Pharmokinetic Optimization in Drug Research

Pharmacokinetic Optimization in Drug Research. Edited by Bernard Testa,Han van de Waterbeemd, Gerd Folkers, Richard Guy© Verlag Helvetica Chimica Acta, Postfach, CH8042 Zürich, Switzerlaand, 2001

Preface

There are a number of reasons why this book is special. First, it willimmediately be apparent that the work is presented on two media, a printedvolume and a CD-ROM. Less obvious but more important is the fact that it issimultaneously a book of Proceedings and much more, as explained below.But above all, it covers a field of immense current interest and significance indrug research.

In our age of combinatorial chemistry and high-throughput technologies,bioactive compounds called ‘hits’ are discovered by the thousands. However,the road is very long indeed that leads from hits to lead compounds and thento pharmacokinetically well-behaved clinical and drug candidates. As aresult, the screening, design, and optimization of pharmacokinetic propertieshas become a bottleneck and a major challenge in drug research. To shortenthe time-consuming development and high rate of attrition of active com-pounds ultimately doomed by hidden pharmacokinetic defects, drug research-ers are coming to incorporate structure-permeation, structure-distribution,structure-metabolism, and structure-toxicity relations into drug-design strate-gies. To this end, powerful biological, physicochemical, and computationalstrategies are being developed the objectives of which are to increase the clin-ical relevance of drug design and to eliminate as early as possible compoundswith unfavorable physicochemical properties, pharmacokinetic profiles, ortoxicity.

In March 1995, we organized at the University of Lausanne a symposiumon Lipophilicity in Drug Research and Toxicology whose success wentbeyond our most optimistic expectations and the Proceedings (published byWiley-VCH, Weinheim, Germany) of which contain chapters which continueto receive frequent citation. In March 2000, LogP2000 – The Second Lipo-philicity Symposium was organized at the same location as a logical sequel tothe first. Its theme (Lipophilicity in Drug Disposition – Practical and Com-putational Approaches to Molecular Properties Related to Drug Permeation,Disposition and Metabolism) attracted over 300 scientists from industry andacademia. A total of 23 invited lectures and 80 free communications were pre-sented. A number of the latter were also submitted for inclusion in the Pro-ceedings. The ensemble of these texts is included in the attached CD-ROM.

The general themes and objectives of the Second Lipophilicity Symposi-um were the determination, computation, and interpretation of lipophilicityand related molecular properties as factors and predictors of drug permeation,disposition, and metabolism. In other words, the symposium was focused onphysicochemical and computational strategies. But more was needed to offeran urgently needed book covering all major strategies used in lead optimiza-

tion. This is why a number of additional authors were invited to contribute atotal of 10 chapters presenting major biological strategies of importance inthe early phases of lead optimization.

The result is a book of unique breadth and depth in which internationalauthorities and practicing experts from academia and industry present themost modern biological, physicochemical, and computational strategies toachieve optimal pharmacokinetic properties in research series. These proper-ties include gastrointestinal absorption, protein binding, brain permeation,and metabolic profile. Toxicological issues are also of utmost importance.The biological strategies emphasized in the book include cell cultures andhigh-throughput screens. The physicochemical strategies focus on the deter-mination and interpretation of solubility, lipophilicity, and related molecularproperties as factors and predictors of pharmacokinetic behavior. Particularattention is paid to the lipophilicity profiles of ionized compounds, to lipo-philicity measurements in anisotropic media (liposomes/water, IAM col-umns), and to permeability across artificial membranes. Computational strat-egies comprise virtual screening, molecular modeling, lipophilicity, and H-bonding fields and their application in structure-disposition relations.

This book is about both theoretical and technological breakthroughs. Butrather than compiling rapidly outdated information, it aims at offering long-lasting knowledge and stimulating food for thought. This is why its threemain parts (i.e., biological, physicochemical, and computational strategies)are accompanied by two chapters which set the scene, four others whichreview the molecular and biological background of pharmacokinetic leadoptimization, and finally two which conclude the book. All 33 chapters, andnot only the 23 invited lectures, are of course also included in the CD-ROM.

This book would not exist without the generous help we received frommany different parties. The symposium received considerable support from anumber of institutions and companies. The organizers and scientific advisorsof the symposium offered valuable input and much time. My co-editorsdeserve special gratitude for their enthusiastic cooperation and their friend-ship. Now we can relax and wish great satisfaction to our readers.

Lausanne, January 2001 Bernard Testa

VI PREFACE

Contents

Part I. Setting the Scene

Pharmacokinetic Challenges in Lead Optimization 3Giovanni Gaviraghi*, Robert J. Barnaby,and Mario Pellegatti

Modelling in Preclinical and Clinical Drug 15DevelopmentLuc P. Balant* and Marianne Gex-Fabry

Part II. Molecular and Biological Background

Structure of Liposomal Membranes in Relation 33to PermeationOle G. Mouritsen*, Hans K. Andersen,Jesper S. Andersen, Jesper Davidsen,Lars K. Nielsen and Kent Jørgensen

Relations of Molecular Properties with Drug 51 Disposition: The Cases of Gastrointestinal Absorption and Brain PenetrationHan van de Waterbeemd* and Dennis A. Smith

Structure-Metabolism Relations and the Challenge 65of Predicting BiotransformationBernard Testa* and Gabriele Cruciani

Concepts in Prodrug Design to Overcome 85Pharmacokinetic ProblemsBernard Testa* and Joachim M. Mayer

Part III. Biological Strategies

Methodologies in Cell Culture 99Heidi Wunderli-Allenspach

Biological Models to Assess Drug Bioavailability 117Ronald T. Borchardt

Biological Models to Study Blood-Brain Barrier 127PermeationStefanie D. Krämer*, N. Joan Abbott,and David J. Begley

Biological Models to Study Skin Permeation 155Nabila Sekkat and Richard H. Guy*

Biopharmaceutical Aspects of Nasal and 173Pulmonary Drug DeliveryPaolo Colombo*, Daniela Cocconi, Patrizia Santi,Ruggero Bettini, Gina Massimo, Pier Luigi Catellani,and Claudio Terzano

The Significance of Plasma-Protein Binding 189in Drug ResearchSaik Urien, Jean-Paul Tillement*, and Jérôme Barré

High-Throughput ADE Screening 199Olivier Kretz* and Alessandro Probst

In Vitro Models for Early Studies of Drug Metabolism 217Jiunn H. Lin and A. David Rodrigues*

Addressing Toxicological Issues in the 245Lead-Optimization Phase of Drug Discovery and DevelopmentPhilip Bentley

Part IV. Physicochemical Strategies

Physicochemical Parameters as Tools 257in Drug Discovery and Lead OptimizationBernard Faller* and Frank Wohnsland

Lipophilicity Profiles: Theory and Measurement 275John Comer* and Kin Tam

VIII CONTENTS

High-Throughput Measurements of Solubility 305ProfilesAlex Avdeef

Electrochemical Aspects of Drug Partitioning 327Frédéric Reymond, Véronique Gobry,Géraldine Bouchard, and Hubert H. Girault*

Biolipid pKa Values and the Lipophilicity of 351Ampholytes and Ion PairsRobert A. Scherrer

Recent Advances in Reversed-Phase-HPLC 383Techniques to Determine LipophilicityChisako Yamagami

Liposome/Water Partitioning: Theory, Techniques, 401and ApplicationsStefanie D. Krämer

Importance of the Mobile Phase in Immobilized 429Artificial Membrane ChromatographyKimberly L. Morse* and Charles Pidgeon

High-Throughput Artificial Membrane Permeability 447Studies in Early Lead Discovery and DevelopmentManfred Kansy*, Holger Fischer, Krystyna Kratzat,Frank Senner, Björn Wagner, and Isabella Parrilla

NMR Spectroscopy for the Study of Drug- 465Phospholipid InteractionsRoberta Fruttero

Part V. Computational Strategies

Virtual Screening of Molecular Properties: 485A Comparison of Log P CalculatorsMark E. Duban, Mark G. Bures, Jerry DeLazzer,and Yvonne C. Martin*

CONTENTS IX

Quantitative Structure-Absorption Relationships 499Han van de Waterbeemd

Hydrogen Bonding: The Last Mystery 513in Drug Design?Hugo Kubinyi

Molecular Hydrogen-Bonding Potentials (MHBPs) 525in Structure-Permeation RelationsGiulia Caron*, Sébastien Rey, Giuseppe Ermondi,Patrizia Crivori, Patrick Gaillard, Pierre-Alain Carrupt,and Bernard Testa

VolSurf and Its Application in Structure-Disposition 539RelationshipsGabriele Cruciani*, Sara Clementi, Patrizia Crivori,Pierre-Alain Carrupt, and Bernard Testa

Molecular-Modeling Approaches to Predict 551Metabolism and ToxicityAntonius M. ter Laak* and Nico P. E. Vermeulen

Part VI. Conclusion

Molecular Biology, Drug Design, and Drug Delivery: 591Bringing It All TogetherVincent H. L. Lee*, Sharon K. Wu, and Chun Chu

Pharmacokinetic Lead Optimization: 615Fine Art vs. Blind TechnologyBernard Testa

Subject Index 627

X CONTENTS

LogP2000 – The Second Lipophilicity Symposium

Major Sponsors

Fondation Herbette (Université de Lausanne), CHGlaxoWellcome SpA, Verona, ItalyKontaktgruppe für Forschungsfragen, Basel, CH

F. Hoffmann-La Roche AG, Basel, CHNovartis Pharma AG, Basel, CHAres-Serono SA, Geneva, CH

Sirius Analytical Instruments Ltd., Forrest Row, UKUniversité de Lausanne, CH

Sponsors

3M Pharmaceuticals, St. Paul, MN, USAAbbott Laboratories, Abbott Park, IL, USAAstraZeneca, Macclesfield, Cheshire, UKBASF AG, Ludwigshafen, GermanyBoehringer Ingelheim Pharma KG, Biberach an der Riss, GermanyEuropean Centre of Pharmaceutical Medicine, Basel, CHMerck & Co., Inc., USANestlé SA, Vevey, CHPfizer Global Research and Development, Sandwich, Kent, UKpION Inc., Cambridge, MA, USASerono Pharmaceutical Research Institute, Geneva, CHSmithKline Beecham Pharmaceuticals, Harlow, Essex, UKSwiss National Science Foundation, Berne, CHWarner-Lambert Co., Ann Arbor, MI, USA

Part I. Setting the Scene

Pharmacokinetic Challenges in Lead OptimizationGiovanni Gaviraghi*, Robert J. Barnaby, and Mario Pellegatti

Modelling in Preclinical and Clinical Drug DevelopmentLuc P. Balant* and Marianne Gex-Fabry


Pharmacokinetic Challenges in Lead Optimization

by Giovanni Gaviraghi*, Robert J. Barnaby, and Mario Pellegatti

GlaxoWellcome S.p.A, Medicines Research Centre, Via Fleming 4, Verona, Italy,Phone: +39 045 921 83 84; Fax: +39 045 921 80 71

1. Introduction

The timing of pharmacokinetic and metabolic studies in pharmaceuticalresearch and development has changed dramatically in the last five years.Traditionally, industrial Drug Metabolism Departments have performed fairlystandardized studies required for drug registration, including so-calledAbsorption-Distribution-Metabolism-Excretion (ADME) studies (PK defini-tion, mass balance, metabolic profile, and metabolic identification) and bio-analytical studies. In addition to this traditional role, a great deal of emphasishas recently been put on integrating some of these studies in the early stagesof the research and development process. This new emphasis has necessitateda considerable increase in human resources in Drug Metabolism Departments,particularly employed in the lead-optimization phase of projects.

The major reason for this trend is the fact that, in the pharmaceuticalindustry, the most successful drug is quite often not the most potent one butrather the one that has the optimum balance of suitable potency, safety, phar-macokinetics, formulation, drug-drug interactions, and manufacturing cost.Some years ago, the traditional process for discovering a new drug was thatresearch chemists and pharmacologists would combine forces to identify themost potent molecule in the chosen pharmacological model, which was pre-dicted to have some relevance to the disease in humans. Little attention waspaid to the study of drug delivery, pharmacokinetics, duration of action,metabolism, solubility, and formulation. The project was passed over to an-other Division, quite often based on another site or another country, whoseobjective was to ‘develop’ the compound. This process design inevitably pro-duced many pharmacologically active compounds which could never becomedrugs due to insurmountable ‘developability’ problems, typically poor oralbioavailability, high clearance, low solubility, or formulation difficulties.


Pharmaceutical companies accepted that the attrition rate of compoundswas high, but whilst sales profits were high and competition was low, this wasnot a particular problem. A number of years ago, several factors started tocause a rapid change in this situation. Government spending on nationalhealth and particularly on drugs was cut world-wide, registration authoritiesbecame much stricter in authorizing drugs with no significant improvementon existing medications or with significant drug-drug interactions. This, andthe rapidly increasing cost of new technology needed to remain competitiveand overhead costs, started to eat away at pharmaceutical companies’ profits.Clearly, in this environment, the high attrition rate was no longer acceptable,and companies started looking harder at when and why drugs were failing tomake it to the market. Many surveys were performed, and it became evidentthat poor human pharmacokinetics was a major reason for failure.

Currently, the overall costs of bringing a new medicine to the market liebetween £100 and £300 million [1], and because the attrition rate is so high,much more money is spent on compounds that fail to make it compared tothose that do [2]. In addition, the more advanced the development stage of thecompound is, the more money is spent, thus any method capable of identify-ing high-risk projects early on in the process allows to fail quickly and cheap-ly and hence enables the dedication of the always limited resources to lower-risk, higher-return projects.

As pharmacokinetics has been recognized as being one of the major fac-tors for project failure, there has been a huge drive to perform these studiesmuch earlier in the process and, more importantly, before the drug candidateis selected (in the lead-optimization phase) so that only compounds with highpotency and good pharmacokinetic properties are chosen for development [3].

Of course, technology development has also played its part in enablingthis change in emphasis for pharmacokinetics studies. Major advances havebeen made in the last decade, in particular in bioanalytical chemistry. Methoddevelopment and sample assessment have been greatly facilitated by majorimprovements in liquid chromatography-mass spectrometry (LC-MS) suchthat high-sensitivity bioanalytical methods can be developed and samplesassayed within a few hours instead of a few weeks as before. Development ofin vitro models to study individual disposition parameters has been funda-mental in helping to identify the crucial physiological factors affecting drugdisposition.

The major driving force [4] behind the rapidly increasing amount of phar-macokinetic information has been the development of high-throughputscreening and combinatorial chemistry, which have enabled pharmaceuticalcompanies to multiply the probability of rapidly selecting a large number ofpotential drug candidates. Clearly, these technologies would not serve thepurpose if the compounds selected could not be rapidly screened for develop-

4 PHARMACOKINETIC OPTIMIZATION IN DRUG RESEARCH

able pharmacokinetic parameters and the best-balanced compound(s) select-ed. Therefore, companies have also been investing heavily in developinghigh-throughput in vitro ADME screens and in vivo screens.

In conclusion, the new challenges are to rapidly improve productivity inproducing in vivo pharmacokinetic information, to develop high-throughputin vitro ADME screens to help to obtain information on discrete drug-dis-position processes, and, more recently, to interpret and apply these data aswell as to predict what effect chemical structure has on individual pharmaco-kinetic parameters in order to allow the medicinal chemist to design a betterpharmacokinetic profile into the molecules (in silico modelling).

2. Accelerated In Vivo Studies

In vivo studies are still the cornerstone of pharmacokinetic studies in leadoptimization, because the living organism is a very complex system and thedisposition of a molecule can be determined by a multitude of physiologicalprocesses occurring, either sequentially or simultaneously. There is still littlebasic understanding of these processes, and consequently we are still in a rel-atively poor position to predict, for example, oral bioavailability or metabol-ic turnover from mere knowledge of the chemical structure. However, this situation is changing mainly due to the increased importance assigned to therapid optimization of pharmacokinetics in early phases of discovery andimproved technology and throughput. Many pharmaceutical companies andacademic centers are devoting more resources and money to research andtechnology development in this area, hence it is likely that major advances inour understanding of the processes involved will occur in the coming years.

In vivo studies will be needed for some time to come, not least becausethe regulatory authorities request them. There are no universal approaches tothe type of in vivo study that should be performed. Each project will have itsown problems, and studies have to be designed appropriately. In order towork at maximum efficiency, an important aspect to consider before design-ing a study is to know exactly which type and quality of information isrequired. Determining factors will be the phase of the project and the numberof compounds that need to be studied. Typically, at the start of a lead-optimi-zation project, hundreds of compounds of pharmacological interest may haveto be studied. In vivo studies, despite the availability of high-throughput ana-lytical techniques, have a relatively low throughput, thus it is not very attrac-tive to perform standard pharmacokinetic studies on this number of com-pounds. There are several approaches one can adopt to ‘filter down’ this num-ber to a manageable one (e.g., <10), so that definitive pharmacokinetic com-parison studies can be performed and the best candidate(s) chosen. N-in-1 or

PHARMACOKINETIC OPTIMIZATION IN DRUG RESEARCH 5

cassette dosing involving dosing multiples of compounds (typically 5 to 10 ata time) has been used for a number of years [5] [6] and increased throughputconsiderably. Correlation with discrete compound dosing is good (Fig. 1) ifcertain precautions are taken. Care has to be taken due to drug-drug interac-tions, especially via the oral route, where local concentrations in tissues con-taining metabolizing enzymes (typically, most drug-drug interactions aremetabolic in nature) are higher than after intravenous administration. Fromexperience, problems are more probable when the total dose is high. There-fore, at GlaxoWellcome (GW), we limit the total dose administered to 1 mg/kgorally. The incorporation of a project-reference compound, which ideally iseliminated by the same route as the other compounds in the cassette, enablesan internal control to check for any significant drug-drug interactions. If drug-drug interactions are a problem with the chemical classes under study,throughput can be still be improved by using cassette analysis. Compoundsare dosed singly, but samples from different compounds are pooled beforeassaying with LC-MS/MS in order to save on analysis time.

Another approach is to use limited time-point protocols to reduce thenumber of samples. For example, a large number of compounds could berank-ordered for clearance by taking samples at one or two early time pointsinstead of a normal full concentration vs. time profile. All these approachesare suitable for obtaining approximate information with the aim of filteringdown a large number of compounds. All these approaches depend on LC-


Fig. 1. Comparison of calculated clearances after intravenous cassette and discretedosing during a GW lead-optimization project

MS/MS (typically electrospray triple-quadrupole) as the analytical endpoint.Improvements in the robustness, sensitivity, and software of LC-MS/MShave made the instrument fundamental to the achievement of relatively high-throughput in vivo pharmacokinetic studies. Combined with fast, genericHPLC methods, automated software routines, and automated sample prepar-ation, analytical methods can be developed in the course of an afternoon andthe biological samples from an in vivo study run overnight. The software rou-tines link to pharmacokinetic parameter-calculation software, thus enablingthe results to be available the next day. In addition, the improved software androbustness of the system has made possible the use of the instrument by rel-atively inexperienced analysts. The majority of personnel now working inDrug Metabolism departments are able to perform quantitative LC-MS/MSanalyses, leaving the qualitative work to more experienced personnel.

An example of the value and impact of rapid in vivo pharmacokineticinformation on a lead-optimization project can be taken from a GW VeronaCNS project, which has as its objective to ‘fast-follow’ a competitor whichhad announced a positive proof of mechanism study in phase-2 clinical trials.Speed was clearly essential. Quite early on in the project, it became clear thatin vitro affinity was not the only crucial parameter for determining in vivopotency in the animal pharmacological models. Brain penetration wasobserved to vary by factors of 102–103 within the same chemical class. A pri-mary brain-penetration screen was rapidly set up in rodents by measuringbrain-plasma ratios 5 minutes after a bolus intravenous injection, and usingcassette dosing and analysis by generic high-speed gradient LC-MS/MSmethods. Intravenous dosing was performed to provide the best estimate ofbrain-penetration potential without the complicating factors of varying oralbioavailability and pharmacokinetics. Using this approach, approximatelyone hundred compounds were rapidly screened and a drug candidate selectedin a very short time. The compound selected with the best in vivo potency inthe pharmacological model was not that with the highest affinity but with thehighest brain penetration.

3. High-Throughput In Vitro Studies

In vivo studies provide information about the complete complex systemwhich, by its nature, does not lend itself to aiding drug design, because theeffect of chemical structure on individual processes cannot be ascertained.For example, it is not easy to derive relationships between chemical structureand oral bioavailability, as a number of processes (solubility, stability, perme-ability, and intestinal and hepatic metabolism) combine together to producethe overall result. Studying those individual physiological parameters in vivo


can be done in most cases, but requires considerable expertise in animal sur-gery and cannot be considered for a large number of compounds due to ethi-cal aspects. Therefore, there has been considerable drive in the two areas ofmetabolism and intestinal permeability [7] to set up high-throughput in vitroscreens so that compounds can be selected on the basis of their properties inthese screens and also to rapidly increase corporate databases so that algo-rithms linking physicochemical properties and chemical structure to the phys-iological process can be developed, hence enabling prediction of these prop-erties (see Sect. 4).

3.1. Absorption

It is not surprising that a lot of effort has been put into setting up in vitrohigh-throughput screens for the most important physiological processesdetermining drug disposition, metabolic turnover, and oral absorption. Mostmodels of absorption use immortalized cell lines of intestinal origin, and mostoften, Caco-2 cells were studied. Caco-2 cells suffer from a major drawbackin that they take 3 weeks to grow to confluence. Recently, a number of alter-native cell lines have been studied to avoid this disadvantage. The Madin-Darby canine kidney-cell line (MDCK) grows to confluence in only 3 daysand, despite being a kidney-cell line, has been shown to form the majority ofthe physiological structures of the intestinal epithelia when grown in mono-layers in culture. In addition, MDCK has been shown to give results similarto Caco-2 for compounds that are transported by transcellular passive diffu-sion (Fig. 2) [8]. This cell line is being used by GW in their high-throughputscreen for absorption. A specially designed device has been developed toenable the transport study of 48 compounds at a time on one 70-mm filter(cells on sheets) [9]. Automation of the liquid handling during the transportexperiment and LC-MS as analytical end-point allows to screen the perme-ability through the MDCK monolayer of up to 100 compounds per week withlimited resources. Throughput could be increased still further by automatingcell culture and pooling samples for LC-MS/MS analysis.

Results from the in vitro screen are often difficult to relate directly toabsorption potential because of the sigmoidal nature of the relationshipbetween permeability and absorption and the fact that the in vitro screenmeasures a rate of transfer at a single time point rather than a percentage ofcompound transferred. However, the results can be arranged into 2–3 groups,depending on their permeability values, and indicate whether compoundsshould show good or poor absorption.


3.2. Metabolism

Traditional microsomal methods used for identifying metabolizingenzymes or studying potential drug-drug interactions by determining inhibi-tion constants have been adapted to develop high-throughput screens capableof determining the metabolic turnover of 1000 compounds per week per per-son. Typically, compounds are incubated for a fixed time with human or ani-mal hepatic microsomal preparations and suitable co-factors, and the %-metabolism compared to some control is measured with HPLC-MS. In GW,incubations are performed in 96-well plates and all liquid handling performedby a robotic arm. After on-line incubation, reactions are stopped by additionof acetonitrile, supernatants produced by centrifugation and injected directlyonto a HPLC-MS system. Ultra-fast gradients ensure a universally genericsystem. Detection is performed by single-quadrupole mass spectrometryyielding the mass of the molecular ion. Compound and library identity on theplate is provided by a bar-code, and molecular weights extracted from com-pany databases are transferred into the mass-spectrometry sequence file sothat the molecular ion is automatically monitored. Metabolic turnover is thenautomatically calculated and the results posted directly into an Oracle data-base. Other hepatic preparations can also be used as well as other tissuehomogenates, which is particularly relevant when the metabolic transforma-tion is due to cytosolic or phase-2 enzymes or when there is a specific inter-est in understanding, for instance, the relevance of gut metabolism for com-pounds subject to a substantial first-pass effect.


Fig. 2. Alternative cell line for GI permeation:MDCK is a faster-growing alternative for measurement of passive diffusion

The results from this screen are frequently very useful for eliminatingmolecules with metabolic clearance (to reduce metabolic clearance is a fre-quent objective of discovery projects). However, there are several limitationsto the use of these data in predicting in vivo clearance [10], and the applicabil-ity of the screen depends on the chemical classes under study. Drug metab-olism is by nature a multifactorial process, and there are usually multiplepathways involved. The microsomal preparations used do not contain allpotential metabolizing enzymes, and, therefore, prediction will be poor whenthe compounds under study are metabolized by these enzymes. Poor in vivoprediction can also result due to a large effect of protein binding on hepaticextraction and drug/tissue binding. In this case, in vivo metabolic clearance islower than that observed in vitro due to the fact that protein binding limitshepatic extraction and also limits the concentration of free drug after hepaticextraction has occurred. Furthermore, enzyme content and stability in micro-somal preparations are very variable, so it is essential to standardize as muchas possible and include positive controls in all determinations.

Due to the complex nature of microsomal preparations that contain themultitude of P450 enzymes, use of these data to predict metabolic rates ofindividual enzymes will be impossible, although, with the increasing size ofthese databases, some models can be developed predicting overall metabolicturnover. A useful development of this technology is the screening of a largenumber of compounds with heterologously expressed recombinant humandrug-metabolizing enzymes. Here, more information can be generated relat-ing chemical structure to turnover, which is important for assessing the struc-tural features needed in a molecule to be a substrate for each P450 enzyme.

So far, we have only mentioned two high-throughput in vitro screens, butof course information generated on other important disposition parameters,such as protein binding, plasma stability, P450 induction, and involvement ofefflux pumps, would also be highly valuable for specific projects.

Although information from high-throughput screens is invaluable in rap-idly selecting the optimal drug candidates, it is essential that a person withsubstantial experience in pharmacokinetics and drug metabolism interpretsthe data, both in considering the conditions in which the assay was performedand in combining information from various sources, such as protein bindingand solubility, to help draw the correct conclusions. Lastly, information froma number of screens has to be combined to predict the fate of those moleculesin vivo, e.g., whether the compound will be orally bioavailable and whetherthey will pass the blood-brain barrier. The interpretation of these data requiresconsiderable experience; however, help from software tools and in silico datamodelling is essential to deal with the large number of compounds involved.

In addition to the high-throughput in vitro screens mentioned, other moretraditional in vitro methods are also finding their way into the drug metabo-


lism chemist’s armoury in the quest for selecting the best drug candidates.Traditionally, drug-drug interaction studies are performed in phase 1 or 2 togenerate important information on potential interactions in large-scale phase-3studies and when the drug reaches the market. However, if the objective of aproject is to ‘fast-follow’ a marketed drug or class of drugs, for example, allof which have drug-drug interaction problems, a very good marketing claimfor the follow-up drug would be a lower incidence or complete lack of theseeffects. Therefore, the selection of the best compound should also be basedon this factor, in this case, necessitating the identification of the enzymesinvolved and calculation of the necessary metabolic kinetic parameters. Thiswould also be advisable if the candidate drug(s) had a narrow therapeuticwindow. Therefore, under certain circumstances, these types of studies oughtto be performed at very early stages before the candidate drug has beenselected. Furthermore, in this context, throughput has to be improved if thebest candidate is to be selected rapidly.

4. In Silico Modelling Developments

Generating large quantities of in vitro data from high-throughput ADMEscreens will be very important to directly feedback to project chemists so thatthe drug candidates possessing optimal pharmacokinetics can be identified.However, this is only the short-term objective of the application of thescreens. Building large databases with both in vitro and in vivo informationand combining the information with essential physicochemical propertieswill be extremely important to begin to understand the relationship betweenchemical structure and physiological parameters, such as passive diffusionthrough the intestinal epithelia or penetration through the blood-brain barrier.In a cyclic iterative process (Fig. 3) [7], models are developed from data andare used to predict parameters, more data are produced, comparisons aremade between predicted and measured parameters, and the information fedback into to the models to further refine them. The larger the database and thebroader the chemical space covered, the more predictive the models willbecome. When the models are sufficiently refined, in the not too distantfuture, drug disposition will be largely predicted on a very large set of vir-tual compounds, and only those predicted to possess good pharmacokineticproperties will be synthesized and undergo in vitro and in vivo studies inorder to rapidly identify the best compounds.

All the major factors influencing drug disposition will be the target fordevelopment of predictive in silico models. However, again, the most impor-tant areas for the majority of drug-discovery projects are intestinal absorp-tion, blood-brain barrier penetration, and metabolic clearance. The study of


the parameters that are important for the passage of molecules through bio-logical membrane barriers has been the subject of countless articles and con-ference presentations. A number of software packages are now commerciallyavailable, which will aid prediction of intestinal absorption. Knowledge ofthe rules governing intrinsic absorption has grown considerably, however, thechallenge for the metabolism chemist is still to combine knowledge of all thepossible factors (e.g., solubility, effect of bile salts, stability, efflux pumps,intestinal and hepatic metabolism) involved in the absorption process in orderto predict the overall situation in vivo. Furthermore, brain penetration is amulti-factorial process depending frequently on protein binding and, morerecently discovered, on multi-drug resistance efflux pumps such as P-glyco-protein. Another area of high importance is the prediction of metabolism.Some advances have been made in determining structure-binding relation-ships for some P450 iso-enzymes, but others are proving more difficultbecause of, for instance, flexible enzyme conformations and synergic effects.New important areas are the development of algorithms for the prediction ofthe effects of efflux pump transporters and P450 enzyme induction.

A vital factor in the development of useful predictive models will be thequality of the data contained in the database. A large quantity of data will notmake up for poor quality, and the models will not be so successful if the datathey are based on are of poor quality or come from low-quality screens. Highquality is therefore essential. It is tempting for short-term project objectivesnot to pay too much attention to quality. A normal problem with combina-torial chemistry is that the libraries produced tend to possess a lower-than-


Fig. 3. The future of lead optimization?

optimal purity. For large numbers of compounds, this will indeed not matterthat much. Of course, some mistakes will be made, e.g., a low percentage ofgood compounds may be filtered out, or a low percentage of poor compoundsmay be identified as good. However these ‘mistakes’ will be identified bymore definitive tests later on. On the other hand, it is a mistake to build a data-base on data arising from compounds with low purity. These should not beentered into the database. Another crucial factor is the control of the screensproducing the data. GW has established a standardized protocol world-widefor all its ADME screens. If biological material is used, the source is strictlycontrolled, and multiple positive and negative controls are utilized in allassays. Assays must pass the acceptability criteria of these standards forresults to be accepted into the database. These checks will certainly reducethe throughput, but the rewards for the extra effort will be felt in the longterm, resulting in a corporate database containing data of a sufficient qualityto efficiently aid prediction through in silico models.

5. Conclusions

The requirement for high-throughput screens for ADME factors is rapid-ly growing in the pharmaceutical industry. The drive for this need is theobservation that a large proportion of drugs fail in development due to poorpharmacokinetics, and companies are now looking to ‘design in’ good phar-macokinetic properties into molecules before they are selected for toxicolog-ical and clinical studies. This will enable high-risk projects to be halted soon-er and hence reduce costs and help to identify projects with a higher probabil-ity of success. Combinatorial chemistry has provided the industry with thepossibility of rapidly detecting pharmacologically interesting hits and select-ing drug candidates. Therefore, high-throughput screens have to be developedto provide the possibility of quickly selecting the most developable drug can-didates. Some progress has been made, but throughput for most screens is stillin the order of hundreds of compounds per week, and further improvementswill have to be made for companies to make full use of biological high-throughput screening and combinatorial chemistry technology. A major chal-lenge in the forthcoming years will be to build predictive in silico modelsbased on high-quality in vitro and in vivo data present in corporate databases.Complexity is high due to the multi-factorial nature of biological processesaffecting drug disposition. Technology, of course, will improve, but a key fac-tor will always be the presence of both highly specialized and broad-basedscientists with considerable experience who are able to interpret largeamounts of data, develop and apply models, and combine the results to pre-dict the physiological outcome.


The ultimate goal is to provide complete in silico predictions on a num-ber of pharmacokinetic parameters before the compounds are even synthe-sized and finally to study only those compounds that have been selected bythe in silico models.

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Modelling in Preclinical and Clinical Drug Development

by Luc P. Balant* and Marianne Gex-Fabry

Department of Psychiatry, University of Geneva, CH-1225 Chêne-Bourg, Switzerland, Fax: +41 22 305 5799; e-mail: [email protected]

1. Introduction

Research and development of new drug entities are becoming more com-plex with the advancement of basic sciences [1]. At one end of the drug-development process, computer-calculated structures of receptors are pro-gressively used to design new chemical entities with specific pharmacologi-cal/therapeutic activities. At the other end of the drug-development process,new software is presently under development to simulate large-scale clinicaltrials including hundreds of patients in order to optimize clinical trial designsbefore they are performed in the real clinical world. As a consequence, mod-elling is more and more important in new drug development. It must bestressed that modelling is not a new discipline. It has been performed foryears, especially for pharmacokinetic data obtained in healthy volunteers dur-ing Phase-1 studies [2]. More recently, as the power of computers has in-creased, more complex modelling is being performed, and efforts are beingmade to integrate knowledge obtained during all phases of drug development.The present review does not cover the different aspects of modelling duringdrug development with equal emphasis: the classical ones are briefly sum-marized, whereas more space is devoted to specific areas where intensive sci-entific activity is ongoing.

2. Models: Mechanistic vs. Empirical

There have been many proposals for the terminology used to characterizemodels. An elegant presentation of the distinction between mechanism-basedmodels and empirical models has been given by Thakur [3]: ‘A mechanisticmodel, as the name implies, should have as many features of the primary


system built into it as observations or data will allow. Such a model shouldbe consistent with the observed behaviour of the system – retrodiction –; itshould further be predictive of the system’s future behaviour or behaviourunder perturbation – prediction –. One must have some knowledge of the pri-mary system in terms of structural connectivity and functional mechanisms.[…]. On the other hand, when the system under study is complex and hardlyanything is known about its structural connectivity and functional mecha-nisms, yet one has to produce hypotheses about it based on some externalcharacteristics such as a dose-response (secondary system), one often relieson mathematical functional forms for such a system. These mathematicalfunctions are empirical models’.

If this distinction between mechanistic and empirical models is accepted,one may conclude that, until today, in the majority of cases, pharmacokinet-ics (PK) has used empirical models. Pharmacodynamics (PD) has searched touse mechanistic models more diligently than pharmacokinetics, but it is onlyin recent years that the concept of mechanism-based modelling has reallybeen promoted as a tool useful not only for academic work, but also for amore rational drug-discovery and -development process.

3. Theory-Derived Physicochemical Properties

Physicochemical properties include calculated molecular weight, solubil-ity, lipophilicity, hydrogen bonding, potential, or dynamic molecular surfaceproperties which are fundamental attributes of xenobiotics. They are exten-sively used in Quantitative Structure-Activity Relationship (QSAR) investi-gations, and efforts have been made to use them, as early as possible, in thediscovery of new drugs not only for prediction of pharmacological activity,but also for toxicology, membrane-passage forecast, and clearance estimation[4] [5]. At the same level, one may consider genetic information obtainedfrom molecular biology, which is a fundamental property of individuals. Thisinformation may be associated with ‘kinetics-oriented properties’ of individ-uals, such as the genotype for metabolizing enzymes, or with ‘dynamics-oriented properties’ such as receptor polymorphisms. Presently, there havebeen few attempts to use this information for modelling purposes, with noto-rious exceptions such as molecular modelling of cytochromes P450.


4. In Vitro Methods

4.1. In Vitro/In Vivo Extrapolation for PK and PD

It is very important to determine (or at least predict), at an early stage ofdrug development, the kinetic properties of a drug candidate. This meansabsorption, distribution, metabolism, and excretion (ADME) [6]. As impor-tant is the prediction of the release characteristics of the active principle froma pharmaceutical formulation and its dynamic properties. These include char-acterization of the molecular targets for biological effect, the concentration-effect relationship, the reversibility and time course of effects, as well as po-tential adaptive changes. Finally, the possibility of either kinetic or dynamicinteractions must be assessed.

One major aim of in vitro/in vivo modelling is to provide screening toolsfor drugs at a very early stage of development. Such screening tools shouldallow ‘high-throughput’ capacities: they should be rapid, easy to use, requiresmall amounts of compound, be relatively inexpensive, and, last but not least,result in reliable predictions. These predictions may serve as a basis for thechoice of the second animal species for early toxicology studies (e.g., mon-key or dog) beside the usually studied rat.

From a kinetic point of view, a series of methods have been developed.They include various subcellular and cellular systems for measuring permea-tion, membrane transport, absorption, distribution, metabolism, and excre-tion. These systems also include in vitro testing in cells modified by geneticengineering. Among such systems, one may list:

• Partition coefficients (usually water/octanol) [7] [8]• Caco-2 cell monolayers (for gastrointestinal absorption) [9]• Plasma protein binding (animal and human)• Microsomes (animal and human, for hepatic metabolism) [10] [11]• Hepatocytes (animal and human) [10] [11]• Enzyme systems expressed by means of recombination (animal and

human).

From a pharmacodynamic point of view, one may mention systems avail-able to determine in vitro both affinity and intrinsic efficacy of new drug can-didates.

Most PK and PD models display a relatively good predictive value froma qualitative point of view, but usually lack accuracy to predict quantitative-ly the in vivo behavior. Another problem is that the results obtained fromin vitro models are strongly depending on experimental conditions so thatimportant differences are often observed between results published by differ-ent laboratories. There is, however, considerable work presently done to im-


prove the predictability of such models. As stated recently by Tucker duringa symposium on the prediction of in vivo metabolism in man from in vitrodata: ‘In Vitro Veritas? Not yet, but we are getting closer!’ [12].

4.2. Biopharmaceutical In Vitro/In Vivo Correlations

In vitro methods and modelling are also very important for the develop-ment of pharmaceutical formulations [13] [14]. According to Dunne et al.[15]: ‘A major goal of the pharmaceutical scientist is finding a relationshipbetween an in vitro characteristic of an oral dosage form and its in vivo per-formance. One such relationship between drug dissolution (or absorption) invivo and that in vitro is known as an ‘in vitro-in vivo correlation’ (IVIVC)whose importance stems from the fact that it may be used to minimise thenumber of human studies required during drug product development, assist insetting meaningful in vitro dissolution specifications and justify biowaiversfor scale-up and post approval changes’. As a consequence, more elaboratemodels are under development to predict in vitro-in vivo relationships.Another point of importance addresses the experimental protocol for thein vivo study. As a matter of fact, a multitude of variables can influence rateand extent of absorption after intake of an oral formulation. For the measure-ment of consistent in vivo parameters within and between pharmacokineticstudies, it is of primary importance that such variables are recognized andcontrolled [16].

5. Animal Experimentation

The pharmacokinetic development programme is well defined for xeno-biotics of smaller molecular weight. Products from the biotechnology areamay present particular problems. At an early stage of drug discovery (e.g.,prior to the ‘first-into-man’ administration), a ‘classical’ kinetic programmemay include:

• Single- and multiple-dose studies in up to 3 or 4 species• Toxicokinetics• Preliminary metabolic elucidation.

Similarly, a ‘classical’ pharmacodynamic programme may include:• Pharmacological testing• Safety pharmacological testing• Preliminary toxicology.


The use of models to improve the predictability of such investigations isbeing considered.

5.1. Animal Pharmacokinetics and Toxicokinetics

A few years ago, the development programme of a new drug dependedheavily on animal pharmacokinetic studies as a tool to predict behavior inman. Every drug company had a ‘standard programme’ for drug candidateswhich, for example, included both intravenous and oral administration of‘standardized’ doses to rats. Today, more emphasis has been given to human-derived subcellular or cellular systems, as described above. Animal pharma-cokinetic studies are nevertheless still of crucial importance because theyconstitute the bridging studies to validate animal exposure in toxicologicalinvestigations and ‘extrapolability’ of these results to humans.

In toxicokinetics, various modelling methods are used, including popula-tion methods when sparse sampling is performed. More recently, it has beenadvocated to apply pharmacokinetic/pharmacodynamic modelling in toxicol-ogy [17].

5.2. Animal Pharmacodynamics

This area of research has seen important developments in recent years.For example, animal models have been used successfully to develop newconcepts of drug treatment or they have been refined in a way to demonstratethat the concentrations at which measurable pharmacological and toxicologi-cal effects occur are similar in experimental animals and humans. Geneticallymodified animals play a role that becomes more important as the causes ofdiseases are now better understood at the molecular level.

5.3. In Vivo PK/PD Modelling in Animals

Until recently, animal pharmacokinetic and pharmacodynamic investiga-tions were usually performed in different departments of the pharmaceuticalcompany. To some extent, pharmacokinetics was performed with doses thatwere dictated more by analytical sensitivity than by effects in animal models.On the other hand, pharmacologists were usually not interested in blood con-centrations at which effects were obtained. This separation of activities isnow progressively vanishing, and more integrated approaches are tested[18–21]. The principle of PK/PD modelling is briefly summarized by Deren-


dorf and Meibohm [22]: ‘Pharmacokinetic/pharmacodynamic modelling linksdose-concentration relationships and concentration-effect relationships,thereby facilitating the description and prediction of the time course of drugeffects resulting from a certain dosing regimen. […]. Application of PK/PD-modelling concepts has been identified as potentially beneficial in all phasesof preclinical and clinical drug development’.

5.4. Mechanism-Based PK/PD Modelling

As emphasized by Van Der Graaf et al. [18], the need for a more mecha-nism-based approach in PK/PD modelling is increasingly being recognized:‘Receptors are the most important targets for therapeutic drugs. In the pastdecade, the tremendous progress in the area of molecular biology has yielded many new insights in the structure and function of receptors. […].The science of pharmacology has been built upon the concept that physico-chemical properties of receptors are often highly conserved among individu-als and species. This concept has been the cornerstone of modern, ‘rational’drug discovery that is based on the search for novel ligands that displayselectivity for a particular receptor system of interest’.

5.5. ‘Scaling up’ and ‘First-into-Man’

For drug candidates, an important aim of modelling at this stage is to pre-dict the first dose to be administered to the first volunteers of Phase-1 inves-tigations. Attempts have been made for this type of prediction [23] using thesimple allometric approach (i.e., clearance and volume of distribution extra-polation based on body weight only). This simple method has been largelyunsuccessful in prospective prediction, essentially because interspecies dif-ferences in drug metabolism, as well as dynamic effects, are neglected by themodel [24]. More elaborate physiological models, integrating kinetics anddynamics, probably represent a better approach and should be more inten-sively investigated [25–29]. Recently, modelling based on the populationapproach led to promising results. Information on this subject is, however,still scarce.


6. Phase-1 Studies

6.1. Pharmacokinetic Modelling

Phase-1 studies are usually conducted in order to characterize the basicproperties of a new drug in humans in the expected therapeutic dose range(i.e., to determine its ‘fingerprint’). This includes usually:

• Single- and multiple-dose kinetics at three dose levels• In vivo metabolic profile• Bioavailability investigations• Formal PK drug-drug interaction studies.

Phase-1 PK is the area where modelling has been most extensivelyapplied not only for drug development, but also for investigations of drugbehavior not necessarily aimed at fulfilling regulatory purposes. As a conse-quence, this is the area where most experience is available, and it representsa pivotal body of knowledge from which it is possible to judge modellingefforts in other areas of drug investigations.

Pharmacokinetic data analysis always postulates models. It is thus neces-sary to challenge the term ‘model-free pharmacokinetics’, which (in the opin-ion of the authors) is a ‘non-sense’ concept because data analysis is alwaysbased on some form of hypotheses. Different categories of pharmacokineticmodels can be distinguished:

• Empirical models. The calculation of the area under the concentrationvs. time curve (AUC) by the trapezoidal rule and of Cmax and Tmax byvisual inspection of the curve, as often performed in bioavailabilitystudies, is a good example of an empirical model.

• Compartmental models are still the most commonly used models forpharmacokinetic data analysis, and there is presently little reason tobelieve that the situation will change in the near future.

• Clearance-based models used for classical pharmacokinetic data ana-lysis do not fundamentally differ from compartmental models. Clear-ance-based models are most useful for comparison of the behavior ofdrugs in health and disease, where the objective is the quantification ofchanges in systemic availability, clearance, and volume of distribution.

• Full physiological models including blood flow to all major organs ofdistribution and elimination are not considered as classical tools inPhase-1 studies. The main drawback of these models is the high num-ber of variables, which necessitates even more data points if reason-able estimates of the parameters are to be calculated.

• Recently, population modelling has also been applied in the data-richsituations encountered in Phase-1 studies [30].


It is probable that when medical imaging will become more easily avail-able for the study of drugs under development, new modelling methods willbe necessary to handle real-time data, multi-tissue concentrations, and short-lived isotopes among other factors.

6.2. Pharmacodynamic Modelling

Pharmacodynamic modelling usually cannot be performed in healthy vol-unteers for therapeutic effects or surrogate endpoints of clinical manifesta-tions of a disease. However, one area of PD that has been neglected is modelling unwanted effects that may often be observed in healthy subjects aswell as in patients. Despite limitations for the measurement of therapeutical-ly relevant effects in healthy volunteers, the present tendency is (wheneverpossible) to integrate PK and PD modelling already in Phase-1 studies. Ac-cording to Van Peer et al. [31]: ‘Early investigation of pharmacokinetic-phar-macodynamic relationships in Phase 1 may facilitate the further clinical de-velopment of a new drug. Although some pharmacology assessments inPhase 1 are often only surrogates for the therapeutic effect, PK-PD model-ling of those effects provides in general crucial information on the drug’spotency in vivo’.

7. Phase-2 Studies

7.1. Primary Aims of Phase-2 Modelling

The early studies in patients (Phase 2a) are performed in order to confirmthat the expected therapeutic effect can be observed at doses that are well orat least acceptably tolerated. The Phase-2b studies are performed in order to‘prepare the ground’ for the large-scale (and expensive) Phase-3 clinicaltrials. Presently, attempts are made to see whether it is feasible to replace partof the Phase-2b programme by simulations using specific software [32]. Thisnew approach still needs to be thoroughly investigated before it can be vali-dated. In particular, it is not yet clear how much a priori knowledge on thenew drug and the target population is needed to obtain reliable predictions forthe outcome of Phase-3 clinical trials. Activity in this field is presently goingon in a few centers.


7.2. Integrated PK/PD Models

PK/PD modelling has been promoted for many years by scientists [33] [34].However, until recently, these proposals have encountered little interest in thepharmaceutical industry because pharmacokinetic and pharmacodynamicdepartments essentially used to work without strong concertation. As discussedby Sheiner and Beal [35], recent work has focused on the development of newmodels that allow to account for different kinds of pharmacodynamic data: ‘Asinterest in using PK/PD models to represent and forecast clinical outcomesincreases, the task of defining appropriate structural and statistical models forsuch PD responses becomes pressing. These responses are often non-continu-ous; that is, dichotomous, categorical, time-to-event, or counts. Generalisa-tions of the so-called indirect PD models recently discussed by Jusko and co-workers [36], provide a flexible framework for structural modelling of the com-plex dynamical relationships between PK and PD’. These models are underintensive development both in academia and in the pharmaceutical industry,and many methodological issues remain to be addressed [37].

7.3. Biomarkers

One area of great interest and activity is the development, validation, andapplication of biomarkers. Biomarkers are characteristics that are used as in-dicators of normal biological processes, pathogenic processes, or pharmaco-logical responses to a therapeutic intervention. They are essential in the glo-bal assessment of the efficacy and safety of drugs on the basis of PK/PD. Todate, there are unique opportunities for the development of new biomarkers(e.g., on the basis of genomics, proteomics, and new imaging techniques).However, a critical issue is their validation. Ideally, biomarkers are function-al and mechanistic. They can be developed in animal studies on the basis ofa combination of in vitro and in vivo technologies. Subsequently, biomarkerscan be validated as surrogate endpoints of the clinical effect on the basis ofclinical trials. The development and validation of biomarkers requires a mul-tidisciplinary approach involving specialists in disciplines such as pharma-cology, toxicology, advanced PK/PD data-analysis, and the clinical sciences.

7.4. Modelling Pharmacodynamic Variability

According to Levy [38] [39], prediction of effective drug concentrationsfor individual patients, taking into account pharmacodynamic variability fac-tors, is a field where modelling may play an important role.


8. Phase-3 Studies

The kinetics of a drug are most relevant in the population in which thedrug is going to be used. The kinetics in normal young men are only relevantin obtaining the ‘fingerprint’ of the drug, but kinetic parameters of primeimportance such as clearance, volume of distribution, half-life, and systemicavailability should be known in the relevant population. Therefore, drugslikely to be affected by pathophysiological factors prevalent in the target pop-ulation should be the subject of some kind of ‘population-kinetics’ studies.This is particularly pertinent for drugs with a narrow therapeutic range.Before population-kinetics studies are carried out, the basic pharmacokineticinformation should be available. Therefore, the objective of population stud-ies is not to replace, but rather to supplement the information provided by pre-vious thorough evaluation of drug kinetics, metabolism, and dynamics.Different types of population approaches may be considered, depending onstudy design and objectives.

8.1. The Pharmacokinetic Screen

The concept of pharmacokinetic screen has been advocated by the USFood and Drug Administration (FDA) [40] [41]. For a long time, this has beenthe object of controversy, but today, more positive views are usually ex-pressed. The pharmacokinetic screen involves the determination of one con-centration in virtually all patients in Phase-3 trials. The pathophysiologicalcondition, as well as the dosage regimen, are recorded, and statistical methodssuch as multiple linear regression are used to explore the relationship betweendose-corrected plasma levels and certain pathophysiological features.

8.2. Formal Pharmacokinetic Studies

Such studies are usually performed to identify populations at risk (e.g.,the elderly, patients with renal insufficiency or hepatic failure) or to investi-gate about possible drug-drug interactions. The study design is formal in thesense that parallel groups or cross-over studies are usually performed.

8.3. Population Pharmacokinetics: Basic Concepts

Even though Sheiner and Beal introduced population pharmacokinetics inthe 1970s [42] [43], it took almost 20 years for the methodology to be gener-


ally accepted as a useful tool during drug development. The approach is par-ticularly beneficial if intensive blood sampling is not attainable, such as inchildren and patients with cancer and AIDS, but it may also be applied inother situations encountered in Phases 1 to 3 of drug development. This typeof integrated modelling has been one of the main areas of activity of theEuropean Concerted Action COST B1 [44–46].

The approach relies on several samples per patient, drawn at differenttimes with respect to the previous dose. For a successful study, it is importantto be confident about the compliance with the dosage regimen and with thetiming of the samples relative to the most recent dose. By means of specificsoftware, average pharmacokinetic parameters, their inter-individual variabil-ity, their possible dependence on co-variables, and the unexplained residualvariability can be estimated [44] [47–49]. Despite the fact that powerful soft-ware is now available [50], more work is needed before this methodology(which is still largely in the hands of specialists) can be used by a broader setof scientists with a degree of confidence that is compatible with the require-ments of drug development. In particular, study design, quality of data, covar-iate assessment communication, protocol design, or strategies for data analy-sis need to be refined [51]. This is one of the aims of the COST B15 Action‘Modelling during Drug Development’. Today, many pharmaceutical compa-nies have introduced this approach or use it routinely during drug develop-ment. Advocacy by the FDA for pharmacokinetic screening during Phase-2and -3 studies was an important factor in the widespread adoption of thisapproach even though, to some extent, it also led to opposition and resistancewithin some companies. An even more important factor was the gradual real-ization by the pharmaceutical industry that the approach was cost-effective inrevealing clinically important information about the determinants of interpa-tient pharmacokinetic and pharmacodynamic variability [47]. In addition,population modelling is expected to act as the vehicle to propagate informa-tion within and between the successive phases of clinical evaluation. In par-ticular, decisions at Phase-1/Phase-2 and Phase-2/Phase-3 transitions may bebetter informed due to modelling and simulation.

8.4. Regulatory Authorities and Population Methods

Regulatory authorities tend to have diversified opinions about populationmodelling in Phase-3 studies. Some authorities, such as the FDA, encouragethe use of population methods, whereas in Europe, various attitudes may beencountered, and no formal ‘Notes of Guidance’ exist on the subject [52] [53].


8.5. Computer-Assisted Simulation of Clinical Trials

A further step in modelling is provided by attempts to simulate the out-come of clinical trials performed under a variety of conditions [54–56]. Asemphasized by Gieschke et al. [57]: ‘Computer simulations have been suc-cessfully applied in various industries (e.g. automobile, aerospace) to makeproduct development more efficient. It is now suggested to use simulations insupport of clinical drug development for predicting clinical outcomes ofplanned trials. The methodological basis for this approach is provided bypharmacokinetic and pharmacodynamic mathematical models together withMonte Carlo techniques. It is hoped that computer simulation helps to evalu-ate consequences of design features on safety and efficacy assessment of thedrug which are not easily obtained otherwise’.

8.6. Simulation of Specific Clinical Trial Designs

Computer-assisted simulation of clinical trials has been advocated to testspecific designs that are not commonly used. Examples are provided by therandomized concentration-controlled trial [58] [59] or the pharmacologiceffect-controlled randomized clinical trial [60] [61].

8.7. Modelling Specific Aspects of Clinical Trials: Compliance

Non-compliance or partial compliance represents an important challengein the analysis of Phase-3 clinical trials, and various modelling approacheshave been developed to overcome this problem [62].

9. Postmarketing Studies

Once a drug is on the market, its ‘scientific life’ is by no means ended. Asa single example, one may imagine that an unexpected drug-drug interactionis observed. To understand the mechanisms and potential kinetic and/ordynamic consequences of this interaction, it is necessary to take into con-sideration and analyze all information gathered during the pre-registrationphases. Here again, integrated modelling strategies are probably the tool ofchoice.

Therapeutic drug monitoring is particularly well suited for providing alarge amount of data in patients. The use of the population approach has beenadvocated to individualize dosing regimen in individual patients [63] [64].


Population models permit the application of Bayes’s formula to obtain im-proved estimates of an individual’s pharmacokinetic and phamacodynamicparameters in the light of observed responses [63]. The construction of data-bases summarizing the PK/PD behavior of the drugs to be monitored duringclinical use will certainly also contribute to the validity of monitoring proce-dures for patient-care individualization [65].

10. Concluding Remarks

This review has attempted to highlight some of the areas where work ispresently in progress in the field of ‘Modelling during Drug Development’. Itdoes not pretend to have covered the whole field of PK/PD modelling.Artificial neural networks [66] [67], the fractal approach [68], or fuzzy logic[69] are among the techniques currently tested in the field of new drug devel-opment. A survey of the integration of pharmacokinetic and pharmacodynam-ic principles in clinical drug development in a major pharmaceutical companyhas been performed recently [70]. It has shown that the use of the PK/PD-guided approach had contributed to making clinical drug development morerational and more efficient. The recommendation was that opportunities toapply the PK-PD approach should be identified in each project and a project-specific strategy for the PK-PD-guided approach should be defined during thevery early phases of drug development. This is a welcome change as com-pared to earlier developmental strategies based essentially on ‘trial and error’methodologies. It is probable that the ability to model drug behavior andeffects more efficiently has greatly promoted the more scientifically basedstrategies of drug discovery and development, without forgetting their morerational use in individual patients.

The authors would like to thank the members of the Management Committee of theEuropean Action COST B15 entitled ‘Modelling during Drug Development’, of which Luc P.Balant is the chairman, and, for their help in the preparation of the manuscript, V. Nitsche(Austria), P. Kremers and A. Van Peer (Belgium), J. Martinkova (Czech Republic), K. Brøsen(Denmark), O. Pelkonen (Finland), G. Paintaud and F. Mentré (France), U. Gundert-Remy andJ. Kuhlmann (Germany), P. Macheras (Greece), G. M. Pacifici (Italy), M. Danhof and J. H. G.Jonkman (Netherlands), S. G. Dahl (Norway), J. Benitez (Spain), J. Holoman and M. Durisova(Slovak Republic), G. Alvan and M. Karlsson (Sweden), J. L. Steimer (Switzerland), O. Kaya-alp (Turkey), A. R. Boobis and L. Aarons (United Kingdom), K. Pithan (European Commission).

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Part II. Molecular and BiologicalBackground

Structure of Liposomal Membranes in Relation to PermeationOle G. Mouritsen*, Hans K. Andersen, Jesper S. Andersen,Jesper Davidsen, Lars K. Nielsen, and Kent Jørgensen

Relations of Molecular Properties with Drug Disposition: The Cases of Gastrointestinal Absorption and Brain Penetration

Han van de Waterbeemd* and Dennis A. Smith

Structure-Metabolism Relations and the Challenge of PredictingBiotransformation

Bernard Testa* and Gabriele Cruciani

Concepts in Prodrug Design to Overcome Pharmacokinetic ProblemsBernard Testa* and Joachim M. Mayer


Structure of Liposomal Membranesin Relation to Permeation

by Ole G. Mouritsen*a), Hans K. Andersenb), Jesper S. Andersena),Jesper Davidsenb), Lars K. Nielsena) and Kent Jørgensena) b)

a) MEMPHYS-Center for Biomembrane Physics, Department of Chemistry,Technical University of Denmark, Building 206, DK-2800 Lyngby, Denmark;

* Phone (secretary): +45 45 25 24 63; Fax: +45 45 93 48 08; e-mail: [email protected]) Department of Pharmaceutics, The Royal Danish School of Pharmacy, Universitetsparken 2,

DK-2100 Copenhagen, Denmark

1. Introduction

1.1. Overview

The conventional textbook picture of the fluid lipid-bilayer component ofbiological membranes as a fairly structureless ‘fluid mosaic’ solvent is farfrom being complete [1]. As illustrated in Fig. 1,a, the lipid bilayer displaysdistinct, although subtle, elements of transverse as well as lateral structureand organization. Whereas the trans-membrane structure is well-known to beof seminal importance for binding, penetration, as well as permeation ofmolecular compounds, the importance of the lateral structure is much lessanticipated [2–4]. Recent experimental and theoretical studies have shownthat the lipid bilayer displays static and dynamic structural organization on asmall scale [4], e.g., in terms of differentiated lipid domains in the nanometerrange [5–7]. Evidence for this type of lateral structure in model membranesis discussed, and it is shown how trans-membrane permeation can be con-trolled by the heterogeneity associated with the lateral membrane structure.The new insights into lipid-membrane structure are considered in relation todevelopment of new liposome-based drug-delivery systems [8] [9].

1.2. Lipid Bilayers and Model Membranes

The lipid-bilayer component of biological cell membranes plays a keyrole in a wealth of biological processes both as a carrier, a barrier, and a tar-


get. Simple and well-defined lipid-bilayer systems such as uni- and multila-mellar liposomes (cf. Fig. 1, b) are therefore often used as models of biolog-ical membranes [10]. A detailed knowledge of the physical and physicochem-ical properties of bilayer membranes, in particular their structure and dynam-ics on the molecular level, is hence essential for modern drug research [8] inrelation to i) the interaction of drugs with membrane-bound receptors, ii) drugtargeting, penetration, and permeation of cell membranes, and iii) the use ofliposomes as smart drug-delivery systems. Recently, reviews with focus onthe physical properties of lipid-bilayer membranes in relation to drug researchhave been published [8] [11].

1.3. Membrane Permeation and Liposome-Based Drug Delivery

Due to their biocompatibility and their ability to effectively encapsulatetoxic drugs, liposomes [12] [13] and particularly polymer-coated ‘stealth’liposomes [14], as illustrated in Fig. 1, c, are used as microcarrier systems indrug delivery. Their long vascular circulation times enable the liposomes toeffectively evade the immune system. The questions concerning stabilizationof the liposomes in the blood stream as well as the mechanisms of targeting,destabilization of the carrier liposomes at the target membrane, and, finally,effective transport and adsorption of the released drug across the target mem-branes all involve problems related to the permeability of lipid bilayers. Thepermeability in turn is controlled by structural properties of the bilayer, i.e.,the transverse and the lateral molecular organization [15]. It is not an easymatter to study and characterize lipid-bilayer structure due to the fact thatlipid bilayers under most physiological conditions are fluid and liquid crys-talline [16] and, therefore, are very soft and sustain a considerable degree of


Fig. 1. Schematic illustration of a eukaryotic cell membrane with a lipid-bilayer core (a), aconventional liposome (b), and a polymer-coated ‘stealth’ liposome (c). A phospholipase A2

(PLA2) molecule is shown bound at the ‘stealth’ liposome surface.

disorder [17]. One of the routes to study and understand how order and organ-ization develop out of this disorder and, ultimately, how lipid membranes reg-ulate biological function [18], proceeds via an investigation of the phase equi-libria in lipid systems.

2. Phase Structure and Lateral Organization of Lipid Bilayers

A lipid bilayer is a complex and structured fluid with considerable dy-namics, a special trans-bilayer molecular profile [19], and a highly non-trivial lateral organization of the membrane components on many differentlength- and time scales. This picture anticipates the physical fact that themolecular constituents of the bilayer are large, amphiphilic molecules withmany internal degrees of freedom and that the membrane assembly is a many-particle system which, by basic laws of Nature, displays correlated and coop-erative dynamical modes involving many molecules.

The amphiphilic nature of the lipid molecules, which is the main reasonfor their spontaneous self-assembly into lipid-bilayer aggregates in an aque-ous milieu, implies that the bilayer and the adjacent parts of the aqueousembedding medium have a particular molecular interfacial structure and can-not be considered as separable entities. The special interfacial structure is ofparamount importance when it comes to the interaction of the membrane withforeign molecular components, such as drugs, proteins and peptides, and thetrans-membrane penetration of such species [20]. On the mesoscopic scale,the lipid bilayer can be considered as a soft and flexible interface that is char-acterized by continuum-mechanic physical properties like interfacial tensionand elastic bending moduli. The bilayer exhibits a substantial degree of in-plane fluctuations and nanoscale heterogeneity, which in turn control themechanical moduli of the membrane and, hence, the repulsive entropic inter-actions with approaching macromolecules and other neighboring membranesand surfaces.

In the lamellar state, the lipid bilayer owes its lateral structure and specif-ic physical properties to the underlying phase equilibria of the lipids [17]. Thetransitions between the different phases are conveniently identified by peaksin the specific heat, which can be measured by differential-scanning calorim-etry. An example is shown in Fig. 2, where the specific heat for a dispersionof multilamellar liposomes made of DC18PC (where DCnPC stands for satu-rated diacylglycerophosphatidylcholine with n C-atoms in each acyl group) isshown as a function of temperature. Among several different phase transitionsoccurring in this bilayer, the so-called main transition is signaled by the largepeak. At the main transition, the lipid bilayer goes from a solid (gel) phase ofconsiderable acyl-chain order to a fluid phase characterized by a high degree


of acyl-chain conformational disorder (cf. inserts in Fig. 2). Other responsefunctions, such as lateral compressibility and bending rigidity, are also foundto exhibit singular behavior at the main transition, pointing to the fact thatphospholipid bilayers become soft at the main transition. We shall, in the fol-lowing, focus on the homologous series of lipids DCnPC, with n=14, 16, 18,which all display a main transition as shown in Fig. 2. Within this lipid series,the thermodynamic properties, such as transition temperature and heat oftransition, vary almost linearly with n.

3. Lateral Membrane Organization and Permeation

3.1. Single-Component Lipid Bilayers

At the main transition, the lipid bilayer displays an anomalous permeabil-ity behavior [21], as shown in the case of Na+ permeation through DC16PCliposomes in Fig. 3. This permeability anomaly is in fact a fairly generic phe-nomenon which is not only found for small cations but also for a number ofother small and larger molecular compounds [22]. This points to the possibil-ity that the passive trans-membrane permeation is a non-specific physicalphenomenon controlled by the physical structure of the bilayer. At the phasetransition, which is strongly influenced by fluctuations [23], the bilayer struc-ture breaks up and defects are formed.


Fig. 2. Specific heat (heat capacity) as a function of temperature for multilamellar liposomesof DC18PC. The inserts show schematically the structure of the different lipid phases.

Computer simulation on molecular interaction models of lipid bilayers[16] [24] provides a convenient way to investigate the lateral structure oflipid bilayers near the phase transition. In Fig. 4, snapshots of microconfigu-rations obtained from computer-simulation calculations of a series of differ-ent lipid bilayers close to their respective main transition are shown. As illus-trated in the case of DC14PC in Figs. 4,a,d, lipid domains are formed near thetransition, fluid domains within the gel phase below the transition, and geldomains in the fluid phase above the phase transition. The lipid-domain for-mation is a dynamic phenomenon which is a consequence of the density fluc-tuations accompanying the phase transition. This phenomenon has beentermed dynamic heterogeneity. By comparison of the frames in Figs. 4,a–c,we find that, when compared at the same relative temperature, lipid bilayersformed of longer lipid chains show less lateral heterogeneity.

It has been suggested that this kind of lipid-domain formation in the tran-sition region causes the leakiness of liposomes observed near their transitiontemperature [22]. In fact, the experimental data for Na+ permeation in Fig. 3can be directly related to the calculated amount of interface of the lipid domainsin microconfigurations as those displayed in Figs. 4,a–d. This is shown by thetheoretical curve in Fig. 3, which is calculated based on the simple assump-tion that the transmission coefficient is much larger in the interfaces of thedomains that in the remaining parts of the bilayer. This correlation suggeststhat lipid bilayers become less permeable as the acyl-chain-length isincreased. Based on this insight, it is also possible to predict how membrane


Fig. 3. Relationship between passive permeability of Na+-ions (•) measured by radioactive tracer experiments [21] and computer simulation of lipid-bilayer heterogeneity (---)

permeability can be altered by incorporation of certain molecules into thelipid bilayer, as well as how various solutes influence the permeability. A par-ticularly important example is cholesterol, which in large amounts lowersbilayer permeability but in small amounts (~3%) makes the bilayer leakier[25].

There is accumulating evidence in favor of the microscopic picture pre-sented above of lateral heterogeneity in lipid bilayers [2]. A piece of indirectexperimental evidence is presented in Fig. 5, which shows the results of atwo-probe fluorescence-energy-transfer experiment carried out on liposomesystems for three different lipids [26]. In this type of experiment, one takesadvantage of the possibility of designing a donor-acceptor fluorescent pair ofprobes that are lipid analogs. Provided that the donor and acceptor have dif-ferent affinity for the two different lipid phases, a decrease in fluorescence-energy transfer, i.e., an increase in donor fluorescence intensity, is expectedto occur if the bilayer decomposes into domain patterns like those shown inFigs. 4,a–d. The data in Fig. 5 indeed reveal a peak in the donor intensity atthe transition for all three lipids. The peak intensity and the width of the peak


Fig. 4. Snapshots of simulated lateral configurations of lipid bilayers containing 10 000 acylchains. a)–c) Density fluctuations in the fluid phase of DC14PC, DC16PC, and DC18PC, at atemperature two degrees above their respective phase transition. The interfaces of the lipiddomains are highlighted. d) Same as a) at a temperature two degrees below the phase transition.e) Non-equilibrium solid-fluid phase separation in a binary mixture of DC14PC-DC18PC.f ) Compositional fluctuations in the fluid phase of a 1:1 binary mixture of DC14PC-DC18PC.

increase with decreasing chain length. It is interesting to note that the activ-ity of phospholipase A2 (PLA2), which hydrolyzes phospholipids, has beenfound to be sensitive to the structural heterogeneity of the lipid-bilayer sub-strate and that the activity depends on acyl-chain length and temperature in away which is very similar to that shown in Fig. 5 [27]. A set of data illustrat-ing this remarkable effect is presented in Fig. 6.

Fluorescence-transfer experiments do not lead to a measure of the spatialscale of the lipid domains. Evidence based on neutron scattering has recentlysuggested that these domains are in the range of nanometers [7]. A direct vis-ualization of lipid domains is possible in lipid monolayers, which are trans-ferred from an air-water interface to a solid support, by means of Langmuir-Blodgett techniques [5] [6]. By varying temperature and surface pressure in aLangmuir trough, the monolayers are prepared in a thermodynamic stateequivalent to that of bilayers. After the transfer to solid mica plates, themonolayer structure is imaged by atomic-force microscopy [6]. An exampleof the lateral small-scale structure obtained by this approach applied to aDC14PC monolayer in the transition region is shown in Fig. 7, a. It is seenthat the monolayer is subject to a considerable degree of nanometer-scalelipid-domain formation.


Fig. 5. Fluorescence intensity of the donor molecule of the donor-acceptor pair, N-(7-nitro-benzofurazan-4-yl)-1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (NBD-PE) andN-sulforhodamine-8-labeled-1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (N-Rh-PE),in three different lipid bilayers as a function of temperature around the respective phase-transi-

tion temperature Tm

3.2. Two-Component Lipid Bilayers

When several different lipid species are present in a liposome, a morecomplex phase behavior arises due to phase coexistence, as described by thethermodynamic phase diagram. This has some dramatic consequences for thepermeability of the liposome.

Fig. 8 shows the phase diagram of a binary mixture of DC14PC andDC18PC, for which the chain-length mismatch leads to a non-ideal mixingbehavior and a gel-fluid coexistence region. In the spirit of the analysis of the


Fig. 6. Activity of phospholipase A2 (measured by the so-called inverse lag time, –1 [27])as a function of temperature for three different lipid bilayers. The peaks occur at the respective

transition temperature of the lipid bilayer in question.

lipid-bilayer behavior provided above for a one-component lipid bilayer, wepresent in Figs. 9, a–c results in the case of DC14PC-DC18PC for the thermalvariation of the specific heat, the donor fluorescence intensity in a two-probefluorescence-energy-transfer experiment [28], and the permeability of a smallnegative ion (dithionite, S2O4

2–) [29]. It is observed that all three measures,and in particular the permeability, track the phase boundaries. This impliesthat the binary liposomes become leaky at the borders of the coexistenceregion. Furthermore, it is interesting to note that the level of the permeability(as well as the fluorescence and specific heat) in the coexistence region isconsiderably higher that in either of the two phases. This suggests that themixture is not fully separated and, therefore, possibly not in thermal equilib-rium. The microstructure may hence look like the non-equilibrium computer-simulation image in Fig. 4, e. In fact, fluorescence and Fourier-transform-infrared (FT-IR) spectroscopy have provided evidence for the phase-separa-tion process in binary lipid mixtures to be extremely slow [30], and a non-equilibrium domain pattern may therefore persist during the time-scale of theexperiment. Recent fluorescence-microscopy studies of single, giant unila-mellar liposomes composed of DC12PC-DC18PC mixtures [31] [32] have alsorevealed large coexisting gel-fluid domains in the m-range on the surface ofthe liposomes.

Similar findings of increased permeability across the phase-coexistenceregion is found for other compositions than the one discussed above.However, the narrower the coexistence region, the more difficult it becomes


Fig. 7. Lateral structure of lipid monolayers transferred to a solid support and subsequentlyimaged by atomic force microscopy. The different gray tones reflect height differences.A) Lipid monolayer of DC14PC in the fluid phase near the critical point. Image size is 5×5 m.Maximum height difference is about 5 Å. B) Lipid monolayer of a binary mixture of DC14PC-DC18PC near the coexistence region. Image size is 250×250 nm. Maximum height difference is

about 2 Å.


to discern special features at the phase boundaries. For the more ideal mix-ture DC14PC-DC16PC, the phase boundaries cannot be gauged from thepermeability measurements, and there is just a broad peak with a maximumsomewhere inside the coexistence region [33].

It is possible to take advantage of these special mixing properties of lipidbilayers to design liposome systems with particularly strong enhancer effects.Examples are mixtures involving short-chain lipids like DC10PC [34]. Fig. 10illustrates the permeability-enhancing effect of this lipid when incorporatedinto liposomes of DC14PC. Also for this mixture, the peak in the specific heatoccurs at the same point as the peak in the permeability [34]. The short-chainlipid molecules enhance the fluctuations and hence the lateral heterogeneityof the liposomal membranes, leading to dramatic leakage.

Fig. 8. Phase diagram of the binary lipid-bilayer mixture DC14PC-DC18PC exhibiting gel-fluid coexistence


Fig. 9. Thermal scans across the phase diagram of a 1:1 lipid-bilayer mixture of DC14PC-DC18PC (cf. Fig. 8). a) Specific heat. b) Fluorescence intensity of the donor molecule NBD-PE alone or together with the acceptor molecule N-Rh-PE. c) Permeability of dithionite ions

(S2O42–) measured from the rate of fluorescence quenching of NBD-PE.


Lateral lipid-domain formation is not only restricted to phases involvinggel domains. Mixtures of lipids in the fluid phase can exhibit domain forma-tion due to compositional fluctuations [35], which correspond to a small-scalelocal demixing of the two lipid species. An example of an experimental obser-vation of this type of behavior is shown in Fig. 7, b, which is an atomic-force-microscopy image of a DC14PC-DC18PC lipid monolayer transferred onto asolid substrate [5]. Domains enriched in one of the species are found to occuron the nanometer scale.

4. Permeation and Liposome-Based Drug-Delivery Systems

Liposomes used for drug delivery often consist of simple mixtures ofphospholipids and cholesterol [12–14]. In addition, small amounts of lipopo-lymers are incorporated into the liposomes, as illustrated schematically inFig. 1, c, in order to provide for longevity in the blood stream. Such polymer-coated liposomes, e.g., by poly(ethylene glycol) (PEG), are known as

Fig. 10. Permeability enhancement of DC14PC liposomes by short-chain lipids DC10PC,measured by quenching of the fluorescence intensity, P200, of NBD-PE by S2O4

2–. Results areshown as a function of temperature, and the composition across the phase diagram of theDC10PC-DC14PC mixture is shown in the insert. A, B, C, and D refer to compositions

xDC10PC = 0, 0.03, 0.10, and 0.30, respectively.


‘stealth’ liposomes [14]. The versatility of liposomal drug-delivery systems isobvious considering the great variability in lipid compounds one can use todesign liposomes associated with particular stability and targeting potentials.Insight into the structural properties of lipid-bilayer membranes of the typedescribed above, in particular with respect to the determination of the optimalmolecular composition, is a prerequisite for optimal design of smart deliverysystems. We shall only mention one specific example where permeation iscontrolled by enzymatic activity.

It was recently discovered that PLA2 has an unexpected influence on‘stealth’ liposomes [36] [37] in that the presence of the lipopolymers enhanc-es the PLA2-catalyzed hydrolysis of the phospholipids of the liposomes. Theactivity is conveniently measured in terms of the so-called lag time, . Lag-burst behavior is characteristic of PLA2 activity on many phospholipid sub-strates. During the lag phase, hydrolysis products are locally accumulated inthe bilayer, leading to defect sites at which a dramatic enzyme attack sets inat the burst. As shown by the data in Fig. 11, the lag time decreases and hencethe enzyme activity increases as increasing amounts of the lipopolymersdipalmitoylphosphatidylcholine(DPPE)-PEG2000 are incorporated. This typeof assay can be used as a model system to study the potential of ‘stealth’ lipo-somes to act as a smart delivery system at target tissues that have elevatedlevels of endogenous PLA2, such as inflammed and cancerous cells [9]. InFig. 12, release profiles are shown for liposomes that initially are encapsulat-ing a fluorescent model drug (calcein), which permeates through the lipo-somes in response to the PLA2-assisted hydrolytic degradation of the lipidbilayer. A clear effect is observed as the lipopolymer content is increased. A

Fig. 11. Lag time (), at 37° of PLA2-catalyzed hydrolysis of large unilamellar DC16PC lipo-somes ‘doped’ with increasing amounts of DPPE-PEG2000 lipopolymer


Fig. 12. Release profiles of calcein at 37° during PLA2-mediated degradation of large unilamel-lar DC16PC liposomes ‘doped’ with different amounts of DPPE-PEG2000 lipopolymer. From

right to left, the three curves correspond to 0, 2, and 5% DPPE-PEG2000, respectively.

Fig. 13. Zeta potential of SOPC liposomes as a function of DSPE-PEG750 lipopolymer concen-tration measured in HEPES buffer at pH 7.5 [38]


possible explanation for this phenomenon is provided by the data in Fig. 13,which shows the zeta potential as a function of the concentration of DSPE-PEG750 lipopolymers incorporated into stearoyl(oleyl)phosphatidylcholine(SOPC) unilamellar liposomes [38]. The correlation in the decrease in zetapotential with the decrease in lag time (Figs. 11 and 12) suggests that thechanges in the enzyme activity may be due to a change in the surface poten-tial and hence the affinity for binding of PLA2 to the liposomal surface.

5. Conclusions and Outlook

Liposomes are self-assembled lipid systems, and their structural proper-ties (lateral organization, permeability, as well as stability) are therefore to alarge extent controlled by non-specific physical interactions. Insight into themolecular control of the physical properties of liposomes is hence importantfor manipulating and tailoring the liposomal properties in relation to specificdrug-delivery purposes. In a liposomal formulation, the drug assumes analtered pharmacokinetics characteristic of the liposomal carrier, and it can, inprinciple, be targeted to diseased tissue by using a combination of physico-chemical and pathophysiological factors at the sites of the liposome carrierand the target membrane, respectively [9].

Trans-bilayer permeability of biological membranes is a crucial factor forthe transport and delivery of drugs [39]. Our understanding of the relationshipbetween the detailed molecular structure of a drug molecule and its ability topenetrate and permeate a biological membrane is very modest [40]. Advancesin new biophysical experimentation involving probe molecules along withmolecular simulations of increasing accuracy hold a promise for some pro-gress. In any case, a deeper understanding of how the physical properties ofsimple lipid bilayers control permeability is needed in order to better evalu-ate drug candidates.

The barrier properties of real biological membranes are of course muchmore complicated than those of simple liposomes designed for drug-deliverypurposes. Since our understanding of how to control lipid-bilayer structure israpidly increasing due to the advent of new experimental techniques andnovel theoretical concepts, it is to be expected that, in the near future, we maysee liposomal carriers as one of the modern microcarrier therapeutic systemsthat come close to realizing Paul Ehrlich’s early vision of a ‘magic bullet’ forthe treatment of diseases.

This work was supported by the Danish Natural Science Research Council, the DanishTechnical Research Council, the Danish Medical Research Council via the Centre for DrugDesign and Transport, and the Hasselblad Foundation. O. G. M. is an associate fellow of theCanadian Institute for Advanced Research.


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Relations of Molecular Properties with Drug Disposition:

The Cases of Gastrointestinal Absorption and Brain Penetration

by Han van de Waterbeemd* and Dennis A. Smith

Pfizer Global Research and Development, Department of Pharmacokinetics,Dynamics and Metabolism, Sandwich, Kent CT13 9NJ, UK; Fax: +44-1304-656433;

e-mail: [email protected]

1. Introduction

The ideal situation for the medicinal chemist is that the human metab-olism and pharmacokinetics of a compound can be predicted based on itsphysicochemical properties and molecular structure [1]. If this were so, drugdesign on the basis of simple physicochemical estimates would be immense-ly efficient. Unfortunately, an almost opposite situation exists, and physico-chemical models have become increasingly more complex to match ourunfolding understanding of biology. Physicochemical properties used todayin drug-discovery programs include solubility, lipophilicity, molecular size,hydrogen-bonding capacity, and charge, all of which are considered as impor-tant molecular features since they relate to various aspects of drug disposition[2–4]. However, there are no universal rules for their application or to fore-tell which is the dominant property in any situation. A major complication isthat the search for better molecular descriptors has neither decreased the useof established descriptors, nor the search for improved approaches to deter-mine these. For instance, the traditional measure of lipophilicity is the parti-tion coefficient in octanol/buffer (log P). A large collection of reference datais available in the MedChem database, and its very availability means it willbe extremely unlikely that a new measure of lipophilicity will replace the tra-ditional one.

Each of the relevant molecular properties can be assessed by variousmethods, both experimental and computational. There is, so far, a lag of sev-eral or many years before computational approaches can parallel or even


replace measured values. To add to this complexity, a large number of pro-grams exist to compute a property such as log P [5]. From this starting pointof partition coefficents, hydrogen bonding and molecular size have been iden-tified as major components of lipophilicity [6]. A combination of lipophilic-ity measures has been utilized to express hydrogen bonding, e.g., the differ-ence between partition in octanol/water and alkane/water systems. Since thisis rather tedious experimental work, computed estimates are being developed,for example the polar surface area [7] or the molecular hydrogen-bondingpotentials (see the chapter by Caron et al., p. 523). The array and general util-ity of computed molecular properties are further discussed in the chapter byvan de Waterbeemd (see p. 499).

Even with the variety of available techniques, the complexity of biologi-cal systems implies a never ending search for better physicochemical meas-ures. Experimental approaches also include components of biological sys-tems as mimics instead of simple solvent systems. Thus, lipophilicity is nowbeing assessed using immobilized artificial membranes (IAM), consisting ofphospholipids grafted on a solid support (see chapter by Morse and Pidgeon,p. 429). Such systems may better mimic cellular membranes than octanol par-titioning, but their complexity means that it is difficult to pinpoint the prop-erties to optimize in a drug-design program.

For most drugs, oral administration is the preferred route. Oral bioavail-ability is, therefore, one of the major hurdles in many drug-discovery pro-jects. Several aspects of oral absorption are discussed in this book. Despitethe above caveats, our understanding of the influence of physicochemicalproperties on oral absorption has increased in recent years [2–4]. The mostimportant physicochemical properties for membrane transport and oralabsorption include lipophilicity, molecular size, charge, and hydrogen bond-ing [8]. In the chapter by van de Waterbeemd, an overview is presented onquantitative structure-absorption relationships, summarizing models to pre-dict oral absorption based on physicochemical and structural properties.Although such predictive models may work reasonably well for certain drugclasses, they may be unsatisfactory in other cases. What is now becomingclear is that our understanding of in situ processes influencing membranetransport is far from complete. Furthermore, inadequate or irrelevant biolog-ical data have sometimes been used. In fact, little human oral-absorption dataare available, and many predictions rely on model systems. Most drug com-panies use in vitro systems to estimate intestinal absorption. These includecell-culture monolayers, e.g., Caco-2 or MDCK cells, and the Ussing cham-ber (see the chapters by Wunderli-Allenspach, p. 99, and Borchardt, p. 117).

Cell membranes are both a physicochemical and a biological barrier. Thefirst can be well modelled with physicochemical and structural properties,whereas modelling the properties of biological membranes remains a major


challenge due to factors such as P-glycoprotein (P-gp), a transporter proteinresponsible for secretory intestinal processes, and the cytochrome P450 iso-form 314 (CYP3A4) involved in gut-wall metabolism [9] [10].

In this chapter, we critically discuss recent progress in the understandingof membrane permeability, focusing first on the gastrointestinal barrier and onhow to better predict oral absorption, and second on the blood-brain barrier(BBB) and our ability to predict central nervous system (CNS) penetration.

2. Modelling Oral Absorption

2.1. Mechanism of Membrane Crossing

A number of membrane models have been developed relating lipophilic-ity to flux or transport across a membrane [11]. In a first approximation, amembrane can be considered as a homogeneous organic solvent, often mim-icked with n-octanol. This has been called the solution/partitioning mem-brane model (see Fig. 1) [12].

The bilayer model comprises the phospholipid head-group region and thealkyl-chain portion (see chapter by Mouritsen et al., p. 33). Many drugs arelipophilic bases which are positively charged under physiological conditions.These compounds may interact with negatively charged phospholipid headgroups in the membrane. The transmembrane movement of such compoundsis believed to occur via a flip-flop mechanism with a lifetime of about 1 min(see Fig. 2) [12].

Our growing understanding of the morphology and constituents of mem-branes and their role in drug transport challenges this simple view, and a need


Fig. 1. The solubility/diffusion model describing the transmembrane movement of solutes [12].Drug in the extracellular space (Do) dissolves in the outer surface of the lipid membrane to aconcentration (Dom) determined by its lipophilicity. The drug diffuses down a gradient withinthe membrane to the inner surface of the membrane (Dim). The pool at the inner surface is inequilibrium with the drug in the cytoplasm (Di). This model is simplistic since the membrane

is a bilayer composed of polar and lipid regions.

for more sophisticated membrane models is warranted. The epithelial mem-brane consists of cells connected to each other via tight junctions. Smallhydrophilic drugs may be absorbed via this paracellular pathway. Therefore,old membrane models such as the so-called two-step distribution and the dif-fusional-resistance membrane models had to be extended taking aqueouspores into account (see Fig. 3) [13]. This model shows that combined log Dvalues and molecular weights are important for permeability and that thewell-known sigmoidal permeability-lipophilicity relationship should be con-sidered as a molecular-weight-dependent set of sigmoidal curves. Examplesof such sigmoidal sets can be found in the literature [14] [15]. Whilst intel-lectually appealing and conforming to the behavior expected, the curves suf-fer from a lack of experimental data. Hence, they remain hypothetical andcannot become the norm to guide the design of new compounds. Of impor-


Fig. 2. The flip-flop model describing the transmembrane movement of solutes [12]. A drug inthe extracellular space (Do) dissolves in the outer leaflet of the membrane (Dom). The drugcrosses the membrane by flip-flop events (fi and fo). The drug pool in the inner membrane leaf-

let (Dim) is in quasi-equilibrium with the cytoplasm in the cell (Di).

Fig. 3. Aqueous pore model to describe transport across a membrane [13]. The model includespores and both aqueous (outside of the membrane) and organic (reflecting outer membrane

leaflets) stagnant diffusion layers (DL).

tance here is the fact that the currently used monolayer cell lines (Caco-2 andMDCK) do not have tight junctions similar to those of intestinal epithelia.Thus, data from the most developed model systems are not generally appli-cable.

Studies on the incorporation of lipophilic oligopeptides into phospholipid-membrane vesicles have demonstrated that hydrophobic interactions are themajor driving force of solute accommodation into the region of polar phos-pholipid head groups [16]. Transfer of such compounds into the interior of thephospholipid bilayer depends mainly on H-bonding and ionic interactions,and less on hydrophobicity.

2.2. Tube Models

Drug flow in the gastrointestinal tract and absorption from the small intes-tine have been modelled by so-called tube models [17–20]. The macroscopicmass-balance approach utilizes mass-transfer relationships to estimate theextent of absorption [19]. This oversimplified tube model has recently beenimproved by adding dentritic-type villi to the tube and using probability con-cepts to describe the time evolution of the dissolution and absorption pro-cesses [18].

2.3. Modelling the Role of P-Glycoprotein and CYP3A4

P-Glycoprotein (P-gp) is a membrane transporter first identified in multi-drug-resistant (MDR) tumor cells. Subsequently, it was found to be expressedin normal tissue, including intestinal epithelium and the blood-brain barrier,and in human intestinal cell cultures such as the Caco-2 and MDCK cell lines.P-gp is located on the apical (luminal) side of the intestinal membrane and isinvolved in the secretory transport of a wide range of drugs. This drug-effluxpump affects, for example, the oral delivery of anti-HIV drugs [21].

A number of drug-metabolizing enzymes are expressed in the gut wall,the most important of which is CYP3A4 [22]. An up to 30-fold variation inCYP3A levels in the small intestine has been reported. It is postulated thatboth CYP3A4 and P-gp work in a concerted way to form an efficient barrierto prevent many xenobiotics from entering the human body [23].

A computer model has been presented which incorporates the operationof P-gp in multidrug resistant (MDR) cells [12]. A transport model using pas-sive membrane permeability from Caco-2 monolayers combined with P-gpaffinity data has been suggested [24]. Such studies have demonstrated thataffinity to P-gp may not necessarily compromise the absorption of a com-


pound, as shown for verapamil, which has high permeability. By contrast,talinolol has lower P-gp affinity but also lower permeability and is muchmore affected by efflux. Other models are under development to predict in vivo intestinal metabolism from in vitro data [23].

The gut-extraction ratio can be seen as a function of the rate constant formetabolism in the gut (kmg) in the numerator divided by the sum of the com-peting processes for removing the active drug from the enterocyte, metab-olism (kmg) and absorption into the blood (ka), in the denominator (cf. Eqn. 1and Fig. 4) [23].

ERg = kmg/(kmg + ka) (1)

This model readily explains how the flip-flop effect described above, andthe resultant slower passage, may imply that P-gp efflux favors bases.Moreover, H-bonding capacity, which slows membrane crossing, may furtherincrease the efficiency of P-gp efflux. These factors, which limit membranecrossing, may be confused with P-gp selectivity. Thus, the role of H-bond-acceptor strength in increasing affinity for P-gp is now recognized [25] [26].What is unclear is whether this factor is related to interactions with lipids orproteins. This information on the role of gut-wall metabolism and efflux canthen be used in more elaborate models of oral absorption in which the effectsof CYP3A4 metabolism and P-gp efflux are combined [27] [28].

Beside the well-studied role of P-gp in membrane transport, other trans-porters may also influence permeability. A model for epithelial drug transportinvolving P-gp in MDCK cell predicts that the MDCK apical membranes areless diffusion-permeable than the basolateral membrane for both vinblastineand digoxin [29]. Different expression levels of various transporters on theapical and basolateral sides of the membrane may be one possible explana-


Fig. 4. Gut-extraction ratio composed of rate of metabolism (kmg) and absorption into theenterocyte (ka) [23]

tion. The problem here is how a canine kidney-cell line relates to human drugabsorption. Indeed, relevance becomes questionable when one moves beyondan undifferentiated monolayer as a model of membrane permeability to a cellwith particular expression characteristics.

2.4. The Role of Charge on Membrane Transport

Many metabolic and other pharmacokinetic processes can be related todistribution coefficients (log D) measured in octanol/buffer. Because log Dcontains a correction for the degree of ionization, it is a powerful yet compos-ite descriptor. There is general belief that only uncharged species can crossmembranes. However, a formal electrical charge can be highly delocalizedand therefore be less of an impediment than believed, especially when log Dis sufficiently high. In vitro model systems allow to test such a hypothesis.For instance, the pH-dependent permeation of one rapidly (alfentanil) andone slowly (cimetidine) transported drug across Caco-2 cell layers was inves-tigated (see Fig. 5) [30]. The transport of the unionized form was 150- and30-fold faster than the transport of the ionized form for altentanil and cimet-idine, respectively. At pH values where the fraction of the unionized form islow, the contribution of the transport of the ionized form becomes significantand measurable. The authors suggested a modification of the pH-partitiontheory to predict intestinal absorption. However, the important questionremains whether rates are of any consequence in human drug absorption. Fora small molecule, does passage via aqueous pores (paracellular absorption)dwarf the permeation of ionized drug? One has to question whether thedebate on ionized vs. unionized drug really helps in drug design.


Fig. 5. Structures of a drug rapidly (alfentanil) and slowly (cimetidine) transported throughCaco-2 monolayers

2.5. How Important is Molecular Size?

There are several views on the role of molecular size in membrane cross-ing [31] [32]. It has been proposed that permeability is directly proportionalto the diffusion coefficient of the molecules or to 1/(molecular volume)n.Other authors have made corrections using Mr

1/3 or Mr1/2. To account for vari-

ations in molecular shape is even more difficult. Furthermore, the 3D-distri-bution of properties such as lipophilicity, H-bonding, and atomic charges onthe molecular surface may impact on permeability. This is even more relevantnow that we understand that cell crossing involves membrane and proteininteractions. Whilst bulk properties may account for interactions with lipids,they are extremely unlikely to be applicable to interactions with proteins suchas P-gp. The ‘rule-of-five’ proposed by Lipinski et al. [33] indicates the diffi-culty in deconvoluting the exact role of molecular size. Oxygen- and nitro-gen-containing groups have to be incorporated into a molecule in order tokeep its log P below 5 to prevent poor solubility. And as the molecular weightapproaches 500, the number of H-bond donors and acceptors will have to beincreased beyond the limits of the rule-of-five simply to remain below the logP ceiling.

2.6. Rate vs. Partitioning

Drug distribution in the body is mainly a dynamic process with only pseu-do-equilibrium states in certain organs and membranes. Hence, rates of trans-port into or through membranes may contain valuable information. Often,sink conditions on one side of the membrane prevail and do preclude a realequilibrium situation. These sink conditions may arise simply from transportaway from the membrane or binding to proteins or organ tissue. The Caco-2model for absorption is normally used as an equilibrium model, although thetime course may be chosen such that pseudo-sink conditions prevail. It hasrecently been suggested that using plasma on the basolateral side may be bet-ter to mimic normal absorption [34]. In this study, the presence of proteinssignificantly reduced the efflux of estradiol 17-O-glucuronide by 66%, oftaxol by 75%, of propranolol by 82%, and of quercetin by 94%. The conclu-sion was that failure to consider the effect of plasma binding can result in anoverestimate of basolateral-to-apical efflux and hence in misleading net-fluxcalculations.

Preliminary investigations suggest that the kinetics of cellular uptake intocerebral capillary endothelial-cell monolayers may be better than transmono-layer flux to predict passive diffusion of polar permeants across the BBB in vivo [35].


Caco-2 monolayer permeability coefficients have been compared to humanclinical permeation-rate constants [36]. A rank-order relationship was observed,but no combination of in vitro test systems adequately reflected in-vitro-disso-lution/in-vivo-absorption relationships as a function of formulation.

Compounds that cross membranes rapidly are less likely to be adverselyaffected by either CYP3A4 or the P-gp efflux pump discussed above. Rela-tionships between transport-rate constants and equilibrium-distribution coef-ficients have been investigated in octanol/water systems [37] [38]. The datain Fig. 6 suggest that, with increasing lipophilicity, the rate into a membraneincreases linearly up to a certain log D value and then reaches a plateau value.

3. Measuring and Modelling Brain Penetration

The blood-brain barrier (BBB) is formed by epithelial-like high-resis-tance tight junctions within the endothelium of capillaries perfusing the ver-tebrate brain. Because of the presence of the BBB, circulating drugs gainaccess to brain cells mainly by lipid-mediated transport through the BBB byfree diffusion, although examples of active transport are discovered inincreasing number. Usually, transport via the tight junctions (paracellular) isignored. The usual targets for CNS drugs are seven-transmembrane-domain


Fig. 6. Relationship between the rate constants log k of transport from buffer at pH 7.4 into octanol as a model membrane, compared to log D values [38]

(7-TM) receptors. These receptors have their active site accessed from theaqueous environment surrounding the cell (ECF). The blood-brain barrier anda blood-cerebrospinal-fluid (CSF) barrier function together to isolate thebrain from circulating drugs. The blood-CSF drug-permeability barrier islocalized in the epithelium of the choroid plexus (CP). P-gp is located sub-apically, conferring an apical-to-basal transepithelial permeation barrier.Conversely, the multidrug-resistance-associated protein (MRP) is locatedbasolaterally, conferring an opposing basal-to-apical drug-permeation barrier.Together, these transporters may attenuate or enhance the secretion and reab-sorption of drugs into and out of the CNS.

Much of the data produced on brain penetration concerns the partitioningof drugs into whole brain from blood or plasma. This is a simple model ofbrain penetration compared to reality. Physicochemical models of the datahave shown that brain penetration needs a low H-bonding potential and a rel-atively high lipophilicity. Partitioning systems using cyclohexane/water incombination with octanol/water (log P) have been cited as better model sys-tems than octanol [39].

These extrapolations may not actually help to design CNS drugs whichrequire access to receptors such as 7-TMs. Whole-brain partitioning repre-sents partitioning into the lipid of the brain, and not actual access to drugreceptors. For instance, desipramine partitions into brain and is distributedunevenly [40]. The distribution corresponds to lipid content of the brainregions and not to specific desipramine binding sites. For receptors such as 7-TMs, ECF concentrations determine activity, but CSF concentrations can betaken as a reasonable guide of ECF concentrations. The apparent dramaticdifferences in brain distribution described for total brain become rather smallwhen the free (unbound) concentration of drug in plasma in compared to theCSF concentration. Whole-brain/blood partitioning reflects nothing but aninert partitioning process of drug into lipid material. The lack of informationconveyed by the total brain concentration is indicated by studies on KA-672[41], a lipophilic benzopyranone acetylcholinesterase inhibitor. The com-pound achieved a total brain concentration of 0.39 µM at a dose of 1 mg/kg,equivalent to the IC50 determined in vitro (0.36 µM). Doses up to 10 mg/kgwere without pharmacological effect. Analysis of CSF indicated that the con-centrations of the compound were below 0.01 µM, readily explaining the lackof activity. These low concentrations are presumably caused by a high clear-ance of (unbound) free drug.

Free drug partitioning actually reflects the drug reaching the receptor andeliciting pharmacological effects. Unless active transport systems are in-volved, the maximum CSF-to-plasma partition coefficient is 1. This shouldbe contrasted with the 10- or 100-fold higher affinity of total brain comparedto blood or plasma. The minimum partitioning based on a limited data set


appears to be 0.1. Fig. 7 shows a comparison between lipophilicity (log D)and the ratio cCSF/cPLASMA for a series of different compounds, illustratingthe limited range of partitioning. It is apparent that passage through the tightjunctions is more significant than previously recognized. It should be notedthat the term log D is not a perfect descriptor, and some of the measures whichincorporate size and hydrogen bonding may be better. Clearly, however, theCNS is more permeable than assumed, allowing drugs like sulpiride (com-pound 3 in Fig. 7) to be used for CNS applications.

Sulpiride is one of the hydrophilic drugs shown above. The relationship isexplored further in Fig. 8, which shows that free drug concentrations do notcorrelate with receptor occupancy. In contrast, its CSF concentrations (about4-fold lower than the free plasma concentration) correlate directly with actual receptor occupancy (as measured by positron-emission tomography(PET)) and pharmacological activity. If actual total brain concentrations weremeasured, then these would be considerably lower than CSF concentrations,and, if taken as a measure of CNS penetration, would suggest that the drug isnot acting in a conventional manner.

The fact that hydrophilic compounds have ready access to the CNS, albeitup to 10-fold less than lipophilic compounds, is worthy of further considera-tion. For instance, -adrenoceptor antagonists are known to cause sleep dis-orders. In four drugs studied, the effects were lowest with atenolol (log D–1.6), intermediate with metoprolol (log D –0.1), and highest with pindolol


Fig. 7. CSF Concentration/free (unbound) plasma concentration ratios, compared to logD, forthe neutral and basic drugs ritropirronium (1), atenolol (2), sulpiride (3), morphine (4), cimeti-dine (5), metoprolol (6), atropine (7), tacrine (8), digoxin (9), propranolol (10), carbamazepine(11), ondansetron (12), diazepam (13), imipramine (14), digitonin (15), chlorpromazine (16), and

the acidic drugs salicylic acid (a), ketoprofen (b), oxyphenbutazone (c), and indomethacin (d)

(log D –0.1) and propranolol (log D +1.2). This was correlated with the totalamount present in brain tissue [42], which related to the log D values. Furtheranalysis using CSF data and receptor affinity to calculate receptor occupancydemonstrated that the occupation of the 1 central receptor was high for alldrugs [43]. Propranolol showed a low occupancy, possibly because its active4-hydroxy metabolite was not included in the calculation. In contrast, occu-pation of the 2 central receptor correlated well with sleep disturbances. Theincidence of sleep disturbances is therefore not due to penetration into theCNS but to the 1/2 selectivity of the compounds (atenolol > metoprolol >pindolol = propranolol). The relative receptor occupancies are illustrated inFig. 9. This analysis clearly indicates that lowering lipophilicity to obtain a


Fig. 8. Receptor occupancy calculated from the free (unbound) concentration of drug in plasma, the concentration in CSF compared to that measured by PET scan for sulpiride

Fig. 9. Central receptor occupancy after oral administration of the -adrenoceptor antagonistsatenolol (A), metoprolol (B), pindolol (C), and propranolol (D). The high occupancy of 1-receptors does not correlate with physicochemical properties (lipophilicity). The occupation of2-receptors correlates with sleep disturbances and the intrinsic selectivity of the compounds.

compound devoid of CNS effects is partly based on flawed premises and thatselectivity is more important.

Whilst the aqueous channels offer a greater-than-expected access, the roleof P-gp in the BBB will lower the expected penetration of lipophilic drugs.Experiments using mice deficient in mdr1 (P-glycoprotein) indicate that thepermeability of ivermectin, cyclosporin A, morphine, protease inhibitors[44], and the antihistamine fexofenadine [45] is decreased. None of thesecompounds has ideal membrane-crossing characteristics, and size would ren-der the paracellular passage of cyclosporin A and ivermectin negligible.Under these conditions, the rate of membrane transfer becomes critical (seeSect. 2.6).

4. Conclusion

A number of physicochemical measures and models are available to pre-dict oral absorption, extent of metabolism, and pharmacokinetic behavior.Some of the models have a complexity not far from the biological modelsthemselves. Despite this progress, drug design has not become more straight-forward, demonstrating that our understanding is far from complete. Here, wehave reviewed current insights in permeability, focusing on the gastrointesti-nal and blood-brain barriers. It is clear that our understanding of how to com-bine physicochemical with biological information needs to grow further.However, all systems we are interested in appear to have a paracellular, trans-cellular, and an efflux component. The paracellular route concerns mainlysmall molecules. With increasing lipophilicity, the transcellular pathway be-comes important but also involves an efflux component. The rate of mem-brane transfer is important, and H-bonding and size are probably critical fac-tors influencing this process. As rate decreases, efflux becomes more signifi-cant. Modelling these three dimensions with our present physicochemicalmodels might yield more satisfactions than trying to identify a single physico-chemical parameter closely related to membrane permeability.

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Structure-Metabolism Relations and theChallenge of Predicting Biotransformation

by Bernard Testa*1) and Gabriele Cruciani2)

1) Institut de Chimie thérapeutique, BEP, Université de Lausanne, CH-1015 Lausanne,Switzerland; Tel.: +41 21 692 4521; Fax: +41 21 692 4525; e-mail: [email protected]) Laboratorio di Chemometria, Università di Perugia, Via Elce di Sotto 10, I-06123 Perugia,

Italy; e-mail: [email protected]

1. Concepts in Drug Metabolism

1.1. The Importance of Drug Metabolism in Drug Design

Current drug design is mainly a ligand design aimed at discovering com-pounds with high affinity/activity towards predefined biological targets.Modern high-throughput techniques have rendered this strategy immenselysuccessful, but the road remains very long that leads from a high-affinityligand to a pharmacokinetically and toxicologically well-behaved drug can-didate. To decrease the costly and time-consuming development of activecompounds ultimately doomed by hidden pharmacokinetic and toxicologicaldefects, medicinal chemists must integrate metabolic considerations intodrug-design and lead-optimization strategies. Various aspects of drug metabo-lism of interest to medicinal chemists are listed in Table 1.

Table 1. Aspects of Drug Metabolism of Interest to Medicinal Chemists

• The chemistry and biochemistry of metabolic reactions• The consequences of such reactions on activation and inactivation, toxification and

detoxification• Predictions of drug metabolism based on quantitative structure-metabolism relation-

ships, expert systems, and molecular modelling of enzymatic sites• Prodrug and soft drug design• Changes in physicochemical properties (acidity, basicity, lipophilicity, etc.) resulting

from biotransformation


1.2. Pharmacodynamic Consequences of Drug Metabolism

Biotransformation can conveniently be subdivided into reactions of func-tionalization (the so-called phase-I reactions, which create or modify a func-tional group) and reactions of conjugation (the so-called phase-II reactions),which covalently attach an endogenous moiety to a xenobiotic or a metabolitethereof [1–5]. The two classes of reactions can produce active or inactivemetabolites and may also result in toxification or detoxification. A number ofpharmacodynamic and pharmacokinetic consequences of drug metabolismare listed in Table 2.

There are many different mechanisms by which metabolites may be toxic(Table 3). In some cases, unwanted effects result from an action at a pharma-cological target. More extensively investigated and hence better documentedare mechanisms of toxicity involving a post-metabolic reaction.


Table 2. Major Pharmacodynamic and Pharmacokinetic Consequences of Drug Metabolism

Table 3. Mechanisms Accounting for the Toxicity of Metabolites

Pharmacodynamic consequences• One or more active metabolites are produced• The drug yields one or more metabolites eliciting unwanted or toxic effects (toxification)• The drug is inactive per se (prodrug) but is transformed into an active metabolite

Pharmacokinetic consequences• When only inactive metabolites are formed (inactivation), the rate of metabolism affects

the duration and intensity of action of the drug• A metabolite inhibits a metabolic pathway, producing complex kinetics• The drug induces its own metabolism (auto-induction), resulting in a response that

changes over days• One or more metabolites have physicochemical properties vastly different from those of

the parent compound, for example a very high lipophilicity resulting in tissue accumu-lation and residue retention

Unwanted effects resulting from an action at a pharmacological target• Agonism or antagonism at an unwanted receptor • Interference with physiological enzymes and pathways• Accumulation in membranes of lipophilic residues formes by conjugation reactions

Mechanisms of toxicity involving a post-metabolic reaction• Covalent binding to biological macromolecules (reactive intermediates produced by

phase-I and even phase-II reactions)• Oxidative stress produced by oxygen-activating phase-I metabolites

As much as possible, medicinal chemists want to be able to anticipate thepossible occurrence of toxification reactions, especially the generation ofreactive metabolites able to form covalent adducts with critical biomolecules.Whereas human expertise is irreplacable, there is a clear need for reliable,versatile, and powerful expert systems, as discussed later in this text.

1.3. Binding vs. Catalysis

In schematic terms, three phases can be envisaged in any pharmacologi-cal process, be it a pharmacodynamic or a pharmacokinetic one (Fig. 1). Toelicit an effect, a drug (or any xenobiotic) must first penetrate into the bio-logical system (membrane, organelle, cell, organ, or organism) and into thecompartment of action (penetration step). It must then bind to a biological tar-get (binding step), usually a molecular ‘machine’ (enzyme, receptor, trans-porter, etc.). As a result, the biological ‘machine’ with bound ligand is acti-vated to produce a biological response (activation step) which will cascade to,e.g., an observable pharmacodynamic effect or a metabolic reaction [6][7].Note that this ‘activation’ may also be a blockade or inhibition.

Most significant here are the second and third phases of a biologicalresponse, which are considered below in terms of biotransformation reac-tions.

Indeed, enzyme kinetics allows a metabolic reaction to be readily decom-posed into a binding and a catalytic phase. In spite of its limitations, Micha-elis-Menten analysis offers an informative approach for assessing the bindingand catalytic components of a metabolic reaction [8]. Here, the Michaelisconstant Km represents mainly affinity, whereas the catalytic efficiency is ex-pressed by Vmax or the turnover number kcat.


Fig. 1. The three conceptual steps in the interaction of a drug with a biological system. Thesesteps underlie any pharmacodynamic or pharmacokinetic event (modified from [6][7]).

A point to be stressed, and one which has an overwhelming albeit in-sufficiently recognized significance in drug metabolism, is the vast differencein energy levels existing between the binding step and the catalytic step.Whereas the binding step is a reversible one, typically liberating about10 kcal/mol, the catalytic step implies the breaking and formation of covalentbonds and thus involves significantly higher energy levels (Fig. 2). This basicdifference has fundamental implications in structure-metabolism relation-ships (SMR), such that the binding step is expected to correlate with surfaceproperties (polarity, hydrophobicity, etc.), whereas the catalytic step shouldbe related to core molecular properties (properties of molecular orbitals, reac-tivity, etc.). Steric properties (steric hindrance, topography, etc.), being part-ly intercorrelated with surface and core molecular properties, are expected toinfluence both the binding and catalytic step (see Sect. 2).

1.4. Structural Selectivities in Drug Metabolism

There is a fundamental difference between drug metabolism and pharma-cological processes, such that the enzymatic reactions of metabolism display


Fig. 2. Schematic representation of the energy levels encountered in drug metabolism. Thelower panel presents the molecular properties expected a priori to account for (i.e., to correlate

with) the steps of penetration, binding, and catalysis.

an element of complexity usually not occurring at receptor sites. Indeed, thereis only one type of selectivity occurring at the receptor level, namely thequantitatively or qualitatively different responses elicited by various pharma-codynamic agents. In contrast, two different types of selectivity exist in xeno-biotic metabolism, namely substrate selectivity and product selectivity (Fig. 3)[2][9–12].

Substrate selectivity is defined as the differential metabolism of distinctsubstrates under identical conditions; its analogy with pharmacological pro-cesses is clear. In contrast, product selectivity (defined as the differential for-mation of distinct metabolites from a single substrate under identical condi-tions) has no known correspondence in receptor-mediated events. Both typesof selectivity can be subdivided into subtypes depending whether substrates(or products) are non-isomeric (e.g., homologues, analogues, or congeners),regioisomeric (i.e., positional isomers), or stereoisoisomers (diastereomers orenantiomers). These subtypes are listed and defined in Fig. 3. It is also ofcommon occurrence that product selectivity varies from substrate to sub-strate, in other words that product selectivity is substrate-selective (substrate-product selectivity).

Both substrate and product selectivity are of utmost significance whenattempting to predict biotransformation. Indeed, substrate selectivity offers theconceptual framework to rank substrates according to their relative rate of bio-


Fig. 3. Various types of selectivity encountered in drug metabolism [2][9–12]

transformation by a given enzyme, in a given reaction, in a given organ, in agiven organism, etc. Product selectivity is even more important when a singlesubstrate is considered, since it allows to make sense of the relative rates offormation of metabolites generated by different routes, different enzymes, oreven resulting from attack by the same enzyme at different positions in themolecule. Thus, chemoselectivity will be observed when different types ofatoms are attacked (e.g., O- vs. N-glucuronidation, N- vs. S-oxygenation,C(sp3) vs. C(sp2) hydroxylation), whereas regioselectivity implies that thesame type of atom exists in the two or more positions being attacked (e.g.,ortho- vs. para-hydroxylation). There is even some overlap between chemo-selectivity and regioselectivity, e.g., phenol vs. alcohol glucuronidation, thechemical difference between the ether glucuronides so formed being small.

From a general viewpoint, innumerable publications confirm the value ofthe concepts of substrate and product selectivity, which contribute to a clearerpresentation of data and a facilitated understanding of structure-metabolismrelationships.

2. Structure-Metabolism Relationships

2.1. Overview

As mentioned in Sect. 1.3., different structural and physicochemical prop-erties will be found to play a predominant role in SMR. In in vitro studiesdesigned to determine separately the binding and catalytic steps (Km andVmax), different SMR should be found for each step. In vitro studies restrict-ed to determining reaction rates, as well as in vivo studies, yield metabolicresponses that possibly express the penetration step, and certainly the bindingand catalytic steps. Hence, such metabolic responses will have a more hybridcharacter and should lead to more empirical correlations of limited mechan-istic interpretability (Fig. 4).

The sections to follow will illustrate selected aspects of structure-meta-bolism relationships.

2.2. Relations between Metabolism and the Lipophilicity of Substrates

Comparing the overall metabolism of numerous drugs clearly reveals aglobal relation with lipophilicity. Indeed, there exist some highly polar xeno-biotics known to be essentially resistant to any metabolic reaction, e.g., sac-charin, disodium cromoglycate, and oxaceprol [13–16]. Furthermore, manyin vivo metabolic studies have demonstrated a dependence of biotransforma-


tion on lipophilicity, suggesting a predominant role for transport and parti-tioning processes. A particularly illustrative example is offered by the anti-asthmatic chromone-2-carboxylic acids [17], whose pharmacokinetic behav-ior in the rat has revealed the opposite influences of log D on renal clearance(which decreased with increasing lipophilicity) and metabolic clearance(which increased with increasing lipophilicity).

A similar dependence is demonstrated by -blockers. Propranolol andother lipophilic -blockers are extensively metabolized in humans and showshort plasma half-lives of a few hours. In contrast, the more hydrophilic -blockers (e.g., atenolol) undergo little or no metabolism and are slowlyexcreted by the kidney, with long plasma half-lives of approximately 10–20 h.In this class of drugs, elimination is thus realized by metabolism rather thanexcretion, and a clear trend exists between lipophilicity and extent of meta-bolism.

This global trend is in line with the Darwinian rationale for xenobioticmetabolism, which is believed to have evolved in an animal-plant ‘warfare’,with herbivores adapting to the emergence of protective chemicals (e.g., alka-loids) in plants [18].

The exception to the global and direct relation between extent of metab-olism and lipophilicity is offered by the vast number of human-made, highlylipophilic polyhalogenated xenobiotics which now polute our entire bio-sphere [19–21]. Such compounds, which include polyhalogenated insecti-


Fig. 4. Biological limitations in SMR: schematic relations between biological systems of study,metabolic steps, the plurality of reactions, and resulting consequences in quantitative structure-

metabolism relationships

cides (e.g., DDT), polyhalogenated biphenyls, and dioxins, have a strong pro-pensity to accumulate in adipose tissues. In addition, they are highly resistantto biotransformation in animals due in part to their very high lipophilicity, andin part to the steric shielding against enzymatic attack provided by the halo-gen substituents.

When the results of Michaelis-Menten analyses are examined for quanti-tative structure-metabolism relationships (QSMR), it is often found that lipo-philicity correlates with Km but not with Vmax, in agreement with the a prioriargument discussed in Sect. 1.3. The metabolic hydrolysis of esters of nico-tinic acid in rat liver and brain subcellular fractions offers an illustrativeexample of this rule [22]. For esters covering a broad range of structures(alkyl, cycloalkyl, functionalized alkyl, or aryl esters), Km and Vmax valueswere determined in rat-liver microsomes, mitochondria, and cytosol. No cor-relation with lipophilicity existed for the Vmax response, whereas the Km

parameter was correlated to a lipophilicity parameter measured by RP-HPLC(log kw

o) (Eqns. 1–3) :

Liver microsomes:pKm = – 0.23(±0.05) (log kw

o)2 + 1.8(±0.3) log kwo + 0.36(±0.38) (Eqn. 1)

n = 10; r2 = 0.913; q2 = 0.871; s = 0.202; log kwo opt. = 3.9

Liver mitochondria:pKm = – 0.20(±0.06) (log kw

o)2 + 1.5(±0.4) log kwo + 0.75(±0.47) (Eqn. 2)

n = 12; r2 = 0.816; q2 = 0.729; s = 0.252;log kw

o opt. = 3.75

Liver cytosol:pKm = – 0.094(±0.030) (log kw

o)2 + 1.0(±0.2) log kwo + 0.16(±0.2) (Eqn. 3)

n = 11; r2 = 0.957; q2 = 0.935; s = 0.130;log kw

o opt. = 5.3 (outside explored range)

In other words, this study showed lipophilicity to have a major influenceon the affinity of nicotinate esters for rat-liver hydrolases, but to play no rolein the subsequent catalytic step. A comparable conclusion emerged from theQSMR study of Martin and Hansch on the oxidation of drugs by rat-livermicrosomes [23][24]. Also, the oxidative deamination of homologous ali-phatic amines by rat-liver monoamine oxidase and rat-aorta amine oxidaserevealed bell-shaped relations between Km and chain length, but no relationfor Vmax [25][26].

Given its spectral characteristics, cytochrome P450 (CYP) is of particularinterest to investigate the binding step and the molecular factors that influ-ence it. Indeed, substrate binding to CYP can be detected as a type-I differ-ence UV spectrum (peak at 385–390 nm, trough at ca. 420 nm) and quanti-


fied as Ks (in M units) [2]. The substrate-binding mode to microsomal cyto-chrome P450 has repeatedly been shown to be related to lipophilicity. This isexemplified by an extensive study of the type-I binding affinity of 50 modelcompounds to hamster-liver microsomes. Linear relations were found be-tween Ks and Doct

7.4 [27]. These relations incorporated monocyclic hydrocar-bons, bicyclic hydrocarbons, higher hydrocarbons, homologous carbamates,fatty acids, and fatty-acyl methyl esters, respectively, with r2 values in therange 0.90 to 0.99. The fact that not all compounds could be fitted into a sin-gle regression suggests that size and shape also influenced affinity

2.3. Relations between Metabolism and Electronic Properties of Substrates

Because catalysis is characterized by the cleavage and formation of cova-lent bonds, the catalytic step is expected to be controlled in part by molecu-lar-orbital properties of the substrates. This can be seen in attempts to ration-alize product regioselectivity in reactions of C-oxidation, which are commonto the vast majority of drugs. Such reactions may occur at a number of near-equivalent positions (e.g., ortho- vs. meta- vs. para-position in aromatic rings;α- vs. β- vs. γ-position in alkyl substituents). In fact, metabolic data consis-tently indicate that reactions of C-oxidation occur with high regioselectivity.The same is true for reactions of N-oxidation, as well as for hydrolyses andconjugations.

In the case of CYP-mediated C-hydroxylation, a very large body of evi-dence has led to reliable yet qualitative predictive rules of product regioselec-tivity. C(sp3)-atoms in benzylic, allylic, or penultimate positions, or in posi-tions α to heteroatoms, are favorite targets of hydroxylation (Fig. 5). The fac-tors accounting for product regioselectivity in C(sp3) hydroxylation arechemical and enzymatic. Chemical factors include slightly larger electrondensities in the priviledged positions of attack [28][29], and relative heats offormation of corresponding radicals (following H· loss) [30]. However, otherstructural factors only account for part of the observed regioselectivity,inasmuch as they influence the binding mode and, as a consequence, the tar-get site facing the catalytic group.

Electronic properties may also be of some interest in rationalizing sub-strate selectivity, at least in ranking the reactivity of analogous substrates. Anexample of this type can be found in the microsomal dechlorination of chloro-alkanes at the terminal CH2Cl or CHCl2 group [31]. When examining separ-ately chloroethanes and chloropropanes, a trend becomes apparent betweenthe extent of enzymatic dechlorination and the electron density at the targetC-atom (Fig. 6). It appears that electron deficiency at the C-atom facilitatesenzymatic attack, but the trend is a qualitative one.


An example of a quantitative SMR study correlating electronic propertiesand catalytic parameters is provided by the glutathione conjugation of fivepara-substituted 1-chloro-2-nitrobenzene derivatives [32]. The values oflog k2 (second-order rate constant of the non-enzymatic reaction) and log kcat

(enzymatic reaction catalyzed by various glutathione-transferase prepara-tions) were correlated with the Hammett-resonance – value of the substrates,a measure of their electrophilicity. Regression equations with positive slopes


Fig. 5. A hypothetical molecule showing positions (marked by an arrow) of regioselectiveCYP-catalyzed C(sp3) hydroxylation

Fig. 6. Microsomal dechlorination of chloroethanes () and chloropropanes () at CH2Cl andCHCl2 groups, as related to the electron deficiency at the target carbon (modified from [31])

and r2 values in the range 0.88 to 0.98 were obtained. These results point toa trend relating substrate electrophilicity and ease of nucleophilic substitutionmediated by glutathione, be it enzymatic or non-enzymatic. However, thesmall number of substrates examined (five) forbids any quantitative extra-polation.

Another example is afforded by the microsomal N-demethylation of para-substituted N,N-dimethylanilines [33]. For 12 substrates, a fair correlationwas found between Km and the hydrophobic fragmental constant (), indicat-ing that affinity decreased with decreasing lipophilicity (Eqn. 4) :

log Km = 0.71 + 1.63 (Eqn. 4)n = 12; r2 = 0.68; s = 0.31

Interestingly, the maximal velocity Vmax was found to increase slightlywith increasing lipophilicity, but mainly to decrease with increasing electron-withdrawing power of the para-substituent (as assessed by the Hammett con-stant ) (Eqn. 5) :

log Vmax = 0.39 – 0.94 – 1.56 (Eqn. 5)n = 12; r2 = 0.80; s = 0.23

Eqns. 4 and 5 were derived for N-demethylations mediated by livermicrosomes from phenobarbital-pretreated rats, i.e., when only one or a veryfew enzymes were involved. No correlation was obtained with metabolic datafrom untreated animals, i.e., when a variety of native enzymes were involved.QSMR examples of this type, although rather rare in the literature, are ofinterest in offering mechanistic insights and unveiling qualitative trends.Their capacity for quantitative predictions, in contrast, is very much limitedto the chemical series under consideration.

2.4. Stereochemical Factors and Molecular Modelling in Drug Metabolism

The influence of configurational factors in xenobiotic metabolism is awell-known and abundantly documented phenomenon [9–12][34–36]. Thus,substrate-enantioselective biotransformation is the rule for many chiral drugsand ranges from practically complete to moderate, with only a limited num-ber of proven examples of lack thereof. Product stereoselectivity is also acommon phenomenon in drug metabolism. In many prochiral or chiral drugs,the methylene group is frequently a center of prochirality, the enzymatic reac-tion discriminating between the two enantiotopic or diastereotopic H-atoms.

As opposed to configurational factors (enantio- and diastereoselectivity),the role of conformational factors in drug metabolism has rarely been exam-ined. Thus, an intriguing result was the opposed substrate enantioselectivity


and identical product regioselectivity seen in the hydroxylation of warfarinand phenprocoumon, mediated by -naphthoflavone-induced rat-liver micro-somes. Indeed, the reaction was selective for the 6- and 8-position of (R)-war-farin and (S)-phenprocoumon. This opposed enantioselectivity remainedunexplained for years until it was shown that (R)-warfarin binds to cyto-chrome P450 as the cyclic hemiketal tautomer and in a conformation that ren-ders it topographically equivalent to (S)-phenprocoumon despite oppositeabsolute configurations [37].

para-Chlorophenoxyalkanoic acids differing only marginally in terms ofacidity and lipophilicity showed singularly different pattern of metabolic con-jugation. Thus, the acetic and propionic homologues are essentially inerttowards glucuronic-acid conjugation in mammals. In contrast, the isobutyrichomologue (clofibric acid) is extensively glucuroconjugated in most species.A theoretical conformational study of the three compounds showed that theyall prefer folded conformations, but the isobutyrate, in contrast to its twolower homologues, also has extended conformers of relatively low energy(2–5 kcal/mol). This result led to the suggestion that the conformation of aryl-oxyalkanoic acids recognized at the binding site of glucuronyltransferasemust be the extended one [38]. The above examples show that conformation-al factors may play a subtle and sometimes determining role in biotransfor-mation, and that they must be taken into account in relevant cases for a prop-er assessment of SMR.

But in which metabolic step do stereochemical factors come into play?Clearly, the 3D geometry of substrates is an essential component of the ste-reoelectronic features that govern binding to enzymes and hence make up a ‘haptophore’ (haptein = to bind) also called a ‘pharmacophore’ by analogywith receptor binding. In addition, such stereoelectronic features also governthe positioning of target groups in the catalytic site and as such can influencethe catalytic step. Molecular modelling of substrate-enzyme interactions con-vincingly illustrates these phenomena and allows useful predictions to bemade (see, e.g., [39]). This approach is aptly demonstrated and exemplifiedby Vermeulen et al. elsewhere in this volume (see p. 549).

Quantitative three-dimensional SMR (3D-QSMR) are another powerfuland promising tool for rationalizing the influence of molecular factors onmetabolism and venturing quantitative predictions within congeneric series. Asan example, 38 drugs known to be good substrates of the human cytochromeP450 CYP3A4 were investigated, using as biological data their Km values andthe nature of the metabolite(s) (position and functional group) [40]. Good 3Dmodels were obtained which indeed revealed a common pharmacophore con-sisting in four approximately equidistant groups: two H-bond acceptors, one H-bond donor, and one hydrophobic region. Such models have a predictive(quantitative) character, but their range of extrapolability is difficult to assess.


2.5. Interest and Limitations of Quantitative Structure-MetabolismRelationships

The examples discussed above confirm that different molecular propertiescontrol binding affinity and susceptibility to catalysis, as suggested in Fig. 2.As such, examples of this type can afford valuable mechanistic insights. Incontrast, the extrapolative power of the correlations is doubtful, restrictingtheir value to the explored property space and to the chemical series investi-gated. The contributions of quantitative SMR to drug design thus appear lim-ited at present but certainly not devoid of interest.

Furthermore, the influence of substrate properties on metabolism are butone face of the coin. Enzymatic constraints also contribute significantly to sub-strate binding and to catalysis, and hence to observed substrate and productselectivities. For example, the protein environment of the heme is the key enzy-matic factor accounting for selectivity in cytochromes P450. Already years ago,it has been shown that a difference of a few amino acids between two isozymes[41], or even changing a single amino acid by site-directed mutagenesis [42],may drastically affect selectivity. For example, the relative para/ortho/metaturnover numbers for acetanilide hydroxylation by CYP1A2 were found to be720:11:28, but they changed to 13:13:1 in the Arg455Gly mutant [43].

3. Predicting Drug Metabolism

3.1. Current Systems

Predicting the metabolic fate of any new chemical entity with reasonableconfidence remains a major objective of drug research. Experimental meth-ods combining in vitro metabolism (using, for example, human liver micro-somes or engineered cells expressing some human enzymes) with powerfulhyphenated techniques (LC-MS, MS-MS) have opened the road to high-throughput screening (HTS) applications in metabolism. However, thesetechniques have their limitations and do not replace fast and reliable in silicometabolic screening, to be applied in early phases of drug design to examineadequacy with prerequisites (e.g., fast activation in the case of a potential pro-drug, absence of adduct-forming metabolites, no uptake by given cyto-chromes P450).

As reviewed by Hawkins, one approach to predict metabolism is to usedatabases in the form of either knowledge-based systems or predictive expertsystems [44]. Existing knowledge-based systems are Metabolite [45][46],and the book series Biotransformations [47], which has been produced as asoftware product called Metabolism [46][48]. These databases can be


searched to retrieve information on the known metabolism of compoundswith similar structures or containing specific moieties.

Predictive databases attempt to portray the metabolites of a compoundbased on knowledge rules, defining the most likely products. Existingsystems of this type are MetabolExpert [46][49] and META [46][50].

3.2. Goals of Metabolic Prediction

Reasonable metabolic prediction is indeed a major objective of drugresearch, but it is a very fuzzy and broad one which calls for definition andclarification. A number of goals towards this objective are listed in Table 4.

As suggested in Table 4, predicting all reasonable metabolites (goal 1) rep-resents the simplest goal, although the term ‘reasonable’ is left undefined here.Classifying these metabolites into a metabolic tree (goal 2) calls for addition-al rules, as does the identification of potentially reactive and/or adduct-form-ing metabolites or metabolic intermediates (goal 3). The difficulty reaches newheights when semi-quantitative predictions are sought based on molecularproperties of the substrate (goal 4). The necessary rules must also originate inexisting knowledge, but they should be derived from structure-metabolismrelationships using such statistical tools as multivariate analyses and neuralnetworks. The same is true for the highest level of difficulty, when biologicalfactors are taken into account to modulate the predictions according to animalspecies, genetic factors, age, etc. (goal 5). In other words, the goal of the ulti-mate expert system would be to generate condition-dependent, semi-quantita-tive metabolic trees, a goal that will only be met very progressively.

3.3. The MetaFore Project

The authors present here a general strategy towards an expert system ableto fulfil the goals listed in Table 4. The creation and development of such a


Table 4. Goals in Metabolic Predictions, Classified by Increasing Difficulty

Goal 1: A list of all reasonable phase-I and phase-II metabolites of a given compoundGoal 2: Same as above, organized in a metabolic treeGoal 3: Same as above, plus a warning for reactive/adduct-forming metabolitesGoal 4: Same as above, plus a) a probability of formation based on molecular factors,

and b) a filter against improbable metabolitesGoal 5: Same as above, plus a probability of formation under different biological con-

ditions

predictive database is in progress in the authors’ laboratories and will proceedstage-wise. A first stage aims at meeting goals 1–3, with a second stage meet-ing goal 4, and a final stage to reach goal 5. The latter goal is obviously andby far the most ambitious and difficult one, but even its partial fulfilmentwould go beyond anything available to date.

The chosen strategy begins with two comprehensive lists, one of func-tional groups and the other of the metabolic reactions they may undergo(e.g., Fig. 7 ). To combine the two lists of functional groups and their reac-tions in a 2D table, one must assign a probability to each box in the table(Fig. 8). In an initial phase, these probability factors can simply be set to 0or 1 by a human expert depending whether the given group is known toundergo the given reaction or not (goals 1–3). In later stages, the possibil-ity exists of using neural networks or other rule-generating procedures toassign semi-quantitative values to the probability factors, e.g., 0, 1, 2 and 4(goal 4).

At a higher level of complexity (Fig. 9), the influence of proximal molec-ular factors will be considered. Such factors are well known and include thepresence of activating groups (unsaturated systems or heteroatoms adjacent toa C(sp3), aromatic substituents such as OH or NR2, etc.) or inactivating fea-tures (steric hindrance, ArCOOH, ArSO3H or ArSO2NH2 substituents, etc.).


Fig. 7. Examples of functional groups and their reactions. a) Redox reactions at sp2- and sp-hybridized C-atoms, b) conjugation reactions: methylations.

These factors will be combined in a 3D table with the functional groups andtheir metabolic reactions (Fig. 9). The result will be probability factors whichcan initially be guessed by a human expert (goal 1–3), but should rapidly berendered semi-quantitative by the use of neural networks or other rule-gener-ating procedures (goal 4).


Fig. 8. Factors influencing the metabolism of xenobiotics: functional groups and their reactions

Fig. 9. Factors influencing the metabolism of xenbiotics: proximal molecular factors


At a still higher level of complexity, global molecular properties must beconsidered, e.g., acidity and/or basicity, lipophilicity, perhaps shape, and cer-tainly the presence of pharmacophores indicative of affinity for given iso-zymes (Fig. 10). Such influences will again be expressed as probability fac-tors which will be derived from quantitative SMR. However, because of the

Fig. 10. Factors influencing the metabolism of xenbiotics: global molecular factors

Fig. 11. Factors influencing the metabolism of xenbiotics: biological factors


limitations discussed in Sect. 2, it would be unrealistic to hope for anythingbetter than semi-quantitative factors (goal 4).

The last and most difficult level to consider is that of biological factors(Fig. 11). This is our goal 5, whose fulfilment will always remain very partialand highly approximative. This, however, is not a reason to be discouragedbefore trying, since any reliable result, however incomplete, would be a stepforward.

4. Conclusion

The conclusions to be drawn from this review are both encouraging anddiscouraging. Most of the above discussion has shown that metabolism, likeany other biological response, is heavily dependent on the molecular proper-ties of substrates. And this is an encouraging statement, since it implies thatstructure-metabolism relationships do exist and hence that predictions arepossible. As we have seen, many local predictions (i.e., valid within a givenchemical series) have been reported. These usually account for a reasonablygood percentage of the variance and should allow usable predictions withinthe explored space of molecular diversity and property.

In contrast, lucid researchers cannot fail to recognize that global predic-tions (i.e., valid across chemical series) remain a discouraging challenge. Ofcourse, current expert systems will offer qualitative predictions as to possiblemetabolites, but the danger of false positive and false negative results isalways present. Furthermore, we have seen how quantitative predictionsacross series remain out of reach due to the complexity of molecular struc-ture, the diversity of modes of interactions between substrates and metaboliz-ing systems, and, above all, the many biological factors involved. And if onegeneral conclusion should emerge from all that biologists have learned aboutbiological regulations, it is that biological factors are interdependent in a non-linear manner. In other words, biological factors not only influence thepharmacological responses, they also influence each other. The obviousimplication is that the systematic study of biological factors taken one by oneor few by few will never yield a complete understanding of biological regu-lations.

So what is there to say about the validity, extrapolability and interpretabil-ity of (Q)SMR? Our answer is that a trade-off seems to exist between molec-ular diversity on the one hand and SMR on the other. In concrete terms, weoffer the following two statements as working hypotheses to be tested inmeta-analyses:

• Τhe greater the chemical diversity of the investigated compounds, thesmaller the chance that SMR exist and can be uncovered.

• When such SMR do exist, however, their information content will increasewith increasing range of the explored space of molecular diversity andproperty.

The authors express their gratitude to Prof. Sergio Clementi for his support and encourage-ment. They also thank in advance those colleagues who will offer a constructive criticism ofthe MetaFore project.

REFERENCES

[1] B. Testa, P. Jenner, ‘Drug Metabolism – Chemical and Biochemical Aspects’, Dekker,New York, 1975.

[2] B. Testa, ‘The Metabolism of Drugs and Other Xenobiotics – Biochemistry of RedoxReactions’, Academic Press, London, 1995.

[3] G. J. Mulder, (Ed.), ‘Conjugation Reactions in Drug Metabolism’, Taylor & Francis,London, 1990.

[4] B. Testa, in ‘Burger’s Medicinal Chemistry and Drug Discovery’, 5th Edition, Ed. M. E.Wolff, Wiley-Interscience, New York, 1995, pp. 129–180.

[5] C. G. Wermuth, B. Testa, in ‘The Practice of Medicinal Chemistry’, Ed. C. G. Wermuth,Academic Press, London, 1996, pp. 615–641.

[6] B. Testa, Acta Pharm. Nord. 1990, 2, 137.[7] B. Testa, in ‘Advances in Drug Research’, Ed. B. Testa, Academic Press, London, 1984,

Vol. 13, pp. 1–58.[8] B. Testa, Chirality 1989, 1, 7.[9] B. Testa, P. Jenner, in ‘Concepts in Drug Metabolism’, Eds. P. Jenner, B. Testa, Dekker,

New York, 1980, part A, pp. 53–176.[10] B. Testa, Biochem. Pharmacol. 1988, 37, 85.[11] B. Testa, in ‘Xenobiotic Metabolism and Disposition’, Eds. R. Kato, R. W. Estabrook,

M. N. Cayen, Taylor & Francis, London, 1989, pp. 153–160.[12] J. Mayer, B. Testa, in ‘Pharmacokinetics of Drugs’, Eds. P. G. Welling, L. P. Balant,

Springer Verlag, Berlin, 1994, pp. 209–231.[13] J. L. Byard, L. Goldberg, Food Cosmet. Toxicol. 1973, 11, 391.[14] L. M. Ball, A. G. Renwick, R. T. Williams, Xenobiotica 1977, 7, 189.[15] M. J. Ashton, B. Clark, K. M. Jones,G. F. Moss, M. G. Neale, J. T. Ritchie, Toxicol. Appl.

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Press, London, 1994, Vol. 25, pp. 55–86.[20] M. H. Bickel, Experientia 1982, 38 (Suppl.), 879.[21] W. R. Jondorf, Veterinary Res. Commun. 1983, 7, 277.[22] A. Durrer, B. Walther, A. Racciatti, G. Boss, B. Testa, Pharm. Res. 1991, 8, 832.[23] Y. C. Martin, C. Hansch, J. Med. Chem. 1971, 14, 777.[24] C. Hansch, Drug Metab. Rev. 1972, 1, 1.[25] P. H. Yu, J. Pharm. Pharmacol. 1989, 41, 205.[26] P. H. Yu, J. Pharm. Pharmacol. 1990, 42, 882.[27] K. A. S. Al-Gailany, J. B. Houston, J. W. Bridges, Biochem. Pharmacol. 1978, 27, 783.[28] B. Testa, D. Mihailova, J. Med. Chem. 1978, 21, 683.[29] B. Testa, D. Mihailova, R. Natcheva, Eur. J. Med. Chem. 1979, 14, 295.[30] J. P. Collins, G. H. Loew, J. Biol. Chem. 1988, 263, 3164.[31] A. G. Salmon, R. B. Jones, W. C. Mackrodt, Xenobiotica, 1981, 11, 723.[32] R. Morgenstern, G. Lundqvist, V. Hancock, J. W. DePierre, J. Biol. Chem. 1988, 263,

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Concepts in Prodrug Design to Overcome Pharmacokinetic Problems

by Bernard Testa* and Joachim M. Mayer

Institut de Chimie Thérapeutique, BEP, Université de Lausanne, CH-1015 Lausanne,Switzerland; Tel.: +41 21 692 4521; Fax: +41 21 692 4525;


1. A Case for the Prodrug Concept

Metabolism and disposition are of pivotal importance in drug researchand development (R&D) due to the interdependence of pharmacokinetic (PK)and pharmacodynamic (PD) processes [1–6]. Disposition and metabolic stud-ies should be initiated as early as possible in the testing of lead candidates toidentify potential PK problems such as limited intestinal absorption, inade-quate distribution, fast metabolism, the nature and toxicological potential ofmetabolites, the enzymes involved in the compound’s metabolism and theirpotential for drug-drug interactions, and other relevant aspects.

The major reasons for failure during development are PK or toxicologicalproblems with the candidates. Furthermore, PK issues underlie various diffi-culties encountered with new drugs after they have been marketed [7].Pharmacokinetic defects are not absolute barriers to drug development, butcan delay it and/or make the drug vulnerable in the marketplace to competi-tors, or even result in outright failure.

Avoidance of the forseeable or proven PK defects thus assumes consider-able significance. However, PK and PD optimization may not be compatible,meaning that efficacy at the target may decrease or be lost during PK optim-ization. A telling example of such a situation is provided by the novel drugclass of neuraminidase inhibitors, where target-oriented rational design hasled to highly hydrophilic, poorly absorbed agents, as discussed later in thisarticle [8]. Here, we examine the prodrug concept as an alternative or com-plementary approach to disentangle PK and PD optimization. In other words,rather than attempting to improve lead candidates within a unitary rationaldesign process, PK optimization may be achieved by the application of theprodrug concept to research compounds with high in vitro activity. This may


prove a more realistic objective than attempting to deal with PK and PD opti-mization simultaneously.

2. Modulation of Drug Metabolism by Structural Variations

Many examples of structure-metabolism relationships (SMR) involveoverall molecular properties such as configuration, conformation, electronicdistribution, or lipophilicity [5] [9] [10]. An alternative means of modulatingmetabolism is by structural modifications of the substrate at its target site, adirect approach whose outcome is often more predictable than that of alteringmolecular properties by structural changes not involving the reaction center.Globally, structural variations at the reaction center can aim either at decreas-ing or even suppressing biotransformation, or at promoting it by introducinglabile groups. Metabolic switching is a combination of the two goals, the aimbeing to block metabolism in one part of the molecule and to promote it inanother (Fig. 1).

Inertness towards biotransformation can often be observed for highlyhydrophilic or lipophilic compounds. But high polarity and high lipophilicitytend to be avoided by drug designers because they may result in poor bio-availability and very slow excretion, respectively. However, metabolic stabil-ization can be achieved more conveniently by replacing a labile group withanother, less or non-reactive moiety, provided this change is not detrimentalto PD activity (Table 1) [11].

Metabolic stabilization may present some advantages, as listed in Table 2.Nevertheless, drawbacks cannot be ignored, e.g., too long half-lives and a


Fig. 1. Lead optimization by metabolic manipulation

risk of accumulation. In contrast to metabolic stabilization, metabolic switch-ing is a versatile means of deflecting metabolism away from toxic products toenhance the formation of therapeutically active metabolites and/or to obtaina suitable pharmacokinetic behavior (Table 2).

Metabolic promotion can be achieved by introducing into a lead com-pound a functional group of predictable metabolic reactivity, for example anester linkage. This concept enjoys considerable success in the design of pro-drugs, which are discussed separately below. Another approach rendered pos-sible by metabolic promotion is the design of soft drugs [12]. The concept ofsoft drugs, which are defined as ‘biologically active compounds (drugs) char-acterized by a predictable in vivo metabolism to non-toxic moieties, after theyhave achieved their therapeutic role’, has led to valuable therapeutic innova-tions, such as -blockers with ultrashort duration of action (e.g., [(arylcarbon-yl)oxy]propanolamines and esmolol). In both cases, esterase-mediatedhydrolysis produces metabolites that are inactive due to the loss of the side-chain or due to a high polarity of the para-substituent, respectively.


Table 1. A Few Classical Means to Achieve Metabolic Stabilization

Table 2. Some Possible Advantages of Metabolic Stabilization and Metabolic Switching

For the sake of fairness, it must be mentioned that the design of soft drugsis not without its limitations. Some workers in the field place emphasis on thepredictability of the metabolism of soft drugs. However, it must be recog-nized that this predictability is qualitative rather than quantitative due to themany biological factors involved. A similar limitation also applies to manyprodrugs, as discussed below.

3. Principles of Prodrug Design

3.1. Definition and Interest

Prodrugs are defined as therapeutic agents that are inactive per se but arepredictably transformed into active metabolites [5] [7] [13]. As such, pro-drugs must be contrasted with soft drugs, which, as explained above, areactive per se and yield inactive metabolites. And in a more global perspec-tive, prodrugs and soft drugs appear as the two extremes of a continuum of


Table 3. Prodrugs: A Concept to Overcome Barriers and Enhance a Drug’s Usefulness(modified from [14])

possibilities where both the parent compound and the metabolite(s) contrib-ute in a large or small proportion to the observed therapeutic response.

Prodrug design aims at overcoming a number of barriers to a drug’s use-fulness (Table 3). Based on these and other considerations, the major objec-tives of prodrug design can be defined (Table 3).

3.2. Complementary Viewpoints in Prodrug Design

The successes of prodrug design are many, and a large variety of suchcompounds have proven their therapeutic value. When discussing this multi-disciplinary field of medicinal chemistry, several complementary viewpointscan be adopted, namely a chemical classification, the mechanism of activa-tion (i.e., enzymatic and/or non-enzymatic), the tissue selectivity, the possibleproduction of toxic metabolites, and the gain in therapeutic benefit (Table 4).

In a chemical perspective, it may be convenient to distinguish betweencarrier-linked prodrugs, i.e., drugs linked to a carrier moiety by a labilebridge, and bioprecursors, which do not contain a carrier group and are acti-vated by the metabolic creation of a functional group [15]. In carrier-linkedprodrugs, the carrier moiety is often and conveniently linked to a hydroxy, anamino, or a carboxy group. Derivatization of the latter is often particularlyrewarding in terms of lipophilicity, since a highly polar carboxylate groupbecomes masked inside an ester group whose properties can be broadly mod-ulated.

Relevant examples of bioprecursors are provided by chemotherapeuticagents whose activation occurs by reduction in oxygen-deprived cells. Thus,the one-electron reduction of 3-amino-1,2,4-benzotriazine 1,4-dioxide to acytotoxic nitroxyl radical is believed to account for the antitumour activity ofthis bioprecursor (Fig. 2) [16]. Bioprecursors certainly appear as a viableclass of prodrugs since they avoid potential toxicity problems caused by thecarrier moiety (see below). In contrast, attention must be given here to meta-bolic intermediates.

A special group of carrier-linked prodrugs are the site-specific chemicaldelivery systems [12] [17]. Macromolecular prodrugs are synthetic conju-gates of drugs covalently bound (either directly or via a spacer) to proteins,polypeptides, polysaccharides, and other biodegradable polymers [18]. A spe-cial case is provided by drugs coupled to monoclonal antibodies.

Prodrug activation occurs enzymatically, non-enzymatically, or also se-quentially (enzymatic step followed by non-enzymatic rearrangement). Asmuch as possible, it is desirable to reduce biological variability, hence theparticular interest currently received by non-enzymatic reactions of hydro-lysis or intramolecular catalysis [19]. Reactions of cyclization-elimination


appear quite promising and are being explored in a number of studies. Theirgeneral reaction scheme is shown in Fig. 3,a. An example of cyclization-elimination due to a basic amino group is shown in Fig. 3,b.

The problem of tissue or organ selectivity (targeting) is another importantaspect of prodrug design. Various attempts have been made to achieve organ-selective activation of prodrugs, in particular dermal delivery [21] and brainpenetration [17]. A promising approach appears to be the site-specific chem-ical delivery systems, which may appear as the ‘magic bullet’ of drug design,their selectivity being based on some enzymatic or physicochemical charac-


Table 4. Complementary Viewpoints when Considering Prodrugs


Fig. 2. Postulated one-electron, cytochrome P450 reductase-mediated reductive metabolismof 3-amino-1,2,4-benzotriazine 1,4-dioxide to a cytotoxic nitroxyl radical [16]

Fig. 3. a) General reaction scheme for the intramolecular activation of prodrugs by cycliza-tion-elimination (modified from [19]). b) Activation of basic ester prodrugs of 5-bromo-2′-

deoxyuridine by cyclization of the promoiety (modified from [20]).

teristic of a given tissue or organ. For example, the selective presence of cys-teine conjugate -lyase in the kidney suggests that this enzyme might beexploited for delivery of sulfhydryl drugs to this organ [22]. Other chemicaldelivery systems are the brain-selective dihydropyridine carriers [12] [17], asillustrated by various dihydropyridine carriers. These moieties undergo en-zymatic aromatization to a pyridinium derivative which is polar and, whenformed in the brain, remains trapped there and, upon hydrolysis, yields theactive drug. A large variety of drugs (e.g., neuropharmacological agents, ster-oid hormones, chemotherapeutic agents) have been coupled to dihydropyri-dine carriers, resulting in improved and sustained brain delivery. However, itmay be that relatively facile hydrolysis of these dihydropyridine prodrugsposes a pharmaceutical problem.

The toxic potential of metabolic intermediates, of the carrier moiety or ofa fragment thereof, should never be neglected. For example, some problemsmay be associated with formaldehyde-releasing prodrugs such as N- and O-(acyloxy)methyl derivatives or Mannich bases. Similarly, arylacetylenesassayed as potential bioprecursors of antiinflammatory arylacetic acidsproved many years ago to be highly toxic due to the formation of an inter-mediate ketene.

The gain in therapeutic benefit provided by prodrugs is a question thatknows no general answer. Depending on both the drug and its prodrug, thetherapeutic gain may be modest, marked, or even significant [23]. As suggest-ed in Table 4, a trend is apparent when comparing marketed drugs and candi-dates in R&D. In the case of marketed drugs endowed with useful qualitiesbut displaying some unwanted property which a prodrug form should ameli-orate, the therapeutic gain is usually modest yet real, but may become markedif good targeting is achieved.

In the case of difficult candidates showing excellent target properties butsuffering from some severe physicochemical and/or PK drawbacks (e.g., highhydrophilicity restricting bioavailability), a marked to significant benefit canbe obtained. Here, indeed, a prodrug form may prove necessary, and itsdesign will be integrated into the iterative process of lead optimization. Thispossibility is aptly illustrated by the recently marketed neuraminidase inhibi-tor oseltamivir, which, like zanamivir, is an effective medicine against type-Aand type-B influenza in humans (Fig. 4). Oseltamivir is the ethyl ester prodrugof RO-64-0802, a drug showing very high in vitro inhibitory efficacy towardsthe enzyme but low oral bioavailability due to its high polarity [8]. Followingintestinal absorption, the prodrug undergoes rapid enzymatic hydrolysis andproduces high and sustained plasma levels of the active parent drug. It is inter-esting to compare oseltamivir with zanamivir (Fig. 4), which, like RO-64-0802, is poorly absorbed orally and is administered to humans by means of adry powder inhaler.



Fig. 4. Chemical structure of the neuraminidase inhibitors zanamivir (a highly hydrophilicdrug administered in aerosol form), and the orally available prodrug oseltamivir whose

hydrolysis yield the active drug [8]

4. Prodrug R&D: So What?

As this review has shown, the global benefit brought forth by a prodrugrelative to the active agent may range from considerable to negligible. Thegain will be considerable when the development of an innovative and verypromising agent is blocked by a major pharmacokinetic or pharmaceuticaldefect which appears surmountable by a prodrug strategy. In contrast, thegain will be negligible when the drug’s defect is tolerable or barely improvedby transformation to a prodrug.

What remains to be discussed, however briefly, are specific difficulties en-countered in designing and developing prodrugs, as related to the viewpointsmentioned above (Sect. 3.2 and Table 4). These difficulties, indeed, may rangefrom fair to prohibitive and can occur at all stages of the R&D process:• Careful prodrug design is required to minimize the number of proposed

candidates and maximize the explored space of physicochemical and phar-macokinetic properties. The ability to predict target properties (e.g., solu-bility, extent of absorption, and rate of activation) is a major need in ration-al prodrug design, but global quantitative models simply do not existdespite some claims to the contrary. At present, prodrug designers can relyon some local models or rules to make semi-quantitative or even quanti-tative predictions, and, on at least one global model (the ‘rule of five’), tomake qualitative (yes-no) predictions. This situation should improve in thecoming years for the prediction of physicochemical properties, absorptionand distribution, as discussed throughout this volume. In contrast, quantita-tive predictions of rates of biotransformation remain an elusive goal [10].

• One or several additional synthetic step(s) are needed for each prodrugcandidate being prepared. This implies additional work and efforts fromchemists, and increased production costs which may not be consideredworthwhile.


• As far as molecular properties are concerned, a first issue is the physico-chemical profile of the prodrug candidates and its adequacy with the goalsof the project. Some physicochemical properties can be calculated or esti-mated at the design stage (see above), but experimental verification can-not be omitted. For other properties such as solubility, quantitative predic-tions are more difficult, and experimental assessment is mandatory. Exceptfor the extra work involved, this is a comparatively straightforward issue.

• A truly critical difficulty is the pharmacokinetic behavior (absorption, dis-tribution, etc.) of prodrug candidates and its adequacy with the goals of theproject, first in vitro and ultimately in vivo. High-throughput methods arenecessary to rapidly assess the in vitro pharmacokinetic profile of manyprodrug candidates and to verify or falsify the predictions of the prodrugdesigners. Like with drug candidates, the problem is extrapolation tohumans. The danger is real, indeed, that prodrug candidates selected dur-ing in vitro PK screening programs may prove disappointing in vivo.

• The truly critical difficulty is the metabolic behavior and particularly therate of activation of prodrug candidates. As discussed in Sect. 3.2, the hugediversity of drug-metabolizing enzymes and the large interspecies varia-tions that exist make rational optimization of the rate of activation inhumans an impossible task. Human liver microsomes have become a com-mon tool in metabolic profiling, but even results so obtained may give amisleading preview of in vivo metabolism. Chemically activated prodrugs(Sect. 3.2) offer a most viable alternative but have yet to reveal their fullpotential.

• Another problem is toxicity relative to the underivatized active agent. Byinfluencing the distribution and tissular concentrations of the active agentthey deliver, prodrugs may elicit toxic effects not displayed by the activeagent itself. Furthermore, the carrier moiety may generate toxic fragments(e.g., formaldehyde). Additional and careful in vitro (in the presence ofactivating enzymes) and in vivo toxicological investigations are thereforeunavoidable and costly steps in prodrug development, whatever the lack oftoxicity of the active agent.

The above problems appear to be the major sources of difficulty in pro-drug R&D, not to mention possible complications in registration. No wondertherefore that so many medicinal chemists are critical of prodrugs. However,a lucid view cannot ignore the sunny side, in this case the mere existence ofa number of successful prodrugs. Nabumetone, oseltamivir, and pivampicil-line are just a few examples which come to mind.


5. Conclusion

This text presents a brief overview of the prodrug concept, focusing on itspotential in overcoming pharmacokinetic problems and mainly poor oral absorp-tion. Medicinal chemists, biochemists, and pharmacologists who are eager tolearn more about prodrugs should see this text as an invitation to further studies.They will find a wealth of information in the references provided, and by usingthe simple keyword ‘prodrug’ in a literature search will retrieve an avalanche ofpapers. Clearly, the prodrug field is a lively and fertile one. The telling exampleof oseltamivir should convince any remaining sceptic that a prodrug design mayindeed allow the separate optimization of PK and PD properties.

REFERENCES

[1] B. Testa, P. Jenner, ‘Drug Metabolism: Chemical and Biochemical Aspects’,Dekker, New York, 1976.

[2] B. Testa, ‘The Metabolism of Drugs and Other Xenobiotics – Biochemistry of RedoxReactions’, Academic Press, London, 1995.

[3] B. Testa, J. M. Mayer, ‘Hydrolysis in Drug and Prodrug Metabolism – TheBiochemistry and Enzymology of Hydrolases’, Verlag Helvetica Chimica Acta, Zürich, in preparation.

[4] J. Mayer, B. Testa, in ‘Pharmacokinetics of Drugs’, Eds. P. G. Welling, L. P.Balant, Springer Verlag, Berlin, 1994, pp. 209–231.

[5] B. Testa, in ‘Burger’s Medicinal Chemistry and Drug Discovery’, Vol. 1, 5th Edition,Ed. M. E. Wolff, Wiley-Interscience, New York, 1995, pp. 129–180.

[6] B. Testa, in ‘The Encyclopedia of Molecular Biology and Molecular Medicine’, Ed.R. A. Meyers, Vol. 6, Wiley-VCH, Weinheim, 1997, pp. 259–275.

[7] B. Testa, J. Caldwell, Med. Res. Rev. 1996, 16, 233.[8] J. S. Oxford, R. Lambkin, Drug Discov. Today 1998, 3, 448.[9] B. Testa, P. Jenner, in ‘Concepts in Drug Metabolism’, Eds P. Jenner, B. Testa, Part B,

Dekker, New York, 1981, pp. 53–176.[10] B. Testa, G. Cruciani, in this volume, p. 65.[11] E. J. Ariëns, A. M. Simonis, in ‘Strategy in Drug Research’, Ed. J. A. Keverling

Buisman, Elsevier, Amsterdam, 1982, pp. 165–178.[12] N. Bodor, in ‘Advances in Drug Research’, Ed. B. Testa, Academic Press, London,

Vol. 13, 1984, pp. 255–331.[13] H. Bundgaard, in ‘A Textbook of Drug Design and Development’, Eds P. Krogsgaard-

Larsen, H. Bundgaard, Harwood, Reading, 1991, pp. 113–191.[14] V. J. Stella, W. N. A. Charman, V. H. Naringrekar, Drugs 1985, 29, 455.[15] C. G. Wermuth, in ‘Drug Design: Fact or Fantasy?’, Eds. G. Jolles, K. R. H.

Wooldridge, Academic Press, London, 1984, pp. 47–72.[16] R. J. Riley, P. Workman, Biochem. Pharmacol. 1992, 43, 167.[17] N. Bodor, Ann. N. Y. Acad. Sci. 1987, 507, 289.[18] R. Duncan, Anti-Canc. Drugs 1992, 3, 175.[19] B. Testa, J. M. Mayer, Drug Metab. Rev. 1998, 30, 787.[20] W. S. Saari, J. E. Schwering, P. A. Lyle, J. Smith, E. L. Engelhardt, J. Med. Chem. 1990,

33, 2590.[21] S. Y. Chan, A. Li Wan Po, Int. J. Pharmaceut. 1989, 55, 1.[22] I. Y. Hwang, A. A. Elfarra, J. Pharmacol. Exp. Therap. 1989, 251, 448.[23] L. P. Balant, E. Doelker, in ‘Burger’s Medicinal Chemistry and Drug Discovery’,

Vol. 1, 5th Edition, Ed. M. E. Wolff, Wiley-Interscience, New York, 1995, pp. 949–982.

Part III. Biological Strategies

Methodologies in Cell CultureHeidi Wunderli-Allenspach

Biological Models to Assess Drug BioavailabilityRonald T. Borchardt

Biological Models to Study Blood-Brain Barrier PermeationStefanie D. Krämer*, N. Joan Abbott, and David J. Begley

Biological Models to Study Skin PermeationNabila Sekkat and Richard H. Guy*

Biopharmaceutical Aspects of Nasal and Pulmonary Drug DeliveryPaolo Colombo*, Daniela Cocconi, Patrizia Santi,Ruggero Bettini, Gina Massimo, Pier Luigi Catellani,and Claudio Terzano

Significance of Plasma-Protein Binding in Drug ResearchSaik Urien, Jean-Paul Tillement*, and Jérôme Barré

High-Throughput ADE ScreeningOlivier Kretz* and Alessandro Probst

In Vitro Models for Early Studies of Drug MetabolismJiunn H. Lin and A. David Rodrigues*

Addressing Toxicological Issues in the Lead-Optimization Phase of Drug Discovery and Development

Philip Bentley


Methodologies in Cell Culture

by Heidi Wunderli-Allenspach

Biopharmacy, Department of Applied BioSciences, Federal Institute of Technology ETH,CH-8057 Zürich, Switzerland;

Tel.: +41-1-635 60 40; Telefax: +41-1-635 68 82; e-mail: [email protected]

1. The Potential of Cell Cultures

Cell cultures have become an invaluable tool in drug development. Ingeneral, the potential of such systems is either under- or overestimated. Withrespect to the pharmacokinetic behavior of a drug, the reduction of a complexbody to a cell-culture model can be compared to the reduction of a complex,interactive system to a few isolated basic processes. This reductionisticapproach can, however, provide important clues for individual processes aslong as one is aware of its limitations. It certainly has its merits for the screen-ing of drugs at an early stage in drug development.

Cell cultures have been successfully applied to mimic epithelial and endo-thelial barriers with respect to passive permeation, carrier-mediated transport,and possible metabolization of drug compounds upon passage through cells.Although much effort has lately been put into computational models for theprediction of drug permeation, transport studies with cell cultures remain animportant complementary tool for the screening of compounds. It has to bementioned, though, that the full potential of these models is only available ifgrowth conditions and experimental set-ups are strictly controlled and stan-dardized. For optimization and validation of cell-culture models, criteria haveto be introduced to permit characterization with respect to different aspectssuch as growth curve, electrical resistance, cytoarchitecture, as well asexpression and localization of different proteins (e.g., transporters and tight-junction (TJ) proteins).

In the following, the basic cell-culture techniques are briefly reviewed,and specific problems are addressed that arise if standardized protocols arebeing established. The focus is on barrier permeation. A wide variety of meth-ods for the characterization of cell-culture models will be presented and illus-trated with data obtained from selected epithelial and endothelial cells which


are of interest for transport studies. For detailed procedures, excellent labor-atory manuals are available [1].

2. Basic Cell-Culture Techniques

A large amount of information on cell-culture techniques has accumulat-ed over the years, and specific topics are regularly updated. Table 1 [2–14]presents a selection of relevant reviews. This section does by no means claimto cover the subject exhaustively, but is rather intended to stress importantaspects relevant for the establishment of well-defined cell-culture models fortransport studies.


2.1. General Equipment

Helmrich and Barnes [3] have recently published a comprehensive reviewon equipment and techniques for animal-cell cultures. Basically, animal cellscan be propagated with minimal precautions, in particular if antibiotics are usedin the media. In the long run, however, if standardized conditions are to beestablished and validated, a hood, which provides a sterile environment, ismandatory for the handling of cell stocks, solutions, and culture vessels. For

Table 1. A Short Bibliography of Cell-Culture Equipment and Techniques

Issues References

Laboratory design and equipment Davis [2], Helmrich and Barnes [3]

Materials• reagents, media, serum Helmrich and Barnes [3], Xie and Wang [4]• plastic and glass ware Helmrich and Barnes [3], Brown [5]

Cell-culture methods• sterile techniques Helmrich and Barnes [3], Lincoln and Gabridge [6]• primary cultures Pollard [7], Helmrich and Barnes [3], Hertz et al. [8]• multipassage cultures Helmrich and Barnes [3]• cloning Helmrich and Barnes [3]• freezing/thawing Helmrich and Barnes [3]• serum-free cultures Reid and Luntz [9]• scale-up cell cultures Griffith and Looby [10], Mather [11]• suspension cultures Brown [5]

Contaminations• microbial, particularly Stacey [12], Stacey and Doyle [13],

mycoplasms Lincoln and Gabridge [6]• cell cross-contamination Marcovic and Marcovic [14]

short-term experimental cultures, e.g., transport studies, in which cells are usedfor a limited time (in the range of a few hours) before being discarded, it is suf-ficient to run experiments under good-laboratory-practice (GLP) conditions.Helmrich and Barnes [3] provide balanced information about the types ofhoods available on the market and other relevant equipment such as pipettors,autoclaves, water-purification systems, and filtration devices. Of particularimportance are the incubators for which the investment in good quality and reg-ular maintenance pays off in the long run. As a standard, the CO2/HCO3

– buf-fer system is used to culture cells because of its high capacity. Most media aredesigned such that an atmosphere of 5% CO2 in the incubator leads to a stabil-ization at pH 7.4. To preserve stable conditions in the incubator with respect tothe pH value of media, i.e., of CO2 concentration, and temperature, only long-term experiments and stock cultures should be kept in CO2 incubators. Forshort-term experiments, simple incubators or heating plates can be used over afew hours under the condition that the pH value is kept constant by addition ofHEPES buffer to the standard NaHCO3-containing medium.

2.2. Materials

Media should be used according to the cell-type-specific indications pro-vided by the cell banks such as the American Type Culture Collection(ATCC) and the European Collection of Animal Cell Cultures (ECACC).Most media formulations are commercially available as liquids or powders.With the use of liquid media, possible problems with the water quality areavoided. Preparation from powders has the advantage that storage of liquidsfor unknown periods of time before shipping can be excluded and possibledegradation of certain components of the medium minimized. Relevant infor-mation about the stability of the single components of media should bechecked in the original publications. If additional chemicals are required forexperiments with cell cultures, care has to be taken that at the very leastreagent-grade materials are purchased. If they are added to medium for long-term cultures, they have to be sterilized or the final medium has to be filtered.

For most cell lines used for transport studies such as Caco-2, MDCK(Madin Darby canine kidney), and ECV304 cells (see below), heat-inactivat-ed fetal calf serum is added to the medium. Batch-to-batch variations in seraare common and generous supply should be organized as soon as a batch hasbeen tested and found to be adequate for a particular cell line. For specialapplications, it may be important to replace serum with a defined supplementcontaining compounds such as growth factors and hormones [9].

Cells can be cultivated on different supports. For the propagation of cellstocks, plastic disposable flasks and plates are most common. Some cells


need coating of the culture surface with materials such as collagen, laminin,or gelatine. Other possibilities are glass cover slips or membrane inserts con-sisting of different materials. The latter are mostly used for microscopy andtransport studies. Standard conditions have to be established as growth char-acteristics of cells vary significantly depending on their support [15]. Specialmeasures have to be taken for the laboratory scale-up of cell cultures forwhich special supports providing large surface areas, e.g., as microbeads,have been described [11].

2.3. Cell-Culture Methods

Methods discussed here will be restricted to the cultivation of cell linesand transfected cells. For primary cultures, general reviews are available [3]as well as specific protocols for special tissues such as brain-microvesselendothelial cells [16]. Standard procedures should be established in eachlaboratory for the handling of cell lines, which includes passage frequencyand splitting ratios. Cell cultures should be discarded at regular intervals (e.g.,every 4 to 5 months, i.e., about 40 passages) to avoid variations due to driftor degeneration of cells. To maintain stock cultures of adherent cells, they aresplit at intervals as follows. The medium is removed and replaced with tryp-sin/EDTA solution. After a first washing step for the removal of excess serum,which inhibits the trypsin activity, fresh trypsin/EDTA solution is added andremoved subsequently to leave but a thin film of liquid over the cell layer.Cultures are then incubated at 37° and checked at short intervals. Each cellline has its characteristic time for the detachment of cells, and a significantshortening of this time may be indicative for mycoplasm contamination (seebelow). It is important to wait until most of the cells have come off to avoidselection of rapidly detaching cells. Fresh medium is then added, which dueto its serum contents stops trypsin activity. If incubation with trypsin/EDTAis made with a large volume of liquid, a centrifugation step has to be per-formed to remove the trypsin solution before the addition of fresh medium.This represents a possible source for contamination and cell damage.

Cryopreservation methods available today render maintenance of culturesby repeated subculture unnecessary [3] [17]. This is highly significant in viewof the risks of microbial contamination, culture cross-contamination, labora-tory accidents, and genetic drifts. The most important principles of cell bank-ing have been summarized by Stacey [12].

Related to good cell-banking practices are routine tests for viability afterfreezing and thawing, for microbial contamination (including mycoplasms),and for cell cross-contamination. Comprehensive reviews on these aspectshave recently appeared [6] [12] [14]. Mycoplasms, which are ubiquitous,


deserve special mention. Human isolates represent a large percentage of themycoplasmal contaminants found in cell cultures. Contamination can easilypass unnoticed. Cultures usually survive, but changes are noted, e.g., in thetightness of cell adherence (see above), in the protein-expression pattern, andpossibly in the permeation of drugs. A long list of effects brought about bymycoplasm infections has accumulated [6]. Various mycoplasm tests areavailable. Routine staining of cell nuclei with 4,6-diamidino-2-phenylindole(DAPI) for fluorescence microscopy (see below) provides an easy test formycoplasms, as contaminated cells beside the stained nuclei show significantdotted staining within the cytoplasm. In case of contamination, cells shouldbe discarded and all facilities thoroughly disinfected before a new batch isthawed. To prevent microbial contamination, penicillin and streptomycin areoften routinely included in the culture media. This bears a certain risk ofselecting for antibiotic-resistant bacteria as well as mycoplasms. In general,antibiotics cannot completely protect cell cultures from contaminants. Manyof them are rather bacteriostatic than bacteriotoxic, and most of them showselective toxicity against gram-negative or gram-positive bacteria. Forinstance, those antibiotics inhibiting bacterial cell-wall synthesis will notwork on mycoplasms, because these do not have cell walls at all. For an accu-rate evaluation of the incidence of bacterial and mycoplasm contamination,antibiotics should be removed from cell cultures. Problems may arise intransport studies due to the possible action of antibiotics on cell membranes.

Cell cross-contamination can be a serious problem [14]. The most prom-inent example in the past was the contamination of a large number of celllines with HeLa cells. Today, genetic fingerprinting of cell lines is routinelyperformed at the cell banks. With this approach, an identical genotype hasbeen shown for T24, a human bladder-carcinoma cell line and ECV304, apresumed human umbilical vein endothelial cell line (see also below). Toavoid cell cross-contamination in the daily work, procedures have to bedeveloped which include clearly separated media and solutions for differentcell lines and a strictly sequential handling of different types of cell cultureswith cleaning steps in between.

3. Characterization of Cell Cultures

To follow optimization of cell cultures for transport studies and to vali-date transport models, criteria have to be introduced for the characterizationof the system. A whole palette of methods has been developed to describeimportant characteristics such as cell number, electrical resistance, the cytoar-chitecture of cells as well as the expression and localization of certain pro-teins (Fig. 1). This list is not exhaustive. For instance, the characterization of


cell cultures by transmission or scanning electron microscopy will not be dis-cussed. In the following sections, different methods as listed in Fig. 1 will bepresented and illustrated with relevant data from a selected set of epithelialand endothelial cell cultures (Table 2), which have been characterized in ourlaboratory in view of their use for transport studies. For detailed informationregarding these cells, the reader is referred to the references quoted in Table 2.

3.1. Growth Curves and Transcellular Electrical Resistance Measurements

Growth curves for adherent cells are established by counting the cellsafter trypsinization of a defined culture area at different times after seeding.Cell numbers can be determined with a cell-counter instrument, but also in aNeubauer counting chamber. If cells do not easily detach, the nuclei-releasemethod [32] can be applied. Transepithelial and transendothelial electricalresistance (TEER) measurements, respectively, present an important tool tocharacterize the tightness of a cell layer, whereby two systems are currentlyused: the Endohm-12 resistance chamber connected to an EVOM voltohmeter(World Precision Instruments, Stevenage, Herts, UK), and the Millicell-ERSsystem (MERS 000 01, Millipore). The principle of the two systems is thesame, i.e., the electrical resistance of a culture area is calculated from the flowof an applied current perpendicularly across the cell layer. Of course, care has


Fig. 1. Overview on various methods for the characterization of cell-culture models for transport studies


to be taken that measurements are done under strictly controlled conditions,e.g., always the same type of electrode (clampstick or cup electrode), thesame position of the electrodes, and constant temperature – preferably 37°.

For each defined growth condition (e.g., medium, support), each cell lineshows characteristic growth behavior (Table 3). Starting from a constant indi-vidual seeding density for each cell type, the time period of exponentialgrowth as well as the cell density in the stationary phase (defined by a con-stant cell number) were determined and found to be very reproducible for allcells tested. MDCK, both parent and transfected cells, as well as Caco-2 cellswere confluent within 2 to 3 days. They reached the stationary phase after ca. 7 to 10 days in culture, and the cell density at that stage was 4 to 5 × 105

Table 2. Selection of Well-Characterized Cell-Culture Models for Transport Studies

Cells Origin of cells References Conditions used for characterization

Caco-2 Human colon adeno- Audus et al. [18] EMEM a)carcinoma cell line Briske-Anderson et al. [19] Cyclopore® inserts

Anderle et al. [20]Rothen-Rutishauser et al. [15]

MDCK Madin-Darby canine McRoberts et al. [21] EMEM a) or kidney epithelial cell Butor and Davoust [22] DMEM b)line (type-II strain) Jaeger and Kachar [23] Cyclopore® inserts

Rothen-Rutishauser et al. [24]Braun et al., in preparation

MDR1-MDCK MDCK cells trans- Pastan et al. [25] DMEM b) withfected with the Horio et al. [26] 80 ng colchicine/mlmdr1 gene which Hämmerle et al. [27] Cyclopore® insertscodes for P-gp

T24 Human bladder Bubenik [28] Mc Coy’s 5A c)carcinoma cell line Cyclopore® inserts

ECV304 Human umbilical Takahashi et al. [29] M199 d)vein endothelial Takahashi and Sawasaki [30] Cyclopore® insertscell line

PBMEC/C1-2 Porcine brain micro- Teifel and Friedl [31] M199 d)vessel endothelial Cyclopore® insertscell line

a) Minimum Essential Medium with Earle’s Salts (Gibco BRL # 41500-018) containing 20% fetal calf serum. b) Dulbecco’s modified Eagle’s medium with Glutamax-I (DMEM,GibcoBRL) containing 10% fetal calf serum. c) McCoy’s 5A medium (Sigma) with 2 mM

L-glutamine containing 10% fetal calf serum. d) M199 with 10 mM HEPES containing 10%fetal calf serum. All media contained 7.5% NaHCO3, 100 units penicillin/ml and 100 µg strep-tomycin/ml. Incubations were at 37° under a 5% CO2 atmosphere.

cells/cm2. For the TEER, large differences were found. MDCK parent cellsshowed type-II characteristics [33]. Their TEER was around 200 to 250 Ω cm2, whereas MDCK parent cells transfected with the mdr1 gene,which codes for the P-glycoprotein (P-gp), i.e., MDR1-MDCK cells, repro-ducibly showed high TEER values (>1000 Ω cm2) with fluctuation between1 000 and 10 000 Ω cm2. Different growth behavior from MDCK and Caco-2 was found for T24 and ECV304 cells, whereby significant differences werealso found between the two even if starting at the same seeding densities. TheT24 cells reached confluence after 3 days in culture and levelled off at about 2.5 × 105 cells/cm2, whereas ECV304 were confluent after 1 day in cultureand grew to a cell density of 8 × 105 cells/cm2 within 15–21 days. For T24cells, TEER remained low (50–75 Ω cm2), while ECV304 cells reached250–350 Ω cm2 in the stationary phase.

PBMEC/C1-2 cells, which originally had been designed to provide anoptimal transport system, did not reach a stationary phase. Under all condi-tions tested in our laboratory, including coating of the culture surface withcollagens I or IV, laminin, fibronectin as well as gelatine before seeding, cellsstarted to detach before cell numbers levelled off, i.e., between days 10 and14. At that stage they had reached a TEER value of ca. 150 Ω cm2.

It is interesting to note that confluence does not coincide with attainmentof a constant TEER value. Different patterns have been observed. In MDCKparent cells, a steep increase in the TEER value is noticed at early times assoon as confluence is reached. The value then drops, which has been associat-ed with the installation of channels in the plasma membrane [24] [34], andthen remains constant throughout the exponential and stationary phases. Thesituation is different for ECV304 and for the T24 cells. In both cases, an


Table 3. Growth Characteristics of Different Cells in Culture

Cells Seeding Time to Time to Cell density TEERdensity reach con- reach statio- in stationary in stationary[cells/cm2] fluence nary phase phase phase

[d] [d] [cells/cm2] [Ω cm2]

Caco-2 1 × 105 2–3 ~10 4 × 105 750–800MDCK 5 × 104 2 ~ 7 5 × 105 180–250MDR1-MDCK 5 × 104 2 ~10 5 × 105 >1000

(1000–10 000)T24 5 × 104 3 15–20 2.5 × 105 50–75ECV304 5 × 104 1 15–21 8 × 105 250–350PBMEC/C1-2 5 × 104 3 a) 7 × 105 a) 100–150 a)

a) Cell density and TEER were measured at day 10. Cells detach between days 10 and 14before reaching stationary phase (for growth conditions, see Table 2).

increase in TEER values is noted beyond confluence, however, a constantlevel is reached before cell numbers stabilize in the stationary phase (data notshown).

3.2. Characterization by Confocal Laser Scanning Microscopy (CLSM)

3.2.1. General Aspects

For the daily follow-up of cell cultures, phase-contrast microscopy is animportant tool. Subtle changes in size and shape as well as the appearance ofvacuoles or other cell inclusions can be indicative for contaminations or defi-ciency symptoms. More detailed information can be gained from fluores-cence microscopy, particularly in combination with CLSM. Fluorescentprobes to specifically label nucleic acids or proteins such as F-actin are avail-able, and a large selection of fluorescence-labelled secondary antibodies ofdifferent species can be purchased to detect labelling with primary antibodiesof a variety of distinct proteins and organelles. The recent developments inCLSM have significantly enhanced the potential of fluorescence microscopy.In particular, the progress made in 3D-image processing has added the thirddimension to cell-morphology studies (Fig. 2). With respect to cell-culturetransport models, cross-sections (z-scans) of preparations can be analyzed,and conditions can be unambiguously defined under which cells form eithermonolayers or multilayers. This is achieved by staining of nuclei and cyto-skeletal components such as F-actin or tubulin. Another issue is the polariza-tion of epithelial cells in monolayers and multilayers. Apical and basolateraldomains of the cytoplasmic membrane are defined through their separationby TJ, which can be visualized by markers such as ZO1, occludin, and oth-ers. In polarized cells, the protein pattern of the apical and basolateral mem-branes is different. P-glycoprotein (P-gp) for instance is integrated into theapical cytoplasmic membrane. Therefore, the localization of P-gp givesimportant clues on the state of polarization of cells.

The possibility of using three to four markers concomitantly permits thetracing of a specific marker in the context of its localization within the cell.This is particularly important for control specimens in which the specificmarker is lacking. Without markers for the cytoskeletal structures and nuclei,it remains open whether the focal plane was chosen correctly or not. Besidethe static localization of fluorescent markers in fixed specimens, kinetic stud-ies in living cells can also be performed. Examples are transport studies withfluorescence-labelled compounds, LDL-uptake in endothelial but not in epi-thelial cells (see below), or follow-up of metabolic processes in which themetabolite becomes fluorescent [35]. In the following sections, examples of


CLSM applications are presented for the characterization of different cellsregarding cytoarchitecture, TJ formation, polarization (as illustrated by P-gpexpression), LDL-uptake, and carrier-mediated transport activity.

3.2.2. Characterization of Cytoarchitecture

To gain information on the cytoarchitecture, cell layers can be fixed with3% paraformaldehyde, permeabilized with 0.2% Triton-X-100 before beinglabelled with fluorescein-phalloidin, specific for F-actin, and DAPI, whichstains cell nuclei. In contrast to most other staining procedures, this labellingdoes not need any antibodies. With optical x,y-sections of such preparations,information can be obtained on the regularity of the cell layer. The 3D-datastacks provide also information on the z-axis. For instance, as illustrated inFig. 3, monolayers are characteristic for parent MDCK cultures (Fig. 3,a).The transfected cells, i.e., MDR1-MDCK [27], however, form irregular mul-tilayers (Fig. 3,b). Under several growth conditions, multilayers are alsofound in Caco-2 cells (Fig. 3,c) [15]. PBMEC/C1-2 cells, i.e., immortalizedporcine brain-microvessel endothelial cells, show the same irregular growthin multilayers already at early times in culture as the transfected MDCK cells(data not shown).


Fig. 2. Confocal laser scanning microscopy. Schematic representation of optical sectionsand some typical representations of 3D-data stacks.


Fig. 3. Monolayer vs. multilayer formation. Cells were cultured as described in Table 2 andprepared for CLSM [24]. F-Actin was labelled with oregon-green-phalloidin and cell nucleiwith DAPI. Data are represented as sections with the respective x,z- and y, z-projections (seeFig. 2), in which monolayers and multilayers can easily be distinguished from the arragement

of the nuclei.

3.2.3. Characterization of Tight Junctions

CLSM combined with antibody labelling of TJ-related proteins such asoccludin and ZO-1 provides important information on the state of the TJs. Inparent MDCK cells (Fig. 4,a) and Caco-2 monolayers [15], a very regular TJnetwork is found near the apical face of the cell layer. As could be shown byRothen-Rutishauser et al. [24], TJs form as soon as contact between cells ismade, and a complete network is formed at confluence, several days beforethe stationary phase is reached. In multilayers formed by Caco-2 and MDR1-MDCK cells, TJs are localized not only at the upper surface of the cell layer,but also between the layers [15]. Clear polarization as found with the parentMDCK cells is, however, lost. ECV304 cells also form a fairly complete TJnetwork after confluence, although it is much less regular than that foundwith, e.g., MDCK cells (Fig. 4,b). A very incomplete TJ network is foundwith PBMEC/C1-2 cells even after 10 days in culture (Fig. 4,c). In T24 cells,although they show high expression of ZO-1, TJs are barely formed, and the protein is localized throughout the cytoplasm (data not shown). This cor-responds perfectly well with the very low TEER values of 50–75 Ω cm2

found.

3.2.4. Characterization with Respect to P-gp Localization

Labelling with anti-P-gp antibodies revealed low levels of the transporterin all tested cells but the MDR1-MDCK (Hämmerle, unpublished). In MDCKparent cells, faint labelling was found throughout the cytoplasm (Fig. 5,a).Intensive fluorescence was detected in the apical membranes of MDR1-


Fig. 4. Tight-junction formation. Cells were cultured as described in Table 2 and prepared forCLSM [24]. TJs are made visible by labelling of the ZO-1 protein with the respective antibody(white). Nuclei were stained with DAPI (grey). (a) MDCK type-II cells, parent strain; (b)ECV304 cells; (c) PBMEC/C1-2 cells. Data are represented as 3D-reconstructions (SFP,

see Fig. 2) to clearly illustrate the localization of the TJs.


Fig. 5. P-gp localization within MDR1-MDCK and MDCK parent cells. Cells were cultured asdescribed in Table 2 and prepared for CLSM [24] [27]. P-gp was labelled with mouse anti-P-gp antibodies and a cyanine 3-conjugated goat anti mouse secondary antibody. Nuclei werestained with DAPI. x,z-Projections are shown (see Fig. 2), i.e., cross sections through the

cell layer.

1) 1,1′-Dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate acetylated low-densitylipoprotein.

MDCK cells in the uppermost layer, i.e., these cells were typically polarized(Fig. 5,b). Significant labelling of P-gp was also detected throughout thecytoplasm of these cells, and some apical accumulation of label occurred alsoin lower layers, although polarization was not clearly distinguishable.

3.2.5. Characterization with Respect to LDL-Uptake

To differentiate between endothelial and epithelial cells, LDL-uptake hasbeen introduced as a highly specific endothelial characteristic [36]. DiI-Ac-LDL1) uptake is tested in living cells in the CLSM. Cells are incubatedwith DiI-Ac-LDL at 37° and pictures taken at various time intervals.Preparations are then fixed with 3% paraformaldehyde and nuclei labelled fororientation within the cells. PBMEC/C1-2 cells showed a very significantuptake of DiI-Ac-LDL (Fig. 6,a), and uptake was also found in ECV304cells, although much less (Fig. 6,b). In T24 cells, only minute traces werefound (data not shown), and no DiI-Ac-LDL uptake was observed with MDCKcells (Fig. 6,c).

3.2.6. Characterization with Respect to Metabolic Activity

CLSM can also be used to study the metabolic activity of enzymes in cellcultures. For instance, samples are incubated with the coupling reagent 5-nitro-salicylaldehyde (NSA) and substrates such as Ala- or Leu-MNA (4-methoxy-2-naphthylamine) to explore aminopeptidase activity in skin and in HaCaTcell cultures, which have been suggested as a model for the viable epidermis

[35] [37]. The amino-acid derivatives are hydrolyzed by the enzyme, and lib-erated MNA is coupled to NSA to form an insoluble, fluorescent compound,the appearance of which can be followed in living cells. Significant amino-peptidase activity could be demonstrated in HaCaT cell cultures (Fig. 7).

3.2.7. Characterization with Respect to Colocalization of Markers with Cell Organelles

In recent years, specific fluorescent dyes have become commerciallyavailable for the identification of cell organelles such as lysosomes [38] andmitochondria [39]. These specific markers provide additional tools for thelocalization of compounds within cells.

3.3. Characterization by Western-Blot Analysis of Protein Expression

For the characterization of cell cultures, it may be important to determineage-related expression of special proteins such as P-gp and other transporters,as well as TJ proteins. SDS polyacrylamide-gel electrophoresis (SDS-PAGE)and Western blots are state-of-the-art in any cell culture laboratory. Thisapproach is complementary to the CLSM studies discussed above. With thenecessary controls, blots can be used for identification and quantification ofproteins, whereas CLSM, which only permits qualitative or at most semi-quantitative analysis, is suited for the localization of proteins within the cell.For P-gp as well as for ZO-1, good correlation was found between the resultsobtained by blots as compared to CLSM. One has to be aware, however, that


Fig. 6. LDL-Uptake into different cells. Cells were cultured as described in Table 2 and DiI-Ac-LDL (white) uptake followed in the living cells, which were then fixed and prepared forCLSM as described (Sect. 3.2.4). Nuclei were stained with DAPI (grey). (a) PBMEC/C1-2cells; (b) ECV304 cells; (c) MDCK parent cells. Data are represented as 3D-reconstructions

(SFP, see Fig. 2).


Fig. 7. Aminopeptidase activity in HaCaT cells. HaCaT cells were cultured as described [37]. Totest for aminopeptidase activity, cells were incubated at 37° in a microscopic chamber in the pres-ence of the coupling reagent NSA, and the cell layer was focused in the differential interferencecontrast (DIC) mode. At 0 min, Ala-MNA was added as a substrate and the appearance of thefluorescent metabolite monitored in the confocal mode. Pictures were taken at intervals: (a) 0 min

DIC only; (b) 0 min confocal mode; (c) 10 min confocal mode; (d) 20 min confocal mode.

specific antibodies that react in Western blots do not necessarily react inCLSM preparations and vice versa. Hence, both methods should be used inparallel to avoid false negative results.

3.4. Characterization of Transport Processes

For transport studies, different experimental designs have been developed(for a review, see chapter by Krämer et al., p. 127). The two-chamber-system


is most commonly used, for instance also to test the tightness of cell layerswith mannitol (see Sect. 3.4.1). Another possibility, which may be attractiveunder certain conditions such as the P-gp assay with transfected cells (seeSect. 3.4.2), is the one-chamber-system.

3.4.1. Paracellular Transport

Paracellular permeation of small hydrophilic compounds depends on thetightness of a cell layer. With a set of FITC-labelelled dextrans in the molec-ular-weight range of 4 to 150 kDa, paracellular transport was tested in rat-alveolar epithelial cells [40]. An inverse relationship between permeation,expressed by Papp, and molecular weight was found. Traditionally, the tight-ness of cell layers is tested with mannitol (Mr 182). A large variabilitybetween laboratories has been reported for Papp values [41].

Both mannitol transport and TEER measurements are suitable to test forthe tightness of cell layers. TEER measurements are helpful to follow thedevelopment of tight cell layers over time. Due to a wide variability in theTEER values of culture inserts, single TEER measurements do not providesufficient information and cannot easily be compared between different cul-tures. In conjunction with transport studies, the measurement of mannitoltransport is established as a relevant control for paracellular permeation andthus the tightness of the cell layer.

3.4.2. Carrier-Mediated Transport

Different carriers and transporters have been described in various types ofcells. They have mostly been identified as integral membrane proteins with atypical set of specific substrates. Transporters involved in multidrug resis-tance (MDR) are clinically relevant, as this phenomenon is one of the majorproblems encountered with chemotherapy of tumors, CNS drug targeting, andanti-HIV therapies. P-gp is the most prominent member of the MDR family(coded for by the mdr1 gene). It has primarily been described as a drug effluxpump with a broad spectrum of substrates. Easy-to-handle models to identifyP-gp substrates and inhibitors at an early stage in drug development are ofparticular interest. To produce such a model, MDCK cells were transfectedwith the mdr1 gene [25]. The MDR1-MDCK strain displays high expressionof P-gp and shows the typical localization of this membrane protein in theapical surface of cells (see Fig. 3).

To screen for P-gp substrates or inhibitors, respectively, MDR1-MDCKcells can be used [27]. The MDCK parent strain, which has practically no


P-gp activity, serves as a reference. If rhodamine123 is used as a substrate, P-gp-mediated transport can even be visualized in the CLSM. The rho123assay can also be applied to screen for inhibitors.

For the characterization of transporters in cell cultures, a combinedapproach comprising expression, localization, and function studies, as forinstance presented for P-gp (see above), is most promising.

4. Conclusion

The successful use of cell cultures for transport studies depends on thecareful characterization of the respective systems (cells, medium, support).This is true for small-scale basic-research projects as well as for high-throughput assays. If data from different laboratories are compared, cultureconditions have to be kept in mind, as small differences can lead to signifi-cant differences in the respective results. It must be stressed, that optimizedand validated cell-culture methodology is mandatory. CLSM takes an impor-tant place in the characterization of cells as presented here. True enough, thisis not a methodology available in every laboratory. Equipment is quite expen-sive and expertise is needed for routine application. In this respect, it is com-parable to electron microscopy. The importance of getting information aboutthe z-axis, which is identical with the direction of transport through the celllayer, favors the use of CLSM. Compared with electron microsopy, CLSMhas significant advantages. In particular, the preparation time is much short-er, and specimens need not be dehydrated. Furthermore, not only fixed, butalso living cells can be observed.

I would like to thank the ETH Zürich Biopharmacy group for contributing CLSM micro-graphs. Thanks are due to Barbara Rothen-Rutishauser, Maja Günthert, Annette Braun, andStefanie D. Krämer for carefully reading the manuscript. I would also like to thank PeterBoderke and Hans Peter Merkle for providing unpublished data on the aminopeptidase activ-ity in HaCaT cells.

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Biological Models to Assess Drug Bioavailability

by Ronald T. Borchardt

Department of Pharmaceutical Chemistry, The University of Kansas, 2095 Constant Avenue,Lawrence, KS 66047, USA; Tel: 785/864-3427; Fax: 785/864-5736;


1. Introduction

In recent years, the pharmaceutical industry has become interested inassessing the ‘developability’ of compounds by determining their pharmaceu-tical, biopharmaceutical, pharmacokinetic, metabolic, and toxicological char-acteristics early in the drug-discovery process, i.e., at the stages of lead selec-tion and lead optimization [1]. The motivation for this paradigm shift in drugdiscovery research is in part economic, i.e., the quality of the compoundsnominated for drug candidacy needs to increase in order to reduce the attri-tion rate in preclinical and clinical stages of development [2]. This paradigmshift has also occurred because history shows that when this strategy of inte-grating discovery and development was employed, it led to a higher percent-age of clinically successful drug candidates [3].

The ‘developability’ of a potential drug candidate is dependent on manyfactors, but ADME characteristics (A, absorption; D, distribution; M, metab-olism; E, elimination) are among the most important [1]. If a compound is tobe administered orally, the characteristic of primary interest is its oral bio-availability. Unfortunately, the term oral bioavailability means differentthings to different scientists, depending on their disciplines, i.e., medicinalchemists vs. pharmacologists. This often leads to differences in the interpre-tation of actual ADME data. The pharmacokinetic definition of oral bioavail-ability (which should be the only definition used) is ‘the fraction of an oraldose reaching the systemic circulation’ [4].

There are multiple factors that can affect oral bioavailability, includingdisintegration of the dosage form, dissolution of the drug molecule, metab-olism in the intestinal tract or intestinal mucosa, permeation across the intes-tinal mucosa, and hepatic first-pass metabolism and/or clearance [4]. Theconfusion among scientists often occurs when attempts are made to equate


oral bioavailability with intestinal mucosal permeation. The following type ofcomment is often heard. ‘Well, if the compound exhibits good intestinal per-meation, it must have good oral bioavailability’. This generalization might betrue or it might be false! Ultimately, the ‘absolute oral bioavailability’ of acompound can only be determined by carrying out i.v. and oral dosing of thecompound in animals and by comparing the areas under the plasma vs. timeprofiles.

As the pharmaceutical industry underwent this paradigm shift in drug dis-covery, it became readily apparent that determination of the ‘absolute oralbioavailabilities’ of large numbers of compounds at the stages of lead selec-tion and lead optimization was not feasible [1]. Therefore, most pharmaceu-tical companies have taken the ‘reductionist’ approach to the problem andhave established high-throughput screens (HTS) to rapidly assess those fac-tors (e.g., solubility, metabolism, ability to permeate) that ultimately influ-ence the oral bioavailability of a compound [1]. Again, it is important toremind readers that none of these factors in isolation can be used to predictoral bioavailability unless the critical factor(s) (e.g., ability to permeate ormetabolism or solubility) that limits oral bioavailability has been clearly iden-tified for that compound! At the stages of lead selection and lead refinement,this critical factor(s) for a particular drug candidate is generally not known.Therefore, data forthcoming from HTS on the solubility, permeation, andmetabolic stability of a molecule are only useful for rank ordering com-pounds. These data should be ultimately used to select compounds for animalstudies in which the interplay of all these ‘developability factors’ are present.

Since other contributors to the Proceedings of this Symposium willaddress the way in which metabolism and solubility influence oral bioavail-ability, this chapter will focus on factors that influence permeation across theintestinal mucosa.

2. Barrier Properties of the Intestinal Mucosa

Since much of the research effort ongoing in my laboratory in the decadeof the 1990s has been focused on peptides and peptidomimetics, I will con-centrate here on the barrier properties of the intestinal mucosa that restrict thepermeation of this structural class of molecules. However, the concepts putforward here are generally applicable to more traditional types of drug candi-dates forthcoming from organic chemistry.

To a large extent, the clinical development of peptides and peptidomimet-ics has been prevented because of their unfavorable ADME properties. Theproblem of metabolic lability by hydrolytic pathways (e.g., peptidases) has,for all practical purposes, been resolved by medicinal chemists through struc-


tural manipulation of the peptides (i.e., introducing peptidase-resistant bonds)to produce peptidomimetics [5–7]. Until recently, medicinal chemists havehad less success in manipulating the structures of peptides and peptidomimet-ics to achieve good permeation of cell membranes (i.e., intestinal mucosa)while still retaining high affinity for the macromolecular target [5–7].

Permeation of peptides and peptidomimetics across the intestinal mucosacan occur via the paracellular pathway (pathway A, Fig. 1) or transcellularpathway (pathway B, Fig. 1). In general, hydrophilic peptides (e.g., opioidpeptides) and peptidomimetics (e.g., RGD peptidomimetics) are restricted tothe paracellular pathway, which consists of aqueous pores (average size insmall intestine, approx. 7–9 Å) created by the cellular tight junctions [7].Those aqueous pores limit peptide and peptidomimetic permeation based onsize and charge [8]. Hydrophilic peptides and peptidomimetics, whose per-meation is restricted to the paracellular pathway, typically exhibit oral bio-availabilities of <1–2% [7]. It should be noted that some hydrophilic mole-cules that structurally resemble di- and tripeptides (e.g., β-lactam antibiotics)show good intestinal permeation and good oral bioavailability (> 50%)because they serve as substrates for the oligopeptide transporter (pathway C,Fig. 1) [9][10].

In contrast to hydrophilic molecules, hydrophobic peptides, hydrophobicpeptidomimetics, or hydrophobic prodrugs of hydrophilic peptides and pepti-domimetics that lack charge and exhibit low hydrogen-bonding potential cantraverse the intestinal mucosa by passive diffusion via the transcellular path-way (pathway B, Fig. 1) [11]. Previously, it was thought that the enzymaticbarrier to peptide transport consisted only of brush-border membrane andcytoplasmic peptidases [12]. Recently, however, it was shown that a specificisozyme (3A4) of cytochrome P450 plays an important role in the metabolismof peptides (e.g., cyclosporin) and peptidomimetics (e.g., HIV protease inhib-


Fig. 1. Pathways of peptidomimetic transport across the intestinal mucosa. A: Passive diffu-sion via paracellular route. B: Passive diffusion via transcellular route. C: Transporter-facili-

tated (e.g., oligopeptide transporter). D: Transporter-restricted (e.g., by efflux transporters).

itors) [13]. In addition, the intestinal mucosa has been shown to contain effluxtransporters (e.g., multidrug-resistance protein (MDR1 in humans, also calledp-glycoprotein)) that restrict transcellular permeation of hydrophobic pep-tides (e.g., cyclosporin) and peptidomimetics (e.g., HIV protease inhibitors)[14–17] as well as hydrophobic prodrugs of peptides and peptidomimetics[18].

3. Methodologies Used to Assess Intestinal Mucosal Permeation

Methodologies used to assess intestinal mucosal permeation of drug can-didates can be grouped into three general categories: i) computational[19–23], ii) experimental using physicochemical surrogates [24–27], and iii)experimental using biological surrogates (see discussion below). Becauseother chapters in the Proceedings of this Symposium will discuss options i)and ii) in detail, the discussion here will focus on option iii), i.e., experimen-tal using biological surrogates.

While the computational and physicochemical approaches have provenuseful for predicting the passive diffusion of drugs across the intestinal muco-sa (e.g., pathways A and B, Fig. 1), adequate surrogates for predicting trans-porter-facilitated (pathway C, Fig. 1) or transporter-restricted (pathway D,Fig. 1) have not been developed. For example, in Fig. 2 is shown a hypothet-ical plot of the intestinal mucosal permeation coefficients (Pe) for a series ofdrugs and their respective apparent octanol-water partition coefficients(PCapp). It is obvious that compound Y is quite hydrophilic, yet shows goodpermeation characteristics that are not predictable from its PCapp value. Incontrast, compound Z is quite hydrophobic but shows poor permeation char-acteristics that are not predictable from its PCapp value. An example of a‘type-Y’ compound is valacyclovir [28], which is a substrate for the oligopep-tide transporter (pathway C, Fig. 1). An example of a ‘type-Z’ compound isindinavir [15], which is a substrate for MDR1, an efflux transporter (path-way D, Fig. 1). It is compounds like Y (e.g., valacyclovir ) and Z (e.g., indin-avir) that require the use of biological surrogates of the intestinal mucosa toassess a compound’s ‘true’ permeation characteristics and its pathway of per-meation (Fig. 1).

Historically, pharmaceutical scientists have used a wide variety of bio-logically based methodologies to estimate intestinal mucosal permeation ofdrugs and drug candidates [29]. In the 1990s, many academic and industrialscientists have focused on the use of in vitro models consisting of culturedhuman intestinal epithelial cells [30–32] or intestinal mucosal tissue [33], in situ models consisting of perfused intestinal mucosa [34], and in vivo mod-els consisting of cannulated animal models [34]. Each of these models has its


advantages and disadvantages, which have been reviewed elsewhere [29–35].I would like to take this opportunity to offer a few general comments aboutthe utility and the limitations of using cell-culture systems to predict intesti-nal mucosal permeation. These general comments would also be applicableto the other biological model systems mentioned above.

While cultured human intestinal mucosal cells (e.g., Caco-2 cells) havebecome quite popular in academic and industrial laboratories [31][35][36]during the 1990s, only permeability data for compounds that are passivelydiffused (pathway A, Fig. 1) have been correlated with human permeabilitydata [32][37]. Similar in vitro/in vivo correlations have not been establishedfor compounds that are metabolized by cytochrome P450-3A4 in the intesti-nal mucosa, actively transported (e.g., via the oligopeptide transporter) acrossthe intestinal mucosa (pathway C, Fig. 1), or actively effluxed (e.g., viaMDR1) to the luminal side of the intestinal mucosa (pathway D, Fig. 1). Suchin vitro/in vivo correlations may in fact be difficult to establish using the cur-rently available cell-culture methodology (e.g., Caco-2 cells) for the follow-ing reasons: These cultured cells may not express the same complement oftransporters or, if expressed, they may not be at the same level as in humanintestinal mucosal cells. The levels of expression of these transporters in the


Fig. 2. Hypothetical plot of Pe vs. log PCapp for a series of hypothetical molecules. CompoundY is a hydrophilic compound showing unusually high permeation values whereas compound Z

is a hydrophobic compound showing unusually low permeation values.

cultured cells may be highly dependent on growth conditions and thus mayvary from laboratory to laboratory. The influence of the transporter on per-meation of a molecule is known to be dependent on its concentration [11] andthe presence or absence of excipients [38]. Therefore, it may be difficult if notimpossible to duplicate exactly in vitro the ‘milieu’ in the lumen of the humansmall intestine. While the establishment of in vitro/in vivo correlations forsubstrates for these transporters (pathways C and D, Fig. 1) may be proble-matic, there is no doubt about the utility of these cell-culture models for con-ducting mechanistic studies that help guide the selection of compounds to befurther studied in vivo [32][35][36].

With respect to studying the permeation of compounds that are substratesfor cytochrome P450-3A4 in Caco-2 cells, it is important to note that thesecells express very low levels of this isozyme [39][40]. Therefore, investiga-tors have pursued two different strategies to assess the influence of this meta-bolic enzyme on intestinal mucosal permeation. One strategy simply involvesdetermining independently the permeation characteristics of molecules inCaco-2 cells and their metabolic lability in liver microsomes. Alternatively,Caco-2 cells have been induced to express higher levels of cytochrome P450-3A4, and then the permeation and metabolic lability of molecules can beaddressed simultaneously [40]. Both strategies yield valuable mechanisticinformation, but neither will necessarily predict the in vivo behavior of com-pounds. Therefore, the results of these types of experiments should be usedas a guide for selecting compounds and planning oral bioavailability experi-ments in animals.

4. Application of Cell-Culture Systems to Estimate Intestinal MucosalPermeation Characteristics

4.1. Via Passive Diffusion (Pathway A, Fig. 1)

In the previous Symposium, our laboratory, in collaboration with Dr.Philip Burton’s laboratory, described how the Caco-2 cell-culture system wasused to elucidate the importance of hydrogen-bonding potential in determin-ing the permeation of peptides [11]. Subsequently, our laboratory [41][42] hasemployed Caco-2 cells to demonstrate that cyclic prodrugs of hydrophilicpeptides (e.g., [Leu5]-enkephalin and its metabolically stable analogDADLE) synthesized using a phenylpropionic-acid linker or a coumarinic-acid linker and a coumarinic-acid-based cyclic prodrug of a RGD peptido-mimetic [43] have significantly enhanced cell-permeation characteristics.

For example, the coumarinic-acid-based cyclic prodrug of DADLE wasca. 31-fold more able to permeate Caco-2 cell monolayers than was DADLE


itself [41]. Mechanistic-type studies showed that this cyclic prodrug was atranscellular permeant (pathway B, Fig. 1), whereas DADLE was a paracel-lular permeant (Pathway A, Fig. 1), and that the cyclic prodrug was not a sub-strate for an efflux transporter (pathway D, Fig. 1) [41]. In vivo studies toconfirm these observations are now ongoing in our laboratory.

4.2. Via Passive Diffusion Modified by an Efflux Transporter(Pathway D, Fig. 1)

When attempts were made to employ a third prodrug strategy using an(acyloxy)alkoxy linker to enhance the permeation of [Leu5]-enkephalin andDADLE, the results were quite different than those described above, i.e., thepermeability coefficients of the (acyloxy)alkoxy prodrugs across Caco-2 cellmonolayers in the apical-to-basolateral direction were 200–300 times lowerthan the values observed with phenylpropionic-acid- and coumarinic-acid-based cyclic prodrugs of the same opioid peptides [18]. In fact, the (acyl-oxy)alkoxy-based cyclic prodrug of DADLE had a permeability coefficientca. four times lower than that of DADLE itself. By studying the permeationof this (acyloxy)alkoxy-based prodrug in the basolateral-to-apical direction,we showed that this cyclic prodrug was a substrate for an efflux transporterin Caco-2 cell monolayers (pathway D, Fig. 1) [18]. In vivo studies to con-firm these observations are now underway in our laboratory.

Another interest in our laboratory has been the determination of theeffects of peptide-bond bioisosteres on transport by passive diffusion (path-way B, Fig. 1), passive diffusion modified by efflux transporters (pathway D,Fig. 1) and active transport by the oligopeptide transporter (pathway C,Fig. 1). Recently, we determined the effect of a pyrrolinone peptide-bond bio-isostere on substrate activity for MDR1 in Caco-2 cells [44]. Specifically, wedetermined what effect replacement of a peptide bond with a pyrrolinone bio-isostere in an HIV protease inhibitor had on the permeation characteristics ofthese peptidomimetics. The results showed that the pyrrolinone-based HIVprotease inhibitor was a better substrate for MDR1 in Caco-2 cells and had alower intrinsic permeability than did the peptide-bond-containing HIV pro-tease inhibitor [44].

4.3. Via Oligopeptide Transporter (Pathway C, Fig. 1)

Recently, we have used Caco-2 cells to elucidate the stereochemical pre-ferences of the oligopeptide transporter for peptide substrates [45][46]. Forthese studies, we used a series of diastereoisomers of Val-Val and Val-Val-Val.


For each di- and tripeptide, its metabolism, binding to the apical oligopeptidetransporter, cellular uptake and transcellular transport using Caco-2 cell mono-layers were determined. For example, with the tripeptides, we observed thatthe L-L-L and L-L-D tripeptides were rapidly metabolized in Caco-2 cellhomogenates. The other six stereoisomers of Val-Val-Val were completelystable in the Caco-2 cell homogenates. Five of the stereoisomers (L-L-L, L-L-D,L-D-L, D-L-L, D-D-L) significantly inhibited the cellular uptake of [3H]cepha-lexin (a known substrate of the oligopeptide transporter). The other stereo-isomers had no effect on the uptake of [3H]cephalexin. When the cellularuptake of the stereoisomers was determined, the D-L-L and L-D-L tripeptidesshowed the highest intracellular concentrations. Moreover, the cellular uptakeof the D-L-L and L-D-L tripeptides was inhibited by Gly-Pro, whereas Gly-Proshowed moderate-to-no inhibitory effect on the cellular uptake of the otherstereoisomers. The permeability coefficients of the stereoisomers across theCaco-2 cell monolayers were very low and almost identical. Gly-Pro had noeffect on their transepithelial transport. These results suggest that the interac-tion of the Val-Val-Val stereoisomers with the AP oligopeptide transporter(s)could be a good predictor of their cellular uptake. However, since the majortransepithelial transport mechanism of Val-Val-Val stereoisomers is passivediffusion via the paracellular route, the binding of these molecules to the oligo-peptide transporter(s) is not a good predictor of their transepithelial transport.

5. Conclusion

Recently, in the introduction to a book entitled ‘Integration of Pharma-ceutical Discovery and Development’ [3], Ralph Hirschmann wrote that ‘theconventional strategy employed in the industry had been flawed in that themedicinal chemist focused his or her attention almost exclusively on but twoissues: potency and specificity. The oral bioavailability issues were left forthe endgame’. This drug-discovery mindset has changed in the decade of the1990s because pharmaceutical scientists have improved their understandingof the factors that influence oral bioavailability and because methodologieshave been developed to determine these factors rapidly on large numbers ofcompounds. Thus, these characteristics of a molecule can now be determinedin the lead-selection stage as well as the lead-refinement stage of drug dis-covery. I am optimistic that as our knowledge about the factors limiting oralbioavailability increases in the future, ever more appropriate biological meth-odologies will be developed to expedite the ever more accurate characteriza-tion of ‘developability’ of compounds in drug discovery. I am confident that,because of this paradigm shift in drug discovery, better and safer drugs willbe produced by the industry in the future.


Financial support from the United States Public Health Service (GM51633, DA09315,GM08359) is gratefully acknowledged.

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Biological Models to Study Blood-Brain Barrier Permeation

by Stefanie D. Krämer*, N. Joan Abbott and David J. Begley

Centre for Neuroscience Research, GKT School of Biomedical Sciences, King’s College London, London SE1 1UL, UK; Fax: +44 20 7848 6569;


1. Introduction

The control over drug passage across the complex barrier between theblood and the brain is a challenging goal in drug discovery. The blood-brainbarrier (BBB) consists of a combination of morphological properties andmetabolic, carrier, and immunological functions maintaining the homeostasisof the central nervous system (CNS). The BBB is formed by the tight endo-thelial cell layer in the brain capillaries and controls the exchange of nutri-ents, hormones, metabolites, drugs, toxins, macromolecules, and immunolog-ical cells between the blood and the brain in both directions. CNSTherapeutics have structural characteristics which allow them to cross theBBB; ideally, drug compounds for peripheral organs and tissues should beunable to cross the BBB to avoid central-nervous side effects.

The predictive approaches for BBB passage range from theoretical mod-els based on calculated physico-chemical parameters to in vivo animal experi-ments. The physico-chemical parameters used in correlation functionsbetween estimated and observed BBB passage are basically the same as usedfor the prediction of intestinal or of skin absorption but with distinct weight-ing factors for the different barriers [1]. Generally, theoretical models statis-tically predict BBB passage and fail if specific transport mechanisms areinvolved in permeation. For this reason, in vitro biological models, thoughmuch more labor-intensive and time-consuming, are often employed for theprediction of brain uptake.

Although none of the biological models covers all BBB functions, theyoccupy an important position between the computational approach and ani-mal experiments. The broad variety of BBB models offers tools for the inves-tigation of the different features. Biological in vitro models can give informa-


tion on specific mechanisms which cannot be gained from animal experi-ments. The work with these models is usually more convenient than animalexperiments and they are more suitable for the screening of a series of com-pounds. Most biological models for BBB permeation studies are based on cellcultures with relevant BBB characteristics. In this review, typical BBB char-acteristics and functions will be outlined and a summary of in vitro biologi-cal BBB models as described in the literature will be given. In the last part,techniques for permeation studies will be discussed.

2. Characteristics and Functions of the BBB

2.1. Anatomy and Physiology

Anatomically, the BBB which has an estimated surface area of 12 m2 inhumans [2] is composed of the brain-capillary endothelium as shown inFig. 1. BBB Endothelium forms a much tighter interface than peripheralendothelia. In the periphery most small solutes can diffuse between the bloodand the tissue through the intercellular clefts of 50–200 nm width [3] [4]. Bycontrast, the gaps between capillary endothelial cells in most parts of thebrain are tightly sealed by the zonulae occludentes or tight junctions (TJ).


Fig. 1. The cell types forming the blood-brain interface of a capillary in mammalian brain par-enchyma. E, endothelium; BM, basement membrane; P, pericyte; A, astrocytic process. Arrow-heads indicate the interendothelial clefts closed by tight junctions. Reproduced from [130],

with permission of S. Karger AG, Basel.

Several proteins are involved in TJ formation and function (see Sect. 2.2.2)and lipids are also associated with the junctional complex between the cells(for a review, see [5–8]). The TJ divides the plasma membrane into an apical(luminal) and a basolateral (abluminal) side. The cells are polarized; the twosides show distinct patterns of membrane proteins [9] and presumably also oflipids, as demonstrated for epithelia [10].

It has been shown that the TJ acts not only as a ‘gate’ for the paracellularpermeation of solutes, but also as a ‘fence’ in the plasmalemma. The TJ hin-ders the migration of molecules, e.g., lipids, between the apical and basolat-eral sides in the outer lipid leaflet of the plasmalemma, while molecules in theinner leaflet can move freely around the whole cell [10].

The tightness of the brain endothelium is reflected in its high transendo-thelial electrical resistance (TEER). Measured values for pial endothelium are1500–2000 Ω · cm2 [11] whereas the value estimated from isotopic electro-lyte tracer-permeability studies is ~8000 Ω · cm2 for brain parenchyma [12].For comparison, TEER values of peripheral capillaries can be as low as4–20 Ω · cm2 [11].

As a result of the tightness of the BBB endothelium caused by the contin-uous net of tight junctions, the paracellular pathway is negligible for mostcompounds under physiological conditions [13]. This means that passive,non-facilitated permeation is mainly restricted to lipophilic compoundswhich are able to traverse the lipid membranes of the cells. Pardridge hassuggested that the upper Mr limit for such transcellular non-facilitated pas-sage is around 600, but there are several exceptions [14] [15]. It is likely thatthe majority of larger molecules are excluded as a result of active efflux byP-glycoprotein (P-gp) (see Sect. 2.2.3). Many macromolecules, such as pro-teins that are transported across other endothelia by endocytosis/transcytosis,show negligible penetration in brain capillaries, consistent with the lowerfluid phase endocytotic activity in brain endothelial cells [16–19]. However,specific receptor-mediated endocytotic and transcytotic mechanisms exist inbrain capillaries for some proteins with key functions, such as transferrin [20][21] (see Sect. 2.2.4). Brain endothelia have a higher density of mitochondriathan most other endothelial cells [22], a reflection of their higher rate ofmetabolism subserving specific barrier and transport functions (seeSect. 2.2.5).

There is evidence that the typical characteristics of brain endothelial cellsare induced by the interaction with other cell types in their environment suchas microvascular pericytes, neurons, astrocytic foot processes, perivascularmicroglial cells, and circulating leukocytes [15]. A scheme of a brain capil-lary and the surrounding astrocyte foot processes is shown in Fig. 1. In vivo,the distance between the abluminal surface of the endothelial cells and theastrocyte foot processes is approximately 20 nm [23]. This space contains the


microvascular basement membrane which plays a key role in the inductionprocess [24] [25] (see Fig. 1).

2.2. Biochemical Properties of BBB Endothelial Cells

2.2.1. Endothelial and BBB Markers

For the characterization of in vitro cultures, the cells are screened for typ-ical marker proteins which are predominantly present in one specific cell typeor indicate a specific cell function, e.g., endothelial marker or marker ofmetabolically active cells. Although these markers are not always exclusive-ly found in a single cell type, their presence or absence gives good evidencefor the phenotype of the studied cells. The marker proteins are analyzed byeither immunofluorescence staining for microscopy or after gel electrophore-sis using Western blotting, by activity tests for enzymes or by transport stud-ies for carrier proteins. In addition to the presence of such markers, theirlocalization in the cells provides important information about the identity andstate of the cells. The physiological function of some markers is known, whileothers require further investigation.

Typical barrier markers are the TJ proteins which are involved in the seal-ing of endothelial or epithelial cell layers. The BBB contains a large varietyof different carriers and receptors that mediate the uptake of nutrients andother substances into the CNS as well as the efflux of waste products andtoxic compounds. Furthermore, a relatively high density of metabolic en-zymes is part of the barrier function. Such carriers, receptors, and metabolicenzymes are often used as non-specific BBB markers and are described inmore detail in the following paragraphs.

Typical markers for endothelial cells are the Factor-VIII-related antigenor von Willebrand Factor (vWF), the low-density lipoprotein(LDL)-receptorand the uptake of DiI-labelled-acetylated LDL (DiI-Ac-LDL), the transferrinreceptor, and a specific cluster of differentiation antigens. Further endothelialcharacteristics are non-thrombogenicity, low leukocyte adherence, release ofvasoactive compounds such as nitric oxide, endothelin-1 and prostacyclins,lectin binding, and the presence of angiotensin-converting enzyme (ACE)and monoamine oxidase (MAO). Alkaline phosphatase (ALP) and γ-glutamyltranspeptidase (γ-GTP) are useful markers for a BBB phenotype, since theyare present in high concentrations in brain endothelium and are up-regulatedby inductive influence from astrocytes [3] [26–28].


2.2.2. TJ Proteins

Continuous TJ networks are found in most epithelia and many endothelia,including the brain-microvessel endothelium. The TJ network of culturedcells can be visualized by fluorescence immunostaining of TJ or TJ-associat-ed proteins. The TJ network of an epithelial cell line has for example beendemonstrated using laser scanning confocal microscopy [29].

Several transmembraneous and cytosolic proteins have been recognizedas TJ or TJ-associated proteins, and the identification of further TJ-relatedproteins can be expected. Considerable research on the function of such pro-teins and the understanding of the TJ is underway (for reviews, see [5] [7] [8][30–32]). Proteins involved in TJ formation and function include the trans-membraneous occludin and claudins, the cytoplasmic membrane-associatedguanylate kinases (MAGUK) ZO-1 and ZO-2, the cytoplasmic 7H6 antigen,cingulin, and symplekin. Cadherin-dependent cell adhesion at the adherensjunction after cell-cell contact and perijunctional actin are also involved in theassembly and function of the TJ.

Such proteins are associated with the TJ of both brain and peripheralendothelia. However, the induction of BBB-characteristics in primary bovinebrain endothelial cells using astrocyte co-culture or cyclic AMP (cAMP, seeSect. 4.4) also influenced the TJ structure of these cells as shown with freeze-fracture electron microscopy indicating different TJ arrangements in BBBand peripheral endothelia [33]. Furthermore, ZO-1 forms a continuous bandin brain endothelia but is discontinuous in peripheral endothelia [34], andbrain endothelia express much more occludin than peripheral endothelia [34].There is evidence that the phosphorylation pattern of TJ proteins is relevantfor the tightness of the cell monolayer [32].

2.2.3. Transport Proteins

As passive permeation across the BBB is restricted to lipophilic solutes,brain endothelial cells possess a number of carrier mechanisms transportinghydrophilic nutrients, nucleotides and precursors for neurotransmitters intothe brain, and other compounds, e.g., metabolites, out of the brain. Fig. 2shows a scheme of different transport mechanisms at the BBB. Some trans-port systems simply facilitate the passage of their substrates along the con-centration gradient (equilibrative transport) while others are able to transportthem against an electrochemical gradient and require either a direct source ofenergy, e.g., ATP dephosphorylation, or an indirect source of energy, e.g., asodium gradient maintained by Na+/K+-ATPase (for reviews, see [22] [26][35–40]).


GLUT-1, a glucose transporter present in brain endothelium, facilitatesthe transport of hexoses. It is not insulin-responsive or sodium-dependent, incontrast to some other GLUT isoforms in other tissues. Other nutrients for thebrain, such as short-chain carboxylic acids and ketone bodies, are transport-ed by their own specific transport systems. A number of amino-acid carriershave been described for the BBB. Of special importance is the L-system (orLNAA-system) which facilitates the uptake of large neutral amino acidsneeded in the brain for the synthesis of neurotransmitters. It is present at boththe luminal and abluminal sides of the BBB. Sodium-dependent amino-acidcarriers are also present at the BBB, including some that mediate amino-acidefflux out of the brain, such as the acidic-amino-acid carrier which keeps thelevel of neurotoxic glutamate low in the brain. Di- and tripeptides and evenlarger peptides are also transported at the BBB by specific mechanisms.Glycopeptides are probably transported by GLUT-1 [41]. An uptake mecha-nism is also known for choline. The uptake of DNA and RNA precursors ismediated by the transport systems for nucleosides and purine bases.

Some drug compounds can enter the CNS via these transport systems,e.g., L-DOPA and H1-antagonists. The possibility of using such transportsystems for drug delivery has been widely discussed [40] [42–44]. The BBB


Fig. 2. Major transport mechanisms at the BBB. Scheme of a brain-capillary endothelial cell.GLUT-1 facilitates glucose uptake into the brain; L- and ASC-systems transport neutral aminoacids into the brain; A-system transports glycine; a sodium-dependent acidic-amino-acid car-rier transports glutamic acid and aspartic acid out of the brain; peptide transporters transportsmall peptides into the endothelium; specific receptors bind blood-borne larger peptides, whichmay be endocytosed; P-glycoprotein effluxes a variety of xenobiotics. Modified from [131].

also contains several efflux systems of relevance for drug therapy [22] [44][45]. The multi-drug resistance protein P-glycoprotein (P-gp, MDR-1) is aneffective efflux pump at the BBB with a broad substrate specifity [46] [47].The BBB passage of many potential CNS- or tumor therapeutics is hinderedby P-gp.

P-gp substrates are generally lipophilic and many carry a positive chargewhich can be delocalized, but until now, no common structural characteristiccan be identified which distinguishes substrates from non-substrates (for re-views, see [48] [49]). This makes it impossible, at present, to predict whethera compound will be recognized by P-gp.

The efflux of the P-gp substrate rhodamine 123 from P-gp-transfectedMadin-Darby canine kidney (mdr1-MDCK) cells and the inhibition of theefflux by the P-gp inhibitor verapamil has recently been shown in a confocallaser scanning microscopy study [50].

In spite of claims that P-gp is present on astrocytic endfeet at the BBB[51] [52], the majority of studies show P-gp predominantly localized to theluminal brain-endothelial membrane under normal conditions [53–56].

2.2.4. Receptors and Receptor-Mediated Transport

LDL, acetylated LDL (Ac-LDL), and HDL particles bind to confluentmonolayers of brain-microvessel endothelial cells by the LDL receptor, thescavenger receptor, and the HDL receptor, respectively [3]. Saturable bindingto isolated human brain capillaries has been demonstrated for insulin, insulin-like growth factors (IGF-1 and IGF-2), transferrin, and leptin (reviewed in[39]). Receptor binding is the first step in endocytosis and transcytosis. Thereis evidence that transferrin and insulin are taken up by endocytotic events[20] [21] [57]. A lactoferrin receptor, the internalization of lactoferrin afterbinding, and transcytosis across the BBB have recently been shown on differ-entiated bovine brain-capillary endothelial cells [58].

The use of receptors such as the transferrin and insulin receptors and ofnon-specific endo- or transcytosis for the delivery of peptides to the brain hasbeen investigated and discussed by several authors [2] [14] [40] [59].

2.2.5. Metabolic Enzymes

The BBB is metabolically active [60]. Enzymes involved in free-radicalformation and degradation, such as epoxide hydrolase, MAO, cytochrome P450 reductase and nitric-oxide synthase are relatively prominent (reviewedin [61]). A variety of phase-I and phase-II metabolic enzymes are present at


the BBB, including cytochromes P450 (reviewed in [22] [26]). This has to beconsidered in CNS targeting of metabolically degradable compounds.

2.2.6. Lipids

Lipids extracted from brain tissue contain a relatively high amount of longpoly-unsaturated acyl chains, e.g., docosahexaenoic acid [62]. The influenceof dietary fatty acids on the fatty-acid pattern of brain-endothelial cell mem-branes and its effect on BBB efficiency has been reviewed in [63]. For ex-ample, α-linolenic-acid (18:3 n-3) deficiency in rats has been reported to resultin increased permeability of the BBB to the paracellular tracer sucrose [64].

2.3. Pathological Conditions

BBB Lesions and BBB damage are associated with multiple sclerosis,Alzheimer’s disease, AIDS-related dementia, cerebral edema, ischaemic dis-orders, brain tumors, and infectious diseases such as bacterial meningitis [3][65] [66]. Furthermore, activated lymphocytes, macrophages, and certaintypes of metastatic cells can cross the intact BBB (summarized in [32]).

Several factors which are enhanced in inflammation have been shown toincrease the permeability of the BBB: histamine (affects TJ), bradykinin (affects TJ and induces fluid-phase endocytosis), angiotensin II (inducesfluid-phase endocytosis), arachidonic acid and eicosanoids, serotonin, cyto-kines interleukin 1a and 1b, macrophage inflammatory proteins 1 and 2,tumor-necrosis factor (TNF), and interleukin 2. Other factors modifying theBBB permeability are platelet-activating factor and complement as well asStreptococcus pneumoniae factor(s) [37].

3. Criteria for Good BBB Models

Considering the BBB characteristics summarized above, an optimal BBBmodel would feature the following properties: 1) A continuous TJ network, 2) high TEER and low permeability for small hydrophilic compounds such as mannitol or sucrose, 3) BBB-specific carriers for influx as well as efflux,4) low unspecific endocytosis activity, and 5) BBB-characteristic metabolicenzymes. In particular, the large variety of carriers and enzymes poses amajor challenge for the development of a suitable BBB model for in vitrostudies. While passive diffusion across cell membranes can satisfactorily bepredicted from physicochemical parameters (reviewed in [1]), the prediction


of carrier-mediated transport and of metabolism for a given compound isextremely difficult without an adequate biological model. In addition to thelisted properties 1)–5), the expression and localization of other specific endo-thelial and BBB markers as reviewed above are used for the evaluation ofBBB models. Methods applied for the analysis of specific markers aredescribed by Wunderli-Allenspach in this volume (see p. 99).

4. Biological BBB Models

4.1. Use of the Different Model Types

In vivo techniques for the study of BBB passage are reviewed in [26]. Theestablishment of in vitro biological BBB models started with the isolation ofmicrovessels from brain. Microvessels have been and are still used for theidentification and localization of BBB markers and for binding, metabolism,uptake, and efflux studies. An advantage of isolated microvessels is that theendothelial cells maintain all BBB specific characteristics [39].

The use of pial microvessels, which are more accessible than cerebralmicrovessels, has been discussed in [67]. Pial and cerebral microvessels havemany properties in common, e.g., impermeability to electron-dense tracers ofvarious sizes, but deviate in others such as the TJ architecture.

Cerebral microvessels and capillaries are further used for the preparationof primary brain-microvessel or capillary endothelial cells. Sub-cultured pri-mary cells of brain-endothelial origin are widely used as in vitro BBB mod-els. They can be maintained in culture over a limited number of passages.

The immortalization of primary cells leads to cell lines, which are a con-venient alternative to primary cells. Cell cultures used as BBB models havebeen generated not only from human and animal brain endothelia but alsofrom umbilical-cord vein, aorta, and some epithelia [3] [26]. Cells in culturegenerally lose some BBB characteristics, but many relevant BBB propertiescan be re-induced in these cells under the influence of astrocytes or gliomacells. Cultured cells are used for the investigation of transport processes bypermeation, uptake and efflux studies of drug compounds through the celllayer and into or out of the cells, respectively. In addition, cultured cells aresuitable models for the investigation of expression, function, and localizationof specific proteins under defined conditions and under the influence of dif-ferent factors (cf. contribution of Wunderli-Allenspach in this volume).

The preparation of endothelial-cell plasma-membrane vesicles and theisolation and reconstitution of specific membrane proteins into defined mem-brane vesicles provide systems for the investigation of specific processes atthe BBB. The different models are described below.


4.2. Primary Brain-Microvessel and Capillary-Endothelial Cell Cultures

Primary cultures are generated from freshly isolated tissue. According to[68], about 200–250 million cells can be isolated from one bovine brain and20–50 million from 10 rat brains. The isolated cells can be sub-cultured for alimited number of passages. The use of primary brain-microvessel or capil-lary-endothelial cells as BBB models has the advantage that specific BBBproperties of the in vivo cells are still expressed in the model. Such propertiesare often lost upon transformation into cell lines. However, even in primarycultures, not all the in vivo characteristics are maintained under standard cul-ture conditions. The sub-cultivation of the cells can lead to the loss of themarker enzymes ALP and γ-GTP [69–71]. This might be due to the lack of aspecific environment containing a variety of regulating factors. Trypsin, asused for the detachment of the cells for passaging, can also affect endothelialproperties [72]. A key finding for the development of in vitro biological BBBmodels is that the imitation of the CNS environment as described in Sect. 4.4can re-induce many of the lost BBB characteristics in cell cultures.

4.2.1. Isolation by Enzymatic Digestion

As shown in Fig. 3, the protocols for the isolation of primary cells can bedivided into methods using enzymatic digestion, mechanical techniques, andenzymatic digestion in an early step, but only mechanical techniques for theisolation of the cells from the microvessels. The purity of the cells can bechecked by positive staining for Factor-VIII-related antigen and absence ofstaining for glial fibrillary acidic protein [70].


Fig. 3. Techniques for the isolation of brain-endothelial cells. Redrawn from [81].

In the protocol summarized in [73], minced bovine gray matter is enzy-matically dispersed with dispase to liberate the microvessels. The microves-sels are separated from tissue debris by dextran centrifugation. Pericytes andastrocytes are then dissociated from the isolated microvessels by incubationwith collagenase/dispase and Percoll gradient centrifugation. The isolatedbrain microvessels can be stored at –70° and can be grown on tissue-cultureplates or on microporous filter membranes.

The growth surface is generally pretreated with rat-tail collagen and fibro-nectin. Cells are seeded at a density of 50 000 cells · cm–2. Using this method,confluence was reached by day 7 or 8 and reasonably tight monolayers on day9 or 10. Experiments were carried out between days 9 and 12. After day 16,morphological and functional changes were apparent [73].

Monolayers of primary cells are usually more permeable to small hydro-philic compounds than the in vivo brain endothelium. A typical TEER valueis 160 Ω · cm2 [73]. Furthermore, primary cells undergo some de-differentia-tion in culture, resulting in down-regulation of GLUT-1 [74], ALP, and γ-GTP[75]. The cells isolated by enzymatic digestion methods are of capillary, arter-iolar, and venular origin. Also, complete separation of pericytes from theendothelial cells cannot be guaranteed. Enyzmatic digestion often leads to theloss of specific characteristics [76] [77]. Surface molecules may be removedfrom the cells by the added enzymes [3]. The enzyme-ratio-dependent influ-ence of collagenase and dispase in the digestion steps on several surface pro-teins has been investigated [78]. The choice of medium and culture surface isimportant for the re-expression of such digested molecules.

4.2.2. Non-Enzymatic Isolation

To avoid the influence of digestive enzymes on cell-surface proteins, non-enzymatic or mechanical isolation techniques have been developed [79–81].Microvessels are isolated by mechanical homogenization from the brain tis-sue. Microvessels, consisting mainly of capillaries, are seeded onto dishescoated with an extracellular matrix secreted by bovine corneal endothelialcells, allowing capillaries, but not arterioles and venules, to adhere. The lat-ter two can therefore easily be discarded. Five days after seeding, the firstendothelial cells migrate out of the capillaries and start to form micro-colo-nies, which can be harvested by micro-trypsinization when sufficiently large.The cells are then sub-cultured and can be stored in liquid nitrogen.

This isolation procedure results in pure capillary-endothelial cells. Thecells can be kept in culture up to passage 8 with a life span of about 50 cumu-lative population doublings. Techniques including both enzymatic andmechanical steps have been described [77] [82].


4.3. Cell Lines

Cell lines are derived from immortalized primary cells. Immortality of acell can either follow a spontaneous gene transformation or artificial transfec-tion with immortalizing virus genes. Alternatively, immortal tumor cells aremaintained in culture and used as cell lines.

4.3.1. Endothelial Cell Lines

Endothelial cells originating from bovine, porcine, rodent, and humanbrain and other organs have been immortalized for the establishment of cell lines [83–85]. Endothelial cell lines usually lose some of the typical in vivo characteristics of the original cells. TEER is again lower than invivo, reflecting a lower complexity of TJ formation, the permeability ofhydrophilic small compounds is higher, and the expression of ALP, γ-GTP,and GLUT1 is often reduced (reviewed in [71]). However, as described for the primary cell cultures, many of the relevant BBB characteristics canalso be re-induced in cell lines (see Sect. 4.4), making them useful BBBmodels.

The rat-brain microvessel endothelial cell line RBE4 was established bytransfection with the E1A adenovirus gene [86]. RBE4 cells express Factor-VIII-related antigen and lectin binding sites typical of endothelia. The induc-tion of other BBB markers is described in Sect. 4.4. RBE4 cells also expressP-gp [87]. They form well-organized monolayers, and although these are nottight enough for the ranking of drug permeability, paracellular permeability issufficiently low for toxicological assays [88]. RBE4 cells have been used fora variety of biochemical studies on the BBB and particularly for uptake andefflux studies to investigate carrier functions [89].

4.3.2. Cell Lines of Non-Endothelial Origin

The ECV304 cell line, originally introduced as an immortalized humanumbilical cord vein-endothelial cell (HUVEC) line, expresses several pheno-typic features characteristic of endothelia and the BBB, either in monocultureor when exposed to factors from glial cells (see Sect. 4.4) [90–92]. P-gpexpression and up-regulation may depend on the source of the ECV304 cells[91].

The cell line has recently been found to express a genotype identical toT24, a cell line originating from a bladder carcinoma which makes the originof ECV304 from HUVEC unlikely. Nevertheless, the combination of endo-


thelial and barrier properties shown by the ECV304/C6 co-culture modelmakes it a valuable model for transendothelial permeation studies (seeSect. 4.4).

Epithelial cell lines such as MDCK and Caco-2 (human colon adenocar-cinoma) spontaneously form continuous TJ networks, leading to relativelyhigh transepithelial electrical resistances [29] [93] and low permeability tosmall hydrophilic compounds [94]. Caco-2 cells express P-gp, and MDCKcells which express only very small amounts of the multi-drug-resistance pro-tein, have been transfected with the P-gp mdr1 gene [95]. The transfected cellline expresses high amounts of P-gp at the apical surface. As these phenom-ena are typical characteristics of the in vivo BBB, such epithelial cell lineshave been used as alternative BBB models for permeability and P-gp-effluxstudies [50] [96–99]. However, it has to be kept in mind that epithelial cellsdiffer in many aspects from endothelial cells, and this can influence the trans-port processes across the cell monolayer.

4.4. Induction of BBB Characteristics in Cultured Cells

As described above, primary brain-endothelial cell cultures and estab-lished brain endothelial cell lines tend to lose relevant BBB characteristics.They allow a relatively high paracellular passage of small compounds ascompared to the tight in vivo BBB. The TEER of such cultures is typicallylow. Several of the lost characteristics can be re-induced by the co-culturewith other brain cells; most extensively studied is the influence of astrocytesor glial cell lines. Alternatives to co-culture are the use of culture mediumwhich has been conditioned by such cells, and the addition of single factorsto the culture medium. BBB Characteristics can even be induced in non-brain-endothelial cell lines.

4.4.1. Influence of Astrocytes and Glial Cells on Barrier Cells

The astrocytes used to induce BBB characteristics in barrier models areusually isolated from fetal or newborn rat brain (see, e.g., [100]). Rat orhuman glioma cells are also frequently used, e.g., the rat glioma-cell line C6[32]. In co-culture, the barrier cells and the astrocytes or glioma cells are sep-arated by a porous filter membrane. A frequently used setup is the filter-insertsystem as shown in Fig. 4. There are several different types and materials of disposable membrane inserts commercially available for the use in 6- or12-well tissue-culture plates. They resemble small plastic beakers with thepermeable membrane as the base and are either suspended on the rim of the


well or stand on feet in the well. The membrane is a fixed distance above thebottom of the well, immersed in the medium in the well. The insert also con-tains medium. Cells cannot migrate between the well and the insert, but theinsert membrane is permeable to small-Mr solutes. Cells can be grown oneither side of the membrane. Usually, the barrier cells are grown on the topside of the membrane and the astrocytic cells are either grown on the oppo-site side of the membrane or on the bottom of the well (reviewed in [81]). Asin the in vivo situation, the base of the endothelial cells faces the inducingcells.

In cultures of endothelial cells with conditioned medium from astrocyticcells, the culture medium usually consists of one part fresh medium to onepart medium which has been incubated with an astrocyte or glial cell-line cul-ture for several days [77] [90] [101–103].

In a number of studies, co-culture of primary endothelial cells or endothe-lial cell lines with astrocytes or glial cell lines induced BBB characteristics in the endothelial cell layer. TEER Values are generally increased, but not tovalues as high as in in vivo, and the permeability of small hydrophilic com-pounds is decreased (reviewed in [32]).


Fig. 4. Filter-insert setup as used for the co-culture of barrier cells with astrocytic cells andfor transport studies

Astrocytes or transformed glial cells and neurons in co-culture, or the useof their conditioned medium, have been shown to enhance the expression ofALP, γ-GTP, ACE, MAO, Factor-VIII-related antigen, GLUT-1, P-gp, andLDL- and transferrin receptors in endothelial cells (reviewed in [3] [71] [81]).

The RBE4 cell line in co-culture with astrocytes and C6 glioma cellsdeveloped three-dimensional structures, which expressed γ-GTP and ALP ona particular matrix support [86]. Recently, a transfected bovine brain-endo-thelial cell line was established showing up-regulated BBB characteristics inco-culture with astrocytes [85].

Co-culture of primary bovine brain-endothelial cells with astrocytes alsoincreased the amount of brain-typical, long poly-unsaturated acyl chains inthe phospholipid fraction of the endothelial cells [104].

Astrocytes and glial cells, as well as culture medium conditioned bythese cells, are also able to induce BBB characteristics in cell lines that donot originate from brain microvessels. One example is the ECV304 cell linementioned above. These cells express many BBB markers and showincreased TEER and decreased permeability to small hydrophilic com-pounds under the influence of C6 glioma cells [90–92]. The ECV304/C6 co-culture system is therefore a frequently used model for BBB permeationstudies (see, e.g., [105]). As with Caco-2, some heterogeneity has beenreported in cells from different sources [91], a factor that will need to betaken into account.

In addition to astrocyte-conditioned medium, medium supplemented witha hypothalamic extract [106] or with bovine brain homogenate [107] wereshown to have BBB-inducing effects on endothelial cells.

4.4.2. Three-Dimensional BBB Co-Culture Model

Janigro and co-workers [108] developed a BBB model consisting of co-cultured endothelial and glial cells in a three-dimensional, pronectin-coatedpolypropylene hollow-fibre structure. The endothelial cells line the lumen ofthe fibres, while the glial cells are grown on the outside of the fibres. Thisallows the exposure of the endothelial cells to flow-induced shear stressapplied by a pump that forces the medium through the hollow fibres. Theflow through the fibres also enables sampling from both sides of the barrier.The fiber walls are porous (0.5 µm) to allow diffusion of nutrients and othersolutes but not of cells. Under co-culture and flow conditions, the endothelialcells showed several BBB-specific characteristics. Among them was thepotassium net flux from the extraluminal space into the luminal space whichwas not observed in cultures of endothelial cells alone. The hollow-fibrestructure and the arrangement of the cells is in some respects similar to that


of the brain capillaries. However, in contrast to brain capillaries, the perime-ter of the hollow fibres is lined by more than one endothelial cell, and theshear force in the fibres may be higher than in the capillaries in vivo (dis-cussed in [28]).

4.4.3. Influence of Specific Factors on Monocultures

The mechanism by which astrocytes and other brain cells induce BBBcharacteristics in the barrier cells is not fully understood. Whether a small,soluble molecule or a membrane-bound compound is involved, and whetherit is a peptide, lipid, organic, or inorganic molecule has been intensively dis-cussed (see, e.g., [109]).

However, some well-defined factors with beneficial effects on the endo-thelial cells have been described. Growth factors, such as glutamine, throm-bin, vascular permeability factor, platelet-derived endothelial-cell growth fac-tor, and transforming growth factor beta, promote the formation of confluentbrain-microvessel endothelial cell monolayers and may cause angiogenesis ata later stage (reviewed in [3]).

Tumor necrosis factor alpha (TNF-α) induced the transcription of GLUT-1-mRNA and actin-mRNA in a bovine brain-capillary endothelial cell line[107]. All-trans-retinoic acid re-induced γ-GTP activity and P-gp expressionin immortalized rat brain-capillary endothelial cells [110]. Basic fibroblastgrowth factor (bFGF) had a similar effect as astrocytes or the C6 glioma cellline on RBE4 cells and on a transfected bovine brain-endothelial cell line:RBE4 cells developed three-dimensional structures, which expressed γ-GTPand ALP [86]. In the presence of bFGF, ALP activity and the uptake of theGLUT-1 substrate 2-deoxyglucose was increased (without an increase inGLUT-1-mRNA expression) and paracellular L-glucose permeability wasdecreased in the transfected bovine cell line [85].

4.4.4. Influence of Specific Factors on Endothelial Cells in Co-Culturewith Astrocytes

The effects of several additional factors on endothelial cells grown underthe influence of astrocytic cells have been tested. Elevation of intracellularcAMP reversibly enhanced TEER in brain-endothelial cells pretreated withastrocyte-conditioned medium [32] [77]. It was concluded that cAMP may actas second messenger indicating that phosphorylation of TJ-related proteins isimportant for BBB permeability. Dexamethasone has been shown to decreaseparacellular sucrose permeability in primary rat-capillary endothelial cells


[111], and dexamethasone and butyric acid increased the TEER in co-cul-tured ECV304 cells [112].

4.5. Plasma-Membrane Vesicles and Reconstituted Proteoliposomes

The formation of plasma-membrane vesicles enriched in apical or baso-lateral plasma-membrane domains allows the study of transport and enzymat-ic properties of the luminal and abluminal side of the BBB separately. Thepreparation and characterization of such membrane vesicles are described in[113].

The endothelial cells are isolated as described above. The cells arehomogenized, and the membrane domains are separated on a Ficoll gradientby ultracentrifugation. The different membrane fractions are identified bytheir specific markers, e.g., γ-GTP which is active in the luminal, and A-system amino-acid transporter with activity in the abluminal fraction. Therespective membrane fractions consist of sealed vesicles that are relativelyimpermeable to the passive diffusion of sucrose. They show a primarilyright-side-out orientation and are reasonably free of subcellular membranes(reviewed in [113]). Plasma-membrane vesicles have been successfully usedfor transport studies and for the assignment of membrane-associated proteinsto the apical or basolateral plasma membrane, respectively (reviewed in[113]).

The reconstitution of single plasma-membrane proteins into defined lipidvesicles has been used to study specific proteins. For example, the multi-drug-resistance protein P-gp has successfully been isolated from the plasma-membrane fraction of P-gp-expressing cells and reconstituted into liposomesof different lipid compositions to advance the characterization of this atypi-cal transport protein [114–116].

5. Transport Studies

Transport studies across BBB models are carried out to estimate BBBpassage of a compound in vivo, to find out the permeation route(s) of a com-pound, and to investigate transport processes at the BBB.

5.1. Permeation Studies in Two-Chamber Systems

Transport studies across cell layers are usually performed in two-chambersystems with the cell layer separating the two chambers from each other


(transport techniques have been reviewed in [73]). The cells are grown to atight layer on a permeable membrane or filter insert. This arrangement alsoallows the measurement of the TEER before transport studies are done. Thestudied compound is added to one chamber, and its appearance in the otherchamber is measured over time by either liquid-scintillation counting forradio-labeled compounds or any other suitable analytical technique.

For transport studies, the chambers contain a biocompatible buffer solu-tion which is usually supplemented with serum albumin at a low concentra-tion, e.g., 0.1%. This not only keeps the barrier intact but also enhances thesolubility of lipophilic compounds. Protein binding may also influence theapparent permeability of compounds with high protein affinity [117].

As a control for the tightness of the cell layer, a small hydrophilic com-pound, e.g., mannitol or sucrose, is included. These predominantly permeatevia the paracellular route, and their permeability coefficients are low if the TJare occluding. In the case of sucrose, the stability has to be confirmed, sincethe hydrolysis product glucose can undergo carrier-mediated transport andcan therefore lead to a higher apparent permeability.

Using permeable membranes and filter inserts for compounds with highpermeability coefficients, it must be established that the passage through thesupport material is not the rate-limiting step in the permeation process.Diffusion across cell-free membranes or filters gives information on thepermeability of the material. Diffusion through the unstirred water layer at thecell boundary can also be rate-limiting [118] [119].

5.1.1. Inserts

The same system as described for co-culture and shown in Fig. 4 is usedfor transport studies across a cell layer allowing sampling from both sides ofthe cell layer. If the cells are grown in co-culture, the astrocytic cells aregrown on the bottom of the well rather than on the opposite side of the mem-brane. Shortly before the transport study, the insert is moved to a cell-freewell to prevent any influence of the astrocytic cells on permeation and on theaqueous concentration of the compound in the receiver medium.

5.1.2. Diffusion Chambers

Using diffusion chambers, the permeable membrane with the cell layergrown on it is tightly fixed between two chambers (Ussing chamber). Thisallows a vertical position of the cell layer and therefore a horizontal diffusionof the studied compounds which might be preferred in some cases. The cham-


bers contain transport medium (see above). Depending on the chambermodel, they contain openings for sampling, for electrodes to measure theTEER, and for gas bubbling to stir the medium. As alternative to the gas bub-bling, the chambers have cavities for magnetic fleas.

Stirring not only mixes the chamber contents but also influences theunstirred water layer at the cell boundary. As the unstirred water layer can berate-limiting for the diffusion of some compounds, stirring should be constantin all experiments. A water jacket allows temperature control of the system.In brief, diffusion chambers have several advantages over the disposable fil-ter-insert systems. However, the handling of the different models is time con-suming, technically demanding, and not suitable for high throughput whilethe work with inserts is relatively easy.

5.1.3. Experimental Designs

After the compound is added to the donor chamber of the transportsystem, several overlapping processes can be observed. The increase of theconcentration of the compound in the receiver chamber over time depends notonly on the the permeation of the compound through the cell layer but also onthe distribution of the compound between the aqueous compartment, thecells, and the chamber/membrane material, and also on the permeability ofthe compound through the unstirred water layer and the insert membrane.

It is not always easy to distinguish the permeability from the other pro-cesses. In some cases, a lag phase appears before the amount linearly increas-es in the receiver chamber. This lag phase reflects the equilibration of a com-pound with very high affinity to the cells (e.g., binding to cell proteins)between the cells and the aqueous phase [96]. After a linear increase, the drugconcentration in the receiver chamber starts leveling off. This is when back-permeation becomes significant once the concentration gradient betweendonor and acceptor chamber starts to decrease significantly.

The most straight-forward approach is to determine the permeability co-efficient from the linear phase where distribution equilibrium is reached andback-permeation is not yet relevant. Therefore, the concentration in the donorchamber has to be high enough to ensure a constant concentration gradientbetween donor and receiver chamber over a sufficiently long time span to col-lect several samples to describe the linear phase.

Alternatively, the receiver chamber content is changed after every samplingto a compound-free medium, and the concentration in the donor chamber isestimated from difference calculations from the final concentration measure-ment in the donor chamber and the amount of compound appearing in thereceiver chamber. If strong binding of the compound to the cells or the cham-


ber material occurs, the concentration in the donor chamber at the start of thelinear phase cannot simply be calculated from the difference between the addedcompound and the compound in the acceptor chamber at this time point. Thisis important for the calculation of the apparent permeability coefficients.

Some permeation processes are polarized. This can be tested by the com-parison of the permeabilities from apical to basal and from basal to apical.Several transport mechanisms are unidirectional, e.g., P-gp-mediated efflux,and lead to differences between the permeability coefficients in the differentdirections. Passive permeation and equilibrative carrier-mediated transport,e.g., GLUT-1, usually result in similar permeabilities in both directions.Specific transport mechanisms can be confirmed by the decrease or increasein permeability after adding inhibitors. Saturable processes can be detectedby permeation studies with a large concentration range of the compound.Most carriers are saturable leading to a leveling-off when the permeability isplotted against the drug concentration. However, at high drug concentra-tions, it must be shown that passive permeation is not altered by the highconcentration and that the cells are not affected in any way by the com-pound.

5.1.4. Data Treatment and Calculations

The calculation of the apparent permeability is not standardized. Severaldifferent equations are in use [3] [120]. However, they are all based on thesame parameters, namely the drug concentration in the donor compartment,the drug amount appearing in the receiver chamber over time, and the culturearea of the cells. As discussed above, the parameter which is most difficult todetermine is the concentration in the donor compartment at the beginning ofthe linear increase of drug amount in the receiver chamber.

The apparent permeability coefficient Papp [cm · sec–1] across a cell layercan be calculated according to Eqn. 1 [120] which is based on Fick’s first lawassuming that the concentration in the receiving chamber is negligible ascompared to the concentration in the donor chamber.

(Eqn. 1)

where is the transport rate [µg · sec–1], A equals the area of the permea-

ble membrane, i.e., the cell layer area, and c0 is the initial concentration in thedonor chamber ([µg · ml–1] or [µg · cm–3]). As mentioned above, c0 is notalways the real concentration in the donor chamber after the compound hasequilibrated between the cells, the culture materials, and the aqueous phase.

dQdt

Pcapp = ⋅ ⋅

dQdt A 0


Eqn. 1 could therefore lead to too-low Papp values for very lipophilic com-pounds or compounds with specific binding to any cell structure if c0 is notcorrected for the distribution of the compound between cells, culture materi-al, and aqueous phase.

Other approaches are based on the clearance concept. The average clear-ance over time is calculated from the cleared volumes at several time points[121]. The cleared volume (Vcl) is the sum of the cleared incremental volumes(Vclt) at each time-point t (Eqn. 2):

(Eqn. 2)

where Xt is the drug amount in the receiver chamber at time t and cdtis the

concentration in the donor chamber at time t. The slope of Vcltvs. t equals the

average clearance (Cl). Accurate clearances can only be calculated from a lin-ear relationship between cleared volume and time (see above). The averageclearance corresponds to the permeability-surface area product PS ([ml · min–1]or [cm–3 · min–1]). To correct for the PS of the permeable cell support, the PSis sometimes corrected as shown in Eqn. 3:

(Eqn. 3)

where PSe is the PS of the cell layer alone, PSt is the measured PS of thewhole system, and PSf is the measured PS of the permeable membrane alone.PSe is then divided by the area of the membrane insert to get the endothelialpermeability coefficient Pe [cm · min–1]. Rearrangement of Eqn. 3 leads toEqn. 4.

(Eqn. 4)

where PBBB [ml · min–1 · cm–2] is the BBB permeability (equivalent to Pe), Athe surface area of the endothelial monolayer (surface area of the insert mem-brane), Cltest and Clcontrol are the clearances [ml · min–1] from the test (filterwith cells) and control (filter without cells) experiments [122] [123]. Cl iscalculated as shown in Eqn. 5 from the amount appearing in the receiverchamber (X) and the concentration in the donor chamber (cd):

(Eqn. 5)

However, correction for PSf (Eqn. 3) can lead to misinterpretation of theresults from permeability studies [94].

Cl Xd

=⋅c t

PBBBtest control

control test

CI CIA(CI CI )

= ⋅−

1PS PS

1PSe f

= −1t

Vct

t

tcl

d

X=


5.1.5. Compensation for High Paracellular Permeability

As described above, the tightness of a BBB model can be quantified bythe permeability measurement of sucrose or mannitol. The in vivo BBB isclose to impermeable for these hydrophilic compounds, but their size allowsparacellular diffusion if the TJ net is not completely occluding, as is the casein many in vitro models. Using the permeability data of these small hydro-philic compounds compared to the permeability of other molecules, the par-acellular leakiness of the model may be corrected [124] [125].

5.2. Uptake and Efflux Studies

Studies on the uptake of a compound into the cells do not need a two-chamber system. The cells are grown on a simple tissue-culture surface, andthe uptake of the compound is determined from either its disappearance fromthe aqueous phase or its appearance in the cells in presence and absence ofinhibitors of the transport mechanisms. This technique is suitable for thestudy of different transport systems. In [126], the uptake kinetics into cerebralcapillary endothelial cell monolayers is compared with transmonolayer fluxmeasurements for the prediction of passive diffusion across the BBB in vivo.For polar compounds, uptake kinetics were predictive for BBB passage.

After the compound has been taken up by the cells and the aqueous medi-um has been replaced by compound-free medium, the efflux of the compoundout of the cells can be measured. Such efflux studies have provided a usefultool for the screening of P-gp substrates [50].

5.3. Correlations between in Vitro and in Vivo Permeability

Published studies on the correlation between in vitro BBB model permeabil-ity and in vivo BBB passage are still rare (see, e.g., [81] [127–129]). However,depending on the studied series of compounds, the correlations look promising.Outliers from the correlations are often substrates of transport systems. A directcomparison of results from the various models is difficult, due to the differentcells, experimental setups and techniques, and data-analysis methods.

6. Conclusions

From the characteristics of the in vivo BBB, good brain uptake can beexpected for lipophilic compounds which can readily permeate the cell mem-


branes, provided they are not substrates for P-gp or any other efflux mecha-nisms. Another approach for brain delivery is the design of drug compoundswhich are recognized by inwardly directed carrier proteins if the capacity ofthe transporters is high enough to deliver therapeutic levels of the compoundto the brain. Receptor-mediated endo- and transcytosis have been consideredfor brain delivery of specific peptide-compounds.

For the prediction of drug uptake into the brain, several biological mod-els are under investigation ranging from whole brain microvessels, to primarysubcultured cells and established cell lines, to membrane vesicles. Amongthem, cell cultures have generated most interest. BBB Characteristics lostduring culture can be re-induced under certain culture conditions. Most workhas been done on the co-culture of endothelial or other barrier cells withastrocytic cells. The latter enhance the tightness and induce BBB characteris-tics in some cultured barrier cells leading to frequently used in vitro BBBmodels.

Research on BBB models is a relatively young field. None of the pub-lished models has yet been adopted by a significant number of laboratories.The publication of new models is frequent, and the conclusive evaluation ofpublished models in terms of their predictability for BBB passage will stilltake some time. The broad variety of different in vitro models allows one tochoose a model which is suitable for the studied issue. Tight cell monolayerswith high TEER and low permeability to small hydrophilic solutes are usedfor permeation studies, P-gp-expressing cells for P-gp-mediated efflux stud-ies, and cells expressing specific transport systems are used for the investiga-tion of transport mechanisms. Parallel studies on a number of models providecomparisons of value in establishing the optimal model(s) for particularapplications.

The valuable comments and discussions from and with Annette Braun, Diana Dolman,Cécile Klingler, and Heidi Wunderli-Allenspach are gratefully acknowledged. S.K. has aTravelling Research Fellowship from Wellcome Trust, UK.

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Biological Models to Study Skin Permeation

by Nabila Sekkata) and Richard H. Guy*b)

a) Section de Pharmacie, Faculté des Sciences, Université de Genève, 30, quai E. Ansermet,CH-1211 Genève 4, Switzerland

b) Centre interuniversitaire de recherche et d’enseignement, Universités de Genève et Lyon,Campus universitaire, F-74166 Archamps, France; e-mail: [email protected]

1. Introduction

Over the last 30 years, there have been significant efforts to develop suit-able models for the study of skin permeation [1]. The reason for this effort istwo-fold: first, there is a need in the pharmaceutical industry to test the per-formance of formulations for topical and transdermal drug delivery; second,with respect to toxicity, it is important to evaluate the permeation of variouschemicals across the skin to permit an effective and quantitative evaluation ofrisks following dermal exposure. Furthermore, there are additional economicand ethical criteria which have led to the use and application of these biolog-ical models. In the former case, it is clearly far too expensive to test everypossible formulation, i.e., to conduct screening in vivo in human volunteers(or even patients). In the latter case, it is obviously unethical to test the skinpermeation of potentially toxic substances in human subjects. Thus, as aresult, the development of biological models has been a priority for the field,and the identification of relevant models has proven to be a key objective.The models which have been developed can be subdivided into three groups:

1. In vivo models.2. In vitro, or as sometimes referred to, ex vivo models.3. Cell-culture models.

Each of these classes will be discussed in turn in this chapter. The natureof the results obtained in each case will be summarized, and the advantagesand disadvantages of each approach will be identified. Our objective, there-fore, is not to exhaustively summarize every publication on the subject of bio-logical models applied to the problem of percutaneous absorption, whichwould be (and indeed has been [2]) the material for an entire book in itself!Rather, our goal is to specify what has been done, which are the successful


models, which are the best ways to accomplish a meaningful evaluation ofskin permeation, and to define the circumstances under which the decision ofwhen to use in vivo vs. in vitro vs. cell-culture methodology can be rationallymade.

2. In Vivo Models

2.1. Skin Permeation in Man

In vivo models for skin permeation are simply divided into either humanexperiments or those conducted using animals. Obviously, percutaneous pen-etration studies performed in man provide the most relevant information pos-sible [3]. However, these experiments are not always easy to carry out, theyare sometimes expensive, and they can involve unacceptable risks to the vol-unteers who are taking part. Among the advantages, in addition to the rele-vancy and realism provided by a human in vivo experiment, it is at least theo-retically possible to obtain important pharmacokinetic information as it per-tains to the absorption of compounds across the skin. On the other hand, how-ever, there are certain disadvantages, not the least of which is the variabilityassociated with human percutaneous absorption [4] and the fact that not allexperiments are conducted in the same homogenous population of volunteersunder identical conditions in different laboratories, nor on the same anatomicsite, and so on.

The methods used for human skin-absorption experiments can beextremely rigorous, i.e., a full pharmacokinetic evaluation has to be perfor-med after application of a formulation or a transdermal patch, for example, tothe skin surface [5] [6]. Such an experiment would involve measurement ofplasma concentrations, calculations of input rate, elimination rate, clearance,etc. Such experiments are absolutely essential in the development of transder-mal drug-delivery systems. On the other hand, one may argue that such detailis hardly necessary when evaluating the performance locally of a genericform of, e.g., hydrocortisone. Therefore, a range of different studies havebeen performed and the information obtained therefrom varies in both qual-ity and quantity.

Historically, human experiments were performed using small quantitiesof radiolabelled permeant applied to the skin in a small volume of organic sol-vent which quickly evaporated [7]. Thereafter, the elimination of the admin-istered radioactivity in the urine and/or feces was determined and, from thisinformation, the percentage dose absorbed was deduced. Now, while thisexperiment is of interest from a practical standpoint in that it is conducted inman, it has certain limitations in that the ‘measurement compartment’,


i.e., the excreta, is far-removed from the application site on the skin, and anyconclusions about the percutaneous absorption phenomenon, therefore,beyond the cumulative amount absorbed, are difficult to deduce. For exam-ple, kinetic information is not fully delineated because, of course, the rate ofelimination in the urine is determined not only by the absorption rate but alsoby the renal clearance of the compound from the body. One also has to ques-tion the ethics of applying radioactive substances to human skin in vivo – apractice which is accepted in some countries, but absolutely forbidden else-where.

More recently, an alternative in vivo experiment has replaced the evalua-tion of drugs in a compartment distant from the application site with, instead,determination of the permeant in the compartment to which it is being admin-istered (and which represents typically the rate-limiting barrier to skin per-meation), i.e., in the stratum corneum [8]. In its simplest form, post-applica-tion of a drug-containing formulation, this method involves removal of thestratum corneum by repeated adhesive-tape-stripping and quantitation of theamount of substance absorbed by extraction and analysis of the individualtapes (typically, a chromatographic approach is sufficiently sensitive for theassay). This technique holds much promise for the comparison of differentformulations containing the same drug and the determination of bioequiv-alence, for example, and as a relatively facile approach by which to screenformulations in a model of most relevance. At the present time, the U.S. Food& Drug Administration (FDA) is developing guidelines for this so-called ‘dermatopharmacokinetic’ experiment, and a number of laboratories areactively pursuing the optimization of the method and the identification of thelimits of this particular approach for quantifying ultimately the topical ‘bio-availability’ of substances applied to the skin [9].

It should be noted that this current activity was somewhat anticipated ina series of papers published by Rougier, Dupuis, and co-workers severalyears ago [10]. In this work, the stratum corneum was also removed after ashort period of contact between the applied vehicle and the skin, and thequantity taken up into the barrier layer was determined. It was shown thatthis quantity was highly correlated with (and was predictive of) the percent-age dose applied which would have ultimately permeated into the body fol-lowing an identical exposure (see Fig. 1). While this relationship is hardlysurprising given the brief period of skin exposure involved (30 min), themethodology provided an extremely facile approach by which to evaluatethe body burden of a chemical following its brief contact with the skin – aninvaluable aid, therefore, when assessing the potential for toxicity after der-mal exposure to cosmetic or personal-care products (shampoos, condition-ers, soaps, etc.) which typically remain on the skin for only relatively shortperiods of time.


2.2. Animal Skin Permeation

The use of animal models in vivo to study skin permeation representsanother considerable body of work [11] [12]. Many different species havebeen examined, not the least of which include the rhesus monkey, the pig(mini- and weanling pigs), the rabbit, and many different species of rodents(rats, mice, guinea pigs, both hairy and hairless). The advantage of using ananimal model as opposed to man is clear, i.e., animals are generally moreaccessible, and the procedures which can be used in animals can be moreinvasive than those possible in a human volunteer. The disadvantages of usinganimals, on the other hand, are also quite obvious, not the least of which isrelevance, i.e., is permeation across the animal’s skin representative, or pre-dictive, of that which would be seen in man? Furthermore, animal experi-ments are not necessarily inexpensive (especially if one uses a rhesus mon-key, for example), may be as time-consuming as a volunteer study given that


Fig. 1. Correlation between percutaneous absorption in vivo in man (measured by urinaryexcretion over 4 days) and the stratum corneum levels of the same chemicals determined fol-lowing a 30-min application. The sites of administration examined were the arm (A), abdomen

(V), post-auricular (P), and forehead (F). Redrawn from [10].

the animals will need to be acclimated to the environment of the experiment,and may require specialized equipment such as metabolism cages, methods toseparate excreta, and so on [13].

What has been deduced from the use of animal models to date? First ofall, it is clear that animal models do exist where the permeation of chemicalsacross the skin is generally well-correlated with that in man. The most not-able examples are the rhesus monkey and the pig, including the minipig andweanling pig (see Fig. 2) [14] [15]. Unfortunately, these animals are neitherthe easiest nor the most economic to use; the rhesus monkey, in particular,represents an expensive proposition for experiments of this type. With respectto more ‘classical’ laboratory animals, such as the hairy rat, mouse, rabbit, orthe guinea pig, the literature teaches that percutaneous absorption in thesemodels is much higher than that in man (see Fig. 3) [11]. What does this meanin terms of the practical utility of such species? Clearly, if a quantitative pre-diction is required, then these animals will always over-estimate permeationin man. In addition, in terms of mechanistic relevance, one must ask why theskin of these species is more permeable? The obvious answer is that theyoffer the possibility for a significant role for skin uptake via follicular path-ways, much more so than across the generally ‘unhairy’ skin of humans. As


Fig. 2. Percutaneous absorption (% dose absorbed) of hydrocortisone, benzoic acid, andtestosterone in vivo in man and in rhesus monkey. Redrawn from [11].

well, these rodents typically have a thinner stratum corneum than man.Nevertheless, given that the standard laboratory rat is the animal of choice formany aspects of toxicological testing, there is a certain logic associated withthe use of this animal for the estimation of dermal exposure potential.However, in our opinion, this argument is far from convincing given that thedifferences in percutaneous absorption between rat and man can be as greatas a factor of 10.

Similarly, for the hairless rodent species, the permeation of chemicals isagain higher than that through the skin of man, presumably due, at least inpart, once again to the thinner barrier layer (and perhaps a different lipid com-position) compared to the human equivalent. Differences range from 2–3-foldup to nearly an order of magnitude. On a more positive note, though, it shouldbe said that the hairless species (both in vivo and in vitro), in a relative sense,are reasonable predictors of trends in absorption; that is to say, good per-meants across human skin are also good permeants across hairless rodentskin. In addition, the relationships between permeation and physicochemicalproperties, for example such parameters as the permeant’s octanol-water par-tition coefficient and molecular weight, seem to be similar for these hairless-


Fig. 3. Relative percutaneous absorption of four chemicals in different animal species in vivo.Redrawn from [11].

skin models and that of human beings [16]. This point is discussed further inthe ‘In Vitro Models’ section below. However, there is an important problemwith respect to the use of hairless species when one needs to examine theimpact of formulation components, such as penetration enhancers, on thetransport of drugs across the skin. It has been demonstrated repeatedly thatthe more fragile stratum corneum of a hairless animal exaggerates the effectsof these excipients and may significantly over-predict their effects in man.

A final point worth making about the hairless rat, for example, is that inthe ‘Rougier-Dupuis’ experiment described earlier, it was shown that the cor-relation between the amount of chemical recovered in the stratum corneumafter a 30-min exposure and that which would have ultimately been absorbedsystemically under identical conditions obeyed a relationship almost identicalto that in human beings (for the same series of model compounds transport-ing across the two different skins) [10].

2.3. ‘Hybrid’ Models

There have been other in vivo models developed which are more sophis-ticated, more complicated and, in consequence, less practically useful. Forexample, it was shown several years ago that it was possible to graft humanskin onto the backs of athymic mice [17]. The viability of the skin after thegraft had ‘taken’ was good, and a functional cutaneous microcirculation couldbe demonstrated to be operable. Rejection could be reduced by treating theanimals with immunosuppressive agents. An in vivo model was thereforeavailable which allowed permeation across human skin to be followed in asmall (and, in theory) more easily handled rodent. Importantly, furthermore,the human skin retained its permeability characteristics. Nevertheless, this isa limited model because there is a time-limit on the duration of experimentspossible with these animals, and there is clearly a price to pay, in terms oftime and effort, in establishing a regular and consistent supply of suitable ‘subjects’. Another similar model involved the so-called skin sandwich flap[18]. In this case, following a complex surgical procedure, an external skin ‘sandwich’, consisting of the host athymic rat skin on one side and graftedhuman skin on the other, was raised on the animal and perfused via singleafferent and efferent blood vessels (much like an isolated perfused organpreparation). It followed that quite detailed pharmacokinetic analysis of theabsorption and distribution of topically applied drugs, across human skin,could be undertaken, at least within the constraints of the period available forexperimentation (necessarily short because anesthesia of the animal wasrequired). In the end, it must be said that neither of these models has been usedto a great extent, primarily because of their technical complexity and expense.


While useful perhaps as research tools, these approaches are not practical forroutine use, and literature citations describing their use are now very rare.

2.4. Conclusion

With respect to in vivo models for percutaneous absorption, it is clear thatthe most useful model is a human being, with the rhesus monkey and pig pro-viding appropriate but not always convenient or practical alternatives. Othermodels exist, as we have described, some of which having clear advantages(easier to handle, for example), but often many more limitations. This gener-al conclusion is no different than was reached at a 1989 workshop on in vivopercutaneous absorption sponsored by the FDA and the American Associationof Pharmaceutical Scientists (AAPS) [19]: ‘In general, percutaneous absorp-tion in the pig and monkey [...] is in most cases similar to that in man, where-as in the rat, and especially in the rabbit, it is greater than that observed inman. On the basis of the currently available data, the only animals in whichpermeation data are consistently qualitatively and quantitatively similar tohuman permeation data are the pig (particularly the weanling pig) and therhesus monkey’.

3. In Vitro Models

An in vitro experiment to measure the percutaneous absorption of anactive substance is a much simpler proposition than an in vivo measurement[20]. However, with this simplicity comes a number of limitations, of course,and the question of relevance must always be at the forefront of one’s mind:can the transport of a compound across an excised piece of skin ever be indic-ative of what will happen in the in vivo situation (see Fig. 4 [21])? The liter-ature is replete with papers describing in vitro percutaneous-absorption stud-ies. The range of compounds studied and the variety of skin membranes usedis quite staggering. As in vivo, the range of animal species (in addition toman) which have given their skin for such permeation experiments is verybroad.

3.1. Overview and Methodology

First, however, some general comments about in vitro skin-permeationexperiments have to be made. Typically, one needs, first and foremost, a dif-fusion cell, which can be divided into donor and receptor chambers with the


separating membrane provided by the skin. The donor chamber should becapable of holding the formulation, the solution of the permeant which is tobe applied, or the material matrix in which the active substance is to bebrought into contact with skin (e.g., a transdermal delivery system or contam-inated soil). The receiver chamber should contain a medium into which thepermeant will diffuse and in which it can subsequently be quantified by anappropriate analytical method (HPLC, liquid-scintillation counting, etc.). Thereceptor phase may be ‘static’, i.e., one simply accumulates the permeatingsubstance therein as a function of time, or it may be perfused to mimic themicrocirculatory apparatus of the underlying viable skin tissue, and serialsamples are collected and analyzed so as to provide directly an ‘instantaneous’ measure of the percutaneous flux. Perfusion rates are usuallyset so as to obtain samples which are of reasonable volume and allowstraightforward analysis of the permeating species.

The variety of donor phases, skin membranes, and receptor phases usedin in vitro permeation experiments is considerable. The diversity of potentialdonor phases has already been mentioned and clearly depends on the specif-ic situation and application envisaged. In the pharmaceutical sciences, one


Fig. 4. In vivo/in vitro comparison of scopolamine percutaneous absorption across humanskin. Redrawn from [21].

considers creams, ointments, gels, transdermal patches, and so on. Withrespect to risk assessment, the donor phase can range from the chemical ofinterest dissolved in an organic solvent, which can evaporate after applicationto, as mentioned above, a solid matrix such as contaminated soil, and so on.In the cosmetic- and consumer-products fields, a wide diversity of productsprovide donor phases for in vitro screening, such as shampoos, conditioners,various types of make-up, perfumes, etc.

3.2. Models: Human vs. Animal Skin

As far as the receptor phase is concerned, many different media have beenexamined, ranging from the very simple, i.e., an aqueous solution, perhapscontaining saline or saline buffered to physiological pH (or another pH inwhich an ionizable permeant may be more easily solubilized), to a proteinsolution or a solution including a surfactant, with the intention once again ofsolubilizing materials which are otherwise poorly soluble in aqueous solu-tion. In some diffusion cells, there is no receptor phase per se, and the per-meant post-application is simply quantified in the different layers of the skinbarrier employed.

With respect to the skin, every conceivable ‘slice’ of the tissue has prob-ably been examined in in vitro experiments: full-thickness skin, skin derma-tomed to a particular depth (from a few to several hundred µm), heat- or otherwise separated epidermis, the dermis, skin from which the barrier hasbeen removed (e.g., by tape-stripping) and, most simply, the stratum cor-neum alone, isolated by enzymatic treatment of the tissue.

There have been numerous review articles, and even entire books dedicat-ed to the subject of in vitro skin permeation [2] [22]. Again, it is not our inten-tion here to review everything which has been written before; such an effortwould be overwhelming and is frankly unnecessary because the existing lit-erature is of significant quality. Rather, it is our objective to highlight themanner in which such studies have been performed and to identify the impor-tant revelations which have resulted from the application of this flexible anduseful methodology. It should be noted once again that the FDA and theAAPS conducted a workshop on the principles and practices of in vitro skinpermeation almost 15 years ago [23]. At this workshop, many of the concernspertaining to this technique were raised, and ultimately a consensus wasreached on perhaps the best (‘generic’) way in which to perform in vitro skin-permeation studies. These guidelines remain today both useful and sensible,and represent in our opinion the right starting point for anyone who is aboutto conduct such an experiment. The publication resulting from this workshopalso points out many of the potential problems and sources of variability


which may be observed in these notoriously variable experiments; that is tosay, the variability associated with in vitro skin-permeation experiments tendsto be rather high [4]. The reason behind this problem is that there are manypossible sources of variation built into an experiment, which requires one firstto excise a piece of tissue in a particular manner, separate the tissue into theappropriate layers which are needed, subsequently to mount that membranein an appropriate diffusion cell (avoiding damage to the skin, etc.), to uni-formly apply the donor phase to the skin surface, and to provide a suitablereceptor medium into which the permeant will diffuse. How long can such anexperiment be conducted before the membrane no longer resembles a realpiece of skin? What is the right temperature at which to carry out an in vitroexperiment? How should a piece of tissue be ‘qualified’ as acceptable for usein such a study (water permeability, electrical conductance, visual inspec-tion)? In addition, there are questions relating to the viability of the skin usedand to whether it retains metabolic activity (and, if so, how can that be main-tained and for how long?). Once again, there is a significant amount of liter-ature which addresses this issue. In passing, we might mention here a remark-able model, the ‘isolated perfused porcine skin flap’, which is perhaps themost complete approach available for such an ‘ex vivo’ experiment – more onthis below [24].

As far as the range of models available for in vitro experiments is con-cerned, the selection is as diverse as that which has been used in vivo. In otherwords, the options are human skin, or skin from any one of many animalmodels. The FDA/AAPS workshop at the end of the 1980s clearly concludedthat human skin was preferred, whenever possible, for in vitro skin-permea-tion experiments [23]. The principal sources of human skin for in vitro exper-imentation are cosmetic-surgical procedures, cadavers, or tissue banks. Notsurprisingly, tissue obtained immediately following plastic surgery is pre-ferred, in that the skin is fresh, it can be maintained metabolically active, atleast for some period of time, and the source and condition of the tissue to beused can be assured at the moment that it is removed (see Table 1). On theother hand, with skin obtained from a cadaver, or from a tissue bank, one doesnot always know the origin of the tissue, nor what may have happened to thetissue between the time that it is harvested and the moment that it is used inan in vitro experiment. Equally, it is very unlikely that such tissue wouldretain much in the way of metabolic activity, and it cannot be used, therefore,in experiments which require skin viability to be intact [25].

The preparation of skin tissue for an in vitro experiment depends, ofcourse, on the nature of the investigation envisaged. The stratum corneumand/or stratum corneum + epidermis can be separated by a combination ofheat plus trypsinization. Dermatoming is perhaps a more common method toreduce full-thickness tissue to a more manageable barrier, which does not


include a thick layer of dermis which, under in vivo conditions, would notcontribute significantly to the total diffusional resistance of the skin (in thatpermeants are likely to have been ‘resorbed’ by the cutaneous microcircula-tion before having the opportunity to transport passively into the deeperregions of the dermis).

There is no question, however, that in vitro skin-permeation experimentshave yielded a wealth of information about the percutaneous penetration pro-cess, in particular with respect to the key structure-penetration relationshipsinvolved [26]. Algorithms have evolved from this work which permit, at thepresent time, the permeability coefficient of a chemical across the skin fromaqueous solution to be predicted within a reasonable degree of accuracy[27–31]. Furthermore, these experiments have allowed both formulationeffects to be successfully determined, and the action of permeation enhancersto be evaluated in a manner that is, at least, indicative of what may happen inthe in vivo situation, and the screening and development of transdermal drugformulations to be accomplished in a relatively facile way [32]. A wealth ofother important applications of this methodology have also been reported inthe literature and are discussed elsewhere [33].

As is the case in vivo, many different animals have been used for the pur-pose of in vitro measurements. The rat, once again, is quite common as arevarious hairless species and, in particular, the hairless mouse has proved to bea popular, if controversial at times, model for such work. This controversy hasarisen because hairless mouse skin is more permeable and, as mentionedabove, is more sensitive to the effects of formulation components [34].Nevertheless, it must be stated categorically that structure-permeation rela-tionships across human and hairless mouse skins in vitro are really quite sim-ilar (see Table 2) [35]. The hairless mouse loses its credibility when it is mis-treated, for example by ‘attacking’ it with large volumes of organic solvents,when it reveals its weaker barrier function in comparison to human skin (mostlikely because it is a membrane with a much thinner stratum corneum) [36].


Table 1. Viability of Human Skin in Vitro as Measured by the Conversion of Glucose to Lactateby Anaerobic Metabolism. Data from [24].

Time after death [d] Lactate with dermatomed Lactate with heat-separatedskin [mmol/l/d] epidermis [mmol/l/d]

0.75 19.8 ± 8.9 2.0 ± 1.12 5.9 ± 4.1 1.8 ± 0.83 8.0 ± 4.8 0.6 ± 0.54 6.5 ± 1.7 0.7 ± 0.46 6.8 ± 3.0 0.2 ± 0.18 4.6 ± 2.3 0.2 ± 0.1

13 2.0 ± 0.6 0.9 ± 0.4

For obvious reasons, only relatively few experiments have been per-formed using rhesus monkey skin in vitro. On the other hand, pig skin hasbeen used frequently and, most recently, skin from the ear of the pig hasproved to be a very popular and quite reliable facsimile for the human cuta-neous barrier (see Table 3) [37]. The pig-ear skin is preferred because it canbe removed from the animal immediately post-sacrifice before the carcass issubject to further treatment at the abattoir. The tissue provides a barrier whichis both physiologically and functionally similar to human skin. There nowexists an ever-increasing literature testifying to the usefulness of pig-ear skinas a model for passive skin permeation and as a membrane amenable to thestudy of the effects of more sophisticated permeation-enhancement technolo-gies such as iontophoresis, electroporation, and sonophoresis.

Among other models, which have been considered beyond the ‘classical’hairy laboratory species, one can cite the use of shed snake skin as a mem-brane that has shown some parallels with human skin permeability [38]. Theadvantage of this particular model is self-evident: snakes shed their skins ona regular basis, thereby providing a renewable source of tissue and one that israther ‘user-friendly’ to the animal in that it does not have to be killed in orderto give up its skin. While there have been some positive comparisons betweenthe permeability of shed snake skin and that of the human barrier, the twomembranes are, however, structurally quite dissimilar. It must therefore be


Table 2. Predictive Algorithms for in Vitro Skin-Permeability Coefficients (Kp). Values arebased on the equation log Kp ([cm · sec–1]) = log (D0/h) + f · log Koct – ″ · Mr (cf. [27]), whereKoct and Mr are the permeant’s octanol-water partition coefficient and molecular weight,respectively, and (D0/h), f and ″ are constants (± s.d.) derived from the multiple regression

analysis of the different datasets A through D. Data from [26].

Dataset log (D0/h) a) f 103 · r2 N b)

A –5.8 ± 0.3 0.81 ± 0.10 13.0 ± 4.0 0.90 23B –6.0 ± 0.2 0.70 ± 0.09 5.0 ± 0.3 0.82 42C –5.8 ± 0.4 0.62 ± 0.06 4.2 ± 0.1 0.89 19D –6.3 ± 0.8 0.71 ± 0.06 6.1 ± 0.6 0.67 93

a) D0/h in cm · sec–1. b) Number of chemicals in dataset.

Table 3. Comparison of Human and Pig-Ear Stratum corneum (SC). Values of SC thickness(h), and of the diffusivity (D), and permeability coefficient (Kp) of water across the membrane.

Data from [49].

Skin source h [m] 109 · D [cm2 · s–1] 107 · Kp (cm · s–1)

Pig ear (n = 13) 11.8 ± 4.0 3.2 ± 1.5 1.6 ± 0.3Human (n = 20) 10.9 ± 3.5 3.0 ± 1.5 1.7 ± 0.4

asked whether it is reasonable to anticipate, when using different formulationcomponents, different enhancer methodologies, etc., that shed snake skin willrespond in a manner similar to that of human tissue. At the present time, how-ever, a clear answer to this question is not available.

Finally, we return to the extremely sophisticated and complete isolatedperfused porcine skin-flap (IPPSF) model developed by Riviere and col-leagues [24]. In effect, this model utilizes a flap of pig-abdominal skin, whichis kept under a very carefully controlled environment, and which is perfusedby a nutritious fluid medium to maintain tissue viability for a significant peri-od of time. The IPPSF provides a reasonable surface of tissue across whichpercutaneous penetration can be observed, and the perfusion fluid can be eas-ily collected and subsequently analyzed for the permeating substance. Havingestablished the viability of this particular model, Riviere et al. have conduct-ed a series of experiments demonstrating different applications of the technol-ogy to problems in skin permeation [24]. Most significantly, a sequence ofpapers examining iontophoretic delivery [39–41] allowed the electrotransportof a drug in vivo to be predicted with remarkable accuracy. However, it mustbe said that this methodology has not been widely adopted, primarily be-cause, of course, it is very difficult technically and very expensive to estab-lish and maintain.

4. Cell-Culture Techniques for Skin-Permeation Studies

It is now established that, beginning with human keratinocytes isolatedfrom, e.g., neonatal foreskin, an epidermal sheet of remarkable resemblanceto the in vivo membrane can be cultured [42]. Generically speaking, this isaccomplished by first growing the cells submerged in a culture medium untilconfluence is achieved, and then raising this layer to the air-liquid interface.This means, from this point, that the keratinocytes receive all nutrients fromonly the lower surface of the culture and, as in the in vivo situation, those cellsbeing pushed progressively out of the culture fluid embark on the process ofdifferentiation. Ultimately, after a period of 2 to 3 weeks, the outermost celllayer achieves terminal differentiation and manifests characteristics remark-ably similar to those of normal stratum corneum: completely cornified cellssurrounded by a lipid intercellular matrix. Such a system, then, is potentiallya renewable, reliable, and relevant model for skin-permeation studies, i.e., thesupply of human skin would no longer be a problem, variability would pre-sumably be significantly reduced, and ideally, in the end, the use of animalexperiments would be eliminated.

But to what extent does this approach satisfy the criteria for a faithful andpractical model? Are these air-liquid, reconstituted epidermal sheets reason-


able facsimiles of the human membrane in vivo? Much of the work designedto address this particular question has been conducted in industry and is not,therefore, fully in the public domain. The answer must be: not yet or, at least,little information to answer the question affirmatively exists at this time. Theliterature is relatively light on the quantitative evaluation of the permeabilityof these skin-culture models. Those publications which have appeared indi-cate, in general, that epidermal equivalents are more permeable than thehuman barrier [43] [44]. On the positive side, however, as one examines thelimited literature which is available, it can be said that the differencesbetween the permeability of these cultures and that of human skin appears tobe narrowing with time. For example, an early publication in 1993 [45]showed that the absorption of a small molecule (benzoic acid) was muchhigher across a skin equivalent than across excised hairless mouse skin (seeFig. 5). Subsequently, other authors demonstrated that the permeation ofwater through these barriers was between 3- and 10-fold greater than thatacross human skin in vivo [46]. In that case, it appeared that the support on


Fig. 5. Cumulative permeation of benzoic acid across hairless mouse skin and across a humanepidermal equivalent grown at the air-liquid interface. Redrawn from [44].

which the keratinocytes were grown had an important role in the ultimatequality of the barrier both histologically and functionally. More recent com-parisons of permeability have shown, first of all, that the rank-ordering ofcompounds with respect to their permeation across the culture appears to fol-low that through human skin, which is per se a positive feature [47].

Nevertheless, the equivalence between the skin-culture models andhuman skin has yet to be demonstrated, and the reason for this lack of agree-ment remains largely unknown. Certainly, the lipid organization and contentof the skin culture (i.e., its stratum corneum) does not yet appear to have thesame quality and functionality as that of the human barrier, and, no doubt, thishas a lot to do with the differences that are observed in percutaneous perme-ability [48]. In the end, is it reasonable for us to expect that cell-culture mod-els will ultimately replace the use of either in vitro human or animal skin orin vivo human or animal experiments? The answer to that question willdepend upon resolving why it is that the barrier has yet to manifest the sameproperties as that of the human stratum corneum. This will depend, further-more, on whether the production of these cultures can be accomplished suffi-ciently efficiently as to render them economically accessible to all those whowould like to use them. It must be said, at the present time, that the evolutionof an air-liquid keratinocyte culture manifesting the best possible stratumcorneum barrier requires a period of up to 3 weeks. It requires that the cultureis grown under carefully controlled conditions and that appropriate care istaken to avoid contamination and a subsequent loss of the culture batch. As aresult, the enterprises which currently exist to commercialize these systemsare obliged to charge considerably for these models, rendering them inaccess-ible to a large fraction of the potential market. Thus, while one is extremelyoptimistic and positive about this culture development and its ultimate poten-tial usefulness, it is perhaps unrealistic to hope that these systems will replacethe more conventional experiments currently used within the foreseeablefuture.

5. Conclusions

The conclusions which may be drawn from this analysis of the ‘state-of-the-art’ of biological models for skin permeation are straightforward:

a) Either in vivo or in vitro, the best membrane for a skin-permeationexperiment comes from a human being.

b) Good animal models exist (i.e., the rhesus monkey and the pig), whichshow similar permeability behavior to the human barrier both in vivoand in vitro and the same sensibility to (for example) different en-hancement technologies.


c) Other models (e.g., hairless rodents) have been identified which dem-onstrate rank-order permeabilities to different chemicals that are simi-lar to human skin, but which can be significantly more sensitive to theeffects of the application medium.

d) Hairy skin models (including ‘classical’ laboratory animals) are muchmore permeable than human skin.

e) Remarkably sophisticated and complex models, in vivo and in vitro,have been developed but have not been widely adopted because of thetechnical difficulty involved and because they represent expensivepropositions for routine experiments (e.g., formulation screening).

f) Cell cultures of human keratinocytes (‘epidermal equivalents’) holdmuch promise, but have yet to achieve the necessary quality, reprodu-cibility, and accessibility to be considered a practical and predictivemodel.

We thank the Swiss National Science Foundation for financial support.

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1999, 26, 428.


Biopharmaceutical Aspectsof Nasal and Pulmonary Drug Delivery

by Paolo Colombo*, Daniela Cocconi, Patrizia Santi, Ruggero Bettini,Gina Massimo, and Pier Luigi Catellani

Department of Pharmacy, Via Parco Area del Scienze 27/A, University of Parma, I-43100 Parma, Italy; Tel.: + 39 052 190 5086; Fax: +39 052 190 5006;


and Claudio Terzano

Dept. of Cardiovascular and Respiratory Sciences, Aerosol Research Laboratory. University ‘La Sapienza’, Roma, Italy

1. Bioavailability of Pulmonary and Nasal Medicines

The inhalation route for drug administration includes both nasal and pul-monary delivery. Drug release to these sites, mainly done for local effects, isalso of interest for systemic effects as an alternative to the injection of pep-tides and proteins. Inhalation-drug delivery is subject to comparable biophar-maceutical investigations as other dosage forms, with special attention to thedevice for application. It is relevant that an inhalation-drug product consistsof the formulation and the container closure system.

Four aspects govern bioavailability from dosage forms, namely their sol-ubility and dissolution rate, their permeation through membranes, and theirbiostability including first-pass effects.

A biopharmaceutical drug classification for bioavailability studies of soliddrug products recognizes that drug dissolution and gastrointestinal perme-ability are the fundamental parameters controlling rate and extent of drugabsorption [1]. A drug with high solubility and high permeability through bio-logical membranes is considered practically exempt from bioavailability


problems. A drug exhibiting low solubility and high permeability requirescareful formulation work in order to improve its dissolution rate. A drug withhigh solubility and poor permeability is more difficult to formulate becauseabsorption requires enhanced membrane permeability. Finally, a drug withpoor solubility and bioavailability is a problematic candidate for administra-tion.

This classification is an important tool since it allows the selection of thebest candidates among related compounds. In addition, this classificationhelps the galenical development of dosage forms to improve dissolution rate,permeability and stability, and to avoid first-pass effects. In fact, new poly-meric materials and novel drug-delivery systems allow to optimize adminis-tration in terms of route, rate of delivery, membrane transport, and stability inhostile environments.

The aspects related to oral bioavailability have relevance also in the caseof inhalation delivery, but with some site-dependent differences. The noseand lungs are organs evolved for gas exchange, and their physiology differsfrom the typical organs devoted to absorption. It is the primary strategy ofinhalation delivery to avoid toxicity on respiratory mucosa, both in terms ofintegrity (barrier effect) and functionality (mucociliary clearance).

After inhalation, the sites are less aggressive in terms of pH and enzymat-ic content, and hepatic first pass-effects are less relevant. Both aspects influ-ence the dose to be administered, which can be decreased compared to theoral route. The permeability of inhalation mucosa is much higher than intes-tinal mucosa due to a reduced resistance to substance transport. Finally, drugdissolution is less relevant in the case of solids, since the preparations arevery fine powders which expose a high surface area.

In addition to biopharmaceutical aspects, inhalation bioavailabilityrequires depositing the dose in the nose or lung. This aspect is crucial sincetargeting the lung is very difficult because the dose must be formulated in arespirable form. For nasal delivery, the distance to be traveled by the dose toreach the absorption site is shorter.

In conclusion, in the case of inhalation delivery, the four decisive criteriaof bioavailability play a lesser role than for the oral route. In contrast, an addi-tional aspect gains importance, i.e., deposition at the absorption site, since alack of deposition means no absorption.

According to the FDA [2], ‘the classical bioavailability (BA) and bio-equivalence (BE) may usually be inapplicable for all nasal (oral) inhalationaerosols. The dose administered is typically so small that blood concentra-tions are generally undetectable by routine analytical methods. Moreover,studies are complicated by the fact that only approximately 10–15 percent ofthe dose reaches the biological target. The remainder of the dose, trapped inthe mouth and pharynx, is swallowed and adsorbed through the gastrointes-


tinal tract. Thus, even if determination of blood or serum concentrations werepossible, additional and more extensive studies would be necessary to distin-guish the contributions of the drug adsorbed from the nose, lung, mouth andGI tract’.

BA may be established by in vivo (pharmacokinetic, pharmacodynamic,or clinical) and in vitro studies. In this review, deposition, dissolution, andpermeation will be examined together. An in-depth treatment of the subjectcan be found in excellent books [3–5].

2. Deposition Studies

Deposition is the process which causes inspired drug particles to be cap-tured in the respiratory tract through contact with the wet airspace surfaces.

2.1. Nasal Products

Excellent reviews on particle deposition in the nose are available [6] [7].The anterior portion of the nose collects particles larger than 1 m by impact,and particles larger than 10 m are completely trapped in the nose. The actu-al fraction of inhaled material captured in the nose varies considerably amongindividuals and is more variable than deposition in the lungs. These differenc-es may be due to variations in nasal anatomy, including the number and shapeof nasal hairs. The nose also humidifies inspired air. Furthermore, hygroscop-ic particles grow in size more rapidly when inhaled through the nose than themouth [8].

It is recognized that in vitro methods are less variable, easier to control,and more sensitive in detecting differences between products. However, theirclinical relevance is not always clearly established, since availability to localsites of action depends on drug release. In any case, in vitro studies have animportant position in the BA documentation.

Droplet or particle size and deposition patterns within the nose dependupon drug substance, formulation, and device characteristics. Drug dissolu-tion in the case of suspension products, absorption across mucosa, and rate ofremoval from the nose are also relevant. In vitro nasal-deposition studies havebeen performed using casts of the human nose [9] (Fig. 1).

In a recent FDA guidance [2], the recommended approach to assess BAand BE of solution formulations of locally acting nasal products is to rely onin vitro methods. The assumption is that in vitro studies are more sensitiveindicators of drug delivery to nasal sites of action than clinical studies.However, for suspension formulations, due to the difficulty of characterizing


particle-size distribution, in vivo systemic exposure and absorption have to beconducted as well. In detail, the in vitro BA-specific tests for locally actingdrugs delivered by nasal aerosol or spray are:

a) Dose or spray content uniformity through container life.b) Particle size of spray.c) Spray pattern and plume geometry.d) Priming and re-priming.e) Tail-off profile.

2.2. Oral Inhalation Products

The administration and activity of drugs given by the pulmonary route reston three stages. Stage one is the patient handling and using the device. Stagetwo is the uptake of the aerosol into the lung. This involves a coordinationbetween the activation of the inhaler and the inspiratory act, then the partitionof delivered dose between oropharynx and lung. The third stage concerns theregional deposition of the drug and the subsequent pharmacodynamic effects.

Devices have a central position in inhalation-drug delivery and are notconsidered interchangeable. Apart from nebulizers, the most common pulmo-


Fig. 1. In vitro deposition of a nasal powder (size 63–125 m) performed in a silicon cast ofhuman nose (left cavity shown)

nary devices are the pressurized-metered dose inhaler (pMDI) and the dry-powder inhalers (DPI). Pressurized-metered dose inhalers are used at lowflow rates, whereas dry-powder inhalers deliver a higher dose at higher inha-lation rates.

With the growing confidence that the major parameter in inhalation ther-apy is the dose deposited in the airways as a function of particle size, the useof in vitro data to facilitate regulatory acceptance of a copy product grows inimportance. Therefore, specific in vitro tests, although not sufficient to sub-stitute for in vivo studies, are necessary prerequisites to examine bioequiv-alence. It is now accepted that identical patterns of deposition in the respira-tory tract possess a predictive value for therapeutic equivalence. A review andrecent studies with bronchodilators and corticosteroids have shown that thereis a good correlation between the amount of drug deposited in the lungs andclinical efficacy [10] [11].

The major determinant of deposition in the respiratory tract is aerosol par-ticle size. Identical patterns of aerosol characteristics are predictive for ther-apeutic equivalence, but the reverse is not necessarily true. Two differentpreparations might have identical effects despite different particle sizes.There is hence a strong correlation between clinical efficacy and deposition,and between lung deposition and particle size.

Lung deposition of pharmaceutical aerosols is generally less than 100%of the nominal dose. Formulation, device characteristics, and the patientsthemselves can have a significant impact on the bioavailability of inhaleddrugs. This is due to complex biophysical factors associated with the filtra-tion mechanism of the respiratory system. For drugs intended for systemicdelivery, lung-filtration effects are crucial for efficacy, since the extent ofdeposition and drug transport in the body are subject to changes. Inhalation-bioavailability assessment is based on intrinsic drug properties (e.g., charge,size, dissolution rate, permeability, and aggregation) and on factors affectingdrug transport to the systemic circulation (e.g., membrane size exclusion,enzymatic deactivation, tissue extraction, and mucociliary and alveolar mac-rophage clearance).

The amount and sites of particle deposition inside the lung are determinedby the aerosol’s physical properties and patient characteristics. Relevant aero-sol properties are particle size, density and shape, electrical charge, andhumidity growth. Factors related to the subject are individual features of lunggeometry, sex, age, and the breathing pattern used for particle inhalation, i.e.,inspiratory flow rate, tidal volume, and the mode of breathing (nasal or oral)(Table 1). The morphology of the lungs and airways affects the efficiency ofdeposition. Along with the volumetric flow rate, the anatomy of the airwaysspecifies the local linear velocity of the air stream and thus determines wheth-er the flow is laminar or turbulent. There are intra- and interspecies differenc-


es in lung morphometry. In the same subject, the dimension of the airways willchange with lung volume, age, and diseases. Due to intersubject differences inairway geometry, the total deposition fraction has a coefficient of variation ofabout 27% in normal individuals who breathe in the same manner.

2.3. Particle-Size Determination or Micromeritics of Aerosol

In general, particles of 8 m or greater deposit in the nasal regions of theairway, whereas a fraction of particles less than 5 m deposits in the pulmo-


Table 1. Parameters Influencing the Action of Pulmonary Drugs from Aerosol Generation toTransport into the Blood and to Biological Response

nary zones. Particles between 3 and 8 m largely deposit in the tracheo-bronchial zone. Particles smaller than 1 m risk exhalation. In vitromeasurement of particle size is therefore crucial in characterizing aerosolformulations to predict lung deposition. Valuable measurements of totaldrug delivered and ‘respirable dose’ are obtained in vitro using aerosol-sampling devices. According to the European Pharmacopoeia 1997, fourinstruments can measure the aerodynamic properties of fine particles: glassimpinger, metal impinger, multistage liquid impinger, and multistage cas-cade impactor. These instruments have been constructed in order to trap byimpact the aerosol particles on the basis of their capability to fly. The par-ticle-size distribution of aerosol formulation is determined as aerodynamicdiameter.

The science and technology of small particles and powders, defined byDalla Valle in 1948 as ‘micromeritics’, gives a substantial contribution to theunderstanding of the differences in the behavior of respirable powders. Thepowder properties are classified as fundamental (size and distribution, shapeand surface area of particles) and derived (packing and flow). The fundamen-tal properties influence the deposition and the derived ones, which are depen-dent on size and shape, the manufacturing of dosage forms. For particle clas-sification, micromeritics uses the concept of equivalent spherical diameter.Real particles are rarely spherical in shape, and their dimensional definitionwould require more than one size parameter. The advantage of simplifyingthe classification of size to a single number was obtained taking a sphere asthe geometrical reference for the real particle. A sphere is a geometrical solidwhose surface area and volume are exactly calculated from a single number,i.e., the diameter. How can an asymmetric real particle be referred to as asphere? The solution was found by assigning one measured value of the realparticle (volume, surface area, sedimentation rate, projected area, etc.) to ahypothetical equivalent sphere. Then, the sphere possessing the equivalentvalue measured in the real particle is the sphere whose diameter is assignedto the size of the real particle.

The equivalent diameter changes according to the equivalence establishedbetween the sphere and the real particle. The most useful equivalent diameterin aerosol technology is the aerodynamic diameter, defined as the diameter ofthe sphere of unit density that has the same terminal sedimentation velocityas the real particle. Therefore, sedimentation rate, which controls the mostimportant mechanisms of aerosol deposition, is the equivalence established.In this context, Stokes’ law links the velocity of sedimentation of a sphericalparticle to the particle diameter (Eqn. 1):

(Eqn. 1)V = ⋅ ⋅

dSt2

18g


where dSt is the Stokes equivalent diameter, V is the sedimentation velocity, the particle density, g the acceleration of gravity and the viscosity of thefluid.

The aerodynamic diameter, dae, i.e., the diameter of a sphere of unit den-sity, must be calculated from the Stokes equivalent diameter by correcting forparticle density (Eqn. 2):

(Eqn. 2)

where 0 is the unit density. For example, spherical particles having a volumediameter of 1 m and a density of 2 g/cm3 have an aerodynamic diameter of1.4 m.

When the Stokes diameter is not available, but other equivalent sphericaldiameters such as volume diameter dv can be measured, calculation of theaerodynamic diameter must also take the differences in shape into account.Introducing the dynamic shape factor into Eqn. 2 yields Eqn. 3:

(Eqn. 3)

However, it is important to stress that deposition is certainly dependent onthe aerodynamic diameter, but the deposition of particles having the same dae

will vary with the flow rate of inspired air.Real powders are composed of polydisperse particles. The Mass Median

Aerodynamic Diameter and geometrical standard deviation allow their clas-sification. In real cases of aerosol powders, the geometric standard deviationvaries between 1 and 3.5 [12]. Particle-size distribution is critical in aerosoldosage-form preparation since larger particles are easier to manipulate. Theformulator can use bimodal distributions and chimerical size in order to facil-itate the dosage-form preparation.

In aerosol-powder technology, size is crucial but shape is important aswell. Moreover, shape is relevant for powder manipulation: crystals, veryoften irregular in shape, are usually cohesive, whereas spheres are normallymore flowable. One interesting shape for deposition appears to be the fiber,in so far as it is subject to the additional mechanism of interception; this, how-ever, severely limits flowability.

Particle density has been considered as an important parameter for aero-dynamic size definition. Usually, the true density of drug powders rangesaround 1 ± 0.5 g/cm3, but the possibility of preparing particles using polymer-ic material as carrier allows for the preparation of solid particles having abulk density as low as 0.1 g/cm3. This means that two spherical solid parti-cles with the same dae of 6.5 m, but densities of 1.2 g/cm3 and 0.1 g/cm3,show spherical volume diameters of 5.9 m and 20.4 m, respectively. It isevident that the second type of particle exhibiting the same aerodynamic

d dae v 0= ( / ) / 1 2

d dae St 0( )= / /1 2


behavior could be more easily manipulated due to the larger volume size. Theparticles shown in Fig. 2 could be of interest in inhalation-powder formula-tion since their actual volume is larger than calculated from the aerodynamicdiameter due to the fact that they are porous, as indicated by the presence ofholes.

A tight relationship exists between micromeritics and the collective prop-erties of an aerosol, which expresses the behavior of the cloud emitted by theinhalation device. Collective properties, defined by the number of particlesper cm3 or mass concentration per unit volume, govern the emission behav-ior from the inhalation device of the aerosolized dose, i.e., the plume. Theassessment of plume geometry is useful for interpreting the deposition patternof emitted dose. Using high-speed imaging, information can be collected onthe shape and distance traveled by the plume and on its velocity, area, anddensity (Fig. 3). All these factors constitute powerful tools for the develop-ment of formulation and inhalation devices.

2.4. Nasal and Pulmonary Scintigraphy

-Scintigraphy, a non-invasive technique that gives information on totaland regional drug delivery, is frequently used to assess drug delivery from


Fig. 2. Scanning electron microscopy picture of spray-dried particles for inhalation characterized by low bulk density due to their empty structure

different inhaler devices, both for the lung and nasal passages. The nuclearimaging technique is based on a radioactive tracer incorporated in the formu-lation so as to be strongly bound with the drug or dosage form. A gammacamera connected to an image-processing system quantifies the deposition ofthe drug in the nose or lung. Metered dose inhalers, dry-powder inhalers, neb-ulizers, nasal and inhalation sprays can be tested for efficient delivery anddeposition at the appropriate sites.

Nasal drug deposition is very easily studied by scintigraphy, because ofdecreased technical problems compared to the lung. Interesting studies havebeen done in order to compare the mucociliary clearance of powders or solu-tions from the nose after deposition [13]. A variety of radio-aerosol methodshave been devised using 99mTc. This radionuclide acts as a marker for thepresence of the drug and therefore allows measuring both deposition and clin-ical efficacy simultaneously.

The amount of drug or dosage form deposited in a particular region of thelung can be quantified by scintigraphy, allowing a distinction between centralor peripheral zones. The ratio between deposition in the two zones can be cor-related with the aerodynamic properties of the preparation, the performance ofthe delivery device, and the condition of the airways. It has frequently beendemonstrated, in particular with antiasthmatic drugs, that the deposition pat-tern is correlated to the therapeutic response. This has permitted the design ofappropriate dosage regimen in the case of formula modification or introduc-tion of new delivery devices. The possibility of using the scintigraphic pictureof drug deposited as a tool for demonstrating the bioequivalence of two inha-lation-drug products requires a suitable justification. The assumption that two


Fig. 3. Plume recorded with a high-speed camera during a MDI preparation emission andmanipulated with an image-analysis program using the ‘special effects’ routine (Adobe Photo-

shop, Adobe Systems Inc.)

products similarly deposited will give the same response is not yet fully veri-fied. However, this does not reduce the importance of the scintigraphic tool inassisting the formulator of pulmonary medicines. In particular [14], the tech-nique helps to optimize new delivery devices and/or formulations, comparesthe deposition profile of a new formula vs. a marketed product, explains phar-macokinetic data and absorption, and upholds therapeutic results.

3. Dissolution Studies

Once aerosol particles are deposited in the respiratory tract, the key fea-tures for drug retention pertain to membrane permeability, endocytosis, andmucociliary function. The physicochemical properties of deposited particlesconcurrently affect their clearance through dissolution and membrane perme-ability, or by activating some host defense mechanisms, e.g., macrophagefunctions.

Dissolution is a kinetic process by which a solid substance becomes avail-able for absorption. Frequently, it is the rate-controlling step in oral absorp-tion for drugs of low solubility. It is not likely that dissolution can affect therate of absorption in pulmonary delivery, unless an element controlling dis-solution was expressly introduced in the formulation. With most drugs, how-ever, the bioavailability of the fraction deposited is almost 100%. In thissense, lung drug delivery could be considered effective and efficient com-pared to other routes. In fact, the suspension formulation, due to its high sur-face area, is optimal to enable fast dissolution, unless particle aggregationoccurs. Therefore, preformulation studies, to be carried out for optimizing thephysicochemical properties of the drug for nasal or pulmonary delivery, focusmainly on surface characteristics of the powder in order to guarantee theprompt wetting of particles.

Since dissolution is a rate process, the response of the inhalation tract toaerosols depends not only on the amount of particles deposited, but also onthe amount retained over time. Airways and nasal passages possess ciliatedepithelial cells covered by mucous layers. Particles are usually cleared fromthe respiratory tract by mucociliary clearance, a natural defense mechanism.The time a drug remains at the absorption site could be affected by the use ofcompounds having bioadhesion properties as a means to prolong drug-muco-sa contact. It has been shown that formulations including bioadhesive mate-rials are retained in the nasal cavity with half-life clearances of 3 h or longer,compared with 15–20 min for standard formulations [15]. Mucoadhesivecompounds are synthetic or natural polymers that interact with the mucuslayer. Chitosans have been proposed because of their bioadhesive properties.In combination with bioadhesion, they provoke a transient opening of the


tight junctions of the membrane, enhancing in this way the absorption of thedrug [16]. Finally, liposomes are attracting interest for drug delivery to nasaland pulmonary mucosa. In fact, they are known to sustain the release ofentrapped drugs and to decrease their mucociliary clearance [17].

The poor nasal bioavailability of many substances, in particular of pep-tides and proteins, can be substantially improved by the use of absorptionenhancers. The acceptability of these enhancers is not only dependent on theirpromoting effect, but also their safety profile must be evaluated. Nasal drugformulations must not alter the histology and physiology of the nose, in thesense that the mucosa must retain its functionality as a barrier towards exter-nal substances and microrganisms. In any case, damages induced must bereversible. Moreover, they should be systemically inert without toxic or irri-tating side effects [18–21].

Particles that deposit in the non-ciliated portion of the lungs are clearedmechanically, by dissolution or by macrophage uptake. So, inhaled drug andforeign particles of size >_ 3 m may be absorbed (uptaken) from the lung pri-marily by alveolar macrophages. It was shown that very large porous parti-cles ( 0.4 g/cm3) could be deposited in the lung in spite of their size [22].These large particles (dv > 5 m; dae < 5 m) can avoid phagocytic clearancein the lungs, until the particles have delivered their therapeutic dose. Thisattribute can be particularly useful for controlled-release inhalation therapies,in order to prolong the time of drug delivery and then of drug action.

4. Permeation Studies

Cell cultures and excised mucosa membranes provide in vitro models forstudying permeation through the respiratory epithelium. In vitro models allowthe separation of permeation across the epithelium from deposition. A superbreview on this subject is available [23].

4.1. Cell Cultures

The aims of recent studies have been to determine effective permeabilitycoefficients (Fick’s law in steady-state conditions), to identify and visualizethe permeation pathways (confocal laser scanning microscopy), to discrimi-nate between passive and active transport processes (directional transport),and to shed light on mechanisms of absorption, toxicity, and metabolism.Thus, it was found that tight junctions limit passive diffusion by the paracel-lular route and that transcellular transport can be either passive or active. Theidentification of passive transport processes in diffusion chambers must be


assessed considering that passive diffusion is characterized by equal fluxrates from mucosa to serosa and vice versa.

Nasal respiratory epithelium is the most widely used barrier in transportstudies and consists of four different cell types: nonciliated columnar cells,goblet cells, basal cells, and ciliated columnar cells. The sampling sites ofepithelial material for primary cell culture should be restricted to regionswhere drugs are deposited for delivery. The most relevant region is the respir-atory area, i.e., the pseudostratified columnar epithelium in the region of themedium and inferior turbinates.

Apart from the techniques to sample nasal epithelium, the development ofprimary cell cultures to model permeation must consider factors such as cellgrowth, viability, metabolic activity, and support membrane. To overcome theproblem of the supply of human nasal tissue, primary cultures of epithelial cellswere transformed to cell lines with extended in vitro lifespan. Peter [24] usednasal cell line RPMI 2650 originating from an anaplastic (squamous cell) nasalseptum. This cell line is closely related to normal human nasal epithelium withrespect to its karyotype, cytokeratin-polypeptide pattern, and presence of mucoidmaterial on the cell surface [25–27]. It was used in particular to develop a nasalin vitro model to study peptide permeation and concurrent metabolism [28].

The absorption of drugs administered via the pulmonary route can bestudied in the isolated perfused-lung model and in cell cultures. Intact lungmodels, due to their complexity, do not distinguish between permeation bar-riers present in the alveolar epithelium and other pulmonary tissues. Cell cul-tures of isolated alveolar epithelial cells provide the most accessible means tostudy the mechanisms of transport across the alveolar epithelium, which con-stitutes the major barrier to macromolecular drug absorption into the pulmo-nary circulation. Recently, cell-culture models of human alveolar epithelialcells have been developed which are suitable for drug-transport studies [29].In vivo, the alveolar epithelium consists of cuboidal type-II and type-I cellswhich cover 93% of the surface of the alveolar spaces. Existing lung-epithe-lial cell lines show mainly alveolar type-II cell properties. Therefore, lung-cell lines available to date do not appear to be suitable models for transportstudies. However, it was found that human alveolar epithelial cells grown inprimary culture are capable of forming a tight epithelial barrier, morphologi-cally similar to the in vivo epithelium and valuable as an in vitro model forpulmonary drug-transport studies.

4.2. Tissue Samples

Excised nasal mucosa is frequently used to study nasal transport andmetabolism. Rabbit tissue has been used for the majority of studies [30–35].


In addition, mucosa from ovine [36] [37], bovine [38] [39], and human ori-gins [40] has also been employed. In the case of bovine samples, the mucosaconsists in the epithelium and part of the connective tissue, carefully separat-ed from the lateral cartilage before insertion into the diffusion chamber. Theexcised specimens have a surface area of 3–4 cm2 and a thickness of approx-imately 100 m.

To obtain rabbit nasal mucosa tissue, animals must be killed and the nasalcavity fully opened. After removing the lateral wall, the entire nasal septumis isolated [9]. The tissue thickness varies from 50 to 350 m [30] [41].Fig. 4 shows the results of nasal transport through rabbit mucosa using asdonor a dry powder or a solution prepared from this powder. The powderallowed a more rapid transport of test drug (thiocolchicoside) due to the for-mation of a transient supersaturated solution in contact with the mucosa.Viability testing is a requirement for in vitro experiments with excised tissues,and this is carried out with electrophysiological measurements and cell-stain-ing assays. Viability tests are also useful to detect the cytotoxicity of drugsand permeation enhancers.


Fig. 4. Nasal in vitro transport of thiocolchicoside through rabbit mucosa using as donor a drypowder or a saturated solution prepared from the same powder

5. Conclusion

Nasal and pulmonary delivery assessment requires a complex ensembleof studies involving biological, clinical, physical, technological and mechan-ical techniques. The studies on inhalation must therefore be performed notonly on the basis of the properties of the drug, but also taking into account theeffects of formulation, propellants, and other inhaler characteristics.

The field is in fast evolution due to the availability of new drugs and drugcandidates, which possess the requisites of dose, permeability, and safety forinhalation. Several FDA draft guidances confirm the wide interest in theseroutes of delivery.

Financial support by the Italian Ministry of Universities (MURST ex 40%) and the ItalianNational Council of Research (CNR 99.0084.CT11) is acknowledged.

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The Significance of Plasma-Protein Bindingin Drug Research

by Saik Urien, Jean-Paul Tillement*, and Jérôme Barré

Laboratoire Hospitalo-Universitaire de Pharmacologie, Faculté de Médecine, 8, Rue du Général Sarrail, F-94000 Créteil, France; Fax: +33 149 81 35 94;

e-mail: [email protected], [email protected], [email protected]

1. Introduction

The distribution of drugs in the body occurs from the site of administra-tion, essentially from the blood circulation, and additionally from the lym-phatic system. In a first approximation, it is assumed that blood flow and drugflow are equivalent, namely that the best-perfused and the most voluminoustissues receive the largest fraction of the administered dose. In blood, most ofthe drugs are simultaneously dissolved in the plasma and cellular water (cir-culating cells). Lipophilic drugs are also bound to the circulating proteins,serum albumin, 1-acid glycoprotein (AGP), and lipoproteins [1]. Theseblood proteins can modulate the distribution of drugs to tissues, a possibilitytaken into account when designing for high or low plasma binding. In broadterms, blood binding can either limit or favor the distribution of drugs to tis-sues. Blood binding can also direct drugs to their targets (receptors) in theplasma, blood cells or vessels. These various possibilities are presented here.For technical and methodological aspects, see the chapter by Kretz and Probstin this volume (p. 199).

The notion of blood carrier, together with the blood binding of drugs, sup-poses that the drug-protein interaction is quickly reversible and behavesaccording to the law of mass action. This should be verified because of phar-macokinetic and pharmacodynamic consequences. Indeed, covalent bindingof drugs and metabolites to the circulating proteins is often the cause ofimmuno-allergic reactions, the combination probably being antigenic.


2. Fundamental Aspects of Drug Binding to Plasma Proteins

Assuming that most drugs are lipophilic (which makes possible their oraladministration and absorption by passive diffusion), plasma-protein bindinghas the following characteristics:

1. It allows the transport of drugs in plasma at concentrations much high-er than expected from their water solubility. Hence, the plasma-to-tis-sue gradient of drugs is often greater than expected from a simple sol-ubilization in the plasma water. This phenomenon, which is well-known for many hormones and mainly steroids, is also valid for drugs.

2. There are few systems of active transport of specific drugs towards tis-sues. Corresponding examples are limited to the drugs which are phys-iological substances (hormones, messengers, and transmitters) or tothose that are chemically related and use the same mechanisms. Avariety of multispecific drug transporters have also been described (formore details, see the chapter by Lee et al. in this volume, p. 589).However, in most cases, it can be assumed that the drug will cross thebiological membranes by passive diffusion as a non-ionized molecule.In these conditions, considering the relatively short time of contact ofthe plasma-bound drug with cellular membranes, one can apply Fick’slaw (Eqn. 1):

(Eqn. 1)

where c is the gradient of drug between plasma and tissues (i.e.,between both sides of cellular membranes) and Mr the molecularweight of the drug. Whereas the drug is a small molecule, the drug-protein complex is a macromolecule unable to diffuse via a passivemechanism. Hence, plasma binding acts as a drug reservoir. Further-more, the stability of the complex will influence the diffusion of thedrug into tissues, which is limited to the free form. It is thus clear thatthe rate of dissociation of the drug-protein complex is the determiningfactor of the transfer of drugs to the tissues.

3. The equilibrium between the concentrations of free, plasma-bound,and tissue-bound drug is practically never reached. The amount of drugdistributed into a given tissue at any given time is a function of the fol-lowing factors:– the mass of this tissue,– its binding capacity for the drug, defined by the product N × Ka

where N is the concentration of available binding sites and Ka thecorresponding association constant.

Vr

= k cM


The resulting binding equilibria are not necessarily related to pharmaco-dynamic effects since they involve either acceptors (storage sites) or recep-tors (sites of action).

3. Characteristics of Plasma Binding of Drugs and Pharmacokinetic Consequences

Classically, one distinguishes between two types of drug plasma bindingaccording to their incidence on drug distribution. Plasma binding can berestrictive when it limits the quantitative distribution of the drug. In contrast,it is not restrictive when it does not limit the drug distribution to tissues. Thevolume of distribution affords a measure of this restrictive or non-restrictivefunction of plasma binding. Considering that any drug dissolves in theexchangeable water of the body, 0.6 l · kg–1, a restrictive binding means a VD < 0.6 l · kg–1, roughly superimposable to that of plasma proteins, i.e.,0.1 l · kg–1 for albumin and AGP. In contrast, a larger VD indicates that thegreatest part of the dose penetrates into tissues.

In animal studies, a restrictive binding is characterized by blood concen-trations higher than tissue concentrations (at a given time), and the VD ofthe drug increases either when the concentration of the binding proteindecreases, or when binding inhibitors are present in high concentrations inplasma (e.g., in hyperbilirubinemia).

A small VD is not necessarily associated with a quantitatively importantplasma binding. Highly water-soluble drugs may have a small VD (from 0.1to 0.3 l · kg–1) because they cannot cross cellular membranes; in such cases,administration by the parenteral route may be necessary.

Large VD values indicate non-restrictive plasma binding, namely whensome tissues have a binding capacity greater than that of the circulating pro-teins. This is the case for numerous psychotropic agents, for which there is amarked distribution into lean or adipose tissues.

Plasma binding can also be viewed as a circulating site of storage, releasingthe drug to tissues with higher affinity. This case is self-explanatory, because theabsorption of a drug is followed by its dilution in the blood. The circulating pro-teins will bind and reconcentrate the drug, then will deliver a major fraction ofthe dose to its targets. This is particularly obvious when the receptors are locat-ed in the plasma. Classic examples are colchicine and leukocytes, immunosup-pressants and T-lymphocytes, heparins and coagulation factors. For these drugs,a small VD is an advantage as long as the capacity of binding of the circulatingprotein is lower than that of the receptor. This concept can apply also to anydrug for which an extensive tissue distribution is not necessary, e.g., antihyper-tensives, diuretics, antihistamines, and systemic antimicrobial agents.


4. The Various Profiles of Plasma-Protein Binding

Plasma-protein binding of drugs can be characterized according to theproteins involved. As for binding, it may be important quantitatively and/orqualitatively (as is the case with circulating receptors). Once the binding pro-file of a drug to the various proteins has been assessed, the question ariseswhether binding is restrictive or not.

The binding profile may involve one or several proteins. The simplest sit-uation is when only one protein is involved. The most frequent case is that ofa drug-albumin complex, since albumin is the plasma protein having thehighest concentrations (600–700 M). Albumin has two main binding siteswith high constants of association (104–106

M–1), which can bind drugs by

hydrophobic and electrostatic interactions. At plasma pH (7.4), the protein isnegatively charged, but the sites are charged positively and bind mainlyanionic molecules. Carboxylic acids will bind to site II through hydrophobicforces with additional electrostatic interaction [1]. Site II is also selective forbenzodiazepines, their binding being mainly hydrophobic. Non-carboxylicacids (e.g., enol derivatives) bind to site I (the warfarin site).

The non-steroidal antiinflammatory drugs (NSAIDs) bind to serum albu-min with a high constant of association. Their binding is restrictive, resultingin a small VD (0.1 to 0.4 l · kg–1) similar to that of albumin itself. The enolicNSAIDs (e.g., azapropazone and phenylbutazone) bind to site I or to bothsites in the case of oxicams, whereas the carboxylic NSAIDs (e.g., profens)bind to site II. A typical example of a weak acid (pKa = 4) ionized at plasmapH is observed with tenoxicam, which is bound more than 98% to serumalbumin [2].

A same type of binding can be observed for drugs with a high associationconstant to AGP, as illustrated by mifepristone (Ka = 106

M–1) [3], macrolide

antibiotics (erythromycin), and antiarythmic drugs (lidocaine) [1]. Sincethree variants of AGP exist for which a drug may have different affinities [4],it is difficult to relate a binding to AGP to a restrictive distribution in the body,in contrast to albumin.

A second type of binding to plasma proteins involves basic lipophilicdrugs such as neuroleptics and antidepressants, which are extensively distrib-uted in the body. These drugs usually bind to albumin, AGP, and lipoproteins.Drug binding to lipoproteins is non-restrictive and involves lipid solubiliza-tion of the drug into the lipidic core of the lipoproteins and/or an interactionwith the surface phospholipids [5]. The most typical drug here is imipramine,which is weakly bound to numerous hydrophobic binding sites on albumin(30/mol albumin). Some drugs bind to AGP with relatively high constants ofassociation (Ka = 105

M–1), but its binding capacity is limited because of its

low plasma concentration. Finally, competitions between compounds sharing


the same sites can also be observed. The binding to lipoproteins, which cor-responds to the criteria defined above, is non-restrictive and non-saturable.

Because of multiple binding possibilities, drugs such as imipramine andpropranolol will show a relatively constant free fraction in plasma. Indeed,compensations do occur should one interaction be affected by another drug.

The third type of binding involves very lipophilic, water-insoluble com-pounds which bind mainly to lipoproteins. A classic example is the probucol-lipoprotein interaction [6]. Cyclosporin A is also extensively bound to plasmalipoproteins [7]. Interestingly, drug binding to lipoproteins may elicit a direct


Table 1. Prediction of Drug Plasma-Protein Binding According to Basic PhysicochemicalProperties a)

Acidity and/or Lipophilicity Proteins Percentage Examplesbasicity b) score c) involved of binding

unspecified 0 none undetectable caffeine,(highly water- ketaminesoluble)

weakly acidic + albumin 20–70 phenobarbital(pKa > 6) (moderate(mostly unionized) lipophilicity)

zwitterionic + albumin, 50–95 cetirizine(mostly ionized) (moderate AGP

lipophilicity)

basic + AGP 50–95 lidocaine,(pKa > 7) (moderate methadone(mostly ionized) lipophilicity)

acidic + albumin 80–95 valproic acid,(pKa < 6) (moderate aspirin(mostly ionized) lipophilicity)

basic + + AGP, 80–95 propranolol,(pKa > 7) (marked lipoproteins, imipramine(mostly ionized) lipophilicity) albumin

acidic + + albumin 95–99.9 warfarin,(pKa < 5) (marked piroxicam(mostly ionized) lipophilicity)

weakly basic + + + lipoproteins, 95–99.9 nicardipine,(pKa < 7) (high albumin, diazepam(mostly unionized) lipophilicity) AGP

neutral + + + + lipoproteins 99–99.9 probucol,(very high etretinatelipophilicity)

a) Applicable to most drugs with a molecular weight between 100 and 400. b) Ionization at pH7.4 and 37°. c) Semi-quantitative scale of apparent lipophilicity, ranging from 0 (freely water-soluble) to + + + + (highly lipophilic, water-insoluble).

pharmacodynamic effect, e.g., lipophilic antioxidants such as probucol whichprotect lipoproteins from oxidation. Such drugs, by preventing LDL oxida-tion in situ and the deposit of oxidized-LDL in the arterial wall, may beviewed as anti-atheromatous agents.

Table 1 summarizes the various types of plasma-protein binding accord-ing to some basic physicochemical properties of the ligands.

5. Is it Possible to Design Plasma-Protein Binding and Tissue Distribution?

The issue we discuss here is to predict the tissue distribution of a drug bydesigning an appropriate plasma-protein binding. Put differently, can plasmabinding proteins be used as vectors of an active compound towards a partic-ular tissue? The question cannot be answered at present, but a number ofresults suggest that drug distribution to non-effector tissue sites can be par-tially decreased.

5.1. The Prerequisites for Blood-to-Tissue Transfer

The transfer of propranolol from blood to brain has been reported [8] andcan be investigated using Oldendorf ’s technique [9]. Briefly, a bolus of [14C]-labeled drug in tritiated water is injected in a carotid, and the fraction of drugextracted from the circulating bolus by the brain is determined. Repeating thisexperiment with various plasma proteins at different concentrations allows toexamine the effects of the protein on the transfer process. Assuming that onlythe free form is transferred, three parameters determine the amount of drugthat will enter the brain:

• the rate of transfer across the capillary endothelium,• the rate of dissociation of the drug-protein complex in the capillary,• the transit time in the capillaries.

The faster these rates, the larger the fraction of drug transferred from thecapillary circulation to the brain.

As reported by Pardridge and Landaw [8], the albumin-propranolol inter-action is stable: the drug bound to albumin entering the vascular bed is notreleased and not transferred. This is not the case for AGP-bound propranolol,which is partially dissociated during the transit and transferred to the brain.This model makes it possible to determine the role of the binding protein onthe transfer of the drug. However, it is limited to the immediate effect of theinteraction and must be completed by studies with repeated administration.


5.2. The Distribution of Drug between Plasma and Tissues

At equilibrium, the distribution of a drug between plasma and tissues isthe result of binding in plasma and tissues, respectively. This can be simplyappreciated by Eqn. 2:

(Eqn. 2)

where Vp, VT and fuT are the plasma volume, the tissue volume, and theunbound fraction of drug in tissues, respectively. Assuming that plasma andtissue binding are not saturable, the fu/fuT ratio can be rewritten as:

(Eqn. 3)

where N and K are the binding-site concentration and the affinity constant,respectively. The subscripts T and P stand for tissue and plasma. The ratiofu/fuT is then the balance between the tissue’s affinity and plasma affinity.The greater this ratio, the higher the balance in favor of tissues, and the high-er the VD value. A high VD value (1 l · kg–1) indicates a large and non-selec-tive tissue distribution, whereas a small VD value (< 1 l · kg–1) indicates a lim-ited tissue distribution with possibly a restrictive plasma binding. Hence, onlyif the drug has a low VD value can a restrictive plasma protein binding beviewed as a drug reservoir for a target tissue.

5.3. Restriction of Tissue Distribution

The possibility of restricting the tissue distribution of drugs is demonstratedexperimentally. Drug plasma binding with a high constant of association and asufficiently high binding capacity (expressed in % of the administered dose) lim-its the quantitative distribution of drugs in tissues. This is the case for NSAIDswhich are extensively bound to albumin with a high association constant [1].Here, plasma binding is higher than tissue binding. Moreover, the inflammatorystate is associated with increased capillary permeability resulting in transudationof plasma proteins. The drug-albumin complex is thus ‘attracted’ to the inflam-matory sites, resulting in increased drug concentration in the injured tissues.

5.4. Is It Possible to Target Drug Distribution to a Particular Tissue?

A more reasonable goal should be: ‘Is it possible to restrict significantlythe tissue distribution of a drug to non-effector tissue sites?’. Considering H1-

fufu

1 ( )1 ( )T

T

P= +

+NKNK

V V V fufuD p T

T= +


receptor antagonists (referred to as antihistamines), the first and second gen-eration were lipophilic bases with very high VD values (e.g., chlorphenir-amine, hydroxyzine, and terfenadine). Because of their extensive tissue dis-tribution, these antihistamines were able to easily diffuse into the central ner-vous system or in the heart, resulting in some adverse effects. More recentantihistamines are exemplified by cetirizine, a zwitterionic compound whoseVD value is small (0.4 l · kg–1). Because of its zwitterion nature and modestlipophilicity, the tissue affinity of cetrizine is low [10]. The rather high degreeof plasma-protein binding of cetirizine together with its low tissue binding arethe necessary conditions for a restrictive plasma binding. In other words, themajor fraction of the drug is retained in the blood vessels, in the immediatevicinity of external blood-cell membranes and nearby connective tissueswhere the H1 receptors are located.

6. Conclusion

Some features of drug distribution can be deduced from plasma-protein-binding studies. At the early stage of drug development, plasma-binding char-acteristics together with solubility and lipophilicity data are reliable indica-tors of a large or reduced tissue diffusion. Some plasma-protein-binding datacan also help, since a significant degree of binding to plasma lipoproteins ishighly suggestive of a distribution in tissue lipoproteins (i.e., easy permeationacross biological membranes and binding to adipose tissues). By contrast, thehigh-affinity binding to albumin of a weakly acidic compound is very sugges-tive of a restrictive plasma binding with a low tissue diffusion. Finally, atearly and even at any stage of drug development, the plasma-binding data canbe useful in the interpretation of pharmacokinetic behavior.

Support from the Ministère de l’Education Nationale (EA 427) and Agence du Médica-ment, Projet de Recherche Clinique : ‘Rôle de l’hétérogénéité de l’AGP dans la variabilité inter-individuelle de la réponse aux médicaments’, is gratefully acknowledged.

REFERENCES

[1] J.-P. Tillement, G. Houin, R. Zini, S. Urien, E. Albengres, J. Barré, M. Lecomte, P.d’Athis, B. Sébille, Adv. Drug Res. 1984, 13, 60.

[2] F. Brée , P. Nguyen, S. Urien, P. Riant-Jolliet, E. Albengres, H. Fenner, J.-P.Tillement,Fundam. Clin. Pharmacol. 1989, 3, 267.

[3] B. Grimaldi, J. Barré, J.-P. Tillement, C. R. Acad. Sci. III 1992, 315, 93.[4] F. Hervé, S. Urien, E. Albengres, J.-C. Duché, J.-P. Tillement, Clin. Pharmacokin. 1994,

26, 44.[5] N. Simon, E. Dailly, P. Jolliet, J-P. Tillement, S. Urien, Pharm. Res. 1997, 14, 527.[6] S. Urien, P. Riant-Jolliet, E. Albengres, R. Brioude, J.-P. Tillement, Mol. Pharmacol.

1984, 26, 322.


[7] S. Urien, R. Zini, M. Lemaire, J-P. Tillement, J. Pharmacol. Exp. Ther. 1990, 253, 305.[8] W. M. Pardridge, E. M. Landaw, J. Clin. Invest. 1984, 74, 745.[9] W. H. Oldendorf, L. D. Braun, Brain Res. 1976, 113, 219.

[10] A. Pagliara, B. Testa, P. A. Carrupt, P. Jolliet, C. Morin, D. Morin, S. Urien, J. P.Tillement, J. P. Rihoux, J. Med. Chem. 1998, 41, 853.


High-Throughput ADE Screening

by Olivier Kretz* and Alessandro Probst

Novartis Pharma, Preclinical Safety, Drug Metabolism & Pharmacokinetics Department, CH-4002 Basel, Switzerland

1. Introduction

The advent of combinatorial chemistry has dramatically increased thenumber of compounds flowing through the drug-discovery pipeline. Themassive increase of the cost of drug development, however, forces an earlydrastic selection of those drug candidates which display the greatest likeli-hood of success. In this context, the development of new experimental strat-egies and experimental methods to rapidly screen and select the most prom-ising compounds becomes a real necessity [1] [2].

The early selection of drug candidates is based generally on pharmacolog-ical efficacy, and its determination has shifted from in vivo whole-animal-models to in vitro models. However, although the simple in vitro test modelscan mimic some facets of the in vivo situation, they cannot fully replace themore complex and dynamic in vivo processes. For a therapeutically usefuldrug, disposition, i.e., absorption, distribution, metabolism, and excretion(ADME), must be considered since it will determine the time course of theconcentration of the drug candidate at the pharmacological target and thustrigger the extent and duration of the pharmacological effect. Such aspects areas important as the intrinsic pharmacological activity of the compound.

Early consideration of the ADME properties in the selection of drug can-didates avoids frustrating failures at later development stages. In fact, thedevelopment of many ‘promising’ drug candidates has failed because theoptimization of their bioavailability at the in vivo target was neglected duringthe optimization stage of drug discovery.

In this chapter, strategies and methods for screening compounds withrespect to their ADE properties in systems with a high-throughput will be dis-cussed. High-throughput screening (HTS) in ADE can arbitrarily be groupedin experimental investigations consisting of in vitro and in vivo tests on theone hand, and non-experimental theoretical considerations and ‘in silico’


approaches on the other hand, i.e., the common sense or pragmatic approach,and the computer-assisted approach or virtual screening, respectively.

For both in vitro and in vivo investigations, simplification, automation,and miniaturization of the study design and/or the experimental proceduresare prerequisites for boosting the screening of compounds and entry into theera of ‘high-throughput screening (HTS)’ or ‘ultra-HTS’. On the one side,simplification, automation, and miniaturization permit a reduction in theextent of slow and expensive manual handling, allowing investigations to berun in parallel with shortened overall project duration. Furthermore, miniatur-ization reduces the amount of test compound required for the HTS, an impor-tant prerequisite for the screening of compound libraries where only a fewmilligrams of each compound are available. On the other side, the develop-ment of HTS-compliant analytical procedures (e.g., Q-TOF, LC-MS, API-LC-MS/MS, LC-NMR, MS direct injection of a mixture of compounds) cou-pled to automated handling systems and the improvement in assay detectionlimits, allows analysis of small sample volumes and permits a high through-put of samples, eliminating the ‘analytical bottleneck’ of the past.

2. In Vitro Tests

For the purpose of high-throughput screening, in vitro assays have sever-al advantages over the more complete in vivo tests. They allow investigationof selected, isolated variables/characteristics (e.g., absorption) and concur-rently eliminate the influence of others (e.g., metabolism) which may compli-cate the interpretation of the results. They are generally conducted in eitheraqueous buffers or ‘simple’ culture media, permitting a considerable simplifi-cation of the pre-analytical sample purification, compared to that needed forthe more complex in vivo biological matrices. In addition, they can be rela-tively easily automated and miniaturized and avoid the (sometimes proble-matic) use of live animals whilst maximizing the use of animal- or human-derived test material. They are generally less expensive, require less humanintervention and are less time-consuming than in vivo experiments.

Depending on the circumstances, some of the above-described advantag-es may also become disadvantages when compared to in vivo experiments:simplification/reduction of the biological process and the biological matrix,and suppression of concurrent variables/characteristics may influence the out-come of the experiment in such a way that the results are no longer rele-vant/predictable for the in vivo situation.


2.1. In Vitro Permeation (Absorption) Screening Assays

A pivotal process a drug candidate generally must undergo to reach itsbiological target in vivo is transport/permeation across biological membranes,e.g., from the application site to the systemic circulation (absorption) or fromthe systemic circulation to the peripheral biological target (distribution). Forperorally administered drugs, the passage of the intestinal epithelium is, inaddition to the passage of the mucus gel layer, the lamina propria and theendothelium of the capillaries, one of the major hindrances on their way tothe systemic circulation.

2.1.1. Caco-2 Cell-Monolayer Model

The Caco-2 cell-monolayer model (Fig. 1) [3] is broadly used to identifythose compounds that can or cannot readily cross the intestinal barrier. TheCaco-2 cell line is derived from a human colorectal carcinoma, and when cul-tured, the cells spontaneously differentiate into monolayers of polarized ente-rocytes. After 2–3 weeks in cell culture, the monolayers express high levelsof several brush-border hydrolases and have well-developed junctional com-plexes. Caco-2 cells have also been shown to exhibit drug-metabolizingenzyme activities (phase-1 and phase-2 enzymes) [4], several intestinal trans-


Fig. 1. Schematic representation of the Caco-2 cell-monolayer chamber and of the routes ofpermeation across cell membranes (based on illustrations provided by G. Camenisch,

Novartis Pharma, Basel, Switzerland)

port systems, such as those for large neutral amino acids, bile acids, cobalamin,dipeptides, and the ATP-dependent exsorptive protein P-glycoprotein (P-gp)[5–7].

Early knowledge on the involvement of P-gp in the transport of a testcompound may be of great value for the selection of drug candidates. P-gp islocated in the apical membrane of the enterocyte and mediates luminally-directed transport. The affinity to secretory P-gp in the gastrointestinal tracthas been shown to be a potential source of drug-drug or drug-food interac-tions with respect to the extent and velocity of absorption after oral adminis-tration [8]. Due to its saturability and influence on intestinal permeability,involvement of P-gp may lead to non-dose linearity for drug absorption, dis-continuous absorption profiles, prolongation in absorption times, or to anoverall low systemic availability. Further, it may lead to drug-drug interactionresulting from competitive displacement of two or more P-gp substrate drugs.The impact of this transport process might be of clinical relevance particular-ly for highly potent drugs administered at low doses with steep dose-effectcurves and narrow safety margins.

Caco-2 cell monolayers are grown on polycarbonate filters, and theirintegrity is usually tested by measuring the flux of 3H-labelled mannitol (paracellular marker) and 3H-labelled propranolol (transcellular marker).Additionally, transepithelial electrical resistance (TEER) is determined to testthe tightness and integrity of the monolayer. Permeability is determined byadding the test compound to the apical or the basolateral chamber and deter-mining the rate of its appearance in the opposite chamber.

The standard method for determining apparent permeability Papp [cm/s]through Caco-2 cells is the use of Artursson’s equation [9]:

Papp = Q/(t · A · c0)

where Q/t is the permeability rate [g/s], c0 the initial concentration in thedonor chamber [g/ml], and A is the surface area of the membrane [cm2].

Differences between the apical-to-basolateral and the basolateral-to-api-cal permeability are indicative of the involvement of active/carrier-mediatedtransport processes (cf. Table 1). Determination of the permeability of the testcompound in the absence and the presence of known, selective inhibitors ofthe various active/carrier-mediated transport processes allows identificationof the processes involved.

Caco-2 cell-permeability data show generally a good correlation to oral-absorption data in humans (cf. Fig. 2). Artursson and Karlsson have reportedthat under their experimental conditions, drugs which are completelyabsorbed in humans had permeability coefficients >1 × 10–6 cm/s, whilepoorly absorbed drugs (<1%) had permeability coefficients of <1× 10–7 cm/s[9]. However, since the Caco-2 cell-permeability values may vary between


laboratories and may depend on experimental conditions, the relationshipbetween permeability values and oral absorption in humans must be calibrat-ed for each laboratory and each experimental outline. The validity of theestablished calibration curve should then be confirmed for each experimental


Table 1. Example of Data Obtained Using the Caco-2 Cell-Monolayer Model. Papp: apparentpermeability; AP-BL: apical to basolateral; BL-AP: basolateral to apical; F: predicted fractionabsorbed in humans; Pint: intrinsic permeability (= permeability in the absence of active trans-port mechanisms); Fint: predicted intrinsic fraction absorbed in humans (= predicted fraction

absorbed in the absence of active transport mechanisms).

Compound Conc. Papp F Type of Pint Fint[M] [10–5 cm/min] [%] transport [10–5 [%]

involved cm/min]AP-BL BL-AP

A 5 21.2 ± 0.8 24.5 ± 2.3 95 passive 22.9 95B 5 5.0 ± 0.7 27.7 ± 11.8 12 efflux 16.4 40C 5 87.2 ± 8.9 177.2 ± 12.2 97 efflux 132.2 99D 5 124.6 ± 7.7 7.6 ± 0.4 100 carrier 66.1 99E 5 0.0 81.1 ± 4.1 0 efflux 40.5 < 98F 5 10.0 ± 2.3 11.2 ± 1.4 26 passive 10.6 26

Fig. 2. Example of permeability-absorption relationship with the Caco-2 cell-monolayermodel (based on illustrations provided by G. Camenisch, Novartis Pharma, Basel,

Switzerland)

batch by including markers for low and high permeability (such as, e.g., man-nitol and propranolol, respectively) as quality controls.

A radioligand-displacement assay, which can be used to screen test com-pounds for the potential to undergo P-gp-mediated intestinal secretion, hasrecently been developed by Döppenschnitt et al. [5]. The acceptor proteinwas obtained from Caco-2 cells in which over-expression of P-gp wasinduced by the addition of the cytostatic drug vinblastine to the cell culture,and [3H]verapamil was chosen as the radioligand. Affinity constants to P-gpbinding sites are derived from the concentration dependence of the displace-ment of the radioligand by the non-labelled test compound.

For the few compounds which are not absorbed (passively or actively)across the cell membranes but are absorbed by the alternative paracellularpassive pathway across the tight junctions between the cells, Caco-2 cellmonolayers might not be an optimal model. The junctions in the colonic epi-thelium, and thus also in the Caco-2 monolayer, are tighter than those in thesmall intestinal epithelium (human: 0.8 nm pore radius in jejunum vs. 0.3 nmin the colon), generally the preferred site for absorption. Thus, depending onthe ‘size’ of the compound to be investigated, the permeability determined in vitro in Caco-2 monolayers may underestimate the extent of absorption in vivo.

Overall, Caco-2 cells cultured on permeable supports provide a simple invitro model for characterizing membrane permeability and predicting in vivoabsorption and for identifying and characterizing active transport mecha-nisms to which drug candidates might be subjected. The Caco-2 cell assaycan be fully automated and can be easily used for screening large numbers ofcompounds [10]. It is a most suitable model for comparing/ranking com-pounds within a (chemical) class. Nevertheless, data derived from the Caco-2 model should be regarded as indicative and not necessarily as quantitative.

2.1.2. Parallel Artificial Membrane-Permeation Assay

The parallel artificial membrane-permeation assay (PAMPA) is a furthersimple method for the characterization of passive transcellular diffusion.PAMPA is based on microtiter-plate technology. The wells filled with aque-ous buffer solutions (acceptor compartment) are covered with a microtiter fil-terplate in a sort of sandwich construction (cf. Fig. 3). The hydrophobic filtermaterial is impregnated with a material which mimics mammalian mem-branes, e.g., lecithin. The system is completely artificial without pores andactive transport systems. The test compound is added on the top of the filter-plate, and the flux is calculated by comparing the amount of test compoundin the acceptor compartment after a predefined incubation time with that in


the acceptor compartment of a reference incubation performed with thehydrophobic filter material but without the membrane barrier. The mainobjective of the model is the classification/ranking of compounds withrespect to passive transport, such as that responsible for the transcellularabsorption. Influences of pH changes or effects of surfactants, like bile acids,on transport processes can also be examined using this technique (see alsochapter by Kansy et al. in this volume, p. 447).

The greatest potential of PAMPA lies in the rapid screening of large com-pound libraries for the identification of lead compounds, with respect to pas-sive, transcellular absorption. Kansy et al. [11] showed that the PAMPAmodel allows a rapid and simple classification between compounds (of thesame class) having low, intermediate, and high passive absorption potential.Due to the lack of pores and active transport systems, however, the model isnot suitable for polar compounds with molecular weight < 250 that areabsorbed mainly via the paracellular pathway or for compounds which aresubject to active transport.


Fig. 3. Schematic representation of the PAMPA model and scanning electron micrograph of thepolycarbonate membrane (based on illustrations provided by B. Faller, Novartis Pharma,

Basel, Switzerland)

2.1.3. Other Methods

Everted intestinal rings [12] and brush-border-membrane vesicles(BBMV) [13] are also systems used for assessing membrane permeability.The former technique is a refinement of one of the earliest in vitro absorptionsystems in which an everted intestinal segment was suspended in a buffersystem to measure mucosal-to-serosal transfer. The current methodologyinvolves isolating a rat-intestinal segment, everting, and slicing it into ringswhich are suspended in buffer. BBMVs are prepared by removing the brush-border surface from rat or rabbit intestine and moulding it into vesicles byhomogenization and differential centrifugation. Both everted intestinal ringsand BBMVs are most useful for determining compound-uptake rates ratherthan transepithelial flux (see also chapter by Morse and Pidgeon in this vol-ume, p. 429).

Finally, immobilized artificial membranes (IAM) represent a recentlydeveloped fully artificial system [14] in which the test compound is injectedonto a specialized IAM-HPLC column packed with a phosphatidylcholine sta-tionary phase selected to closely mimic biological membranes. Theoretically,the chromatographic capacity factor for the test compound should correlatewith the water-to-phosphatidylcholine membrane partition coefficient, a use-ful parameter for calculating membrane permeability. The ability of IAM tech-nology to predict membrane permeability is currently being evaluated. Itappears to be well suited for rapid screening, although its applicability will belimited to those compounds which are absorbed by passive processes.

A major challenge in drug discovery is the selection of compounds on thebasis of their ability to cross the blood-brain barrier (BBB), depending on thetherapeutic indication and the location of the pharmacological target. On amorphological level, the BBB consist of the brain-microvascular endotheli-um cells coupled by tight junctions. In addition, the BBB expresses metabol-ic activity (in particular hydrolytic activity) and the P-gp efflux system. Thus,in vitro models such as isolated brain capillaries and cultured primary endo-thelial cells can be of great help for identifying compounds able or unable tocross the BBB.

Brain capillaries are isolated from brain tissue obtained from animals orby post-mortem autopsy from humans by homogenizing and filtering througha nylon mesh (pore size 200 nm) [15]. Isolated capillaries are used to meas-ure uptake of the compounds into the endothelial cells and for the determina-tion of receptors on endothelial cell membranes. This model, however, can-not be used to measure transendothelial transport across the BBB since thereis no separation between luminal and abluminal compartment.

Culture of the endothelial cells on polycarbonate filters produces conflu-ent monolayers with good barrier properties which can be used to study the


transendothelial transport [16]. The brain-microvessel endothelial cell cul-tures permit concurrent investigation of metabolism within and transportacross the BBB. The primary cerebral microvessel endothelial cells in cultureform a highly selective barrier with absence of fenestra, few pinocytic vesi-cles and the presence of tight intercellular junctions. In addition, the cellsform i) an effective metabolic barrier with the presence of monoamine oxi-dases, reductases, and hydrolyzing enzymes, ii) express the P-glycoproteinefflux system, and iii) form an electrostatic barrier due to the presence of sul-fated glycoproteins similarly to what is observed in vivo [17] (see also chap-ter by Krämer et al. in this volume, p. 127).

2.2. Protein-Binding Screening

The ability of drug candidates to bind to proteins of plasma or tissues is animportant variable which influences the disposition and efficacy of a compoundin vivo. Generally, only the unbound fraction of the drug is able to cross mem-branes, exert pharmacological activity, be filtered in the renal tubuli, or becleared by drug-metabolizing enzymes. Nevertheless, the role of protein bind-ing in determining in vivo efficacy is still not universally predictable. For exam-ple, low protein binding may imply low lipophilicity, which could be in conflictwith optimal membrane permeability or receptor/enzyme affinity, and high pro-tein binding is not necessarily a negative attribute since it may help prolong theactivity of some compounds with otherwise short half-lives. Thus, early infor-mation on the plasma-protein-binding properties for selection may be very val-uable (see also chapter by Tillement et al. in this volume, p. 189).

Unfortunately, plasma-protein-binding assays do not lend themselvesreadily to HTS. The suitability of the classically used investigation methods(ultrafiltration, equilibrium dialysis, ultracentrifugation) for a given test com-pound is very much dependent on its physicochemical properties (e.g.,lipophilicity, adsorption to test equipment, molecular weight, molecular size,etc.) and must be determined individually for each drug candidate.Development of new investigation methods which are suitable for screeninga broad spectrum of compounds is currently in progress and hopefully, HTSmethods for plasma-protein binding will soon be available.

3. In Vivo Tests

As discussed above, in vitro systems, although they provide useful infor-mation, may not necessarily reflect the in vivo situation. Simplification/reduc-tion of the (dynamics of the) biological process and of the biological matrix,


and suppression of concurrent (dynamic) variables/characteristics may influ-ence the outcome of the experiment in such a way that the results are no long-er predictable for the dynamic in vivo situation. Since inappropriate/unfavor-able in vivo pharmacokinetic (PK) properties (e.g., poor absorption, low bio-availability, rapid clearance) have frequently been the reason for the discon-tinuation of the development of drug candidates in the clinic, early knowl-edge of the PK properties of drug candidates is of great value for the earlyselection process. Unfortunately, there is no substitute for actual in vivo datain assessing the pharmacokinetic properties of drug candidates. From this, theneed for in vivo HTS methods for the assessment of the PK of a large num-ber of early drug candidates is obvious.

There are several approaches which provide significantly increasedthroughput for in vivo PK investigations. All are based on a reduction of thenumber of animals involved or the number of samples to be analyzed, or both.Reduction of animal numbers is attractive with respect to animal welfare, andreduction of the workload and potentially also time of the in-life phase;reduction of the number of samples is attractive with respect to labor-inten-sity and total time required for the sample analysis.

For a useful HTS assessment of the PK properties of a drug candidate, thein vivo model system (i.e., the animal species) used should be relevant to manwith respect to the property to be assessed (e.g., absorption, bioavailability,clearance, etc.) and of a size that allows optimal combination of the two ‘conflicting’ requirements, namely minimizing the amount of test compoundneeded (especially in the drug-discovery stage) whilst maximizing the amountand size of samples (generally blood) which can be obtained. Furthermore,the bioanalytical method for quantifying the test compound in biologicalfluids should be selective and sensitive as well as suitable (at least with minormodifications) for several test compounds in order to minimize the labor andthe time required for assay development.

3.1. Cassette Dosing

The cassette dosing (also called N-in-one dosing) [18–21] consists of theco-administration of several test compounds as one dose solution to an animaland determining concurrently their PK. Remarkably, the PK of up to 90 com-pounds have been investigated simultaneously in one dog [21]. Generally, thePK of at least one of the test compounds has already been well characterized,thus serving as a reference or standard to verify the applicability of the meth-od under the conditions (i.e., the test-compound mixture) employed.

The attractiveness of cassette dosing lies the reduction of both the num-ber of animals and the number of samples to be analyzed. Potential draw-


backs of the method lie in the possibility of misleading results due to i) inter-actions prior to or during absorption from the gastrointestinal tract (therefore,the method is better suited for i.v. than for p.o. administration), ii) varioussystemic drug-drug interactions (e.g., at the level of cytochrome-P450-medi-ated metabolism, of plasma protein binding, etc.), iii) exaggerated pharma-codynamic or toxic effects (e.g., due to pharmaco-/toxicodynamic synergy orto ‘overdosing’ resulting from additivity of the effects of the combination).Further, insufficient solubility of the compound mixture may prevent admin-istration of a given compound mixture or force a reduction in the dose of anindividual test compound or of all compounds of a mixture. Interferences inthe analytical assay may also prevent the combination of set test compounds(e.g., because of overlapping chromatographic retention times or of matchingmolecular weights either of two test compounds or of one test compound andthe metabolite of a second test compound). Reduction of the total dose and ofthe dose of the single test compounds may avoid or minimize the potential fordrug-drug interactions, exaggerated pharmacodynamic or toxic effects, andsolubility issues. However, this requires a dose-independent disposition of theindividual test compounds.

A valuable alternative to cassette dosing consists in dosing the test com-pounds singly to individual animals (discrete dosing) and post-dose poolingof the samples for cassette analysis. This does not reduce the number of ani-mals but still reduces the number of samples to be analyzed. In addition,except for the possibility of interferences in the analytical assay, it avoids theabove-mentioned potential drawbacks of cassette dosing.

3.2. Other Methods

A further variant is discrete dosing of a test compound with post-dosepooling of samples collected at equally spaced intervals [22] and determina-tion of an average plasma (or serum, or blood) concentration. The PK infor-mation thus obtained is distinctly less complete than that which can be gainedwith either of the two previously described methods but is still very valuableand may be fully sufficient for an early screen. Multiplication of the averageconcentration by the total collection/observation time yields an estimate forthe area under the concentration-time curve (AUC) which may be sufficientto rank numerous test compounds. Additionally, discrete analysis of the sam-ple collected at the last time point provides information on the relative elim-ination rate and the accuracy of the estimated AUC value.

Semi-simultaneous bioavailability estimation [23] consists in administer-ing to the same animal first an intravenous dose of a drug candidate followedby an oral dose at a suitable time post-dose (i.e., post-distributional). Pharma-


cokinetic parameters such as clearance and volume of distribution can bedetermined from the concentration-time curve after i.v. dosing, whereas bio-availability is obtained by deconvolution from the combined i.v. and p.o. PKdata. The major advantage of this technique is that it reduces the inter- andintra-animal variability, and thus allows accurate PK data to be generatedfrom fewer animals. In addition, the total number of plasma samples requir-ing analysis is reduced by the overlapping dosing regimes. However, a criti-cal element of this technique is determining the optimal time post i.v. dosewhen the oral dose can be given. Ideally, some knowledge of the PK of thetest compound is needed to optimize the interval between the two administra-tions.

Alternatively or concurrently, it may be possible to collect plasma sam-ples from animals used in whole-animal pharmacology models and, based onthe concentration/effect relationship established, make a link between in vitropharmacological activity and the behavior of a compound in vivo.

Finally, the potential of transgenic animal models in in vivo ADE screen-ing is certainly not yet fully exploited. Nowadays, transgenic animals aremostly used to investigate pharmacology and/or toxicity. With respect toAD(M)E, transgenic technology can be exploited in different ways: either byintroducing a human-specific gene encoding a desired characteristic (e.g., aprotein, a metabolizing enzyme, a transport enzyme) or by deleting specificanimal genes (knocking-out) from the animal genome and investigating thedisposition of the test compound under these ‘humanized’ conditions. Thesestrategies provide the potential for screening compounds under more ‘human-relevant’ conditions. As an example, the consequences of elevated serum 1-acid glycoprotein (AGP) levels, often seen in disease states (e.g., oncology,inflammation, pain), on the pharmacokinetics and disposition of drugs can beevaluated in a strain of transgenic mice that expresses genetically elevatedAGP levels [24] [25].

4. Theoretical Considerations and in Silico Screening

Although simplification, automation, and miniaturization of experimental(i.e., in vitro and in vivo) investigations have allowed distinct reduction oflabor intensity, time, and the amount of test compound needed and haveincreased the throughput for screening test compounds with respect to theirADE characteristics, experimental investigations remain generally too slow,too costly, and require too much compound for screening the very large li-braries of test compounds in the very early phase of the discovery process. Inthis early phase, theoretical considerations and computational (i.e., in silico)approaches represent a valuable alternative to experimental investigations for


generating potentially useful predictions on the ADE behavior of a com-pound.

4.1. Physicochemical Considerations

Of all the properties which determine a drug’s ultimate in vivo ADEbehavior, its physicochemical properties are possibly the most fundamentaland deserve due attention in the early discovery phase, particularly since several physicochemical variables (e.g., solubility, dissociation constants,lipophilicity) can be relatively easily and predictably manipulated by modifi-cation of the chemical structure.

The rate and degree of intestinal absorption of compounds depend on‘simple’ diffusion processes (e.g., passive transcellular membrane permea-tion, paracellular permeation), on complex biological processes (e.g., metab-olism in the gastrointestinal tract, active transport mechanisms), and on com-pound physicochemical properties (e.g., solubility, lipophilicity, dissociationconstants) which influence passive membrane permeation.

Solubility, lipophilicity, and dissociation constants are important, interre-lated determinants of the in vivo ADE behavior of a test compound. Solubilityof the test compound in the relevant biological fluids (e.g., in the gastrointes-tinal fluids in the case of intestinal absorption) is an important prerequisite forpassive membrane permeation. Solubility in a specific biological fluid, how-ever, depends on the physicochemical properties of the fluid and the lipophi-licity and ionization status of the test compound. For compounds which arepoorly soluble in the gastrointestinal tract (and thus are poorly absorbable perse), appropriate galenical formulation work may provide a solution [26]. This,however, represents a major challenge and might increase the duration andthe costs of development of these projects.

Lipophilicity (e.g., log P) is also a critical variable for the intestinalabsorption (and for membrane permeability in general) of a compound. It hasbeen suggested that compounds with log P values between 0 and 3–4 are themost suitable candidates for absorption by passive transcellular transportacross intestinal epithelia. If lipophilicity is increased, absorption declinesprogressively, mainly due to poor solubility in the gastrointestinal tract,whereas more hydrophilic compounds with log P values below 0 are likely tobe absorbed more slowly via paracellular channels. The paracellular route,however, is governed also by the shape/size of the molecule, since the narrow(5–9 Å) paracellular channels allow transit only to relatively small molecules.

The knowledge and understanding of the dissociation constants (pKa val-ues) of a compound may also be very helpful for the prediction of its in vivoADE behavior. The ionization state of a test compound in a specific body


compartment can be calculated by considering its dissociation constants rela-tive to the pH in the body compartment of interest (e.g., acidic for stomach,basic for intestine, pH 7.4 for plasma, pH 4.5–8 for urine, etc.). The ioniza-tion state of a test compound may affect its solubility, lipophilicity, and suit-ability for passive transcellular diffusion (generally, the un-ionized form isthe preferred moiety for this transport) and/or suitability as a substrate for anactive transport mechanism. For example, an organic acid in its anionic stateor a base in its cationic state are potentially susceptible to active tubularuptake/secretion by the organic anion or cation transporter, respectively [27].If the compound is indeed a substrate for one of these transporters, renalclearance may be high and elimination from the systemic circulation rapid. Aremarkable example is morphine which is taken up by the renal tubular cellsin its cationic form, then conjugated to its O-sulfate, and finally excreted asan anion. Furthermore, it has been observed that compounds with log P val-ues greater than 0 are likely to undergo substantial renal tubular reabsorption.However, this process will only be likely to occur if the compound is presentin urine (pH 4.5 to 8) in an un-ionized state and has not exceeded its solubil-ity limit.

4.2. ADE Processes

Solubility, lipophilicity, dissociation constants, and molecular weight(together with protein binding) influence the route of excretion of a com-pound. The ability to predict a priori the route of excretion is still limited. Toour knowledge, no correlation between a single physicochemical parameterand excretion route has yet been found. Correlation between an arbitrarycombination of the above physicochemical parameters (a so-called rank coef-ficient) and excretion route has been reported for a limited series of com-pounds which differed distinctly with regard to their physicochemical prop-erties [28]. Further, some basic guidelines can be postulated: anionic or cat-ionic characteristics combined with good water solubility, low-to-moderatelipophilicity, low molecular weight (< 500) favor renal excretion. Anions, cat-ions, and non-ionized molecules containing both polar and lipophilic groupsare excreted into the bile provided that their molecular weight is greater thanabout 300, but very-high-molecular-weight compounds, such as, e.g., pro-teins, are poorly excreted via the bile.

The ability to predict a priori the extent of plasma-protein binding is lim-ited, although fruitful attempts have been made to define the molecular bind-ing requirements for certain sites on human serum albumin. In general, bind-ing is likely to increase with lipophilicity; an anionic group may enhancebinding to albumin even further [29] [30]. In contrast, cationic molecules are


virtually precluded from binding to albumin, rather favouring 1-acid glyco-protein.

The use of the computational approaches integrating all the knowledgeaccumulated over the last decades in the in vivo experimentation (i.e., aknowledge-based computer system) is an attractive expansion of the above-described pragmatic approach. Nevertheless, it should be kept in mind thatcomputational approaches to estimate absorption/permeability deal morewith probabilities than with (more or less) exact value predictions.

An approach to estimate absorption based on solubility and permeabilityof drugs (i.e., excluding intestinal-wall metabolism and intestinal-wall active-transport issues) has been elaborated by Lipinsky et al. [31]. The resulting‘rule of five’, in summary, states that a compound has higher likelihood toexhibit absorption problems when two (or more) of the following propertiesare applicable: there are more than 5 H-bond donors (expressed as the sum ofOHs and NHs), there are more than 10 H-bond acceptors (expressed as thesum of Os and Ns), the molecular weight is greater than 500, and the log P isgreater than 5. Lipinsky’s ‘rule of five’ should be seen as a screen for poten-tial absorption problems and not as an absolute test for the absorbability of acompound. The ‘rule of five’ is based on a distribution of calculated proper-ties among several thousand drugs. Therefore, by definition, some well-absorbed drugs will lie outside the parameter cut-offs in the rule. In addition,compounds that are substrates for biological transporter are exceptions to therule.

In conclusion, theoretical considerations and in silico ADE approachescan be used as potential virtual screens or property estimators helping in theearly drug-selection process and in guiding the synthetic chemist in maximiz-ing the ability of a drug candidate to access the therapeutic target in vivo.However, despite the importance of physicochemical properties in determin-ing the ADE profile of a drug candidate, they are as yet primarily roughguidelines and, thus, the ADE profile generated in silico must eventually beconfirmed by in vivo experiments. The potential of this technology lies in thereduction of animal experiments, but certainly not in the eradication of in vivoexperiments. Experimental data generated by the discovery tools outlinedabove can help put the importance of physicochemical properties into theappropriate context.

5. Outlook and Conclusion

A multitude of ADE high-throughput-screening tools, ranging from theo-retical considerations/in silico to in vivo, is available which allows the incor-poration of ADE considerations and profiling into the earliest phase of drug


discovery and to support drug discovery during the selection phase, therebymaximizing the likelihood of success of drug candidates. Unfortunately, thehitherto available screening tools are not well balanced across ADE: whilstseveral tools are available to investigate absorption, the variety of tools toinvestigate distribution and excretion is yet somewhat limited, but efforts todevelop new tools covering these aspects are ongoing.

The range of available ADE HTS tools represents both a chance and achallenge. It allows selection of the most appropriate tool for answering thequestion of interest, and to cope with the number of compounds to be tested,the amount of test compound and time available, and further constraintswhich may be related to the stage of drug discovery/development. But it alsocalls for design and employment of a rational HTS strategy which defines atesting sequence (e.g., theoretical considerations/in silico→ in vitro → in vivo),criteria for selecting the appropriate HTS, continuous validation of the HTS(which should also include testing for false-negatives), and handling andmanagement of the huge amount of data HTS can and will generate.

Automated data evaluation should support and not replace sound, ration-al, and pragmatic thinking in interpreting the HTS results. The limitations ofthe respective HTS tool have to be considered and over-interpretation has tobe avoided. HTS generally allows discrimination between compounds with‘suitable’ and ‘unsuitable’ properties, but only rarely reliable ranking of com-pounds within a narrow subgroup.

Further, a rational HTS strategy has to consider that the design and opti-mization of drugs is multifaceted, and that care has to be taken to avoid thatapplication of ADE HTS eventually results in neglecting other aspects (suchas, e.g., metabolism or toxicity), and thus in selecting compounds withimproved ADE properties (e.g., absorption), but with new issues in otherimportant aspects (e.g., metabolic stability). Optimization of ADE propertiesof drug candidates has to be integrated in that of all other relevant/essentialproperties in order to achieve an optimal balance between ADME, pharmaco-logical, and toxicological properties

The ultimate challenge of ADE HTS is to predict ADE in humans based ondata obtained in silico, in vitro, and from animals in vivo; the value of the pre-dictability, however, can only be proven by assessing the disposition of the testcompound in vivo in humans, in keeping with the ‘saying’: in vivo veritas!

The authors wish to thank Dr. Philip Bentley, Novartis Pharmaceuticals, East Hanover,USA, for his thorough review and valuable correction of the English of this chapter, and Dr.G. Camenisch and Dr. B. Faller, Novartis Pharma, Basel, Switzerland, for kindly providingillustrations for this chapter.


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Drug. Dev. Industrial Pharmacy 1994, 20, 615.[4] T. Prueksaritanont, L. M. Gorham, J. H. Hochman, L. O. Tran, K. P. Vyas, Drug Metab.

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Proc. Natl. Acad. Sci. U.S.A. 1987, 84, 7735.[7] J. Hunter, M. A. Jepson, T. Tsuruo, L. Simmons, B. H. Hirst, J. Biol. Chem. 1993, 20,

14991.[8] J. Hunter, B. Hirst, Adv. Drug. Deliv. Rev. 1997, 25, 129.[9] P. Artursson, J. Karlsson, Biochem. Biophys. Res. Comm. 1991, 175, 880.

[10] P. Garberg, P. Eriksson, T. Schipper, B. Sjöström, Pharm. Res. 1999, 16, 441.[11] M. Kansy, F. Senner, K. Gubernator, J. Med. Chem. 1998, 41,1007.[12] P. S. Leppert, J. A. Fix, J. Pharm. Sci. 1994, 83, 976.[13] M. Kessler, O. Acuto, C. Stroelli, H. Murer, M. Muller, G. Semenza, Biochim. Biophys.

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1998, 12, 1216.[19] J. Bermann, K. Halm, K. Adkinson, J. Schaffer, J. Med. Chem. 1997, 40, 827.[20] T. V. Olah, D. A. McLoughlin, J. D. Gilber, Rapid. Commun. Mass Spectrom. 1997, 11,

17.[21] L. W. Frick, K. K. Adkinson, K. J. Wells-Knecht, P. Woolard, D. M. Higton, Pharm. Sci.

Technol. Today 1998, 1, 12.[22] K. A. Cox, K. Dun-Meynell, W. A. Korfmacher, L. Broske, A. A. Nomeir, C. C. Lin,

M. N. Cayen, W. H. Barr, Reviews Res. Focus 1999, 4, 232.[23] M. O. Karlsson, U. Bredberg, Pharm. Res. 1989, 6, 817.[24] J. W. Holladay, M. J. Dewey, S. D. Yoo, Pharm. Res. 1996, 13, 1313.[25] J. W. Holladay, M. J. Dewey, S. D. Yoo, Drug Metab. Dispos. 1998, 26, 20.[26] B. J. Aungst. J. Pharm. Sci. 1993, 82, 979.[27] W. G. Levine, in ‘Target Organ Toxicity, Volume I – Role of Excretion’, Ed. G. M.

Cohen, CRC Press Inc., Boca Raton, Florida, 1988, pp. 55–88[28] C. H. Fleck, H. Bräunlich, Arzneim.-Forsch./Drug Res. 1990, 40, 942.[29] G. Sudlow, D. J. Birkett, D. N. Wade, Mol. Pharmacol. 1975, 11, 824. [30] S. Wanwimolruk, D. J. Birkett, P. M. Brooks, Mol. Pharmacol. 1983, 24, 458.[31] C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Freeney, Adv. Drug Deliv. Rev. 1997,

23, 3.


In Vitro Models for Early Studies of DrugMetabolism

by Jiunn H. Lin and A. David Rodrigues*

Drug Metabolism, WP75A-203, Merck Research Laboratories, Sumneytown Pike,West Point, PA 19486, U.S.A; Tel.: (215) 652-47 42; Fax: (215) 652-24 10;


1. Introduction

The liver, which constitutes approximately 2.5% of the body weight inhuman adults and contains an abundance of various phase-I and phase-IIenzyme systems, has long been recognized as the major organ for drugmetabolism and elimination. In this regard, the liver often serves as the locusof metabolic drug-drug interactions, where a drug can alter the metabolismand pharmacokinetics of a second drug through various mechanisms ofinduction or inhibition. In addition to drug elimination, the liver also plays asignificant role in drug absorption because of its unique anatomical place-ment between the portal and systemic circulation. This means that a signifi-cant portion of the dose that is absorbed from the gastrointestinal lumen canbe metabolized by the liver before reaching the systemic circulation, a phe-nomenon known as ‘hepatic first-pass metabolism’. For a given drug, it isknown that high hepatic first-pass metabolism always results in low bioavail-ability [1]. Consequently, to design drug candidates with desirable bioavail-ability, optimal metabolic stability and plasma half-life, and minimal drug-interaction potential, mechanistic evaluation of the processes of hepaticmetabolism is important.

Although several in vivo techniques have been established to assesshepatic metabolism, it is often very difficult to address the underlying hepat-ic processes or mechanisms of elimination. As a result, many in vitro models(e.g., isolated perfusion liver, precision-cut liver slices, hepatocytes, subcel-lular fractions, and recombinant enzymes) have been developed to studyhepatic metabolism and have proven to be very useful in basic research (Fig. 1). At the same time, most pharmaceutical companies have recognizedthe utility of these models in the early-‘discovery’-stages of the drug devel-



Fig. 1. In vitro models for studying drug metabolism. Adapted from [30].

Fig. 2. Higher-throughput drug-metabolism screening of compounds using various in vitromodels

opment process [2–7]. This is evidenced by the increasing acceptance ofhigh-throughput screening (HTS) paradigms (Fig. 2). Concomitantly, variousregulatory agencies have acknowledged the usefulness of in vitro humandrug-metabolism data and are requesting that it is provided for inclusion insubmissions and package inserts [8–10].

As shown in Fig. 3, although the complexity and heterogeneity of manyin vitro models is reduced, it is acknowledged that as one migrates from‘whole organ’ to ‘cell-based’ and ‘subcellular-based’ models, the physiolog-ical relevance is compromised. Therefore, it is important to select in vitromodels that are appropriate for specific goals. The purpose of this review isto briefly describe the most commonly used in vitro drug metabolism models,provide examples of applications, and to discuss their advantages and disad-vantages (Table 1).


Fig. 3. Determining clearance using various in vitro models. Intrinsic clearance (CLint) can bedetermined in vitro and scaled to yield estimates of hepatic clearance (CLh). Typically, onetakes into account free fraction of drug in plasma (fu,p) and liver blood flow (Qh). In turn, hepat-ic clearance is related to plasma clearance (CLp), which is the sum of hepatic clearance, renal

clearance (CLr), and all other mechanisms of clearance.


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2. Isolated Perfused Liver

The isolated perfused liver technique, which dates back more than a cen-tury, is still widely used for drug-metabolism studies today, because the iso-lated liver most closely resembles in vivo conditions. In contrast to other invitro systems, organ structure, spatial heterogeneity and architecture remainintact, along with the portal tracts, sinusoids, and space of Disse. There aretwo modes of liver perfusion, namely the recirculating and non-recirculating(single-pass) method, and the choice of mode is dependent on the types ofstudies and kinetic properties of compounds [11]. Recirculation of mediumallows the re-entry of substrates and metabolites into the liver for furthermetabolism, while the single-pass method provides a direct assessment ofmaterial balance for substrate loss across the liver. This approach is more suit-able for low-clearance drugs, because only a small fraction of the substrate ismetabolized during a single pass through the liver. In addition, the recirculat-ing method is often used to generate metabolites in perfusate and in bile. Bycomparison, the single-pass approach permits a direct measure of substrateloss during single pass and is most suitable for kinetic studies employingdrugs with high or moderate hepatic clearance.

Application of the isolated perfused liver technique has proven to be veryuseful for studying drugs that undergo metabolism via two or more pathways.For example, perfused liver has been used to study the simultaneous sulfationand glucuronidation of numerous phenolic compounds (e.g., acetaminophenand 1-naphthol), catalyzed respectively by sulfotransferases and UDP-glucu-ronosyl transferase (UDPGT) [12–16]. Interestingly, for most phenolic xeno-biotics, there is a concentration-dependent switch from sulfation to glucuroni-dation, and studies with perfused liver have revealed that this metabolic shiftis due mainly to saturation of sulfate conjugation. Furthermore, the depletionof inorganic sulfate, which in turn may limit the biosynthesis of co-substrate3′-phosphoadenosine 5′-phosphosulfate (PAPS), can also contribute to thesaturation of sulfate conjugation [17] [18].

Isolated perfused liver has also been used for the assessment of enzymeheterogeneity, with the aid of the multiple indicator-dilution method [19]. Thezonation of enzyme within the tissue can be assessed by comparing the pro-files of parent drug and metabolites during prograde and retrograde single-pass perfusion. For the prograde mode, the liver is perfused with perfusateentering the portal vein and leaving the hepatic vein, while for retrogrademode, the opposite direction from hepatic vein to portal vein is employed.

In addition, isolated perfused liver preparations have been used to assessthe effect of protein binding within blood on hepatic metabolism. By chang-ing the albumin concentration in the perfusate, Rowland et al. [20] demon-strated that the hepatic first-pass metabolism of diazepam in the rat decreased


from more than 90% in the absence of albumin to less than 10% when thealbumin concentration in the perfusate was very high. Similar results wereobserved for tolbutamide [21]. Collectively, these results indicate that thehepatic metabolism of a drug can be reduced if the degree of binding withinblood is increased.

Perhaps, the most important contribution of the isolated perfusion-livertechnique has been the development of organ-clearance concepts and varioushepatic clearance models for describing hepatic elimination kinetics of xeno-biotics [22] [23]. The concept of organ clearance is based on mass-balanceconsiderations, i.e., on the fact that the rate of organ elimination is equal to thedifference between the rate of drug delivery to the organ in the inflow and itsrate of exit in the outflow. Two simple hepatic models have been developed,and the liver is viewed either as a well-mixed compartment (well-stirredmodel), or as an organ consisting of a large number of identical cylindricaltubes, representing the sinusoids, that are arranged in parallel and with hepa-tocytes (parallel-tube model). Although these models have certain similaritiesin their general ability to describe the inter-relationships of plasma-proteinbinding, hepatic blood flow, and enzyme activity in the liver, these models areobviously oversimplified, and occasionally the ability of describing intrahe-patic events is limited. For these reasons, a more sophisticated model termedthe ‘dispersion model’ has been developed [24]. This model is characterizedby two main parameters: the efficiency number (RN), which describes drugremoval by the liver, and the axial dispersion number (DN). The DN is a meas-ure of the distribution of residence times of drug molecules moving throughthe liver. Although the dispersion model reflects more accurately the hepaticphysiology, the mathematical solution for hepatic clearance in this model isquite complex, and its application to experimental data is limited.

Although relatively simple by comparison to the whole body, the perfusedliver is still a complex model for drug-metabolism studies. The issues ofblood flow, protein binding, and hepatic distribution have to be consideredand taken into account. Because of the difficulty of catheterizing the hepaticartery in smaller animals, such as mice, and the high cost of perfusionrequired for larger animals, including humans, most studies have employedrats [11]. Therefore, the application of the isolated perfused liver technique islimited. To our knowledge, there is no report of a drug-metabolism studyemploying an isolated perfused human liver.

3. Precision-Cut Liver Slices

In drug development, early assessment of human drug metabolism is crit-ical for predicting drug interactions and the selection of the appropriate ani-


mal species for toxicity studies. For human risk assessment, in order to ensurean adequate safety margin, one is required to demonstrate that the chosen ani-mal species are exposed to high levels of parent drug and its metabolites. Itis, therefore, important to select animal species that have metabolite profilessimilar to humans. However, a drug’s in vivo metabolic profile in man is typ-ically evaluated at the later stages of drug development, which is often toolate for animal selection. Consequently, numerous groups have attempted topredict metabolic profiles in man using intact cell-based in vitro metabolismmodels, such as suspensions of primary hepatocytes and precision-cut liverslices [25–30].

3.1. Metabolism Studies

There are a number of advantages to using precision-cut liver slices,because they have the structure and functional heterogeneity of the intactliver and maintain a normal metabolic environment with intercellular com-munication. In addition to maintaining intact architecture and metabolic func-tion, their preparation is a relatively simple procedure and does not involvethe use of proteolytic enzymes. Moreover, liver slices retain the cofactors thatsupport both phase-I and phase-II reactions [25] and are useful for obtainingthe complete in vitro metabolite profile of new drug candidates. For example,indinavir, a potent HIV-protease inhibitor, undergoes metabolism at five sites(glucuronidation at the pyridine N-atom, pyridine N-oxidation, phenyl methylpara-hydroxylation, indane 3′-hydroxylation, and N-depyridomethylation),and the metabolite profile obtained from human liver slices accurately re-flects the in vivo human metabolite pattern [26]. Although all of the oxidativemetabolites of indinavir are formed in the presence of NADPH-fortifiedhuman liver microsomes, the N-glucuronide has not been detected when thesame microsomes (native or detergent-activated) are incubated with UDP-glucuronic acid (UDPGA) [27]. Similarly, species differences in the metab-olism of ABT-418 have also been assessed using precision-cut tissue slicesfrom rat, dog, cynomolgus monkey, chimpanzee, and human liver [28–30].ABT-418, a bioisostere of (S)-nicotine and a potent and selective nicotinicacetylcholine-receptor ligand, is metabolized via N-oxidation (catalyzed bymicrosomal NADPH-dependent flavin-containing monooxygenase (FMO))and C-oxidation to a lactam (catalyzed by microsomal P450 and cytosolicaldehyde oxidase). Formation of the lactam predominates in human andcynomolgus liver slices, while the products of both pathways are detected inrat, dog, and chimpanzee liver slices. Because of the instability of cytosolicaldehyde oxidase in dog and human subcellular fractions (S-9), the metabol-ic profile obtained with the dog and human liver slices more accurately


reflects the in vivo metabolic profile. In addition, liver slices have been usedas a model system to study the simultaneous sulfation and glucuronidation ofcompounds such as 1-naphthol [15]. Similar to the in vivo findings, a concen-tration-dependent shift from sulfation at low substrate concentrations to glu-curonidation at high substrate concentrations is observed in rat liver slices.

3.2. Inhibition and Induction Studies

Although limited in number, a few groups have attempted to qualitative-ly evaluate drug-drug interactions with precision-cut liver slices. Rodrigues etal. [31] compared the effects of ketoconazole on terfenadine metabolism inhuman liver slices, microsomal preparations, and cDNA-expressed cytochro-me P450 isoform 3A4 (CYP3A4). At a concentration of 5 µM, ketoconazolecompletely inhibited terfenadine metabolism in all three enzyme models.Similarly, the inhibitory effect of zileuton (a 5-lipoxygenase inhibitor) ontheophylline metabolism by human liver slices accurately reflected the inhi-bition observed in vivo [32] [33]. However, despite these observations, noattempts have been made to estimate the Ki values of inhibitors. More recent-ly, P450 induction has been studied using liver slices. Gokhale et al. [34]were able to maintain rat and mouse liver slices in culture for up to 4 dayswith good morphology and inducibility. CYP3A was highly induced by phe-nobarbital in both rat and mouse liver slices. In rat liver slices, CYP1A1/2activity was induced 4-fold by -naphthoflavone (NF) and 37-fold by dioxin (TCCD). In mouse liver slices, CYP1A1/2 activity was not induced byNF, but was induced (19-fold) in the presence of TCDD. The induction ofvarious P450s has also been evaluated with precision-cut human liver slicesand, in agreement with in vivo findings, rifampicin and omeprazole wereshown to induce CYP3A and CYP1A, respectively [35] [36].

3.3. Challenges

Liver slices may offer an alternative in vitro model for investigating drugmetabolism, but they are only useful for qualitatively assessing routes ofmetabolism. To date, their utility for predicting rates of metabolism has prov-en to be limited [37]. Worboys et al. [38] have demonstrated that, dependingon a drug’s lipophilicity, intrinsic clearance (Vmax/Km) in precision-cut ratliver slices is consistently lower (2- to 20-fold) than that observed with fresh-ly isolated hepatocytes. Therefore, it has been proposed that a distributionequilibrium is not achieved between the cells within the slices and the incu-bation medium due to the slice thickness (~200 µm). Further studies have


revealed that a high concentration gradient may exist across the tissue slice,for drugs with a large volume of hepatic distribution (estimated from hepato-cytes), resulting in the delayed accessibility of substrate to all cells in theslice, and hence a reduced clearance [39–41].

4. Freshly Isolated Hepatocytes

There are various types of functionally different cells in liver, includingparenchymal, Kupffer, sinusoidal and fat-storing cells. The parenchymal cells(hepatocytes), which contain an abundance of various phase-I and phase-IIdrug-metabolizing enzymes, make up 80% of the liver’s volume and 60% ofits total cell number. In the past, mechanical and chemical methods used forcell separation were crude and ineffective. By 1976, however, a two-step per-fusion method (Ca2+-free perfusion followed by a collagenase perfusion) forthe isolation of hepatocytes was successfully developed by Seglen [42]. Alarge yield of metabolically competent hepatocytes with normal morphologycould be obtained, when this procedure was used and followed by incubationin oxygenated albumin-supplemented medium. Modifications of this methodare now used routinely in the field of drug metabolism.


As with liver slices, freshly isolated hepatocytes are useful for studyingmetabolism of drugs, because phase-I and phase-II enzymes are present,along with physiological concentrations of their respective cofactors [39–41][43]. On the other hand, unlike liver slices, the exact drug concentrationreaching the cells can be accurately controlled. In addition, it is possible torecover the entire amount of drug added, which is a necessity when perform-ing kinetic analyses. As a result, there are numerous examples of kinetic dataobtained with isolated hepatocytes. For instance, the kinetic parametersdescribing the O-deethylation of ethoxybenzamide in rat liver microsomes(Km 0.378 mM; Vmax 0.124 µmol/min/g liver) and rat hepatocytes (Km 0.459mM; Vmax 0.0863 µmol/min/g liver) are comparable, after correction for non-specific binding and optimization of cofactor concentrations [44]. Similarfindings have been reported for other compounds such as imipramine anddesipramine [45] [46]. Nevertheless, at the end of the day, a major goal is thequantitative prediction of in vivo metabolic clearance from ‘scaled’ in vitrometabolism data (Fig. 3). In this regard, freshly isolated hepatocytes are prob-ably the best model for the quantitative prediction of clearance. For example,ethoxybenzamide is exclusively metabolized to salicylamide in rats, and the


in vitro Vmax and Km values (3.45 µmol/min/kg body weight and 0.45 mM)obtained with rat hepatocytes are in good agreement with the in vivo findings(3.77 µmol/min/kg body weight and 0.192 mM) [44] [47]. Similarly, Houstonand Carlile [48] have investigated 21 different drugs and have shown anexcellent correlation between the in vitro hepatic clearance values predictedusing rat hepatocytes and the hepatic clearance observed in vivo. At the sametime, Lavé et al. [49–51] have been reasonably successful at predicting hepat-ic extraction using suspensions of freshly isolated human hepatocytes. In fact,it has been proposed that drug clearance in humans can be predicted using acombination of in vivo allometric scaling data (from preclinical species) andscaled in vitro estimates of clearance using freshly prepared hepatocytes (pre-clinical species and man) [51].

4.2. Inhibition Studies

The use of isolated hepatocytes has not been restricted to predictions ofclearance. Zomorodi and Houston [52] have studied omeprazole as an inhib-itor of diazepam metabolism using rat hepatocytes and liver microsomes. Forboth the 3-hydroxylation (28 vs. 108 µM) and N-demethylation (59 vs. 226 µM)pathways, the Ki values obtained with the microsomes were higher than thosereported using hepatocytes. Under in vivo conditions, where diazepam clear-ance was measured in rats receiving infusions of omeprazole (to achieve awide range of steady-state omeprazole concentrations), the Ki value (57 µM)more closely reflected the hepatocyte data. Therefore, it was suggested thatthe lower Ki values in hepatocytes may be related to omeprazole’s high ten-dency to bind to cytosolic proteins, thus providing a cellular reservoir withinthe hepatocytes. Alternatively, an active transporter involved in the uptake ofomeprazole into hepatocytes could result in a higher intracellular omeprazoleconcentration and hence lower Ki values. Regardless of which mechanism,these results suggest that hepatocytes may prove useful as an in vitro modelfor the assessment of drug-drug-interaction potential.

4.3. Challenges

Although freshly isolated hepatocytes have proven to be a very useful toolfor drug metabolism studies, their use is limited by the length of time they areviable in suspension (3–4 hours). In fact, isolated hepatocytes lose 10% oftheir metabolic capacity every hour in suspension [53]. This limitation isespecially problematic when using human hepatocytes, because human livertissue is largely obtained as surgical waste after reduced-size or split-liver


transplantation. Moreover, the timing of liver samples is not always conven-ient for researchers to prepare hepatocytes, and liver tissue often becomesavailable in quantities too large to be used immediately (within 3–4 h).Nevertheless, the introduction of the University of Wisconsin organ solution(UW) has markedly increased the storage time of liver tissue and isolatedhepatocytes (up to 12–18 h). Olinga et al. [54] have studied the effect of coldstorage (0o) in UW solution on the metabolic capacity of freshly isolated hep-atocytes from human liver tissue. Using lidocaine, testosterone, and 7-ethoxy-coumarin as substrates, isolated human hepatocytes could be stored up to 18 h without loss of metabolic activity (phase-I and -II reactions). Cold stor-age has also been used to extend the use of rat and monkey hepatocytes fordrug metabolism studies [54] [55].

5. Cultured Hepatocytes

As stated above, a major problem associated with using human hepato-cytes has been the erratic supply of human liver tissue. Consequently, sever-al culture methods have also been established for maintaining the viabilityand functionality of hepatocytes for 2 weeks or longer. However, there arenumerous factors that can affect the functional activity of hepatocytes in cul-ture. These include medium composition, extracellular matrix components,and cell-cell interactions [56]. In addition, one of the problems associatedwith using cultured hepatocytes is the rapid loss of P450 enzymes and theirmetabolic activity. Although the underlying mechanism is not fully under-stood, it seems likely that the combination of an inability to transcribe P450genes and a failure to stabilize the preexisting P450 mRNAs may account forthe loss of activity in cultured hepatocytes [57]. Interestingly, the rapid lossof monooxygenase activity varies greatly among individual P450s [58] [59].For example, CYP2E1-protein levels in primary cultured rat hepatocyteshave been shown to decline to approximately 89% and 54% of the leveldetected in freshly isolated hepatocytes after 24 and 48 h in culture, respec-tively. At the same time, CYP2B-protein levels decrease to 46% (after 24 h)and 12% (after 48 h) of the levels in freshly isolated cells [58]. Moreover, Emiet al. [60] have reported that a unique form of P450, belonging to the CYP2Csubfamily, is expressed only in cultured rat hepatocytes, and that the level ofexpression is high in cells cultured on collagen (vs. Matrigel). These resultsstrongly suggest that the composition of the P450 pool in cultured hepatocy-tes is not reflective of the liver, and that cultured hepatocytes are not suitablefor qualitative and quantitative drug-metabolism studies, such as identifica-tion of metabolic pathways and prediction of metabolic clearance. It shouldbe noted that the loss of activity in cultured hepatocytes is not restricted to


P450s, but extends to conjugating enzymes [61]. Therefore, efforts have beenmade to identify suitable media (e.g., Chee’s medium) and hormones (e.g.,insulin and dexamethasone) that can minimize the loss of drug-metabolizingenzyme activity in culture [62] [63]. However, these efforts have achievedonly limited success, with only some enzyme activities being preserved.

5.1. Induction Studies

Although the rapid loss of P450 in cultured hepatocytes is a major prob-lem for in vitro drug-metabolism studies, this does not present a problemwhen they are used for the evaluation of drugs as inducers. Cultured hepato-cytes have, therefore, become a popular in vitro system for the assessment ofP450 induction. In drug therapy, there are two major concerns related toenzyme induction. Firstly, induction may reduce the efficacy of therapeuticagents by increasing drug metabolism. Secondly, induction may result in anundesirable imbalance between the toxification and detoxification of drugs[64]. Therefore, knowing that a compound is an enzyme inducer is veryimportant at the drug-discovery stage.

In the past, the potential of enzyme induction has always been assessed bycomparing enzyme activity in laboratory animals before and after chronic drugadministration. However, these animal studies have limited value, as a resultof the well-known species differences in the response to inducers. For exam-ple, omeprazole is a CYP1A2 inducer in humans, but has no inductive effectin mice or rabbits. Similarly, rifampin is a CYP3A4 inducer in humans, butdoes not induce P450s in rodents [1]. Consequently, many investigators haveattempted to evaluate induction potential using primary cultures of humanhepatocytes. This approach is exemplified by the work of Li et al. [65], whocompared the induction potential of rifampin, rifapentine and rifabutin. Theresults consistently showed that rifampin and rifapentine were potent inducersof CYP3A4, while a significantly lower induction potential was observed forrifabutin. Furthermore, the relative induction potency of the three antimicrobi-als (rifampin > rifapentine >> rifabutin) was consistent with clinical findings.

Species differences in CYP3A induction caused by rifampin have alsobeen predicted using cultured hepatocytes. That is, rifampin caused a markedincrease in CYP3A4 protein in cultured human hepatocytes, but was not aninducer in cultured rat hepatocytes [66]. In the same study, Silva et al. [66]also showed that cultured rat hepatocytes could be used to predict enzymeinduction. Dexamethasone caused a marked increase in CYP3A1/2 protein incultured rat hepatocytes, while phenobarbital significantly increasedCYP2B1/2 enzymes. The results were in agreement with the in vivo findings.The study was also expanded to include a series of 13 structurally related


compounds, whereby each compound was evaluated both as an in vivo induc-er of rat P450s and as an inducer of the corresponding P450s in the culturedrat hepatocytes. To obtain a maximal effect, hepatocytes were treated with 50 µM of each compound, and in vivo doses of 400 mg/kg/day (for 4 days)were chosen. On the whole, the in vitro induction of CYP3A1/2 and CYP2B1/2correlated reasonably well with the induction observed in vivo.

It should be emphasized, however, that hepatocytes in culture are notalways a faithful model for predicting induction and should only be used inscreens for the qualitative assessment of compounds. A close examination ofthe results by Silva et al. [66] revealed that the cultured rat hepatocytes failedto predict the in vivo induction for 4 out of the 13 compounds (30%). In addi-tion, in vitro cell-culture conditions can also have a significant impact oninduction. For example, an approximately 50-fold increase of CYP3A1/2 pro-tein has been observed in hepatocytes cultured on collagen. By comparison,a 10-fold increase in CYP3A1/2 protein is observed with Matrigel as the sub-stratum. Conversely, CYP2B1/2 can be induced approximately 50-fold incells coated on Matrigel, while induction of CYP2B1/2 is markedly less [66].These examples illustrate the difficulty of ‘quantitative’ predictions and sug-gest that screening should be based on a relative rank approach [66].

6. Cryopreserved Hepatocytes

Although cultures of primary hepatocytes are viable, for the sake of con-venience, various groups have sought to establish banks of cells. This hasbeen made possible with advances in cryopreservation, since the process ofcryopreservation permits long-term storage (≥ 8 months) of cells in liquid N2

[67] [68]. After thawing, total recovery of cryopreserved rat and mouse hep-atocytes has been reported to be approximately 50% (vs. fresh hepatocytes),with high viability (as determined by trypan-blue exclusion) and functional-ity (measured by neutral-red uptake and protein synthesis) [68]. Similarly, agood recovery (>75%) of viable cells after thawing has been reported forcryopreserved human hepatocytes [69]. In the same study, hepatocytes werestored for a few weeks to 4 years, with only a small fraction of the cells beinglost (<25%) and with minimal loss of cell viability and function.


Cryopreserved hepatocytes are now commercially available, and theirmetabolic capacity has been evaluated with numerous compounds [67] [70].In the case of testosterone, P450-dependent metabolism (metabolic profile


and rate of hydroxylation) in cryopreserved rat hepatocytes is comparable tothat observed with freshly isolated hepatocytes. For benzo[a]pyrene, oxida-tive metabolism is unaffected by cryopreservation, while conjugation isreduced to about 50–60% (vs. freshly isolated cells). Thus, the ratio betweenoxidative metabolites and conjugates can be altered by cryopreservation.Similar observations have been reported by Swales et al. [68]. The effect ofcryopreservation on drug metabolism has also been determined in humanhepatocytes from five organ donors [69]. In most cases, phenacetin O-deeth-ylase activity was decreased in the cryopreserved human hepatocytes, where-as procainamide N-acetylation and acetaminophen conjugation (sulfation andglucuronidation) activity was increased. Overall, these data indicate that it ispossible to generate metabolic profiles with cryopreserved hepatocytes thatare qualitatively (but not quantitatively) similar to those acquired with fresh-ly isolated hepatocytes. Nevertheless, data obtained at Merck (Fig. 4) and by


Fig. 4. Predicting clearance in man using (A) human liver microsomes and (B) cryopreservedhuman hepatocytes. Compounds were singularly incubated with human liver microsomes (0.5 mg protein/ml) or hepatocytes (2106 viable cells/ml), and the time required to consumeparent drug by 50% was determined (LC/MS). Each compound was incubated at a low concen-tration (0.5 µM), assuming that drug consumption was a first-order process ([S] < Km), and thedata were not corrected for non-specific binding (fu,inc = 1). Some of the compounds (1–3) werehighly basic and/or were characterized by a large volume of distribution (> 10 l/kg). The dashed

lines indicate data points that reside within 2-fold of the line of identity (solid line).

others [67] suggest that cryopreserved hepatocytes may prove useful in rou-tine higher-throughput metabolic stability screens.

6.2. Induction Studies

The induction of CYP1A, CYP2B, CYP3A and CYP4A by NF, pheno-barbital, dexamethasone, and clofibric acid, respectively, in cryopreserved rathepatocytes has been evaluated and compared with fresh hepatocytes [71].Madan et al. [71] were able to obtain a total cell recovery and viability of68% and 85%, respectively (vs. freshly isolated hepatocytes). However, themagnitude and specificity of the inductive response to the prototypicalenzyme inducers was similar to that of freshly isolated hepatocytes, suggest-ing that cryopreservation had little effect on the inductive processes. For com-parison, induction of the same rat P450s by NF, phenobarbital, dexametha-sone, and clofibric acid was also evaluated in vivo. Although the absoluteenzyme activities (expressed as pmol/min/mg microsomal protein) weremuch lower in both fresh and cryopreserved hepatocytes, the fold increase inactivity and the pattern of induction were similar to that observed in vivo.Thus, it was concluded that under the conditions examined, cryopreservedhepatocytes appeared to be a suitable in vitro system for evaluating xenobio-tics as inducers of P450. Although the results of this study are promising, itremains to be seen whether cryopreserved hepatocytes will serve as a usefulscreening tool [67] [71].

7. Subcellular Fractions (Focus on Liver Microsomes)

With the advent of differential centrifugation methods, it has become pos-sible to homogenize native liver tissue and to obtain various subcellular frac-tions, such as S-9 (microsomes and cytosol), cytosol (105 000 g supernatant)and microsomes (105 000 g pellet) [72] [73]. Although some drug-metaboliz-ing enzymes are found in mitochondria (e.g., monoamine oxidase, xanthineoxidase, and aromatase), most are located in the cytosol (e.g., sulfotransferas-es, glutathione S-transferases, aldehyde oxidase, N-acetyltransferase (NAT),carbonyl reductases) and microsomal fraction (e.g., P450, UDPGT, carbonylreductase, epoxide hydrolase, FMO). Because these fractions can be storedfrozen, they serve as a very convenient source of enzymes for drug-metab-olism studies and have become widely available. For instance, there is noappreciable loss of human liver microsomal P450 activities after prolongedstorage (at least 2 years) at –80° [74]. As a result, it is now common for dif-ferent laboratories to have access to a ‘bank’ (N > 10 different organ donors)


of liver microsomes and other subcellular fractions [74–76]. However, inter-laboratory comparisons of assay methods have been limited in number [77].

Historically, numerous investigators have used subcellular fractions as ameans of evaluating species-, organ- and organelle-dependent differences(both quantitative and qualitative) in drug metabolism. In many cases, metab-olism data from preclinical species (in vitro and in vivo) have been comparedto in vitro human data in order to predict overall metabolic profiles in man,determine the ratio of various metabolites, and evaluate enantio-, regio-, andstereoselective biotransformations [27] [72] [78–80]. In general, however,most investigators accept that results need to be interpreted cautiously, becausemany factors such as thermal lability (e.g., FMO), latency (e.g., UDPGT), orenzyme loss during tissue processing (e.g., human and dog aldehyde oxidase)can greatly complicate data interpretation [28] [30] [75]. Therefore, it is advis-able to use well-characterized subcellular fractions [72] [75].

7.1. Metabolic Stability Screening and Prediction of Clearance

Attempts have also been made to predict in vivo clearance using hepaticS-9 and microsomes [81–85]. Typically, kinetic parameters (Km and Vmax) aredetermined under linear conditions (with respect to time of incubation andprotein concentration), and the resulting Vmax/Km ratio (intrinsic clearance) is‘scaled’ to give estimates of intrinsic clearance (ml/min/kg body weight) andhepatic clearance (Fig. 3). In some instances, however, it is possible that invitro kinetics are not adequately described by classical Michaelis-Mentenkinetics, as a result of autoactivation, partial inhibition, substrate inhibition,or biphasic saturation curves, which can give rise to erroneous estimates ofintrinsic clearance [86] [87]. Despite the potential pitfalls, a number of groupshave reported that the majority (~75%) of their predictions are successful andare within 2-fold of the observed value [81–85].

More recently, it has been proposed that intrinsic clearance can be deter-mined without the need for time-consuming estimates of Km and Vmax [88].That is, the disappearance of parent compound in liver microsomes is meas-ured at low concentrations (1 µM), under the assumption that parent-drugconsumption is a first-order process (Fig. 4). As a result of the large numberof compounds, the availability of 96-well microplate technology, automatedliquid-handling systems, and improvements in LC/MS, it is envisioned thatmost pharmaceutical companies will conduct their in vitro metabolic stabilityscreens using similar approaches [2] [89] [90]. Nevertheless, it is important todistinguish between simple ‘screening’ and ‘prediction of pharmacokinetics’.In the case of the latter, one has to realize that it is often difficult to study cou-pled (phase-I and phase-II) metabolism in subcellular fractions, which means


that the metabolic profile and the overall rate of metabolism is cofactor-dependent. In the worst case, reactions such as N-glucuronidation of parentdrug are overlooked when using NADPH-fortified liver microsomes.Conversely, P450-dependent metabolism is ignored in liver microsomes for-tified with UDPGA. In this regard, primary hepatocytes are advantageous. Anadditional problem is the non-specific binding of drug to liver-microsomalprotein, the magnitude of which can be compound-specific [88]. Overall, thestrategy used to predict clearance in vivo may depend on whether a compoundis basic, neutral, or acidic (i.e., positively or negatively charged, oruncharged, at pH ~7.4).

7.2. Reaction Phenotyping

The availability of enzyme-specific reagents (e.g., chemical inhibitors andantibodies) has also made it possible to ‘reaction phenotype’ compounds usingsubcellular fractions, i.e., determine which enzyme(s) is(are) involved inmetabolism. For instance, FMO-catalyzed N-oxidation can be readily distin-guished from P450-dependent metabolism by incubating the test drug withmethimazole (FMO inhibitor) or clotrimazole (general P450 inhibitor) [28][29]. In particular, the human liver-microsomal P450 system has been wellcharacterized and the P450 enzyme(s) involved in the metabolism of com-pounds can be readily determined using P450-form-selective chemical inhibi-tors or immunoinhibitory antibodies [91–94]. Expectedly, the number of P450‘reaction phenotyped’ compounds continues to grow, and many pharmaceuti-cal companies are now submitting the data to regulatory agencies [91]. Forhigh-extraction compounds in particular, it is very important to determine ifmetabolism is catalyzed by a single P450 (e.g., CYP3A4), because one mightanticipate significant drug-drug interactions with potent inhibitors of theenzyme (e.g., ketoconazole). In addition, if a polymorphically expressed P450(e.g., CYP2D6, CYP2C9, or CYP2C19) contributes to a significant portion ofthe P450 reaction phenotype in human liver microsomes (> 30%), then phar-macokinetic studies with genotyped subjects may be warranted. At the presenttime, many compounds that are primarily metabolized by polymorphicallyexpressed P450s are screened out, which indicates that P450 reaction pheno-typing is being performed earlier in drug discovery [2] [91].

7.3. Inhibition Studies

Similarly, the availability of P450-probe substrates has also greatly facil-itated inhibition studies with human liver microsomes [75] [91] [92] [95] [96].


One can now evaluate new drug entities as inhibitors by studying their effecton human liver-microsomal CYP2C9 (e.g., tolbutamide hydroxylase),CYP2C19 (e.g., S-mephenytoin 4′-hydroxylase), CYP2D6 (e.g., dextrome-thorphan O-demethylase), CYP3A4/5 (e.g., testosterone 6-hydroxylase),CYP1A2 (e.g., phenacetin O-deethylase), CYP2C8 (e.g., taxol 6-hydroxy-lase), CYP2A6 (e.g., coumarin 7-hydroxylase), and CYP2E1 (e.g., chlor-zoxazone 6-hydroxylase) activity. In each case, the test compounds are singu-larly dissolved in an appropriate solvent [97] [98] and the IC50 for each activ-ity (concentration of inhibitor required to inhibit activity by 50%) is deter-mined at a predefined concentration of substrate ([S]/Km≤1.0). If possible,the test compounds are run in parallel with suitable positive controls (e.g.,ketoconazole, CYP3A4; sulfaphenazole, CYP2C9; quinidine, CYP2D6;CYP1A2 and CYP2C19, fluvoxamine) and in many instances will bescreened out based on their in vitro IC50 values alone. For example, potentreversible inhibitors of CYP3A4 (IC50 < 1 uM) are terminated, and the screen-ing data are simply used to establish an SAR [2] [99]. This appears to be acommon practice, as many laboratories are switching to higher-throughput(non-liquid chromatographic) inhibition screens [100–102]. Inhibition studieswith human liver microsomes are particularly useful, because the results canbe evaluated in light of P450 reaction phenotyping and metabolic stabilitydata in the same preparations (Fig. 2).

As in the case of metabolic stability studies, one has to differentiatebetween simple IC50-based ‘screening’ and the ‘prediction’ of drug-druginteractions. Predictions ostensibly require accurate estimates of Ki, which isdetermined using multiple concentrations of inhibitor and substrate.However, many investigators have acknowledged that even semi-quantitativepredictions are challenging and have reported examples of failures as well assuccesses [95] [103–105]. From an in vitro standpoint, care has to be taken tominimize the non-specific binding of the inhibitor to microsomal protein andto clearly distinguish between reversible and ‘preincubation-dependent’(mechanism-based) inhibition [103]. On the other hand, the extrapolation toin vivo requires some knowledge of pharmacokinetics, namely the plasma (ortissue) levels of the inhibitor ([I]) and the fraction of the dose (substrate only)that is metabolized by the inhibited pathway. Therein lies the problem,because some investigators have used the concentration of total (free andbound) inhibitor in plasma, others have employed the free concentration ofinhibitor in plasma, while some have opted to use a liver/plasma partitionratio in their estimates of [I]/Ki in vivo [95] [103–106]. Therefore, it will cer-tainly take some more time before investigators agree upon the best approachto predicting metabolic drug-drug interactions.


8. cDNA-Expressed Drug-Metabolizing Enzymes

The recent explosion in recombinant technology has afforded researchersthe ability to perform in vitro metabolism studies with in-house or commer-cially available preparations of heterologously expressed drug-metabolizingenzymes. These enzymes can be used in the form of ‘transgenic cell’ lines(e.g., HepG2, COS, and V79), enriched membrane fractions (e.g., from B-lymphoblast, E. coli, Sf9, or T. ni cells) or purified protein (Fig. 1). The avail-ability of cDNA-expressed proteins is not restricted to the human liver-micro-somal P450 system, but includes P450s and a variety of other drug-metabo-lizing enzyme systems (e.g., FMO, NAT, glutathione S-transferase, sulfo-transferases, and UDPGT) from a plethora of tissues, organelles, and animalspecies [30] [107] [108]. Recent publications describing the cloning and het-erologous expression of various canine (e.g., CYP2D15 and CYP3A12) andmonkey (e.g., CYP2D17 and CYP1A2) P450s are of particular interest tomost pharmaceutical companies, because these two species are commonlyused in the toxicological evaluation of compounds [109–112].

8.1. General Applications

Many of the mammalian drug-metabolizing enzyme systems are com-plex, comprising of at least two different enzymes, or ‘isoforms’, that ofteninteract differentially with drugs, and whose expression is under the controlof numerous genetic and environmental factors. Therefore, one of the attrac-tive features of recombinant technology is that one can greatly ‘simplify’drug-metabolism studies and unambiguously assign the results to a particularform(s) of the enzyme (Fig. 3). This is very important when attempting tostudy a particular biotransformation in the absence of competing enzymesand reactions [30]. Although useful, it has to be acknowledged that systemscontaining cDNA-expressed enzymes are artificial, because the enzyme is notpresent in its native environment and is often ‘over expressed’ [30] [91]. Forthe P450s, in particular, this may be an important consideration, since mem-brane fractions containing the cDNA-expressed enzyme differ considerablyfrom native liver microsomes [30] [91]. This means that one has to be con-cerned with differences in membrane composition, the lipid-to-protein ratio,non-specific binding, and the ratio of NADPH-P450 reductase to P450 andcytochrome b5. Moreover, many P450s (e.g., CYP2D6) are often heterolo-gously expressed at levels (≥160 pmol/mg) that far exceed those present innative liver microsomes (≤10 pmol/mg). Consequently, one has to use well-characterized cDNA-expressed enzymes and, as much as possible, relate thedata to the levels of protein and activity in native tissue [91].


Over time, heterologously expressed enzymes have found numerousapplications, ranging from bioreactors to toxicity testing [30] [107] [108][113] [114]. For example, recombinant P450s have been used to verify theusefulness of P450-form-selective substrates, chemical inhibitors, and anti-bodies [30] [91] [94] [97] [115]. In addition, it is now possible to use recom-binant P450s to confirm inhibition (Ki and IC50) and kinetic (Km and Vmax)data obtained with native liver microsomes [30] [107] [116]. However, insome cases, it is expected that the apparent Km and Ki values will vary con-siderably between native tissue and recombinant enzymes [30] [91]. Similarstudies have been performed with other enzyme systems such as FMO, NAT,and UDPGT [117–120]. In fact, the availability of cDNA-expressed NAT2,FMO3, and UGT1A1 has allowed investigators to determine that these pro-teins represent the primary loci of well-established polymorphisms [118][121] [122]. At the same time, with the aid of various recombinant human-liver CYP2C proteins, it has been possible to ascribe polymorphic (S)-mephenytoin 4′-hydroxylase activity to CYP2C19 (see [30] and refs. citedtherein).

8.2. Structure-Function Studies

Recombinant drug-metabolizing enzymes have also become powerfultools for use in mechanistic studies. Recently, Perret and Pompon [123] stud-ied the effect of cytochrome b5 on the uncoupling of monooxygenase activitycatalyzed by purified cDNA-expressed CYP3A4, while Korzekwa et al. [87]were able to evaluate the non-hyperbolic kinetics of a number of substratesusing CYP2C9 and CYP3A4 expressed in HepG2 cells. Similarly, Kenworthyet al. [124] carried out inhibition studies using B-lymphoblast microsomescontaining cDNA-expressed CYP3A4 and evaluated the suitability of ten dif-ferent substrates.

In the absence of X-ray crystallography data, researchers have had toemploy a number of strategies to obtain putative structural information that ispertinent to human drug-metabolizing enzymes. For the P450s, some structu-ral models have been based on amino-acid-sequence alignments – ‘homolo-gy modeling’ – between human P450s (e.g., CYP3A4 and CYP2D6) and non-mammalian P450s (e.g., CYP101 and CYP102) for which high-resolution X-ray-diffraction data are available [125–128]. A second approach has been toconduct metabolism studies with the heterologously expressed products ofsite-directed mutagenesis, so that the amino-acid residues required for catal-ysis, inhibition, substrate binding, protein folding, or interaction with auxil-liary proteins (e.g., cytochrome b5) can be identified. For instance, Halpertand co-workers [129] [130] have used the recombinant forms of mutated


CYP3A4 to investigate the amino-acid residues that influence cooperativity,substrate oxidation, and activation by -naphthoflavone. Likewise, Junget al. [131] were able to locate three amino-acid residues that conferred high-affinity binding of sulfaphenazole to mutant CYP2C19. In native micro-somes, and when incubated with wild-type recombinant CYP2C9 andCYP2C19, sulfaphenazole is highly selective for CYP2C9 (IC50 < 5 µM) anddoes not inhibit CYP2C19 (IC50 > 100 µM). Overall, this type of informationis useful, and many attempts will be made to evaluate it in light of existingmolecular-orbital calculations, homology and pharmacophore models[131–135]. In due course, heterologous expression systems will be used togenerate sufficient quantities of holoenzyme, so that suitable crystals can beprepared as a prelude to X-ray-diffraction studies.

8.3. Screening

Presently, the widespread use of recombinant drug-metabolizing enzymesis limited to enzyme reaction phenotyping and high-throughput inhibitionscreens (Fig. 2), although it is only a matter of time before transgenic cell-based high-throughput enzyme-induction screens also become available[136] [137]. For P450 inhibition (IC50) screening, it has been proposed thatone can use cDNA-expressed P450s and a broad range of fluorescent-probesubstrates in a high-throughput format [107] [138] [139]. The approach hascertain advantages, because one is able to minimize sample processing,employ semi-automated liquid-handling stations, and use rapid 96-well-platefluorescence readers (Fig. 5). In addition, the issue of substrate specificity isavoided when using singularly expressed recombinant P450s. However, aswith human liver microsomes, it is important to use fully characterized assaysand prospectively evaluate the kinetics of the substrate, the effects of solventsand the potency of positive controls. In the case of P450 reaction phenotyp-ing, one can conduct the studies in an integrated manner, whereby humanliver-microsome data are directly related to the turnover rates obtained withthe recombinant proteins. Towards this end, the immunoquantiation of thevarious P450s in native liver microsomes has proven to be an important mile-stone, and numerous strategies for data integration have been reported [91][107] [140–142]. Moreover, the usefulness of recombinant P450s extends tothe reaction phenotyping of compounds that are metabolized by members ofthe same subfamily (e.g., CYP3A4 vs. CYP3A5; CYP2C19 vs. CYP2C9 andCYP2C8), and the evaluation of allelic variant forms (e.g., CYP2C9*1 vs.CYP2C9*3). The latter has proven to be important because of the paucity ofliver tissue from genotyped subjects [30] [143] [144].


9. Conclusions

During the 1990s, researchers in the field of drug metabolism have prof-ited from recombinant technology, the increased availability of human tissue,automation, and analytical instrumentation. As a result, a number of in vitrodrug-metabolism models are now available, which can be utilized at all stag-es of the drug-development process [1] [2]. Therefore, it is possible to opti-mize the drug-metabolism profile of compounds and to obtain clinically rel-evant human drug-metabolism data prior to dosing in man. However, the cor-rect application of these models necessitates that they are validated and thattheir strengths and weaknesses are fully characterized (Table 1). This isimportant, because problem solving often necessitates the integrated use ofmultiple models [2] [30] [75] [91] [95]. Unfortunately, it is obvious from adetailed review of the literature that a considerable amount of progress is stillrequired before the various in vitro approaches are fully validated and stan-


Fig. 5. Screening for CYP3A4 inhibition using a semi-automated assay. RecombinantCYP3A4 (rCYP3A4) is incubated with substrate (trifluoro(benzyloxy)coumarin; BFC) and therate of O-dealkylation (to yield a fluorescent product) is measured in the absence or presenceof inhibitor. Incubations are performed in 96-well plates, with a Genesis RSP 150 (Tecan) as

the liquid-handling system. Examples of Merck data are shown.

dardized. These same issues will also apply to the in vitro-in vivo correlationsused to predict pharmacokinetics, metabolic profiles, and metabolism-baseddrug-drug interactions [1] [2] [103]. Towards this end, it may be necessary todevelop physiologically-based pharmacokinetic models that incorporate invitro drug-metabolism, transporter, and in silico (computational) data [3][145] [146].

As the 1990s have drawn to a close, most pharmaceutical companies haveturned to genomics and accelerated compound synthesis as a means of rapid-ly generating increased numbers of therapeutic targets and lead compounds[2–7]. These events have posed additional challenges and have forced manydrug-metabolism investigators to switch from ‘problem solving’ to higher-throughput (e.g., 96-well) ‘screening’ paradigms. Because progress to fullyautomated HTS (e.g., 384-well) is inevitable, it is anticipated that largeamounts of information will be generated in the course of HTS and thatmetabolism investigators will have to become adept at using databases.Proficiency in data reduction, data modeling, and pattern recognition will alsobecome a necessity [2] [147–149]. Nevertheless, it will be important to per-form these drug-metabolism screens in a rational issue-driven manner, byopting to use the most effective combination of HTS and non-HTS approach-es [1] [2].

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Addressing Toxicological Issues in the Lead-Optimization Phase of Drug Discovery

and Development

by Philip Bentley

Novartis Pharmaceuticals Corporation, 59 Route 10, East Hanover, New Jersey 07936, U.S.A.;Fax: +1 973-781-37 57; e-mail: [email protected]

1. Introduction

The advent of combinatorial chemistry and the availability of rapidscreening methods to detect compounds with desired pharmacological prop-erties have altered the dynamics of the drug-discovery process. The greatdiversity of potential lead structures generated in the early research phaseresults in a desire for methods to rapidly screen for undesirable effects, andfor computer models to determine structure-toxicity relationships.

This new situation presents a tremendous challenge to toxicologists work-ing in drug discovery. The number of potential toxicological effects is almostas great as that of pharmacological endpoints, and developing screens for theplethora of toxicities is essentially impossible. Toxic effects may be broadlyclassified into a few distinct categories (Table 1).

Each of these areas is addressed during the toxicology assessment of anew drug candidate either before the first clinical trials or subsequent to more

Table 1. Classification of Toxic Effects

Genotoxicity The ability of a compound to induce mutations or chromosomal damage, generally by a direct effect upon DNA.

Carcinogenicity The ability of an agent to induce tumors.

Reproductive toxicity Effects on the reproductive process often with a specific emphasis onthe ability of a compound to induce fetal malformations (teratogenicity).

Target organ toxicity The induction of damage in specific organs as a result of systemicallyavailable drug or metabolites.

Local toxicity Direct effects of a compound at the site of application, e.g., dermalirritation.


prolonged clinical trials. The challenge is to devise methods to screen forundesired effects in the early stages of drug discovery. In some of the areas,screening methods are available which permit rapid detection of the ability ofa developmental compound to elicit specific toxic effects (e.g., genotoxicity).However, screening for target organ toxicities remains problematic. This ispartly because the mechanisms by which compounds elicit toxicities are oftenpoorly understood, making mechanism-based screening a very difficult, if notimpossible, task.

Toxic interactions may be a consequence of the chemical structure of acompound or its metabolites. In some cases they result from an exaggerationof the desired pharmacological activity, e.g., the induction of mesovarialleiomyomas in rodents by -agonists; they may also result from unspecific‘pharmacological’ interactions due to the lack of specificity of a compound,e.g., -1 activity of -2 agonists; or they may result from unintentional inter-actions at targets which are not related to the pharmacological activitythrough either covalent or non-covalent reactions (e.g., enzyme inhibition,membrane perturbation, covalent binding to proteins, oxidative stress, etc.).These latter types of toxicity may be more readily evaluated in in vitro mod-els. If metabolism is required, the situation becomes more complex, necessi-tating either an intrinsic metabolic activity within the in vitro system or theinclusion within the screen of a metabolic activating system. Toxicities result-ing from excess pharmacological activity or inadvertent pharmacologicalactivity are more difficult to screen for, particularly in the latter case with newchemical classes.

It should also be borne in mind that screens are only useful when used todetect toxicities, which would result in termination of drug development.

Most pharmacological agents also possess toxicological activity, thus,generally the emphasis during toxicological assessment of pharmaceuticals istowards risk assessment and the determination of therapeutic margins, ratherthan eliminating a toxic risk. In this respect, screening is particularly useful ifmarketed drugs in a therapeutic class are known to have a certain toxicity, theavoidance of which would lead to a market advantage. In such cases, veryspecific toxicity screens may be developed (see below).

2. Genotoxicity

Genotoxicity is associated with both a carcinogenic and a mutagenic risk.Consequently, it is appropriate to eliminate genotoxic agents as early in thedrug-discovery process as possible. Bacterial mutagenicity testing has longbeen used to screen agents [1], but it is now recognized that testing in mam-malian cells is also required. Thus, a standard genotoxicity test battery would


include in vitro tests for bacterial mutagenicity, mammalian-cell chromoso-mal effects (clastogenicity), and in vivo tests in rodents [2]. The tests used forregulatory assessment are generally not suitable for rapid screening, but mod-ified methods are available which allow high-throughput screening for bothgenotoxic endpoints. Bacterial tests include modified Ames tests [3] or otherbacteriological assays (e.g., assessing reversion of dark mutations of theluminescent bacteria Vibrio fischeri [4]). For a medium-throughput screen,the standard Ames test may be used with assessment limited to 2 or 3Salmonella typhimurium strains to limit resource requirements.

The advent of the in vitro micronucleus assay has permitted more rapidscreening for chromosome-damaging compounds than standard cytogenetics,and the process may be automated or even adapted for rapid screening [5].

Computer models are also available to screen potential lead structures forgenotoxic potential [6][7]. The models are limited by the databases uponwhich they are based and cannot readily predict metabolic processes.Nevertheless, they have improved significantly over the last few years andcan be used as a guide during the conceptual phase of a discovery program.

3. Carcinogenicity

About 50% of the compounds tested in carcinogenicity studies induce anincreased incidence of specific tumors in animals [8]. In most cases, the find-ings can be explained, and a carcinogenic risk to humans can be excluded [9].However, for various reasons, early screening for carcinogenic activity wouldbe advantageous.

1. Carcinogenicity testing is generally performed late in the drug-devel-opment process. (Results are generally not available before Phase-IIItrials are initiated.) At this stage, termination of a project is very expen-sive in terms of lost development costs.

2. Studies to explain the mechanism of carcinogenicity are often time-consuming. Since the results of the carcinogenicity studies are usuallyonly available shortly before intended registration submission, thenecessity for such studies can delay registration, with correspondingloss of income and often subsequent market share.

3. Even though mechanistic studies may indicate that there is no carcino-genic risk to patients during therapy, the marketplace perception maybe otherwise, particularly if drugs in the same therapeutic class aredevoid of carcinogenic activity.

Many carcinogens are genotoxic, and genotoxicity screens obviously caneliminate such compounds from development. However, there is a growing


number of non-genotoxic compounds which induce an increased incidence oftumors during carcinogenicity studies. Screens for such compounds are avail-able (e.g., inhibition of cell-cell communication, inhibition of apoptosis).However, although such screens may be of some limited use within specificcompound classes, their general applicability is questionable. Screening fornon-genotoxic carcinogens is accompanied by problems very similar to thoseassociated with screening for target organ toxicity:

– the endpoints are very varied,– the mechanisms are diverse and poorly understood,– screening would involve a relatively large test battery of questionable

relevance.

4. Reproductive Toxicity

Screening methods are available to detect effects on the testis [10] andembryonic development [11][12]. However, testicular toxins are not so com-mon that general screening for such effects is required. The most reliablesystem for detecting effects on the embryo is the whole-embryo culturesystem [11] in which post-implantation rat embryos are cultivated for 2–3days in the presence of test compounds. This system requires removal offetuses from pregnant animals and is, consequently, relatively laborious,which limits its use for general early screening. It is also very sensitive, flag-ging many compounds which subsequently were not shown to induce terato-genic effects in vivo. However, it may be used very successfully within com-pound classes with known teratogenic potential to screen for those com-pounds which are least likely to have such effects [13]. The cellular assays(e.g., micromass) have similar problems regarding specificity and are conse-quently also most useful for screening within classes with known teratogenicrisk, and for determining structure-toxicity relationships.

5. Target Organ Toxicity Screening

This presents the greatest challenge in screening for adverse effects invitro. Many processes are known to be involved in toxic events. However, ingeneral, there is a poor understanding of the factors which determine targetorgan specificity, and of the molecular events underlying many toxic respons-es. In the absence of such understanding, mechanism-based high-throughputscreening is not realistic. Methods have been described for the cultivation ofcells from most potential target organs, for example bone marrow, liver, kid-ney, lung, heart, central nervous system, skeletal muscle, and testis. Some


toxic endpoints, which can be assessed in cell culture, are shown in Table 1.Alternatively, tissue slices from many organs may be used to assess toxicityin vitro. In this case, histopathological or cytological endpoints may also beused [14]

Despite this plethora of possibilities, there is no standard test battery toassess the potential of a compound to induce specific target organ toxicities,and such an approach could not be recommended. Although it would be fea-sible to establish a test battery, the lack of validation would mean that therewas a very high risk of losing potentially valuable compounds without elim-inating all of those with toxicological problems. Cell lines are available withreporter genes assessing many of the end-points listed in Table 2, but theseshould be used with caution and never in isolation [15]. If such screening isperformed, a battery of tests should be used to give a toxicological fingerprintof the compound.

A more rational approach to target organ toxicity screening is depicted inFig. 1. Compounds in chemical or pharmaceutical classes with known toxic-ities could be screened for those toxicities in specially designed in vitro tests.Such tests can be established for the specific problem and validated with thecompounds with the known toxicities. For all other chemical classes, the toxicprofile of lead candidates is first determined in limited in vivo assays. Suchin vivo tests, generally in rats or mice, use smaller numbers of animals thanroutine regulatory toxicity studies (e.g., 5 animals of one sex per dose groupand only 2 different doses) and are designed for rapid assessment of potentialtarget organs. Once such toxicities have been determined, specific in vitroscreens are developed which mimic the in vivo effects, and are used to selectdrug candidates with a more appropriate toxicological profile. In this manner,more specific in vitro screening may be developed. The tests need only be val-idated with the compound class being used, and the endpoint selected needonly be appropriate for the toxicity being examined. In this manner, a variety


Table 2. Some Toxicologically Relevant Endpoints which Can Be Assessed in Cell Culture

• Alteration of ADP/ATP ratios • Enzyme leakage• Alteration of calcium homeostasis • Formation of inclusion bodies• Alteration of signal transduction • Induction of apoptosis• Cell adhesion • Induction of DNA synthesis• Cell division • Induction of stress genes or heat-shock genes• Cell-cycle perturbation • Inhibition of DNA synthesis• Chemotaxis • Inhibition of mitochondrial oxidation• Cytokine release • Membrane perturbation• Cytoskeletal changes • Organelle proliferation (e.g., peroxisomes, • Cytotoxicity SER, RER, mitochondria)• Enzyme induction • Oxidative stress

of screening systems may be in use simultaneously for different drug discov-ery programs. This approach permits optimization of lead compounds in a veryspecific manner, without the risks involved in random screening. However,even limited in vivo studies require significant quantities of drug candidate, sothe resources spent in chemical synthesis are much greater.

6. Local Toxicity

The screening for irritation potential, particularly dermal irritation, is veryimportant for industrial and agrochemicals, but of limited value for pharma-


Fig. 1. Screening for toxic effects. Compounds in the lead-finding stage of drug discovery arescreened in a high-throughput mode for the desired pharmacological effects. Compounds show-ing the desired profile are then screened for genotoxicity, initially in bacterial assays, followedby an in vitro micronucleus assay. In most indications, genotoxic compounds can then beexcluded, and only compounds negative in both tests are put forward for further evaluation ofthe toxicological potential. At this point, the preferred option would be to profile lead com-pounds in new chemical classes in limited in vivo studies to identify target organs for toxicity.Once such target organs have been identified, specific screens are used for all compounds inthe class to select the least toxic for this end-point. Alternatively, a battery of cell lines with spe-cific marker genes might be used to select compounds with the most appropriate toxic ‘fingerprint’. However, with the present state of knowledge, it is not clear which ‘fingerprint’

would be the most advantageous.

ceuticals, except for dermatological use of transdermal applications. In suchcases, very good models for dermal irritation are available [16]. These canreadily be adapted to a medium-throughput mode and could be used for sec-ondary screening in the lead-finding phase of discovery (i.e., after efficacyand genotoxicity screening).

7. Emerging Technologies

7.1. In Silico Approaches

Computer models for the evaluation of potential toxicities (e.g., Topkat,DEREK, ToxSys) are continually being refined and improved. They are use-ful in certain areas of toxicity assessment (e.g., predicting local tolerabilityand sensitization, and for initial screening during the conceptual phase of newdrug synthesis so that toxic moieties may be avoided as far as possible (forfurther details, see chapter by ter Laak and Vermeulen, p. 549).

7.2. Gene-Array Assays

The advent of chip-array technology, which permits the assessment ofalterations of gene expression during a toxic event, will significantly increaseour understanding of target organ toxicities. Such methods, which allow theexpression of several thousand genes to be monitored, will most probablyresult in the recognition of patterns of altered gene expression which are pre-dictive of certain toxicities. In such cases, it would be possible to developspecific assays to recognize compounds inducing such ‘toxic profiles’. Withthis knowledge, in vitro technology could make significant breakthroughssuch that rapid in vitro screening for limiting target organ toxicities maybecome a reality [17][18].

7.3. Biomarkers of Toxicity

Advances in proteomics and high-resolution NMR analysis of body fluidswill lead to recognition of previously unknown biomarkers. NMR Analysis ofurine from treated rats can already predict certain hepatic, renal, and testicu-lar toxicities [19] [20]. Refinement of these techniques could lead to rapid invivo screening. Such methods may detect the initial biochemical eventsunderlying the development of a toxic lesion, eliminating the necessity fortime-consuming pathological examination, and reducing the duration of ani-


mal treatment required to demonstrate a toxic effect. In such circumstances,it may even be possible to dose animals with mixtures of compounds toscreen for toxic responses in very-short-term studies, in a manner similar topresent-day cassette-dosing for preliminary pharmacokinetic evaluations.

7.4. Transgenic Models

As the mechanisms underlying target organ toxicities become betterunderstood, transgenic animals will be developed to permit toxicity screen-ing. Animals with appropriate reporter genes are currently under develop-ment, and with such animals, cassette dosing of several compounds simul-taneously may also be possible in short-term in vivo studies.

8. Discussion and Conclusions

The major challenges facing toxicologists in the future pharmaceuticalindustry will be to develop screening methods for dose-limiting target organtoxicities. If it were possible to predict such toxicities from in vitro models orlimited, rapid in vivo experiments, screening of compound libraries for unde-sired effects would become a reality. At present, primary screening is limitedto a search for desired pharmacological activities. In secondary screens, sometoxic effects (e.g., genotoxicity) may be assessed in a relatively high-through-put mode. However, there are only very limited methods to screen for criticaltarget organ toxicities (Fig. 1). As a consequence, such toxicity assays are notgenerally incorporated into very early screening procedures. With truly novelcompounds, an indication of target organ toxicities can best be obtained fromlimited animal experiments. However, such studies require relatively largeamounts of compound and are very time consuming. As a consequence, suchassays may at most be used to help characterize new lead structures with adefined pharmacological profile. With such compounds, if an unacceptabletoxicity profile is observed in these limited tests, specific screening tests canbe developed rapidly to assess most target organ toxicities. These may thenbe used to screen structural analogs and to identify more promising drug can-didates for lead optimization and eventually development.

Emerging technologies will, however, change the way that toxic assess-ments are performed, and predictive ‘tools’ for certain toxicities may soon beavailable which will lead to faster in vivo and, presumably, in vitro screening.In this manner, what today seems full of risk and of limited value may in thenear future develop into very powerful tools for drug discovery and develop-ment.


REFERENCES

[1] P. Gee, D. M. Maron, B. N. Ames, Proc. Natl. Acad. Sci. U.S.A. 1994, 91, 11606.[2] ICH Guidelines, Fed. Reg. 1997, 62, 62472.[3] P. Gee, C. H. Sommers, A. S. Melick, X. M. Gidrol, M. D. Todd, R. B. Burris, M. E.

Nelson, R. C. Klemm, E. Ziegler, Mutation Res. 1998, 412, 117.[4] T. S. Sun, H. M. Stahr, J. Assoc. Offic. Anal. Chem. 1993, 76, 893.[5] F. Nesslany, D. Marzin, Mutagenesis 1999, 14, 403.[6] M. D. Barratt, Environ. Health Perspect. 1998, 106 (Suppl. 2), 459.[7] D. M. Sanderson, C. G. Earnshaw, M. Chamberlain, M. D. Barratt, Human Exp. Toxicol.

1991, 10, 261.[8] I. F. Purchase, Br. J. Cancer 1980, 41, 454. [9] G. M. Williams, Cancer Lett. 1997, 117, 175.

[10] H. P. Brun, J. F. Leonard, V. Moronvalle, J. M. Caillaud, C. Melcion, A. Cordier, Toxicol.Appl. Pharmacol. 1991, 108, 307.

[11] R. Bechter, Arch. Toxicol. Suppl. 1995, 17, 170.[12] O. P. Flint, Reprod. Toxicol. 1993, Suppl. 7, 103.[13] R. Bechter, G. D. Terlouw, M. Tsuchiya, T. Tsuchiya, A. Kistler, Arch. Toxicol. 1992, 66,

193.[14] A. E. M. Vickers, In Vitro Toxicol. 1997, 10, 71.[15] M. D. Todd, M. J. Lee, J. C. Williams, J. M. Nalezny, P. Gee, M. G. Benjamin, S. B. Farr,

Fund. Appl. Toxicol. 1995, 28, 118.[16] G. J. A. Oliver, M. A. Pemberton, Food Chem. Tox. 1986, 24, 513.[17] S. Farr, R. T. Dunn, Toxicol. Sci. 1999, 50, 1.[18] E. F. Nuwaysir, M. Bittner, J. Trent, J. C. Barrett, C. A. Afshari, Molec. Carcinog. 1999,

24, 153.[19] J. K. Nicholson, J. C. Lindon, E. Holmes, Xenobiotica 1999, 29, 1181.[20] J. A. Timbrell, Toxicology 1998, 129, 1.


Part IV. Physicochemical Strategies

Physicochemical Parameters as Tools in Drug Discovery and Lead Optimization

Bernard Faller* and Frank Wohnsland

Lipophilicity Profiles: Theory and MeasurementJohn Comer* and Kin Tam

High-Throughput Measurements of Solubility ProfilesAlex Avdeef

Electrochemical Aspects of Drug PartitioningFrédéric Reymond, Véronique Gobry, Géraldine Bouchard,and Hubert H. Girault*

Biolipid pKa Values and the Lipophilicity of Ampholytes and Ion PairsRobert A. Scherrer

Recent Advances in Reversed-Phase-HPLC Techniques toDetermine Lipophilicity

Chisako Yamagami

Liposome/Water Partitioning: Theory, Techniques, and ApplicationsStefanie D. Krämer

Importance of the Mobile Phase in Immobilized ArtificialMembrane Chromatography

Kimberly L. Morse* and Charles Pidgeon

High-Throughput Artificial Membrane Permeability Studies in EarlyLead Discovery and Development

Manfred Kansy*, Holger Fischer, Krystyna Kratzat,Frank Senner, Björn Wagner, and Isabella Parrilla

NMR Spectroscopy for the Study of Drug-Phospholipid InteractionsRoberta Fruttero


Physicochemical Parameters as Toolsin Drug Discovery and Lead Optimization

by Bernard Faller* and Frank Wohnsland

Novartis Pharma AG, WKL-122.P.33, CH-4002 Basel;e-mail: [email protected]

1. Introduction

Inappropriate pharmacokinetics in human volunteers is the primary causeof compound withdrawal of new chemical entities in drug development [1].The paradigm in today’s drug discovery is that the majority of programs areintended for oral therapy while most high-throughput screening techniquestend to shift leads towards a) more lipophilic and therefore potentially lesssoluble compounds, and b) compounds with a higher number of hydrogen-bond donors and acceptors and larger molecular volume [2]. The same kindof shift towards physicochemical properties unfavorable for oral deliverymay also happen in the lead-optimization phase if one does not optimize sol-ubility/permeability in parallel to binding affinity/selectivity. A number oftools to predict oral-absorption problems have been proposed, going from insilico predictions to sophisticated in vitro cell-culture models like Caco-2 [3]or, more recently, 2/4/A1 monolayers [4] for gastrointestinal (GI) permeabil-ity and co-cultures of astrocytes with endothelial cells to mimic the blood-brain barrier.

The challenge remains to find the proper combination of these techniquesin view of their respective potential, limitations, and costs, in order to pick upthe right compounds quickly while keeping the risk of eliminating ‘good’candidates as low as possible. Potential and limitations of a number ofapproaches to get early information on solubility and permeability, the mainfactors governing absorption via passive diffusion, will be discussed withpractical examples. Relations between drug disposition and related physico-chemical parameters are shown in Fig. 1.

2. Water Solubility

A set of 22 chemically diverse generic drugs was used to evaluate differ-ent methods to determine intrinsic water solubility (neutral species solubil-ity). Effects of salts were therefore left out at this stage. The ‘gold standard’intrinsic solubility values were obtained from potentiometric titration (ioniz-able compounds) or from equilibrium-checked shake-flask experiments fol-lowed by HPLC-UV analysis. These reference values were compared withcalculated water solubility values and ‘kinetic solubility’ obtained fromnephelometric titration (Table 1).

2.1. In Silico Approaches

Different computer-based approaches for water-solubility determinationwere examined, from simple semi-empirical equations based on octa-nol/water partition coefficients (calculated and experimental) to more sophis-ticated methods based on hydrogen-bond strength and polarizability.


Fig. 1. Drug disposition and related physicochemical parameters


Tabl

e1.

Exp

erim

enta

l an

din

Sili

co I

ntri

nsic

Wat

er S

olub

ilit

y of

22

Gen

eric

Dru

gsa )

CA

SN

o.C

ompo

und

Mr

pKa

log

Pex

pC

LO

GP

p-SO

LH

YB

OT

SYR

Log

CL

OG

PH

T-so

lP

exp-

Sol

-Sol

(pH

)

1951

-25-

3A

mio

daro

ne64

5.3

9.0B

7.8

8.7

8.1

8.4

8.5

9.6

8.6

n.a.

2561

4-03

-3B

rom

ocri

ptin

e65

4.6

5.4B

4.2

9.3

4.7

m.f

.5.

54.

611

.75.

5 (6

.8)

5786

-21-

0C

loza

pine

326.

87.

9B /

4.4B

4.1

4.2

3.7

51.

44.

54.

64.

2 (1

0)15

307-

79-6

Dic

lofe

nac-

Na

318.

14.

0A4.

54.

75.

63.

84.

75.

03.

84.

2 (1

)42

399-

41-7

Dilt

iaze

m41

4.5

8.0B

2.9

3.4

4.3

6.0

4.5

2.8

3.0

4.3

(10)

5104

-49-

4Fl

urbi

prof

en24

4.3

4.0A

4.0

3.7

4.4

3.4

4.1

4.3

4.4

4.1

(1)

54-3

1-9

Furo

sem

ide

330.

810

.6A

/ 3.5

B2.

61.

24.

73.

23.

32.

3–0

.24.

2 (6

.8)

58-9

3-5

Hyd

roch

loro

thia

zide

297.

710

.0A

/ 8.9

A–0

.2–0

.42.

62.

42.

4–1

.4–0

.83.

0 (6

.815

687-

27-1

Ibup

rofe

n20

6.3

4.4A

4.1

3.7

3.6

3.1

3.3

4.5

4.1

3.6

(1)

2207

1-15

-4K

etop

rofe

n25

4.3

4.0A

3.2

2.8

3.4

3.6

3.3

3.2

3.6

4.1

(1)

3689

4-69

-6L

abet

alol

328.

49.

4B /

7.5A

1.3

2.5

3.1

4.1

3.6

0.7

2.4

3.0

(6.8

)17

5385

-62-

3L

asin

avir

659.

8N

3.3

2.4

4.0*

m.f

.4.

23.

42.

14.

1 (6

.8)

1756

0-51

-9M

etol

azon

e36

5.8

9.7A

4.1

2.0

4.1

4.4

0.4

4.6

1.6

4.3

(6.8

)22

204-

53-1

Nap

roxe

n23

0.3

4.7A

2.8

2.8

3.9

m.f

.3.

22.

73.

04.

1 (1

)72

-69-

5N

ortr

ipty

line

263.

410

.1B

4.4

4.3

4.8

4.5

5.3

4.9

5.1

4.8

(12)

57-4

1-0

Phen

ytoi

n25

2.3

8.2A

2.2

2.1

4.1

3.4

3.1

1.9

2.0

4.1

(6.8

)13

523-

86-9

Pind

olol

248.

39.

6B1.

91.

74.

03.

3–1

.51.

41.

23.

8 (1

0)52

5-66

-6Pr

opra

nolo

l25

9.3

9.5B

3.5

2.7

3.6

3.9

2.6

3.6

2.7

3.8

(10)

130-

95-0

Qui

nine

324.

48.

5B /

4.3B

3.5

2.9

4.8

m.f

.3.

53.

72.

44.

3 (1

0)10

6308

-44-

5R

ufin

amid

e23

8.2

N0.

92.

03.

5*2.

3–0

.8–0

.11.

64.

1 (6

.8)

5067

9-08

-8Te

rfen

adin

e47

1.7

9.5B

5.7

6.1

6.7

8.2

8.1

6.7

8.5

5.4

(10)

1378

62-5

3-4

Val

sart

an43

7.5

4.7A

/ 3.9

A3.

95.

74.

2m

.f.

2.5

4.2

6.7

4.3

(1)

a )So

lubi

lity

data

are

exp

ress

ed a

s ne

gativ

e lo

g of

the

mol

ar c

once

ntra

tion

(col

ums

7–

12).

Abb

revi

atio

ns a

nd n

otes

: log

Pex

p: e

xper

imen

tal l

og P

; CL

OG

P:M

edC

hem

CL

OG

P; p

-SO

L: s

olub

ility

mea

sure

d by

pot

entio

met

ric

titra

tion;

HY

BO

T: c

alcu

late

d so

lubi

lity

base

d on

HA

, HD

, and

pol

ariz

abili

ty o

btai

ned

with

the

HY

BO

Tpr

ogra

m;

SYR

: ca

lcul

ated

sol

ubili

ty f

rom

the

Syr

acus

e pr

ogra

m;

log

Pex

p-So

l: ca

lcul

ated

sol

ubili

ty b

ased

on

expe

rim

enta

l lo

g P

;C

LO

GP-

Sol:

calc

ulat

ed s

olub

ility

bas

ed o

n C

LO

GP;

HT-

sol:

solu

bilit

y of

neu

tral

spe

cies

mea

sure

d by

nep

helo

met

ric

titra

tion,

pH

of

mea

sure

men

t ind

i-ca

ted

in b

rack

ets

(pH

1 (

HC

l 0.1

N),

pH

6.8

(50

mM

Cl-

free

pho

spha

te),

pH

10

(bor

ax);

m.f

.: m

issi

ng f

ragm

ent;

n.a.

: not

ach

ieve

d (o

ut o

f dy

nam

ic r

ange

of th

e m

etho

d); *

: mea

sure

d by

sha

ke-f

lask

/HPL

C-U

V.

2.1.1. Semi-Empirical Approach Based on Octanol-Water PartitionCoefficient

A number of semi-empirical equations linking intrinsic water solubility tolog Poct have been published. We used the following equation by Banerjeeand Yalkowsky [5]:

log Sw = 1.17 – 1.38 log Poct (Eqn. 1)

Log Poct was either calculated using the MedChem CLOGP software orexperimentally measured. Experimental values were obtained by dual-phasetitration for ionizable compounds [6–9] or from shake-flask/HPLC-UV forneutral compounds.

2.1.2. Calculated Solubility Using WS-Kow v1.51

The solubility module of the WS-Kow software from Syracuse Universitywas used. The program is based on a training set of 1450 compounds withknown water solubility. Two equations were derived from this training set:one contains a melting point term while the other can be used when the melt-ing point is unknown. The second option (see Eqn. 2) was tested:

log Sw = 0.796 – 0.854 log Poct – 0.00728 Mr + correction factors (Eqn. 2)

More details about the methodology can be found in the paper by Meylandand Howard [10].

2.1.3. Solubility Derived from Hydrogen-Bond Strengths Using HYBOT

HYBOT is a program that calculates hydrogen-bond donor (HD) andacceptor (HA) strengths using a large database of fragments with experimen-tally determined HA and HD values. According to HYBOT applicationnotes [11], water solubility can be predicted based on HA, HD, and polariz-ability using the following semi-empirical equation:

log (1/Sw) = – 0.42 + 0.17Polarizability + 0.13 HA + 0.08 HD (Eqn. 3)

2.2. Nephelometric Titration (Kinetic Solubility)

The principle of this assay is similar to the method described byLipinski [2] except a microtiterplate nephelometer (Nepheloskan Ascent,


Labsystems, Finland) is used throughout the whole concentration range.Briefly, samples are solubilized in DMSO at 10 mg/ml, and small aliquots ofthis concentrated stock are thrown into 300 l of a buffered aqueous medium.The final concentration of DMSO is kept within 1–2% (v/v) during the titra-tion. Solubility is determined as the last concentration before a reading statis-tically higher than the control (buffer) is recorded. The dynamic range of oursetup is 0.5–200 g/ml.

2.3. Conclusions

Results obtained with the approaches described above are summarized inTable 1 and Fig. 2. Thermodynamic solubility values obtained using the stan-dard shake-flask/HPLC-UV method or the potentiometric titration method[12] using a pSOL instrument were taken as the ‘gold standard’. Inspection ofthe residuals in Fig. 2 and Table 2 shows that experimental solubilityobtained via nephelometric titration (HT-sol) fits well with the reference val-ues and gives significantly better results than the in silico methods tested. Thenephelometric titration method has, however, a number of limitations: a) it


Fig. 2. Intrinsic solubility (solubility of the neutral species) measured by nephelometric titra-tion vs. thermodynamic solubility determinations

works only within a relatively narrow dynamic range (log (1/Sw) 3–6, but for-tunately most of the compounds synthesized in early drug discovery fall inthat range), and b) it overestimates solubility in some cases, particularly withsticky compounds (low zeta potential?) where optical detection can becomedifficult (e.g., diclofenac).

In silico methods potentially work over a broader dynamic range and canbe used to evaluate virtual molecules. However, at present, the standard errorsremain relatively large, at least with the current test set and approaches test-ed. It is difficult to draw definite conclusions about the different in silicoapproaches with a small test set. The approach based on HA, HD, and pola-rizability using the HYBOT software seems to perform better than the log-P-based approaches. The current drawback is that a number of compounds(4/22) could not be calculated due to missing fragments. Hopefully, this situ-ation will improve in the future when more fragments will be included in thedatabase.

3. Physicochemical Methods for Assessing Permeability

Passive diffusion across biological membranes is governed mainly bythree interdependent physicochemical parameters: lipophilicity (log P andlog D), polarity (charge, hydrogen bonding), and molecular volume. Foruncharged compounds, it has been demonstrated that permeability is essen-tially described by hydrogen bonding and molecular volume [13]. A set of 30chemically diverse generic drugs for which absorption data in man are avail-able from the literature was used to evaluate physicochemical methods to pre-dict absorption (Fig. 3).


Table 2. Experimental and Computational Tools to Predict Intrinsic Water Solubility

N a) R2 F

HT-sol 20/22 0.86 106.7Log Pexp 22/22 0.65 37.5CLOGP 22/22 0.39 12.7SYR 22/22 0.48 18.7HYBOT 18/22 0.60 23.1

a) Number of compounds.


Fig.

3.C

hem

ical

str

uctu

res

of th

e ge

neri

c dr

ugs

used

in th

e tr

aini

ng s

et fo

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e pe

rmea

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ty a

ssay

s. N

umbe

rs in

par

enth

eses

ref

er to

the

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tion

abso

rbed

in m

an.

3.1. In Silico Predictions

3.1.1. Octanol Log P or Log D

Octanol/water has been widely used in structure-permeation correla-tions [14–16]. For ionizable compounds, it is accepted that the distributioncoefficient (log D) at a physiologically relevant pH (5 to 7.4) is the parame-ter to consider instead of the partition coefficient. A number of compoundsare, however, not properly predicted by the octanol/water system, mainly dueto its inappropriate hydrogen-bond acidity component. Although useful cor-relations have often been obtained within given chemical series, the predic-tive value of octanol/water tends to vanish when chemical diversity is in-creased [17–19].

3.1.2. Polar Surface Area

Polar surface area (PSA) is a surface descriptor which has been intro-duced some years ago as an alternative to calculated octanol/water partitioncoefficients to measure permeability of drugs [19–22]. Polar surface area isdefined as part of the surface area contributed by nitrogen, oxygen, and con-nected hydrogen atoms. In this study, we calculated PSA using the approachpublished by Palm et al. [21].

3.2. Artificial Model Membranes

Artificial bilayer membranes represent the simplest transcellular diffusionmodel for the gastrointestinal wall. Standard planar bilayer membranes are,however, mechanically too fragile and cannot be used as a routine analyticaltool, particularly if one wants to set up a high-throughput permeability screen.However, it has been shown that microfiltration filter supports can be used tostabilize these membranes without altering their physical properties [23] [24].In 1997, Camenisch et al. [25] used artificial membranes made of cellulosefilters impregnated with octanol or isopropyl myristate to correlate fluxeswith drug permeability measured with CaCo-2 cells. More recently, Kansy etal. showed a correlation between the fraction of drug absorbed in man andflux through hydrophobic filters impregnated with lecithin [26]. Our ownmodel is based on polycarbonate filters coated with hexadecane. The reasonswhich guided this choice were the following: a) thickness of polycarbonatefilters compared to mixed cellulose or polyvinylidene fluoride filters (10 vs. 150 m) to minimize membrane retention, b) previous work by


Thompson [24] who showed that polycarbonate filters are best suited formicrobilayer-membrane formation, and c) our finding that hexadecane formsstable solvent layers when impregnated on polycarbonate filters and theincreasing experimental evidence that distribution coefficients for a numberof aprotic solvents correlate better than octanol (amphiprotic) with bilayer-membrane permeability [20] [27–29].

The resistance to permeability across a lipid-bilayer membrane can beexpressed as the sum of interfacial forces and the resistance generated by thehydrocarbon chains of the phospholipids [30]. If one restricts the system to arelatively narrow molecular-weight range (200–600) then permeability ismainly influenced by the latter component. In this case, organic solvents thatmimic the hydrocarbon chain environment are potentially useful to modelmembrane permeation. Walter and Gutknecht have already reported correla-tions between hexadecane/water distribution coefficients and egg phosphati-dylcholine (PC) permeability for a series of weak acids [28]. Later, the sameauthors reported correlations between egg PC-membrane permeability andpartition coefficients in hexadecane, octanol, olive oil, and ether using a larg-er and more diverse set of compounds [29]. Xiang and Anderson studied thecorrelation between distribution coefficients in various bulk hydrocar-bon/water systems and permeability across egg-PC bilayers [31]. Although1,9-decadiene more closely models the lecithin bilayers, reasonable correla-tions were obtained with hexadecene and hexadecane, all significantly betterthan with octanol. Unfortunately, we were unable to immobilize a 1,9-deca-diene solvent layer between two aqueous phases using polycarbonate filters,most likely because of less favorable physicochemical properties.

3.3. Results and Discussion

3.3.1. In Silico Approaches

Octanol/water distribution coefficients were calculated using MedChemCLOGP (partition coefficients) and ACD pKa DB (ionization constants) pro-grams, as these two software products gave the best results in a previous com-parative test where calculated pKa/log P values of 100 substances were com-pared with experimental data (unpublished results). Fig. 4 shows that overall,percent absorption increases with clog D and tends to reach a maximum forlog D values equal or higher than zero. When calculated log D is substitutedby its experimental value, the correlation slightly improves (Fig. 5).

Fig. 6 shows the correlation obtained with the surface descriptor PSA,calculated as described by Palm et al. [21]. A larger number of compoundswith absorption within the 10–90 percent range were included in the study


compared to the original publication. Clearly, compounds with PSA lowerthan 60 Å2 are all well absorbed. The predictive value of the surface descrip-tor seems lower for compounds with PSA within the range 70–150 Å2. Forexample, ranitidine and piroxicam both have a PSA of 80 Å2 while %-absorp-tion is 50 for the former and 100 for the latter. Hydrochlorothiazide and acyclovir, both having a PSA of 117 Å2, also have significantly different%-absorption values: 67 for the former vs. 20 for the latter.

When the semi-empirical equation based on CLOGP, PSA, and number ofhydrogen-bond donor atoms proposed by Winiwarter et al. [19] was used, nofurther improvement in the correlation was observed (Fig. 7) with the set ofmolecules used in this study.

3.3.2. Experimental Approaches

Fig. 5 shows that percent absorption increases with experimental log D atpH 6.8, although a number of compounds are not correctly predicted (metol-azone overestimated; terbutaline underestimated). In addition, experimental


Fig. 4. Correlation between GI absorption in humans and calculated log D at pH 6.8. Log Dvalues were derived from CLOGP and pKa-values calculated with ACD pKa-DB.


determination of log D requires a significant amount of material and man-power, so it is difficult to measure large numbers of molecules.

Diffusion through hexadecane membranes (HDM) at pH 6.8 shows aninteresting correlation with % absorption (Fig. 8), although here again a num-ber of compounds are not properly predicted. For example, the permeabilityof furosemide, guanabenz, and valsartan was underestimated in this assay.Some compounds of our test set are strongly ionized at the pH of the experi-ment (6.8) and therefore potentially diffuse poorly through the membrane yetbeing well absorbed in man. This behavior can be explained by the physiolo-gy of the gastrointestinal tract, where a pH-gradient is present from the stom-ach down to the colon (Fig. 9). This means that these ionizable compoundshave absorption windows along the intestine. To take this feature of the gas-trointestinal tract into account, the experiment was repeated at pH 5.0, 6.8,and 8.0 and the best permeability value used for each compound (Figs. 10and 11). Clearly, determination of permeability in the HDM assay at differentpH values improves the correlation with percent absorption in man.

The advantage of artificial model membranes lies in the cost/throughputratio of the technique. As pointed out by Camenisch et al. [25] and later by

Fig. 5. Correlation between GI absorption in humans and experimental log D at pH 6.8. Log Dvalues were obtained from dual-phase potentiometric titration with a GlpKa instrument (Sirius

Analytical Instruments, Forest Row, UK).


Kansy et al. [26], this experimental approach allows the measurement of largecompound collections and is better suited for high-throughput assays thanapproaches based on partitioning/distribution-coefficient determination. OurHDM assay can be performed with relatively little effort, and its robustnessallows good inter-laboratory comparisons.

4. Conclusion

Due to patient compliance, the oral route of administration continues tobe the preferred route for drug delivery. In order to reduce the developmenttime of new chemical entities, one needs to move from a sequential to a par-allel optimization of potency/selectivity and biopharmaceutical properties.Physicochemical parameters provide tools to get early information on solu-bility and permeability, two important components in transcellular passivedrug absorption. But even when drug disposition is reduced to these two sim-ple components, several approaches need to be considered.

For water solubility, we have seen that it is still difficult to get accuratepredictions using in silico tools. Calculated solubility using hydrogen-bond

Fig. 6. Correlation between GI absorption in humans and PSA calculated as described in [19]

strengths and polarizability descriptors seems a promising approach althoughthe polarizability term is often difficult to obtain. In addition, the influence ofcounterions can hardly be predicted with the current knowledge. Methods forquick experimental solubility determinations are urgently needed. The turbid-imetric method introduced by Lipinski or our nephelometric titration assayprovide ‘kinetic solubility’ data that fit relatively well with true solubility val-ues but work only within a restricted dynamic range. For compounds withionizable groups, potentiometric titration has shown to be a valuable option,although its capability to characterize the solubility of salts remains to be doc-umented. In our hands, the method also fails to measure poorly soluble weakacids (pKa > 10) and bases (pKa < 3).

We have tested three in silico methods for permeability prediction. Thesurface descriptor PSA seems to be the most promising approach. In contrastto the conclusions drawn by Winiwarter et al. [19], in our test set the intro-duction of additional parameters in a semi-empirical equation based on PSA,CLOGP, and number of H-bond donors did not further improve the correla-tion obtained with PSA alone. One possible explanation might be the inter-correlation between the PSA and CLOGP descriptors. Calculated octan-


Fig. 7. Correlation between GI absorption in humans and calculated permeability (log Peff)based on a semi-empirical equation based on PSA, CLOGP, and number of hydrogen-bond

donors, according to [19]


Fig. 8. Correlation between GI absorption in humans and flux through a hexadecane liquidmembrane immobilized between two aqueous phases at pH 6.8. 100% refers to full equilibrium

between donor and acceptor compartments.

Fig. 9. Surface available for absorption vs. pH of the different compartments in the gastroin-testinal tract

ol/water distribution coefficients did not perform as well as PSA, although thesituation may be different when one looks at closely related compounds.

Experimental octanol/water distribution coefficients did correlate rela-tively well with percent absorption but not better than with PSA. The lattermethod has the advantage that compounds do not need to be synthesized,while experimental log D requires a significant amount of material and man-power. Our view is that the most promising physicochemical approach totranscellular permeability are the assays based on artificial model membranesas they combine high throughput, low compound requirement, and good pre-dictive value, particularly when the pH gradient encountered in the intestineis taken into account. The artificial model-membrane technology allowsaccess to permeability-pH profiles for large numbers of compounds and cor-rection for the unstirred water layer. Membrane-based assays also open up thepossibility to study the role of (some) formulation ingredients and salts onpermeability.

In conclusion, one can say that there is no universal method to get earlyinformation on solubility and permeability. Which method to use mainly


Fig. 10. Correlation between GI absorption in humans and flux through a hexadecane liquidmembrane immobilized between two aqueous phases measured within the pH-range 5–8 (best

permeability taken into account)

depends on the problem which is addressed: in silico methods have theadvantage to generate information for virtual molecules and can be used tocharacterize large compound collections. On the other hand, if one needs tocompare compounds in order to prioritize them for in vivo studies, experi-mental assays are more appropriate.

Finally, we want to remind the reader that this chapter only considerstranscellular passive diffusion and leaves out metabolism, clearance, activetransports, and efflux systems. These additional parameters must be consid-ered as well before doing extrapolations to animals.

REFERENCES

[1] R. A. Prentis, Y. Lis, S. R. Walker, Br. J. Clin. Pharmacol. 1989, 25, 387.[2] C. A. Lipinski, F. Lombardo, B. W. Dominy, P. Feeney, Adv. Drug Delivery Rev. 1997,

23, 3.[3] P. Artursson, J. Karlsson, Biochem. Biophys. Res. Commun. 1991, 175, 880.[4] S. Tavelin, V. Milovic, G. Ocklind, S. Olsson, P. Artursson, J. Pharmacol. Exp. Ther.

1999, 290, 1212.[5] S. H. Yalkowsky, S. Banerjee, in ‘Aqueous Solubility’, Marcel Dekker Inc., Basel, New

York, 1992.[6] F. H. Clarke, N. Cahoon, J. Pharm. Sci. 1987, 76, 611.[7] A. Avdeef, Quant. Struct.-Act. Relat. 1992, 11, 510.[8] A. Avdeef, J. Pharm. Sci. 1993, 82, 183.


Fig. 11. Correlation between GI absorption and permeability in cm/s through the hexadecaneliquid membrane, measured within the pH range 5–8 (best permeability taken into account)

[9] B. Slater, A. McCormack, A. Avdeef, J. E. A. Comer, J. Pharm. Sci. 1994, 83, 1280.[10] W. M. Meyland, G. M. Howard, Environ. Toxicol. Chem. 1986, 15, 100.[11] www.ibmh.msk.su/qsar/molpro/hybot/hybot3.htm[12] A. Avdeef, Pharm. Pharmacol. Commun. 1998, 4, 165.[13] H. van de Waterbeemd, G. Camenisch, G. Folkers, J. R. Chretien, O. A. Raevsky, J.

Drug Target. 1998, 2, 151.[14] R. Collander, Ann. Rev. Plant Physiol. 1957, 8, 335.[15] C. Hansch, J. E. Quinlan, G. L. Lawrence, J. Org. Chem. 1968, 33, 347.[16] A. Leo, C. Hansch, D. Elkins, Chem. Rev. 1971, 71, 525.[17] T. W. von Geldern, D. J. Hoffmann, J. A. Kester, H. N. Nellans, B. D. Dayton, S. V.

Calzadilla, K. C. Marsh, L. Hernandez, W. Chiou, D. B. Dixon, J. R. Wu-Wong, T. J.Opgenorth, J. Med. Chem. 1996, 39, 982.

[18] R. C. Young, R. C. Mitchell, T. H. Brown, C. R. Ganellin, R. Griffiths, M. Jones, K. K.Rana, D. Saunders, I. R. Smith, N. E. Sore, T. J. Wilks, J. Med. Chem. 1988, 31, 656.

[19] S. Winiwarter, N. M. Bonham, F. Ax, A. Hallberg, H. Lennernäs, A. Karlén, J. Med.Chem. 1998, 41, 4939.

[20] H. van de Waterbeemd, M. Kansy, Chimia 1992, 46, 299.[21] K. Palm, K. Luthman, A.-L. Ungell, G. Strandlund, P. Artursson, J. Pharm. Sci. 1996, 85,

32.[22] K. Palm, P. Stenberg, K. Luthman, P. Artursson, Pharm. Res. 1997, 14, 568.[23] J. M. Mountz, H. T. Tien, Photochem. Photobiol. 1978, 28, 395.[24] M. Thompson, R. B. Lennox, R. A. McClelland, Anal. Chem. 1982, 54, 76.[25] G. Camenisch, G. Folkers, H. van de Waterbeemed, Int. J. Pharm. 1997, 147, 61.[26] M. Kansy, F. Senner, K. Gubernator, J. Med. Chem. 1998, 41, 1007.[27] G. Caron, G. Steyaert, A. Pagliara, F. Reymond, P. Crivori, P. Gaillard, P.-A. Carrupt, A.

Avdeef, J. Comer, K. J. Box, H. H. Girault, B. Testa, Helv. Chim. Acta 1999, 82, 1211.[28] A. Walter, J. Gutknecht, J. Membr. Biol. 1984, 77, 255.[29] A. Walter, J. Gutknecht, J. Membr. Biol. 1986, 90, 207.[30] J. M. Diamond, Y. Katz, J. Membr. Biol. 1974, 17, 121.[31] T. X. Xiang, B. D. Anderson, J. Membr. Biol. 1994, 140, 111.


Lipophilicity Profiles: Theory and Measurement

by John Comer* and Kin Tam

Sirius Analytical Instruments Ltd., Riverside, Forest Row Business Park, Forest Row, EastSussex, RH18 5HE, UK

1. Lipophilicity and Ionization

Lipophilicity represents the affinity of a molecule or moiety for a lipophil-ic environment. It is commonly measured by its distribution behavior in abiphasic system, either liquid-liquid (e.g., partition coefficient in octan-ol/water) or by chromatographic methods [1]. Knowledge of lipophilicity isused in a variety of methods that may predict absorption and other transportproperties of drug molecules in the human body.

Many drug molecules contain one or more ionizable groups, and theirlipophilicity is pH-dependent. One published estimate suggests that 75% ofdrugs contain ionizable basic groups, 20% contain acidic groups, while only5% are non-ionizable [2]. A more recent study undertaken in December 1999using Oxford Molecular’s Chem-X software is reported in Table 1. It suggeststhat of 51596 compounds listed in the World Drug Index, 32437 containionizable groups. Of these, 14.5% are acids, 67.5% are bases and 14.6% areampholytes. The extent that these numbers apply to drugs in discovery canonly be guessed, though as the majority of drugs are ionizable, predictions oftheir properties must take ionization into account.

2. Lipophilicity Profiles of Acids and Bases: Theory

2.1. The ‘Four-Equation’ Partition Model

Lipophilicity profiles are graphs which show how lipophilicity (expressedas a log D value) changes with respect to pH. The shapes of the profiles arereadily derived from pH-metric theory. Fig. 1 shows the lipophilicity profilefor a monoprotic base and identifies several important terms, illustrated bythe equilibria shown in Fig. 2, which depicts a ‘four-equation’ partition model


276 PARMACOKINETIC OPTIMIZATION IN DRUG RESEARCH

Table 1. Number of Drugs which Ionize

% of ionizable drugs

Total number of drugs in study a) 51 596Number of ionizable drugs 32,437 b) 100

(62.9% of total)Number with……1 acid, no base Acids 3 762 11.6 14.5…2+ acids, no base 958 2.9

1 base, no acidBases

13 918 42.967.52+ bases, no acid 7 997 24.6

1 acid and 1 base 2 441 7.51 acid and 2+ bases Ampholytes 1 280 3.9 17.91 base and 2+ acids 1 051 3.2Others 1 030 3.2

a) Compounds listed in World Drug Index, 1999. b) Number established via 2D search using theChem-X software.

Fig. 1. Lipophilicity profiles for a weak base with pKa = 8, log PN = 3, and log PI = 0 and –1(profiles for weak acids are transposed left/right). Note the dependence of log PI on ionicstrength of background electrolyte. Note the large difference in log D values between a ‘physiological’ pH of 7.4, the pH in the ‘fed’ and ‘fasted’ states in the ileum (5–6.8) [37], andthe stomach pH (1.5–2.0). This underlines the value of the lipophilicity profile for predicting

lipophilicity as a function of pH.


for the partitioning of a monoprotic base in the octanol-water system. Theseequilibria and the corresponding Eqns. 1 to 4 appear below.

1. X0 + H+ s XH+. This ionization equilibrium of the substance in aque-ous solution is represented by the ‘aqueous pKa’ value. Thus,

(Eqn. 1)

2. X0aq s X0

oct. This equilibrium for the partitioning of the neutral speciesX0 between water and octanol leads to Eqn. 2 for the log P value forthe neutral species. This is the value which is normally quoted as theofficial ‘log P’ of any substance. In this example, the number of pro-tons associated with the neutral species is 0, and its partition coeffi-cient may therefore be written as P0. Thus,

(Eqn. 2)

This notation P0 conforms with the proposal of Caron et al. [3] for asystematic notation for log P constants, where the superscript corre-sponds to the electronic state of the partitioning species, and any sub-script corresponds to the partition solvent, with the exception that nosubscript is used for octanol, which is normally assumed to be thedefault partition solvent. Note that [X]oct represents the concentrationof X in terms of moles dissolved per litre of octanol.

log P00

oct0

aqlog

[X ][X ]

=

p log [XH ][H ][X ]

a 0K =+

+

Fig. 2. ‘Four equation’partition model for ionization and partitioning of a weak base (X). Notethat species XH+ is expected to partition as an ion-pair with an anion from the water layer.

3. XH+aq s XH+

oct. This equilibrium represents the partitioning of the cat-ionic species XH+ into octanol. It is assumed that it partitions as anion-pair with a suitable anion from the aqueous solution. The value ofthe log P of the ionized species varies according to the concentrationof background electrolyte. In this example, the number of protonsassociated with the cationic species is 1, and its partition coefficientmay therefore be written as P1. Thus,

(Eqn. 3)

4. X0oct + H+ s XH+

oct. This equilibrium for the ionization of the sub-stance in octanol represents the ‘limiting pKa’, or ‘Scherrer pKa’(named after Scherrer’s measurements in water-saturated octanol in1984–89) [4] [5]. It is not necessary to know the value of the ScherrerpKa in order to calculate the lipophilicity profile, but it is a useful con-cept. Thus,

(Eqn. 4)

2.2. Calculating Lipophilicity Profiles

The lipophilicity of ionizable drugs is conveniently described by a lipo-philicity profile, or a plot of lipophilicity as a function of pH. In the lipophi-licity profile, lipophilicity is expressed as log D, where the distribution coef-ficient D represents the ratio of concentrations of the drug dissolved in eachof the two phases at equilibrium. Because the ratio of concentration of ion-ized and neutral species changes with pH, the log D term is also pH-depen-dent. The term D for a monoprotic base is defined as

(Eqn. 5)

By combining Eqn. 5 with 1, 2 and 3 above, Eqn. 6 may be derived forthe lipophilicity profile of a monoprotic base,

(Eqn. 6)

The argument above can be extended to cover monoprotic acids, and thento include multiprotic acids and bases. As shown by Avdeef [6], this equationmay be extended to a general form (cf. Eqn. 7) to calculate lipophilicity pro-

DP P K

K= +

+

+

+

0 1a

a

[H ]1 [H ]

D = ++

+

+[X ] [XH ][X ] [XH ]

0oct oct

0aq aq

p log[XH ]

[H ][X ]aoct oct

0oct

K =+

+

log P1 oct

aqlog

[XH ][XH ]

=+

+


files for a single compound with any number of acidic or basic ionizablegroups:

(Eqn. 7)

In this equation, superscripts represent the number of protons associatedwith each partition coefficient in the equation, and the terms representstability constants. Thus, for the first proton added, the ionization constant isKa1, for the second Ka2, and for the third Ka3, and the corresponding stabilityconstants are:

Ka1X0 + H+ s XH+ Ka1 = 1

Ka2XH+ + H+ s XH2

++ Ka1Ka2 = 2

Ka3XH2

++ + H+ s XH3+++ Ka1Ka2Ka3 = 3, etc.

In order to draw lipophilicity profiles, it is necessary to know values for[H+] (which may be derived from pH), ionization constants (Ka1, Ka2, etc.),and the partition coefficients of each species (P0, P1, etc.).

Eqn. 7 will also correctly depict the lipophilicity profile for diproticampholytes whose basic pKa is more than 3 pH units below the acidic pKa,though not for ampholytes with a significant zwitterionic component.

2.3. Calculating Partial Lipophilicity Profiles

A partial lipophilicity profile may be drawn without knowing all the infor-mation required for the full profile, provided it is assumed that ion-pair par-titioning does not occur. For example, a partial profile for a monoprotic sub-stance can be drawn if the values of log P of the neutral species and the pKa

are known, by applying Eqns. 8 or 9. If the log D at a particular pH and thepKa are known, a profile can also be drawn after first calculating P0 or P1

from Eqns. 8 or 9.Monoprotic acids, log P1 and pKa known, no ion-pair partitioning:

(Eqn. 8)

Monoprotic bases, log P0 and pKa known, no ion-pair partitioning:

(Eqn. 9)D PK

=+ +

0

a1 [H ]

DK P

K=

+

+

+[H ]1 [H ]

a1

a

DP P= + +

+ + +

+ +

+ +

01

1 22

2

12

2

[H ] ] + ....1 [H ] [H ] ...

β ββ β

[H P


Partial profiles calculated from these equations are shown below in Fig. 4.These profiles were calculated in a spreadsheet, in which [H+] values were cal-culated from pH at 0.1 pH intervals.

3. Lipophilicity Profiles: Measurement

3.1. Summary of Methods

Lipophilicity profiles are not measured, but calculated from data whichmay include partition coefficients, log D values, ionization constants, and pHvalues. Each of these values may be measured or calculated. In a recent review[3], a number of techniques for measuring partition coefficients (log P values)were described and are summarized in Table 2. References are given for tech-niques 3 to 5, while techniques 1 and 2 are discussed in greater detail below.

3.2. Shake-Flask Technique

In the classical shake-flask technique [18], the sample is dissolved in aflask containing both aqueous buffer solution and partition solvent. Theremust be no undissolved substance present. The flask is shaken to equilibratethe sample between the two phases, and then the phases are allowed to sep-arate. The pH of the aqueous phase is measured, and the concentration ofsample is measured in each phase. From these values, the log D at the experi-mental pH can be calculated. A lipophilicity profile can be obtained by meas-uring shake-flask log D at several different pH values without knowing thepKa value(s). Because of its simplicity and clear relationship to the partition-ing phenomenon, the shake-flask technique is regarded as a benchmark meth-od against which other methods are validated. However, it is tedious andoffers limited precision.

3.3. pH-Metric Technique

The pH-metric technique (first described by Dyrssen in 1952 [19]) pro-vides a method of determining a lipophilicity profile directly from a singleacid-base titration in a dual-phase water-partition solvent system. To use thismethod, the pKa value(s) must be known. This pH-metric method has beenextensively described [14] [20–25].

In a typical pH-metric pKa measurement of a water-soluble sample, aweighed sample of pure substance is dissolved in 0.15 M KCl solution (typi-



Tabl

e 2.

Tech

niqu

es f

or M

easu

ring

Log

P

Shak

e-fl

ask

pH-m

etri

cC

entr

ifug

al p

artit

ion

Cyc

lic v

olta

mm

etry

R

P-H

PLC

chro

mat

ogra

phy

[7][

8][9

][10

][1

1–17

]

Mea

sure

men

tD

irec

t sin

gle

poin

tsD

irec

t lip

ophi

licity

D

irec

t sin

gle

poin

tsIn

dire

ct s

ingl

e po

ints

Indi

rect

sin

gle

poin

tspr

ofile

s

Log

Pra

nge

–3 to

+3

–1 to

+8

–3 to

+3

–8 to

+1

0 to

+5

Part

ition

sol

vent

sva

riou

sva

riou

sva

riou

s1,

2-di

chlo

roet

hane

,m

etha

nol,

acet

onitr

ileni

trob

enze

ne

pH r

ange

0 to

14

1.8

to 1

2.2

0 to

14

0 to

14

2 to

7.4

pH e

stab

lishe

d by

…:

…bu

ffer

sol

utio

n…

full

pH r

ange

…

buff

er s

olut

ion

…bu

ffer

sol

utio

n…

buff

er s

olut

ion

mea

sure

d du

ring

ex

peri

men

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cally 0.001 M sample in 15 ml KCl solution). The solution of 0.15 M KClmaintains a constant ionic strength during the experiment. At constant ionicstrength, the activity coefficient of hydrogen ions is constant, and hence isproportional to their concentration. This allows the pH to be expressed on a‘concentration scale’ [26], which is important because pKa values are calcu-lated from concentrations of substances, not activities.

Typically, the solution is acidified with standardized 0.5 M HCl solutionand titrated with standardized 0.5 M KOH solution under argon at 25°. ThepH of the solution is monitored throughout the titration with a standardizedpH electrode. A table of data points (pH values vs. ml of KOH) is saved forfurther study.

The structure of the molecule is studied to ascertain the number of ioniz-able groups. An equation is proposed for each ionization (e.g., X0 + H+ sXH+, XH+ + H+ s XH2

++, etc.). The pKa values are then estimated using aBjerrum difference curve (Fig. 3). Using these estimated pKa values, togetherwith knowledge of sample concentration and experimental conditions, a sim-ulated titration curve is constructed. This simulated curve is compared withthe experimental titration curve. The estimated pKa values (together with


Fig. 3. The Bjerrum difference curve. a) ‘Blank’ titration of a strong acid with a base (no sam-ple present). b) Titration of strong acid with a base, with sample of weak acid or base alsopresent. c) Curve a is subtracted from curve b to provide a volume difference, plotted as a func-tion of pH. d) Approximation to Bjerrum curve produced by rotating curve c through 90° andrescaling Y-axis to represent average number of bound protons per molecule of sample.

The pKa is equal to the pH where n–H = 0.5 (for monoprotic molecule), 1.5, 2.5, etc.

other variables such as substance concentration, CO2 content of the solution,and acidity error) are allowed to vary systematically until the simulated curvefits as closely as possible to the experimental curve. The pKa values requiredto achieve the best fit are assumed to be the correct measured pKa values. Thiscomputerized calculation technique is called ‘refinement’, and is describedelsewhere [27] [28].

In a typical pH-metric measurement of log P, a weighed sample is dis-solved in 0.15 M KCl solution plus a measured volume of partition solventsuch that the total volume is no more than about 20 ml. The experiment isrepeated as above. When an ionizable substance is titrated in a two-phasesystem, its Bjerrum difference curve is displaced from the aqueous curvebecause some titratable sample ‘disappears’ by partitioning into the non-aqueous phase. For monoprotic acids, the pKa shifts to a higher value; forbases it shifts to a lower value (Fig. 4). If the presence of partition solventwere ignored, this curve could be treated as above to produce so-called poKa

values.A series of equations have been published [29] which relate pKa and poKa

values to partition-coefficient (P) values for monoprotic acids and bases, and


Fig. 4. Relationship between pKa and poKa; partial lipophilicity profiles. a) Typical upwardshift in pKa when weak acid partitions into octanol during two-phase titration. b) Typical down-ward shift in pKa when weak base partitions into octanol during two-phase titration. c) and d)Partial lipophilicity profiles derived using Eqns. 8 or 9, after calculating log PN from

data in graphs a and b using Eqns. 10 or 11.

diprotic acids, bases, and ampholytes. For example, P1 for a monoprotic acidis calculated from

(Eqn. 10)

and P0 for a monoprotic base is calculated from

(Eqn. 11)

where

(Eqn. 12)

The structure of the molecule is studied to ascertain the likely partition-ing equations. Thus, for a monoprotic acid HX0, an equation such as HX0

aq sHX0

oct would be expected. Log P value(s) are estimated for the proposedequations. These estimated log P values relate directly to poKa values accord-ing to the equations above.

Using these initial/estimated log P values together with knowledge ofpKa, the refinement process described above is used to determine the correctmeasured log P values. These data are then used to calculate the lipophilicityprofile.

3.4. Comparison of pH-Metric and Shake-Flask Lipophilicity Profiles

Figs. 5a and 5b show lipophilicity profiles for pindolol (a base) and ben-zoic acid which have been measured pH-metrically and also by the shake-flask technique. Each shake-flask data point represents a separate experiment,while the curves plotted from the pH-metric data were determined from oneor two titrations of about 30 minutes duration. These graphs show that thereis general agreement between shake-flask and pH-metric results for the log Dvalues.

3.5. High-Throughput Lipophilicity Profiles: How Many Log P ValuesCan You Measure in 24 Hours?

For most of the 20th century, analytical chemistry was faster than chemi-cal synthesis. However, this is no longer so. Today, combinatorial chemistscan synthesize hundreds of thousands of new molecules every year. These can

r = volume of partition solventvolume of aqueous phase

Pr

K K0

(p p10 1o a a

= −− − )

Pr

K K1

p10 1o a

= −−p a


be readily screened for appropriate biological activity using high-throughputtechniques. For every 1000 000 compounds synthesized, perhaps 30 000 havesuitable activity. After the active compounds have been selected, the oneswhich may be absorbed and transported within the body must be identified.Traditional pharmacokinetic experiments using animals are too slow for thisjob, so a variety of computational and measurement techniques have beenused for predicting absorption from physicochemical parameters [35].However, it remains to be seen which combinations of these parameters is themost effective.

The pH-partition model has long been used to correlate pKa and partitioncoefficients with absorption through membranes by passive diffusion.Therefore, fast methods of measuring pKa and log P would be desirable.What follows is a brief review of methods for making fast measurements ofpKa and log P, with a view to fast production of lipophilicity profiles. Theinformation about sample throughput is based on private communicationswith practicing analysts.

The shake-flask method can be automated so that a large number of shak-ing and separation experiments in buffered solution can be performed in par-allel. Measurement of concentration in each phase can then be automatedusing HPLC/MS or similar techniques. If six different buffer solutions wereused to span the pH range, and samples measured in triplicate to minimizeerrors, measurement of log D between –3 and +3 of 50 samples per instru-ment per 24-hour period is possible. This technique will produce a lipophilic-ity profile showing six measured log D values vs. pH. It is not necessary toknow pKa values, provided the buffer pH values are sensibly chosen.

The liquid-liquid partition chromatography method of Taylor [11] can beused to measure log D at a single pH at a rate of 40–80 samples per 24-hourperiod. The samples are presented in solution in DMSO in a microtiter plateand measured sequentially. The log D data can be combined with measuredpKa values to produce lipophilicity profiles.


Fig. 5. Examples of lipophilicity profiles. Shake-flask data for pindolol [31] and benzoic acid[32] are plotted against pH-metric data for pindolol [34] and benzoic acid [28].

The pH-metric method can be automated so that a dual-phase titration iscompleted in 35 to 40 minutes. Samples can be analyzed at rates of 34 to 41samples per 24-hour period. Each sample is measured in a single assay withequal volumes of water and octanol. A lipophilicity profile can be drawn foreach sample, assuming that the pKa values are already known. A rapid meth-od for measurement of pKa values has recently been described [36]. In thismethod, samples are presented in DMSO solution in a microtiter plate andinjected at a constant rate into a flowing pH gradient. The gradient pH chang-es linearly from 11 to 3 in two minutes. The solution is passed through a flowcell, and UV absorbance is measured by a diode-array UV spectrophotome-ter. pKa is calculated from the change in UV absorbance as a function of pH.This system can measure pKa values at a rate of 300 samples per day.

In all the above methods, sample throughput does not equate with suc-cessful measurement. It is likely that some assays will fail because inappro-priate conditions were chosen, and some repetition will always be necessary.In particular, high-throughput assays are likely to prove difficult for ampho-lytes and zwitterions.

4. Lipophilicity of Ampholytes and Zwitterions

4.1. Ampholytes and Zwitterions

As mentioned in Sect. 1, almost 15% of ionizable drugs are ampholytes,containing one (or more) acidic together with one (or more) basic groups.Since opposite charges may co-exist at certain pH, ampholytes exhibit inter-esting physiochemical properties. A better understanding of their ionizationpatterns and lipophilicity profiles could improve understanding of theirpharmcokinetic behavior.

Ampholytes can be classified into two types: ordinary ampholytes andzwitterionic ampholytes. Ordinary ampholytes exist in neutral or singlycharged form. A typical example is m-aminophenol with the ionizationscheme given in Fig. 6. The acid pKa is greater than the base pKa, thus givinga neutral monoprotic species at mid-range pH. The pKa values can beassigned to the corresponding ionizable groups unambiguously. Thus, thedetermination of lipophilicity is relatively straightforward. The log D of theneutral m-aminophenol can be obtained pH-metrically or by shake-flaskexperiments at about pH 7. The ‘bell-shaped’ lipophilicity profile is shown inFig. 7.

If the two pKa values are close to each other (e.g., less than 3 pH units),and/or the acid pKa is lower than the base pKa, the molecule is called a zwit-terionic ampholyte. In such compounds, the ionization of one group affects


the other. It is difficult to assign the pKa values to the ionizable groupsbecause it is not possible to decide which of the two ionizable groups disso-ciates first. Depending on the pH values of the media, a molecule with oneacidic and one basic group may exist in four different microforms, namely acation (H2X+), zwitterion (HX±), neutral species (HX0, chargeless), and anion(X–), as shown in Fig. 8.

It is generally accepted that the neutral form (HX0) is more lipophilic andhence more effective in crossing lipid barriers [38]. However, the pKa valuesconsidered on their own disclose no information about the equilibrium thatgenerates HX0. It is necessary to use the microconstants (pk1 through pk4) andthe tautomeric ratio (KZ) to describe the amount of various microforms as afunction of pH. This knowledge can be used to characterize the lipophilicityof the various species existing at physiological pH.


Fig. 6. Ionization scheme of m-aminophenol (ordinary ampholyte). The pKa values were generated using the ACD/pKa software.

Fig. 7. Lipophilicity profile for an ordinary ampholyte (cf. Fig. 6). The log PN value of 0.34 was calculated with the ACD software.

4.2. Measurement of pk Microconstants

Traditionally, the pH-metric titration method is used to measure the pKa

values of ionizable compounds. The determination of microconstants needsthe combination of at least two experimental approaches. The second methodis typically spectroscopic (UV, NMR, CD, etc.) [39] [40]. These approachesinvolve selective monitoring of one (or some) of the ionizable group(s), formolecules where the ionization of other group(s) causes negligible spectro-scopic change. It should be noted that some of these treatments require sev-eral assumptions which may not always be valid [41]. If selective monitoringis hampered by a large number, close proximity, or high similarity of the func-tional groups, the deductive method could be employed. This method makesuse of a close derivative of the parent molecule which contains a reducednumber of ionizable groups. Assuming the masking of one or more function-al groups would leave the acid-base properties of the other(s) unaltered, thepKa values of the derivative can be regarded as the relevant microconstants ofthe parent molecule.

For those molecules where selective spectroscopic determination of one(or some) of the microconstants is not possible and the appropriate deriva-


Fig. 8. Ionization scheme of a diprotic zwitterionic ampholyte

tives are not readily available, the micro-equilibria have been unravelled bymeasuring the tautomeric ratios spectrophotometrically (at a single wave-length) in mixtures of organic solvent and water at the pH of the isoelectricpoint [42]. Extrapolation of the tautomeric ratios to zero percent organic sol-vent content yields the aqueous KZ value, from which other microconstantscan be calculated. The success of this method depends on the availability ofan appropriate analytical wavelength at which there is sufficient dissimilaritybetween the molar absorptivity coefficients of the zwitterion and neutral spe-cies. This is because unambiguous spectral assignment for the zwitterion andneutral species is necessary in the calculation [43].

Interestingly, D’Angelo and Collette reported an alternative method tounravel the tautomeric equilibrium [44]. Specifically, spectral measurementswere performed as a function of temperature. The relative proportions of var-ious microspecies change with temperature and this change is governed bythe Gibbs-Helmholtz relation. However, this method called for an assumptionin the microenthalpy parameters in order to calculate the microconstantsand/or tautomeric ratio.

A procedure has recently been devloped at Sirius for extracting the KZ

value from the multiwavelength data obtained from several titrations of var-ying water/co-solvent ratios, without requiring those assumptions adoptedwhen measurements are made only at the isoelectric point [45]. These meas-urements in co-solvent mixtures are referred to as the ‘KZ method’. In the fol-lowing discussion, the principle of the KZ method will be described. Selectedexamples will be used to illustrate how the method works. In these examples,titrations were performed using Sirius GLpKa™ pH-metric autotitrationapparatus with the D-PAS™ accessory for spectrophotometric measurement,and calculations were made using Sirius pKaUV™ software.

4.3. Multiwavelength Spectrophotometric Titration to Measure pKa

The first stage of measurement is a multiwavelength spectrophotometrictitration, in which a solution of the sample (typically 10–5

M) is lowered (orraised) to a starting pH and titrated with a base (or acid). Titration proceedsin steps of about 0.2 pH units, and at each step the UV absorbance spectra aremeasured via a fiber-optic dip probe attached to a diode-array spectrophoto-meter. In this way, a titration can be performed in about 15 ml of solution inno more than 20 minutes. The data obtained consists of a series of spectraacquired at different pH values. The absorbance data matrix, A, can beexpressed in terms of Beer’s law as follows:

A = C E (Eqn. 13)


where C and E represent, respectively, the concentration-pH profile of the ion-ization system and the molar absorptivity matrix with the inclusion of the opti-cal path length. Each element in the absorbance matrix represents the absor-bance at a particular wavelength and pH. Each element in the concentration-pH profile represents the concentration of one of the ionizable species at a par-ticular pH. Each element in the molar absorptivity matrix represents theabsorptivity of one of the species (X, XH, XH2) at a particular wavelength.

To solve this matrix equation, initial values for the number of ionizablespecies and the pKa(s) must be proposed. This information is used to set upthe concentration-pH profile and the molar absorptivity matrix. The pKa val-ues and molar absorptivities are systematically varied until the equationreaches an optimized solution, such that A – C E is close to zero. The pKa val-ues required to achieve this optimized solution are assumed to be the correctmeasured pKa values. This type of calculation is referred to as ‘target factoranalysis’ (TFA).

For example, consider the diprotic zwitterionic ampholyte labetalol,which is shown in Fig. 9.


Fig. 9. Structures of labetalol, niflumic acid, and pyridoxine


Fig. 10. Absorption spectra of labetalol

Fig. 10 depicts the absorption spectra of labetalol obtained at different pHvalues. Each point on this surface represents an element in the A matrix.Principal component analysis on this data matrix (see below) confirmed thatthree independent light-absorbing species are involved in the ionization pro-cess. The pKa values of 7.41 ± 0.01 and 9.36 ± 0.01 (n = 4) were obtainedusing the TFA method. These are in excellent agreement with those deter-mined pH-metrically. Figs. 11 and 12 show the distribution of species and theresolved molar absorptivity coefficients of labetalol, which correspond,respectively, to the optimized solutions for the C and E matrices.

4.4. Explanation of the TFA Calculation

Principal component analysis [46] [ 47] is first applied to A to calculatean abstract solution for C and E, namely, Cabs and Eabs, which contains onlythe primary eigenvalues (r) and eigenvectors (Qr). The residual standarddeviation [47], IND function [46] [47], eigenvalue ratio [48], and reducedeigenvalue ratio [49] [50] are adopted to identify the number of principalcomponents (independent light-absorbing species). Then, target factor analy-sis (TFA) treatment is invoked. Specifically, Cabs and Eabs are rotated to thesolutions with physical significance, Cp and Ep, via a transformation matrix


Fig. 11. Distribution of species for labetalol as a function of pH. The symbols represent the Cp matrix and the solid line denotes the Ct matrix.

Fig. 12. Molar absorptivity coefficients of labetalol. The symbols represent the Ep matrix.The solid lines were generated using the cubic spline interpolation method.

T as defined below [47] [51] [52]:

T = r–1Cabs

T Ct (Eqn. 14)

A ≈ CabsTT–1Eabs (Eqn. 15)

≈ CpEp (Eqn. 16)

where the superscripts –1 and T, respectively, denote inverse and transposeoperations. The test matrix Ct in equation 14 contains the concentration-pHprofiles of the ionization system which are generated theoretically [53]. Theconcentration pH value (pcH (= – log [H+]) is related to the operational pHreading as obtained from the pH meter by a multi-parameter equation [26].

The SPOIL function as derived by Malinowski [46] [51] is utilized todetermine whether the target transformation has been successful. For a par-ticular A matrix, the SPOIL function depends only on Ct which in turn is afunction of the sought pKa values [53]. The TFA computation optimizes theunknown parameters for a global minimum of the SPOIL function.

4.5. Measuring KZ

It is not possible to resolve the spectral data from the zwitterion (HX±)and the neutral species (HX0) in a single titration experiment This is becausethe concentrations of HX± and HX0 are linearly dependent on each other. Asummation of these two quantities is equivalent to the concentration of HX.The spectral contributions from HX± and HX0 therefore degenerate to oneprincipal component (see upper equation of Fig. 8). Thus, the pKa valuesinstead of the microconstants are obtained in the calculations.

To characterize the ionization processes in terms of microconstants, theknowledge of the pKa values and any one of the four microconstants or thetautomeric ratio KZ are necessary. This can be rationalized by the followingequations:

Ka,1 = k1 + k2 (Eqn. 17)

(Eqn. 18)

Ka,1Ka,2 = k2k4 = k1k3 (Eqn. 19)

Application of selective spectrophotometric monitoring may be difficultsince the ionization of any one of these groups would result in measurableshifts of the UV spectrum. For this type of molecule, it has been proposed thatthe microconstants could be determined after several spectrophotometric

Kkk

kkz

1

2

4

3

HX

HX

C

C 0

= = =±


titrations in different mixtures of organic solvent and water at the isoelectricpH, with the tautomeric ratio calculated from the spectral data at an analyti-cal wavelength using the following relationship [42]:

(Eqn. 20)

where KZ(%) represents the tautomeric ratio in a given % (wt.) solvent mix-ture, AXH± represents the absorbance of the compound in aqueous buffer,AXH0 represents the absorbance of the compound in pure organic solvent, andA(%) represents the absorbance of the compound in a given % solvent. Theaqueous KZ value can be obtained from the intercept of the following equation:

log Kz(%) = WR + S (Eqn. 21)

where R symbolizes wt.-% solvent, and W and S represent the slope and theintercept. It can be seen that the validity of Eqn. 20 is dependent on the fol-lowing assumptions, which are not always true:

1) Spectral contributions from H2X+ and X– are neglected. This may notalways be true for compounds with heavily overlapping pKa values.

2) AXH± and AXH0 correspond to the optical data obtained from 100% ofHX± and 100% HX0. In some amphoteric systems, complete formationof HX± or HX0 may be difficult, regardless of solvent composition. Forinstance, if KZ approaches unity, the correctness of AXH± is question-able.

3) The molar absorptivity coefficients of HX± and HX0 at the analyticalwavelength should either be: EXH± k EXH0, or EXH± K EXH0.

The KZ method overcomes problems 1, 2, and 3 above by deriving theresolved molar absorptivities of HX± and HX0 independently of each other.In this refined approach, multiwavelength spectrophotometric titrationexperiments are performed in different wt.-% mixtures of methanol. For eachtitration, the TFA method is applied to resolve the molar absorptivity spectraof species H2X+, HX, and X–. The molar absorptivity spectra of HX thusobtained are a linear combination of the molar absorptivity spectra of HX0

and HX±, from which the unknown KZ value can be derived. The optical dataof HX can be expressed as follows:

a = Ec (Eqn. 22)

where a denotes the molar absorptivity spectra of species ‘HX’ (i.e., HX± +HX0) obtained at various wt.-% methanol, E denotes the molar absorptivityspectra, and c denotes the concentration-methanol wt.-% profiles of HX0 andHX±.

KzXH (%)

(%) XH

(%)A A

A A0

=−− ±


Each element in the a matrix represents the absorbance of ‘HX’ at a par-ticular wavelength and methanol percentage. Each element in the molarabsorptivity matrix represents the absorptivity of one of the species (XH±,XH0) at a particular wavelength. Each element in the concentration-methanolmatrix represents the concentration of one of the species (XH±, XH0) at a par-ticular methanol percentage. This matrix equation is solved in a slightly dif-ferent way to the TFA solution described above. The following assumptionsare made:

cHX0 + cHX±= 1 (Eqn. 23)

(Eqn. 24)

From Eqns. 19, 21, and 23 it is deduced that:

and (Eqn. 25)

The solution of Eqn. 22 is then optimized by iteratively proposing differ-ent values for W and S in Eqns. 25 and using the resulting values of cHX0 andcHX± in Eqn. 22 until it converges to a minimum. The KZ value which is gen-erated from this solution is assumed to be the correct KZ value.

Figs. 13 to 15 are derived from a series of 10 titrations of labetalol inwater-methanol solutions from 0% to 75% methanol. Fig. 13 shows the molarabsorption spectra (a matrix) for the monoprotic form (HX). Fig. 14 depictsthe molar absorptivity spectra of zwitterion and neutral species (E matrix),while Fig. 15 shows the distribution of zwitterion and neutral species as afunction of methanol content (c matrix). From these data, it can be seen thatthe concentration of the neutral species increases as the methanol contentincreases, suggesting that the formation of neutral species is relatively favor-able in solvent media of low dielectric constants. This is consistent with thefact that neutral species have higher lipophilicity than charged species.

Table 3 lists the optimized log KZ and the microconstant values for labet-alol, niflumic acid, and pyridoxine. These data are consistent with thosereported in the literature [42] [54] [55].

Although this re-formulated method involves fewer assumptions than ear-lier methods, it requires that the molar absorption spectra of zwitterion andneutral species remain constant as the solvent content varies. Fortunately, thespectral dissimilarity between zwitterion and neutral species is generallymore pronounced than the solvatochromic shift caused by the change in sol-vent composition. This KZ method can therefore be used to determine micro-constants in a highly automated fashion.

c 101 10HX

WR S

WR S± =+

+

+c 11 10HX WR S0 =+ +

KzHX

HX

c

c 0

=±



Fig. 13. Molar absorption spectra of labetalol (HX). Data from 10 titrations with methanol content varying from 0% to 75.0%.

Fig. 14. Molar absorption spectra of zwitterion and neutral species of labetalol


Table 3. pKa Values, Microconstants, KZ Values and Log P Values of Labetalol, Niflumic Acid,and Pyridoxine a) b)

Labetalol Niflumic acid Pyridoxine

pKa,1 7.41 ± 0.01 2.28 ± 0.08 4.90 ± 0.05pKa,2 9.36 ± 0.01 4.86 ± 0.05 8.91 ± 0.05log KZ 1.48 ± 0.04 c) 1.94 ± 0.03 d) 0.91 ± 0.01 e)pk1

f) 7.42 2.29 4.92pk2

f) 8.90 4.23 5.86pk3

f) 9.35 4.86 8.86pk4

f) 7.87 2.92 7.95Log PHX 1.21 ± 0.02 g) 3.88 ± 0.01 h) –0.50 ± 0.02 h)Log PHX0

2.70 i) 5.83 i) 0.46 i)

a) All pKa values were determined by multiwavelength spectrophotometric titration at 25° andan ionic strength of 0.15 M. b) Literature values: labetalol log KZ = 1.46 [54], niflumic acidlog KZ = 1.24 [42], and pyridoxine log KZ = 0.90 [55]. c) W = –0.021 ± 0.001 (see Eqn. 21).d) W = –0.044 ± 0.004 (see Eqn. 21). e) W = –0.021 ± 0.001 (see Eqn. 21). f) Calculated usingEqns. 17–19 with the corresponding pKa and KZ values. g) Measured pH-metrically at Siriususing GLpKa; hitherto unpublished result. h) Reference [33]. i) Calculated from log PHX usingEqns. 29–31.

Fig. 15. Distribution of zwitterion and neutral species as a function of methanol content for labetalol

4.6. Distribution of Microspecies as a Function of pH

Based on the microconstant and pKa values, the distribution of microspe-cies can be calculated by solving the following equation [45] [53]:

(Eqn. 26)

where Y denotes the initial concentration of the sample used. The symbols k3

and k4 can be expressed in terms of k1, Ka,1, and Ka,2 by using Eqns. 17 and/or18. Fig. 16 shows the distribution of microspecies for labetalol as calculatedusing Eqn. 26 and the data reported in Table 3. In the subsequent discussion,it will be demonstrated that this information is essential for the generation ofthe lipophilicity profile of the zwitterionic ampholyte.

Y H XHX

XHX

2

00

1 1 1 1[H] 0 0

0 [H] 00 0 [H]

1

3

40 0

=−

−−

±kk

k

[ ][ ]

[ ][ ]


Fig. 16. Distribution of microspecies for labetalol as calculated using Eqn. 29 with the microconstants listed in Table 3

4.7. Lipophilicity Profiles of Ampholytes and Zwitterions Using the Shake-Flask Method

A general expression for the lipophilicity profiles for diprotic zwitterion-ic ampholytes can be written as:

(Eqn. 27)

where the subscripts oct and aq represent the octanol and aqueous phases. Byeliminating the octanol concentrations with the corresponding partition-co-efficient equations, Eqn. 28 is obtained:

(Eqn. 28)

where PH2X+, PHX0

, PHX±and PX–

represent the partition coefficients of cat-ion, neutral species, zwitterion, and anion, respectively.

Fig. 17 shows the lipophilicity profile of labetalol in octanol-water withthe experimental values measured using the shake-flask procedure [31]. At pH K pKa,1, the cation is the predominant species. Therefore, the log D

D =+ + +

+ + +

+ ±+ ± −

+ ± −P P P PH X

2 aqHX 0

aqHX

aqX

aq

2 aq0

aq aq aq

20 –

[H X ] [HX ] [HX ] [X ]

[H X ] [HX ] [HX ] [X ]

D = + + ++ + +

+ ± −

+ ± −[H X ] [HX ] [HX ] [X ][H X ] [HX ] [HX ] [X ]

2 oct0

oct oct

2 aq0

aq aq aq


Fig. 17. Lipophilicity profile of labetalol in octanol/water. The symbols represent experimen-tal shake-flase data [31]. The solid line was obtained by nonlinear fitting of data with Eqn. 28.

value approaches log PH2X+. Similarly, at pH k pKa,2, the log D value is

close to log PX–. With these assumptions, the log PH2X+

and log PX–values

were approximately equal to –0.73 and –0.05, respectively. Because the zwit-terion and neutral species co-exist in solution, it is not possible to obtainlog PHX±

and log PHX0independently from the experimental data shown in

Fig. 17.To determine the partition coefficient of the neutral species, it is assumed

that the zwitterion does not partition into octanol (i.e., PHX±= 0). Since the

distribution of microspecies for labetalol has already been determined (Fig. 16), the remaining unknown in Eqn. 28 is PHX0

. A nonlinear fitting ofexperimental data using Eqn. 28 produced a value of 2.89, which is consis-tent with a log P value of 2.87 ± 0.04 as calculated by ACD/Log P™ software(V.4.01, Toronto, Canada) for the neutral form of labetalol.

4.8. Lipophilicity Profiles of Ampholytes and Zwitterions Using pH-Metric Techniques

Using pH-metric titration in the presence of octanol, the log P value of‘HX’ was found to be 1.21. The following equation can be written for the par-titioning of ‘HX’:

(Eqn. 29)

Substituting the log P expressions and simplifying,

(Eqn. 30)

Similarly, by assuming the zwitterion does not partition into octanol, thefirst term on the right-hand side of Eqn. 30 can be neglected to yield the fol-lowing equation:

log PHX = log PHX0– pk2 + pKa,1 (Eqn. 31)

From Eqn. 31, the log PHX0is found to be 2.70. Note the good agreement

with the value obtained from the shake-flask experiment. The log PHX0

values of pyridoxine and niflumic acid have also beenmeasured by the KZ method (see Table 3).

P Pk

KP

kK

HX HX 1

a,1

HX 2

a,1

0

= +±

PHX oct

aq

oct0

oct

aq

[HX][HX]

[HX ] [HX ][HX]

= = +±


4.9. Lipophilicity Profiles of Zwitterionic Ampholytes

Fig. 18 shows the lipophilicity profiles of labetalol generated as a func-tion of log PHX0

by using Eqn. 28, and assuming that HX± does not partition.The values of log PH2X0

and log PX–were those used in Fig. 17. It can be seen

that the value of log PHX0strongly affects the appearance of the lipophilicity

profile. For log PHX0< 0, a U-shaped lipophilicity profile can be observed.

Fig. 19 shows that when the log PHX±value is varied and the log PHX0

value is fixed at 2.89, the lipophilicity profile shows bell-shaped behaviorirrespective of the value of log PHX±

. It should be noted that an increase inlog PHX0

(> 0, see Fig. 18) or log PHX±(see Fig. 19) could generate very sim-

ilar bell-shaped profiles, suggesting that independent determination of bothparameters is difficult.

In general, while the presence of intramolecular effects may enhance thepartitioning of some zwitterionic species into octanol [54], it is reasonable toassume that for many molecules, the zwitterion does not partition.


Fig. 18. Lipophilicity profiles of labetalol as generated using Eqn. 28 with log PH2X+

.Log PHX±

and log PX–

same as Fig. 17.

5. Conclusion

Equations have been presented for calculating lipophilicity profiles frompH data in conjunction with pKa and log P values. Shake-flask and pH-met-ric methods for measuring pKa and log P have been described, together witha hybrid pH-metric/UV method for measuring protonation microconstants.With the availability of microconstant data, the equations for lipophilicityprofiles can be extended to include zwitterionic ampholytes.

Some 15% of ionizable drugs may be classed as ampholytes, many ofwhich exhibiting zwitterionic characteristics. Lipophilicity of such moleculeshas traditionally been difficult to study. With the new techniques described inthis chapter, it will be possible to re-assess the lipophilicity of zwitterionicampholytes, and therefore to incorporate reliable log P values into the set ofphysicochemical descriptors for this important class of molecules.

We gratefully acknowledge the assistance of colleagues at Sirius who have helped in theresearch described above, in the construction of our instruments and software, and in the prep-aration of this chapter. Thanks are especially due to Ruth Legg, Lynne Trowbridge, Karl Box,Roger Allen, Jason Looij, Killian Cherry, and Kirsty Powell. We also thank Krisztina Takács-Novák of Semmelweis University of Medicine, Budapest, for her assistance and advice con-cerning spectroscopy and the calculation of Kz values, and Tim Mitchell and Ryszard Kobleckiof Cambridge Discovery Chemistry for their statistics on ionization in Table 1.


Fig. 19. Lipophilicity profiles of labetalol as generated using Eqn. 28 with log PHX0

equal to2.89. Log PH2X+

and log PX–

same as Fig. 17.

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High-Throughput Measurements of Solubility Profiles

by Alex Avdeef

pION Inc., 5 Constitution Way, Woburn, MA 01801, USA;Fax: +1 781 935 89 38; e-mail: [email protected]

1. Introduction

As the lipophilicity of a series of compounds decreases, so does their abil-ity to cross biological membranes by passive diffusion [1]. Fick’s first lawapplied to a membrane [2] indicates that passive diffusion of a solute is theproduct of the diffusivity and the concentration gradient of the solute insidethe membrane. The membrane/water apparent partition coefficient, an indica-tor of lipophilicity, relates the latter internal gradient to the external bulk-water concentration difference between the two solutions separated by themembrane.

Membrane permeability of a solute is the composite of its membrane dif-fusivity, its apparent partition coefficient, and of membrane thickness [2]. Inthe simplest model, the transport (i.e., flux) of molecules across a membraneis the product of the membrane permeability and the aqueous concentrationdifference between the two sides of the membrane. As such, permeability is arate constant, a kinetic parameter.

For an ionizable molecule to permeate by passive diffusion most efficient-ly, the molecule usually has to be in its uncharged form at the membrane sur-face. The amount of the uncharged form present at a given pH, which direct-ly contributes to the flux, depends on several important factors, such as pH,binding to endogenous carriers (proteins and bile acids), self-binding (aggre-gate and/or micelle formations), and solubility (a solid-state form of self-binding). Low solubility enters into the transport consideration as a thermo-dynamic ‘speed arrester’, as a condition which lowers the opportunity fortranport. In this way, permeability and solubility are the linked kinetic andthermodynamic parts of transport across a membrane. In this chapter, we willfocus on solubility [3–6], specifically on fast methods for measuring it as afunction of pH. We will present our most recent results in high-throughput



Fig. 1. Structures of the compounds investigated

measurements of solubility-pH profiles using 96-well plate technology withdirect UV-spectrophotometric assays. Fifteen sparingly-soluble commercialdrug molecules have been selected to illustrate the approach (see Fig. 1): thebases amiloride, amitriptyline, chlorpromazine, miconazole, nortriptyline,phenazopyridine, propranolol, terfenadine, the acids diclofenac, furosemide,indomethacin, 2-naphthoic acid, probenecid, the ampholyte piroxicam, and anonionizable molecule, griseofulvin. The results of the work will be com-pared to those of traditional saturation shake-flask measurements [5] and ofnew potentiometric measurements [5] [6].

The relevance of solubility in Fick’s first law will then be addressed, byassociating solubility-pH to artificial-membrane permeability-pH profiles [7],to present a new and possibly useful in vitro classification framework for pre-dicting in vivo passive oral absorption as a function of pH.

2. Solubility-pH Profiles

2.1. Equations

The basic relationships between solubility and pH can be derived for anygiven equilibrium model. The ‘model’ refers to a set of equilibrium equationsand the associated equilibrium quotients. In this section, we will considermonoprotic, diprotic, and triprotic molecules, and we will derive nine pos-sible cases of saturated solutions formed by the precipitation of a singleuncharged species.

2.1.1. Monoprotic Acid, HA

In a saturated solution, the two relevant equilibrium equations for the caseof a monoprotic acid are

H+ + A– s HA K1 = [HA] / [H+][A–] (Eqn. 1)

HA(s) s HA S0 = [HA] / [HA(s)] = [HA] (Eqn. 2)

Eqn. 2 is only relevant under conditions of saturation. It describes theequilibrium between the dissolved acid in a solution containing a suspensionof the solid form of the acid. The concentration of a species in the solid phase,[HA(s)], by convention, is taken as unity. Hence the quotient in Eqn. 2 reduc-es to the concentration of the species in the saturated solution, [HA], which isconstant, and is called the intrinsic solubility of the acid.


Protonation (i.e., formation) constants (e.g., Eqn. 1) will have a numeri-cal subscript, indicating the number of dissociable hydrogens in the protonat-ed species formed (on the right side of the equilibrium expression).

In a saturated solution, solubility, S, at a particular pH is defined as thesum of the concentrations of all of the species dissolved in the aqueous solu-tion:

S = [A–] + [HA] (Eqn. 3)

In Eqn. 3, [HA] is a constant (intrinsic solubility), but [A–] is a variable.The next step involves conversions of all variables into expressions contain-ing only constants and [H+] (as the only variable). Substitution of Eqns. 1 and2 into 3 produces the desired equation.

(Eqn. 4)

Curve 1 in Fig. 2 shows a plot of log S vs. pH for the above case (withlog K1 = 4 used in the simulation). For pH K log K1 (i.e., pH K pKa), thefunction reduces to the horizontal line log S = log S0. For pH k log K1, log Sis a straight line as a function of pH, exhibiting a slope of one (and an inter-cept of log S0 – log K1). The pH where the slope is 1/2 equals log K1 (‘pKa’).We do not use the pKa notation in the equations, because its definition formultiprotic cases is not always consistently presented in the literature,although for monoprotic cases, the current notation and the more commonnotation are equivalent. However, we will use the common form, pKa, whenthe meaning is unambiguous.

SK

SK

S K= + +

= +( )+[HA]

[H ][HA] =

H1

1+ 0

1+

pH11

1 100[ ]– log


Fig. 2. Simulated solubility-pH profiles, corresponding to the nine cases considered in Table 1.The ionization constants used were 4, 7, and 10.

2.1.2. Diprotic Acid, H2A

In a saturated solution, the three relevant equilibrium equations for thecase of a diprotic acid are:

H+ + A2– s HA– K1 = [HA–] / [H+][A2–] (Eqn. 5)

H+ + HA– s H2A K2 = [H2A] / [H+][HA–] (Eqn. 6)

H2A(s) s H2A S0 = [H2A] / [H2A(s)] = [H2A] (Eqn. 7)

Note that [H2A(s)] by convention is defined as unity. For such a case, sol-ubility is

S = [A2–] + [HA–] + [H2A] (Eqn. 8)

In Eqn. 8, [H2A] is a constant (intrinsic solubility), but [A2–] and [HA–]are variables. The next step involves conversions of all variables into expres-sions containing only constants and [H+].

S = S0 (10–log K1 – log K2 + 2 pH + 10–log K2 + pH + 1) (Eqn. 9)

Fig. 2 (curve 2) shows a plot of log S vs. pH for the above case (withlog K1 = 7 and log K2 = 4). For pH K log K2, the function again reduces tothe horizontal line log S = log S0. For pH between log K2 and log K1, log S isa straight line as a function of pH, exhibiting a slope of one (if the gapbetween the constants is sufficiently large). Where the slope is 1/2, the pH =log K2. For pH k log K1, log S is a straight line as a function of pH, exhibit-ing a slope of two. Where the slope is 3/2, the pH = log K1.

2.1.3. The Other Cases: Bases and Ampholytes

The remaining seven cases considered, in addition to the above two cases,are summarized in Table 1. Their detailed derivations followed similar stepsas those indicated above. The corresponding solubility-pH profiles are illus-trated in Fig. 2.

2.2. Gibbs pKa

Although Fig. 2 properly conveys the shapes of solubility-pH curves insaturated solutions of uncharged species, the indefinite ascendency in theplots can be misleading. It is not possible to maintain saturated solutions over12 orders of magnitude in concentration (curves 2, 3, 5, and 6)! At some pointlong before the solubilities reach such high values, salts will precipitate, lim-


iting further increases. Although precipitation of salts is not explicitly cov-ered in this presentation, it is nevertheless worthwhile to consider salt forma-tion in this limiting sense. As the pH change raises the solubility, at somevalue of pH, the solubility product of the salt will be reached, causing theshape of the solubility-pH curve to change from those in Fig. 2 (e.g., curve 1in Fig. 2 becomes the curve in Fig. 3).

As a ‘rule of thumb’ [5], in 0.15 M NaCl (or KCl) solutions titrated withNaOH (or KOH), acids start to precipitate as salts above log (S/So) = 4 andbases above log (S/So) = 3. Consider the case of the monoprotic acid, HA,which forms the sodium salt (in saline solutions) when the solubility product,


Table 1. Solubility-pH Equations: Saturated Solutions of Mono-, Di-, and Triprotic Molecules

Case Equilibrium Equilibrium Solubility EquationsExpressions Constant

1 H+ + A– s HA K1 S/So =Acid HA(s) s HA So 10–log K1 + pH + 1

2 H+ + A2– s HA– K1 S/So =Acid H+ + HA– s H2A K2 10–log K1 – log K2 + 2 pH + 10–log K2 + pH + 1

H2A(s) s H2A So

3 H+ + A3– s HA2– K1 S/So =Acid H+ + HA2– s H2A– K2 10–log K1 – log K2 – log K3 + 3 pH +

H+ + H2A– s H3A K3 10–log K2 –log K3 + 2 pH + 10–log K3 + pH + 1H3A(s) s H3A So

4 H+ + B s BH+ K1 S/So =Base B(s) s B So 10+log K1 – pH + 1

5 H+ + B s BH+ K1 S/So =Base H+ + BH+ s BH2

2+ K2 10+log K1 + log K2 – 2 pH + 10+log K1 – pH + 1B(s) s B So

6 H+ + B s BH+ K1 S/So =Base H+ + BH+ s BH2

2+ K2 10+log K1 + log K2 + log K3 – 3 pH +H+ + BH2

2+ s BH33+ K3 10+log K1 + log K2 – 2 pH + 10+log K1 – pH + 1

B(s) s B So

7 H+ + X– s HX K1 S/So =Ampho- H+ + HX s H2X+ K2 10+log K2 – pH + 10–log K1 + pH + 1lyte HX(s) s HX So

8 H+ + X– s HX K1 S/So =Ampho- H+ + HX s H2X+ K2 10+log K2 + log K3 – 2 pH +lyte H+ + H2X+ s H3X2+ K3 10+log K2 – pH + 10–log K1 + pH + 1

HX(s) s HX So

9 H+ + X2– s HX– K1 S/So =Ampho- H+ + HX– s H2X K2 10–log K1 – log K2 + 2 pH +lyte H+ + H2X s H3X+ K3 10–log K2 + pH + 10+log K3 – pH + 1

H2X(s) s H2X So

Ksp, is exceeded. In additions to Eqns. 1 and 2 above, one needs to add thefollowing equation to treat the case.

Na+A–(s) s Na+ + A–

Ksp = [Na+][A–] / [Na+A–(s)] = [Na+][A–] (Eqn. 10)

Effective solubility is still defined by Eqn. 3. However, Eqn. 3 is solved underthree limiting conditions with reference to a special pH value: a) If the solu-tion pH is below the conditions which lead to salt formation, the solubility-pH curve has the shape described by Eqn. 4 (curve 1 in Fig. 2); b) If pH isabove the characteristic value where salt starts to form (given high enough asample concentration), Eqn. 3 is solved differently. Under this circumstance,[A–] becomes the constant term and [HA] becomes variable.

(Eqn. 11)

where Si refers to the solubility of the conjugate base of the acid (cf. Eqn. 11to case 4 in Table 1), which depends on the value of [Na+] and is hence a con-ditional constant. Since pH k log K1 and [Na+] may be assumed to be con-

= +( ) = +( )++ +K

SK Ksp log –pHi

log –pH

[Na ]1 10 1 101 1

S K K= + = +( )+ +[A ] [H ][A ] [A ] 1 [H ]–1

– –1


Fig. 3. Simulated solubility-pH profile for an acid with an ionization constant of 4 and a saltsolubility 4 orders of magnitude greater than the intrinsic solubility. The true ionization con-stant and the Gibbs’ ionization constant are denoted by filled points at pH 4 and 8, respectively.Below the Gibbs’ pKa, only the free acid is precipitated. Above the Gibbs’ pKa, only the salt is

precipitated. At the Gibbs’ pKa, both the free acid and the salt co-precipitate.

stant, Eqn. 11 reduces to that of a horizontal line in Fig. 3: log S = log Si forpH > 8. c) If the pH is exactly at the special point marking the onset of saltprecipitation, the equation describing the solubility-pH relationship may beobtained by recognizing that both terms in Eqn. 3 become constant (seebelow), so that

S = So + Si (Eqn. 12)

Consider the case of a very concentrated solution of the acid hypotheti-cally titrated from pH well below its pKa to the point where the solubilityproduct is first exceeded. At the start, the saturated solution can only have theunionized molecular species precipitated. As pH is raised past the pKa, thesolubility increases, as more of the free acid ionizes and some of the solid HAdissolves, as indicated by curve 1 in Fig. 2. When the solubility reaches thesolubility product, at a particular elevated pH, salt starts to precipitate, but atthe same time there may be remaining free acid precipitate. The simultaneouspresence of the solid free acid and its solid conjugate base invokes the Gibbs’phase rule constraint, forcing the pH and the solubility to constancy, as longas the two interconverting solids are present. In the course of the thought-experiment titration, the alkali titrant is used to convert the remaining freeacid solid into the solid salt of the conjugate base. During this process, pH isrigorously fixed, in a manner of a ‘perfect’ buffer. This special pH point hasbeen designated the Gibbs’ pKa, that is, pKa

GIBBS [5] [6]. The equilibriumequation associated with this phenomenon is

HA(s) s A–(s) + H+ KaGIBBS = [H+] [A–(s)] / [HA(s)] = [H+] (Eqn. 13)

This is a conditional constant, depending on the value of the background[Na+] or [K+].

Since solubility is fixed during the solid’s interconversion (Eqn. 12), onemay set Eqn. 4 equal to Eqn. 11, to get in logarithmic form the expression [6]

log Si – log So = log K1GIBBS – log K1 (Eqn. 14)

Fig. 3 shows a hypothetical solubility-pH profile, where the difference,Eqn. 14, is four log units in the hypothetical example, which is typicallyfound with simple acids in the presence of Na+ or K+ [5].

In principle, all of the curves in Fig. 2 would be expected to have solubil-ity limits imposed by the salt formation. Under conditions of a constant coun-terion concentration, the effect would be indicated as a point of discontinuity(pKa

GIBBS), followed by a horizontal line of constant solubility, Si. However,this ‘ceiling effect’ may not be relevant in drug-discovery settings, since mostsolubility-pH measurements are performed at the ‘floor level’ with very dilutesolutions, due to limitation of the amount of compound available for suchtesting.


2.3. Solubility and Permeability in Fick’s First Law

Consider the PAMPA (parallel artificial membrane permeability assay)experiment [7], where a microtiter plate well is divided into two chambers,donor at the bottom and acceptor at the top, separated by a 125 m microfil-ter disc, coated with an alkane solution of a phospholipid, under conditionsthat multilamellar bilayers form inside the filter channels. This is schemati-cally illustrated in Fig. 4. Fick’s first law applied to homogeneous mem-branes at steady state [2] may be stated as

J = Dm dCm / dx = Dm [Cm(0) – Cm(h)] / h (Eqn. 15)

where J is the flux, in units of mol cm–2 s–1, where Cm(0) and Cm(h) are theconcentrations, in mol cm–3 units, of solute within the membrane at the twowater-membrane boundaries (at positions x = 0 and x = h, where h is thethickness of the membrane in cm units), and where Dm is the diffusivity ofthe solute within the membrane, in units of cm2 s–1. At steady state, the con-centration gradient, dCm/dx, within the membrane is linear, hence the differ-ence may be used in the right side of Eqn. 15. Steady state takes about 10 s


Fig. 4. The schematic of a microtiter plate well in a PAMPA (see text) assay, where the sampleis introduced in the lower (donor) chamber. CD and CA are the aqueous concentrations in thedonor and acceptor compartments, respectively. Cm(x = 0) and Cm(x = h) denote the concentra-tions of the sample in the membrane at the donor and acceptor boundaries, respectively. Thegradient within the membrane, dCm/dx, is constant at steady state, and the distribution

coefficient Kp = Cm(0)/CD = Cm(h)/CA.

to be established in a membrane of thickness 125 m [2], assuming the solu-tion is well stirred.

The limitation of Eqn. 15 is that measurement of concentrations of solutewithin different parts of a membrane is very inconvenient. However, sinceone can estimate (or possibly measure) the distribution coefficients betweenbulk water and the membrane, log Kp, one can convert Eqn. 15 into a moreaccessible form,

J = Dm Kp (CD – CA) / h (Eqn. 16)

where the substitution of Kp allows one to use bulk-water concentrations inthe donor and acceptor compartments, CD and CA, respectively. These con-centrations may be readily measured by standard techniques. Eqn. 16 is stillnot sufficiently convenient, since one needs to estimate Dm and Kp. It is acommon practice to lump the parameters and the thickness of the membraneinto one composite parameter, called ‘effective permeability’, Pe,

Pe = Dm Kp / h (Eqn. 17)

The PAMPA method, described by the Roche group [7] (see also chapterby Kansy et al. in this volume, p. 447) and commercialized as the PSR4instrument (pION), measures Pe values in 96-well microtiter plate format.

The relevance of Eqn. 16 (which predicts how quickly molecules passthrough artificial membranes) to solubility comes in the concentration terms.Consider ‘sink’ conditions, where CA is essentially zero. Eqn. 16 reduces tothe following flux equation

J = Pe CD (Eqn. 18)

Flux depends on the product of effective permeability of the solute (whichwe may presume to be most likely the uncharged molecular species) times theconcentration of the species at the water-side of the donor surface of themembrane. This concentration ideally may be equal to the dose of the drug,unless the dose exceeds the solubility limit, in which case it is equal to thesolubility. If only the uncharged molecular species permeates appreciably,then Eqn. 18 may be restated as

J = Po Co ≤ Po So (Eqn. 19)

where Po and Co are the intrinsic permeability and concentration of theuncharged species, respectively. The intrinsic premeability does not dependon pH, but its cofactor in the flux equation, Co, does. The concentration of theuncharged species is always equal to or less than the intrinsic solubility of thespecies, So.


2.4. Solubility (Flux Factor)-Permeability and Passive Oral Absorption

In solutions that are saturated at some pH, the plot of log Co vs. pH for anionizable molecule is simply a combination of straight segments, joined atpoints of discontinuity indicating the boundary between the saturated stateand the state of complete dissolution. The pH of these junction points isdependent on the dose level used in the calculation, and the maximum valueof log Co is always equal to log So in a saturated solution.

For a base, the plot of log Co vs. pH is a horizontal line (log Co = log So)at high pH in a saturated solution and is a line with a slope of +1 for pH val-ues less than the pH of the onset of precipitation.

For an acid, log Co is also a horizontal line in the saturated solution (atlow pH), and decreases with a slope of –1 in the pH domain where the soluteis completely dissolved.

We have called the plot of log Co vs. pH the ‘flux factor’ profile, with theidea that such a plot, when combined with intrinsic permeability, can be the


Fig. 5. The plots of log Po, log Co, and log PoCo vs. pH for (a) miconazole, a base, (b) diclofe-nac, an acid, and (c) piroxicam, an ampholyte. The assumed total concentrations were at thedose levels (250 mg, 50 mg, and 20 mg, respectively). All the line segments have either 0 or

±1 slopes and join at the saturation-dissolution pH values.

basis of an in vitro classification scheme to predict passive oral absorption asa function of pH.

Fig. 5 illustrates this idea using miconazole as an example of a base,diclofenac as an acid, and piroxicam as an ampholyte. In the three cases theassumed concentrations in the calculation were set to the respective doses.

3. Experimental Methods

3.1. Traditional Shake-Flask Methods

Solubility measurement [3] [4] under equilibrium conditions is largely amanually intensive process, taking from 12 hours to sometimes as long as 7days at a given pH, but it is a simple procedure. The drug is added to a stan-dard buffer solution (in a flask) until saturation occurs, indicated by undis-solved excess drug. The thermostated saturated solution is shaken as equili-bration between the two phases establishes, typically over a 24-hour period.After microfiltration or centrifugation, the concentration of the substance inthe supernatant solution is then determined using HPLC, usually with UVdetection. If a solubility-pH profile is required, then the measurement needsto be performed in parallel in several different pH buffers. The whole processcan easily take a week to complete. Because of the slowness of the measure-ment, very few discovery-stage molecules have their profiles determined.

3.2. Potentiometric Methods

A new potentiometric method, called the dissolution template titration(DTT), has been recently introduced [5] [6]. The procedure takes as inputparameters the measured (or calculated) pKa and the measured (or calculated)octanol/water partition coefficient, log POW. The latter parameter is used toestimate the intrinsic solubility, So, using the expression [8]

log So = 1.17 – 1.38 log POW (Eqn. 20)

Using the pKa and the estimated So, the DTT procedure simulates theentire titration curve before starting an assay. The simulated curve serves asa template for the instrument to collect individual pH measurements in thecourse of the titration. The pH domain containing precipitation is apparentfrom the simulation, and the data-collection strategy is set accordingly.Enough sample is weighed to cause precipitation during the titration.Titrations of acids begin at low pH and those of bases begin at high pH. KOH(or HCl) titrant is dispensed into the slurry, to drive the pH of the solution in


the direction of dissolution, eventually well past the point of complete disso-lution. As titrant is added, careful measurements of pH are made. The instru-ment dramatically slows down the rate of data taking as the point of completedissolution approaches in the titration. The rate of dissolution of the solid,described by the classical Noyes-Whitney expression [3] [9], depends on thesurface area of the solid, the aqueous diffusivity of the solute, the differencebetween the actual concentration of the dissolved molecules and the equilib-rium saturation concentration, and the stirring rate. As the saturation state isapproached, the time needed to dissolve additional solid exponentiallyincreases. The instrument directly takes this into account [5]. Only after theprecipitate completely dissolves (assessment based on the template), does theinstrument collect the remainder of the data rapidly, in a manner characteris-tic of regular titrators. Typically, 3–10 hours are required for the entire equi-librium solubility data taking. The more insoluble the compound is anticipat-ed to be (based on the template), the longer the recommended assay time. Agraphical analysis follows the data collection to obtain approximate solubil-ity constants. These are subsequently refined by a weighted nonlinear least-squares procedure.

Although the potentiometric method can be used in discovery settings tocalibrate high-throughput solubility methods and computational procedures,it is too slow for normal applications.

Additional potentiometric approaches for measuring solubility have beendescribed in the literature [10] [11].

3.3. High-Throughput Microtiter-Plate Methods

3.3.1. Turbidimetric Assays

The ‘turbidity’ method described by Lipinski and co-workers [12],although not thermodynamically rigorous, is an attempt to rank moleculesaccording to expected solubilities. Versions of the method are practiced atseveral pharmaceutical companies, using custom-built equipment. A 96-wellmicrotiter-plate nephelometer has been introduced recently by LabSystems(Franklin, MA, USA). The instrument is partly automated. Usually, the userneeds to integrate a robotic fluidic system in a customized way.

3.3.2. HPLC-Based Assays

Several pharmaceutical companies have taken the classical saturationshake-flask method and transferred it onto 96-well plate technology and a


robotic liquid-dispensing system. Analyses are performed with reversed-phase HPLC. Often it is necessary to develop the appropriate chromatograph-ic methods, since the discovery compounds may not be sufficiently character-ized at the early stages, although generic fast-gradient methods may be elim-inating the need for method development. In some companies, the DMSO isfirst eliminated by a freeze-drying procedure, before the aqueous buffers areadded. Data handling is often the rate-limiting step in the operations.

3.3.3. Direct UV Assay

The method developed at pION, using the PRS4 instrument (pION),involves the conversion of traditional shake-flask methods to 96-well plateformat. Samples are typically introduced as 10–30 mM DMSO solutions in a96-well polypropylene microtiter plate. The Tecan Genesis robotic liquid-handling system (Tecan, Research Triangle Park, NC, USA ) draws a 3–10 laliquot of the DMSO solution and mixes it into an aqueous buffer solution,so that the final typical sample concentration is 50–150 M and the DMSOconcentration is < 5% (v/v). The solutions are shaken on an orbital shaker for3–6 hours, filtered, and assayed by direct UV spectrophotometry in the190–500 nm domain. The direct UV measurement eliminates the need formethod development and easily lends itself to more complete automation,hence, higher throughput.

The buffers used in the assay are automatically prepared by the PSR4robotic system. The quality controls of the buffers and the pH electrode areperformed by alkalimetric titration, incorporating the Avdeef-Bucher proce-dure [13]. Following the completion of the UV assays, the pH in each micro-titer-plate well was measured to confirm proper value. The pH 9.5 wells typ-ically indicated pH 9.2–9.4, an effect attributed to the absorption of CO2 fromthe air. The correct pH values were used in all the calculations.

The solubility-pH data were fitted to the appropriate equation from Table 1 by a least-squares refinement procedure in the PSR4 software toobtain the apparent intrinsic solubility constants, log So. For comparison pur-poses, intrinsic solubilities and solubility-pH profiles were determined usingthe pSOL Model 3 instrument (pION). All pKa and octanol/water log POW

values were obtained by the GLpKa instrument (Sirius Analytical Instru-ments, Forest Row, E. Sussex, UK).

For the derivation of the flux-factor profile as an in vitro tool for predict-ing passive oral absorptions, artificial membrane permeabilities were meas-ured with the PRS4 instrument at pH 3.9, 5.0, 6.2, 7.4, and 8.5. These wereused in a least-squares procedure to determine intrinsic permeabilities,log Po, following the method of Walter and Gutknecht [14].


4. Results of High-Throughput Solubility-pH Measurements

Table 2 lists the apparent intrinsic solubilities for the fifteen compoundsstudied and compares these values to values derived by the pH-metric tech-nique and by the saturation shake-flask method. Three compounds did notdetectably precipitate at the concentrations studied. Fig. 6 shows the plots ofsolubility (g/ml) vs. pH for the molecules studied. Also included in Table 2are the intrinsic permeabilities of some of the compounds studied.

Fig. 5 displays the artificial membrane intrinsic permeability, log Po, andthe concentration of the unionized form, log Co, as a function of pH, formiconazole, diclofenac, and prioxicam. Also, the resultant flux plots, log J,are shown.


Table 2. Apparent Intrinsic Solubility, Intrinsic Permeability and Ionization Constants

Compound pKa Po Apparent pH-Metric So Shake-flask Jmax (10–10

(cm/s) So (g/ml) a) (g/ml) b) So (g/ml) mol cm–2s–1)

amiloride 8.74 2.7 · 10–7 > 200 c) > 0.002amitriptyline 57chlorpromazine 9.24 d) 19 1.9 e) 71 h)

1.4 f)0.02 g)

diclofenac 3.99 d) 1.8 · 10–2 23 0.8 i) 0.6 i) 132.5 j)

furosemide 3.52 1.1 · 10–4 30 5.9 i) 2.9 i) 0.00110.63

griseofulvin 1.1 · 10–5 38 9 h) 0.01indomethacin 4.18 1.8 · 10–3 7 1.1 k) 1 o) 0.35

0.5 l) 0.9 p)0.6 m)3.0 n)

miconazole 6.07 7.8 · 10–5 11 0.8 0.022-naphthoic acid 4.18 33 22 p)nortriptyline 10.13 > 100 c) 20phenazopyridine 5.12 48 20 37 q)piroxicam 5.07 d) 1.0 · 10–3 11 1.3 r) 3.3 i) 0.33

2.33 d) 5.1 s)probenecid 3.01 4.1 · 10–4 5 0.6 0.07propranolol 9.53 d) 1.3 · 10–1 > 100 c) 70 i) 70 t) > 440terfenadine 9.86 3.1 · 10–3 4 0.1 i) 0.26

0.01 u)

a) Assays at 23°, all assayed solutions contained 0.5% (v/v) DMSO. b) Using the pSOL Model 3 instru-ment. c) The solution concentration was 200 or 100 g/ml and no precipitate was detected. d) Cf. [15][16]. e) Cf. [17], aqueous solution result. f) Cf. [17], extrapolated from 10–45 wt-% dioxane. g) Cf. [17],extrapolated from 15–60 wt-% MeOH. h) Cf. [18], at pH 7.4. i) Cf. [5]. j) Cf. [19]. k) Cf. [17]. l) Cf. [17],extrapolated from 15–35 wt-% dioxane. m) Cf. [17], extrapolated from 10–60 wt-% MeOH. n) Cf. [17],extrapolated from 10–30 wt-% DMSO. o) Cf. [20]. p) Cf. [21]. q) Cf. [22]. r) Cf. [17], extrapolated from1–25 wt-% DMSO. s) Cf. [17], extrapolated from 3–45 wt-% MeOH. t) Cf. [23]. u) Cf. [17], extrapolat-ed from 10–75 wt-% MeOH.

5. Discussion

5.1. Solubility-pH Equations and Plots

The complete set of equations describing the relationship between solu-bility and pH have been collected in Table 1. Although many of these equa-tions have been published before (possibly not all), the use of different defi-


Fig. 6. The plots of concentration vs. pH for the studied molecules, in aqueous buffered solu-tions containing 0.5% (v/v) DMSO. The horizontal lines at 100 or 200 g/ml represent the lim-its of solubility. A concentration below the horizontal line indicates solubility of the sample in

a saturated solution.


nitions for ionization constants of multiprotic molecules may contribute tosome misapplications. All the equations in Table 1 have been consistentlydefined, and all have been tested, as is confirmed by the plots in Fig. 2, whichare based on the equations.

5.2. Solubility-pH Profiles and the Apparent Intrinsic Solubilities

Fig. 6 contains the solubility-pH plots of some of the compounds studied.The horizontal lines in the figure indicate the upper limits of detectable solu-bility. If an actual measurement is at the line, one can only say that the truesolubility is greater than or equal to the value indicated by the upper limit.The two upper limits we explored were either 100 g/ml or 200 g/ml,depending on what we anticipated the solubility to be. We had hoped to seeprecipitate for each of the compounds studied by our selections of upper lim-its. Still, three compounds, amiloride, nortriptyline, and propranolol, did notappear to precipitate. A small crystal was noted in the propranolol well, how-ever, it was not enough to indicate precipitation by the method.Chlorpromazine, amitriptyline, and nortriptyline all indicated considerableturbidity (in decreasing order, respectively) on first addition of the DMSOsolutions to the higher-pH buffers, however, the effect was transitory, andwith the exception of chlorpromazine, very little evidence of crystals wasseen in the respective wells. Miconazole and griseofulvin contained precipi-tate in all wells. Curiously, the amount of precipitate in miconazole for pH8–9 was visually much less than near pH 6. This is not consistent with the pH-partition theory, since the pKa of miconazole is 6.07. It may be that two dif-ferent polymorphs form in the pH range studied.

5.3. Comparisons to Literature Results

Table 2 summarizes the solubility data measured in this study, and com-pares the results to those based on the pH-metric method and the saturationshake-flask method. The agreement between the pH-metric and the shake-flask results is good, supported by the results in Table 2 and a published com-parative study [5]. With the high-throughput solubility (HTS) results, we aresatisfied by the quality of the comparison. The measured values are consis-tently higher, by about an order of magnitude. This may be due to the 0.5 %(v/v) DMSO present in the HTS assays. The amount may not appear large, buton mole-fraction basis, the ratio of DMSO to sample is about 500:1 to 1500:1.If DMSO binds to the sample molecules, it is conceivable that the effect mayelevate the apparent solubility constant over that of the true instrinsic solubil-


ity constant. For this reason, we qualified the results in Table 2 with the‘apparent’ prefix. We are currently attempting to measure drug-DMSO bind-ing constants.

5.4. Flux Profiles as an in Vitro Classification for Passive Oral Absorption

The last column in Table 2 lists the calculated (Eqn. 19) maximum fluxvalues across artificial membranes formed by phospholipid bilayers. Weapplied the apparent intrinsic solubilities determined in this study and theintrinsic permeabilities measured by the PAMPA method (unpublishedresults), listed in Table 2. The logarithms of the maximum flux values (nor-malized to furosemide, the least-transported molecule) are displayed in thetop panel of Fig. 7, along with human oral-absorption values taken from var-ious published sources [24–28], as well as the pH of saturated solutions(where Co = So). The pH is based on calculation of Co at the dose concentra-tions reported in the literature [24–28].

The compounds in Fig. 7 are ordered according to increasing calculatedmaximum relative fluxes. To a satisfactory degree, this ordered ranking ofcalculated fluxes follows the order of the human oral-absorption fractions.However, since the pH values of saturated solutions are very different foracids and bases, the top of Fig. 7 may be somewhat misleading, just as it maybe misleading to make predictions of absorption for a diverse class of com-pounds based on measurements of permeability at just one pH.

Perhaps the more important basis for comparison would be to pick specif-ic pH values and compare rankings of fluxes. To do so requires dose-solubil-ity-pH knowledge. Fig. 5 displays the characteristic pH profiles for bases (a),acids (b) and ampholytes (c). Since log Co changes linearly with pH in unsat-urated solutions, as is simply evident in Fig. 5, one can take the maximumrelative rankings in the top of Fig. 7 and deduce the rankings for pH 6.8 (mid-dle of Fig. 7) and for pH 5.0 (bottom of Fig. 7), simply by taking the pH dif-ference between the onset of saturation under dose concentrations (top of Fig.7) and the desired pH.

At pH 6.8, the calculated flux ranking is in the order propranolol > diclo-fenac > piroxicam > griseofulvin > terfenadine > indomethacin > probenecid> miconazole k amiloride ~ furosemide. However, at pH 5, the ordering isconsiderably different: diclofenac > piroxicam ~ indomethacin > probenecid> griseofulvin > propranolol ~ miconazole k terfenadine k furosemide >>>amiloride. These patterns, obtained at HTS speeds, may give insights intohow intestinal pH variations may affect absorption of candidate molecules[29]. Of course, this is a very simplistic model, based on application of Fick’sfirst law to artificial membranes. Nevertheless, it appears to give useful


Fig. 7. Graphs of the logarithms of the calculated fluxes relative to that of furosemide vs. humanoral absorption fractions. The top bar graph represents the maximum possible values, with thesaturated-solution pH values. The middle and bottom graphs correspond to the relative fluxes at

pH 6.8 and 5.0, respectively. The order of the molecules is the same in the three graphs.

insight, if only of a qualitative nature, and the information can be obtainedquickly, which may be of interest to discovery research.

6. Conclusion

The work described here is just the beginning of a new high-throughputtechnology. Refinement of the protocols continues, in order to make themethod more robust and to port the method to 384-well plate format withconcomitant increase in throughput. We will have the opportunity to addressthe role of DMSO, even in low-percentage concentrations, in elevating theapparent solubilities.

We are grateful for the support received from Wyeth-Ayerst Research and Hoffmann-LaRoche through a consortium for the development of the high-throughput instrument and theassociated methodologies. We thank Manfred Kansy (Roche) for many stimulating discussionsin the area of high-throughput permeability measurements. We gratefully acknowledge the veryable assistance of colleagues at pION: Konstantin Tsinman, Melissa Strafford, and CynthiaBerger.

REFERENCES

[1] V. Pliska, B. Testa, H. van de Waterbeemd, in ‘Lipophilicity in Drug Action andToxicology’, Eds. V. Pliska, B. Testa, H. van de Waterbeemd, VCH, Weinheim, 1996, pp.1–6.

[2] T. F. Weiss, ‘Cellular Biophysics. Volume I: Transport’, The MIT Press, Cambridge, MA,1996, pp. 83–183.

[3] D. J. W. Grant, T. Higuchi, ‘Solubility Behavior of Organic Compounds’, Wiley, NewYork, 1990, 335.

[4] S. H. Yalkowsky, S. Banerjee, ‘Aqueous Solubility’, Dekker, New York, 1992, 149.[5] A. Avdeef, C. M. Berger, C. Brownell, Pharm. Res. 2000, 17, 85.[6] A. Avdeef, Pharm. Pharmacol. Commun. 1998, 4, 165.[7] M. Kansy, F. Senner, K. Gubernator, J. Med. Chem. 1998, 41, 1007.[8] P. Isnard, S. Lambert, Chemosphere 1989, 18, 1837.[9] A. S. Noyes, W. R. Whitney, J. Am. Chem. Soc. 1897, 19, 930.

[10] J. J. Kaufman, N. M. Semo, W. S. Koski, J. Med. Chem. 1975, 18, 647.[11] D. Todd, R. A. Winnike, Abstr. 9th Ann. Mtng., Amer. Assoc. Pharm. Sci., San Diego

(1994).[12] C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Adv. Drug Deliv. Rev. 1997,

23, 3.[13] A. Avdeef, J. J. Bucher, Anal. Chem. 1978, 50, 2137.[14] A. Walter, J. Gutknecht, J. Membrane Biol. 1984, 77, 255.[15] A. Avdeef, Sirius Technical Application Notes 1995, Vol 1.[16] A. Avdeef, K. J. Box, Sirius Technical Application Notes 1996, Vol 2.[17] A. Avdeef, C. M. Berger, M. Strafford, L. Trowbridge, K. J. Box, C. Johansson, P.

Artursson, in preparation.[18] J. Huuskonen, M. Salo, J. Taskinen, J. Chem. Int. Comp. Soc. 1998, 38, 450.[19] A. Chiarini, A. Tartarini, Arch. Pharm. 1984, 317, 268.[20] S. H. Yalkowsky, R. M. Dannenfelser (Eds.), ‘AQUASOL dATAbASE of Aqueous

Solubility’, 5th ed., 1998, College of Pharmacy, Univ. of Arizona, Tucson, AZ 85721.


[21] K. G. Mooney, M. A. Mintun, K. J. Himmestein, V. J. Stella, J. Pharm. Sci. 1981, 70, 13.[22] A. T. M. Serajuddin, C. I. J. Jarowski, J. Pharm. Sci. 1985, 74, 142.[23] W. Schürmann, P. Truner, J. Pharm. Pharmacol. 1978, 30, 137.[24] G. L. Amidon, C. R. Walgreen, in ‘Biopharmaceutics Drug Classification and

International Drug Regulation’, Capsugel Library, 1998, pp.13–27.[25] E. B. Asafu-Adjaye, A. Hussain, Abstract, Natl. Mtg. Am. Assoc. Pharm. Sci., 1997,

Boston.[26] K. Balon, B. U. Riebesehl, B. W. Mueller, Pharm. Res. 1999, 16, 882, and refs. therein.[27] J. D. Irvine, L. Takahashi, K. Lockhart, J. Cheong, J. W. Tolan, H. E. Selick, J. R. Grove,

J. Pharm. Sci. 1999, 88, 28, and refs. therein.[28] M. Yazdanian, S. L. Glynn, J. L. Wright, A. Hawi, Pharm. Res. 1998, 15, 1490.[29] J. B. Dressman, G. L. Amidon, C. Reppas, V. P. Shah, Pharm. Res. 1998, 15, 11.


Electrochemical Aspects of Drug Partitioning

by Frédéric Reymonda), Véronique Gobrya), Géraldine Bouchardb),and Hubert H. Giraulta)

a) Laboratoire d’Electrochimie, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,Switzerland; Tel.: +41 21 693 31 51; Fax: +41 21 693 36 67; e-mail: [email protected]

b) Institut de Chimie Thérapeutique, Section de Pharmacie, Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland

1. Introduction

The aim of this review chapter is to emphasize the fact that, as soon as ionspartition between two immiscible liquid phases, they establish de facto a poten-tial difference between the two phases [1][2]. Consequently, it is not possible totreat the partition of ions without resorting to an electrochemical methodology.

After a short review of the thermodynamic aspects of the partition of ionsand ionizable compounds between two electrolyte solutions, this chaptershows how classical electro-analytical methods such as cyclic voltammetrycan be used to measure thermodynamic quantities such as the standard parti-tion coefficient of ionized drugs, log P0. The same methodology can also beapplied to study interfacial acid-base reactions. Finally, we present the con-cept of ionic partition diagrams, which are predominance-zone diagrams as afunction of the interfacial Galvani potential difference and the aqueous pH.

2. Distribution of Ions between Two Immiscible Electrolyte Solutions

Before treating the distribution of ions or salts between two immiscibleelectrolyte solutions, it is worth recalling the thermodynamic aspects of thedistribution of a neutral compound.

2.1. Partition of a Neutral Compound

Let us consider the partition of a neutral compound N between two phas-es and .


At equilibrium, the chemical potentials in the two phases (i.e., the workrequired to bring one mole of compound N from vacuum to each phase) areequal. It is usual to refer the chemical potential to a standard state associatedto a concentration scale, i.e., molal, molar, or mole-fraction scale. In thischapter, we shall use the molar scale, where the standard state is a virtual orideal solution of solute N at the concentration of one molar (1M), but wherethere would be no interactions between the molecules of N. A standard solu-tion is therefore a virtual solution where the molecules of the solute are onlyallowed to interact with those of the solvent. The chemical potential of N inthis standard state, i.e., the work required to add one mole of solute to this vir-tual phase is called the standard chemical potential 0.

For any real solution, the chemical potential is defined as

(Eqn. 1)

where a is the activity of the solute, the activity coefficient defined suchthat RT ln represents the work of interactions of the molecules of solute Nbetween themselves, and c and c0 represent the concentration in the molarscale and the standard concentration of 1M, respectively. As the numericalvalue of c0 is unity, it is often omitted to simplify the writing of the equations.We shall also here follow this practice.

In this way, when a solute N is allowed to partition between two phases and , the work to bring one mole of solute from vacuum to the two phases

µ µ µ γ= + = + +

0 00RT a RT RT c

cln ln ln


Fig. 1. The chemical potential corresponds to the Gibbs energy of solvation and is thereforea negative quantity. In this example, putting in contact the two phases and will result in a

transfer of the solute N from the phase to the phase .

is the same, and we can write:

(Eqn. 2)

By substitution, we can define the standard partition coefficient of thesolute N and its standard Gibbs energy of transfer from phase to phase as

(Eqn. 3)

It is worth recalling this fundamental thermodynamic aspect of partitionto emphasize the standard character of the partition coefficient defined as afunction of two standard quantities. Just as the standard chemical potentialrefers to a virtual or ideal solution, it is important to keep in mind that thestandard partition coefficient refers to the partition of a solute between twovirtual solutions. Eqn. 3 also shows that RT ln P0 represents the standardGibbs energy of transfer from the phase to the phase .

Of course, it is not possible to measure a standard quantity, but it can beestimated by extrapolation at infinite dilution by

(Eqn. 4)

2.2. Partition of a Salt

Although a salt, when dissolved in polar solvents, is dissociated into ionsor ion pairs, we can from a thermodynamic standpoint consider it as a neutralmolecule. In this way, we can consider the partition of a salt C+A– and definethe standard partition coefficient as

(Eqn. 5)

The standard partition coefficient of a salt will depend on the hydrophilicor lipophilic character of the ions it is composed of. For this reason, it is verytempting to deconvolute this quantity as a function of ionic quantities.

2.3. Electrochemical Potential of an Ion

When trying to define ionic quantities, the additional condition to consid-er is the electroneutrality of the solution. For this reason, it is not possible to

ln ln–

Pa

a RTC A0 C A

C A

C A0,

C A0,

+ –

+ –

+ –

+ – + –=

=

β

α

α βµ µ

ln lim lnPc

ccN0 N

N=

→0

β

α

ln ln–

–,

Pa

a RT

G

RTN0 N

N

N0,

N0,

t, N=

= =

→β

α

α β α βµ µ ∆ 0

µ µα βN N=


add positively (or negatively) charged ions and simultaneously keep thephase electroneutral. To circumvent this difficulty, it is usual to define theelectrochemical potential i of an ion i which takes into account the innerpotential of the phase.

(Eqn. 6)

The term zi F represents the additional work required to bring an electri-cal charge zi F from vacuum ( = 0) to a phase having an inner potential . Itmay be important to recall that the potential of a phase is a constant whichdepends on the surface potential and the excess charge carried by the phase.

The electrochemical potential, therefore, represents the work to bring amole of ions from vacuum to the phase. In calculating this quantity, we dis-tinguish the work associated with all the short-range interactions, namely thechemical potential, and the purely electrostatic work associated with thetransfer of a charge to a phase having an inner potential.

2.4. Partition of an Ion

When salts are partitioned between two phases, the electrochemicalpotentials of the different ions should be equal at equilibrium:

(Eqn. 7)

By substitution, one obtains what is often referred to as the Nernst equa-tion for ionic equilibria expressing the Galvani potential difference equal tothe difference of inner potentials as

(Eqn. 8)

with i

0, the standard transfer potential, defined as the standard Gibbsenergy of transfer of the ion Gt, i

0, expressed in a voltage scale:

(Eqn. 9)

Such values have been tabulated in different reviews for numerous ions[3–5]. Of course, one can also define a standard partition coefficient such as

(Eqn. 10)ln–, ,

PRTi

i i0 = µ µα β0 0

∆∆

αβ

α β α β

φ µ µi

i i

i

i

iz F

G

z F0

0 0 0

+ =→, , ,

– – t,

∆ ∆αβ β α

αβ

α

βφ φ φ φ= = +

– lni

i

i

i

RTz F

a

a0

˜ ˜µ µα βi i=

µ µ φi i iz F= +


The interesting consequence of this definition is that in the case of ionicspecies, we have to define both a partition coefficient and a standard partitioncoefficient related by

(Eqn. 11)

This equation illustrates the specificity of ionic partitioning between twoimmiscible electrolyte solutions, namely that the partition coefficient is poten-tial-dependent [6]. This implies that the partition coefficient depends on thepresence of the other ionic species that will be present in the two adjacent phas-es and thereby define the Galvani potential difference between the two phases.

Eqn. 11 clearly shows that it is meaningless to measure the partition co-efficient of ions as often done by shake-flask or titration methods, as the valueobtained will strongly depend on the nature of the other electrolytes or buffersystems used. The only value worth reporting is the standard partition coeffi-cient as this value is unique for a given ion and solvent pair system. The stan-dard partition coefficient is a thermodynamic quantity that is a function of thestandard chemical potential of the ion in the respective phases. Because itrefers to ideal or virtual solutions, the standard partition coefficient does notdepend on the presence of other species in solution.

2.5. Distribution Potential

To illustrate how a Galvani potential difference is established, we consid-er as before the partitioning of a univalent salt C+A–. For both the cation andthe anion, there is equality of the electrochemical potentials at equilibrium

(Eqn. 12)

(Eqn. 13)

By substitution, the Galvani potential difference can be expressed as

(Eqn. 14)

By taking into account the electroneutrality condition in the two phases(cC+ = cA–), this equation can be rewritten as

(Eqn. 15)∆∆ ∆

αβ α

βαβ α β

β αφφ φ γ γ

γ γ=

++

C

0A0

C A

C A

+ – + –

+ –2RT

F2ln

2F RTa a

a a∆αβ α β α β

α β

β αφ µ µ µ µ= ( ) ( ) +

C

0,C0,

A0,

A0, C A

C A

+ + – –

+ –

+ –

– – – ln

µ φ µ φα α α β β βA0,

A A0,

A– – – –+ = +RT a F RT a Fln – ln –

µ φ µ φα α α β β βC0,

C C0,

C+ + + ++ + = + +RT a F RT a Fln ln

ln ln ln –Pa

aP

z FRTi

i

ii

i=

=

β

α αβ φ0 ∆


The last term associated with the activity coefficients is often negligible.In this case, the Galvani potential difference established by the partition ordistribution of a salt between two phases is called the distribution potential[7]. This value is a constant that depends upon the nature of the ions but noton the total number of moles dissolved between the two phases.

Eqn. 15 clearly shows that as soon as we partition a salt between twoimmiscible solvents, say by shake-flask, we automatically polarize the inter-face between the two solvents and establish a potential difference.

When a salt formed of a lipophilic cation (e.g., tetrabutylammonium)which has a negative standard transfer potential (water vs. oil) o

w0TBA+ and

of a hydrophilic anion (e.g., chloride) with also a negative standard potentialo

w0Cl– partitions into a biphasic system, the interface polarizes negatively,

since the Galvani potential difference established is the half-sum of two neg-ative quantities.

Conversely, when a salt formed of a hydrophilic cation (e.g., potassium)which has a positive standard transfer potential o

w0K+ and of a lipophilic

anion (e.g., tetraphenylborate) which also has a positive standard potentialo

w0TPB– partitions into a biphasic system, the interface polarizes positively,

since the Galvani potential difference established is the half-sum of two pos-itive quantities.

On the other hand, if we partition a hydrophilic salt made of both a hydro-philic cation and a hydrophilic anion (e.g., potassium chloride), the interfacial


Fig. 2. Distribution potential for the partition of TBACl, KTPB and KCl

Galvani potential difference will depend on the relative hydrophilicity of thetwo ions. If the cation is more hydrophilic than the anion, the Galvani poten-tial difference will be slightly positive. Conversely, if the anion is morehydrophilic than the cation, the Galvani potential difference will be slightlynegative.

Similarly, if we partition a lipophilic salt made of both a lipophilic cationand a lipophilic anion (e.g., tetrabutylammonium tetraphenylborate (TATB)),the interfacial Galvani potential difference will also depend on the relativelipophilicity of the two ions. If the cation is more lipophilic than the anion,the Galvani potential difference established will be slightly negative.Conversely, if the anion is more lipophilic than the cation, the Galvani poten-tial difference will be slightly positive.

It should be stressed at this point that the relations derived above arebased on the assumption that equilibrium is established upon partition of thesalt. This implies that the solubility of the salt in the two adjacent phasesshould be sufficient for the equilibrium condition to be fulfilled. The distribu-tion potential established by the partition of a single salt is independent of thephase ratio.

More generally, as soon as more than one salt partitions, say n ions parti-tion, the equilibrium should be analyzed by the n equalities of the electro-chemical potentials of the n ions between the two phases. The Galvani poten-tial difference is then obtained as the solution of a system of 2n + 2 equationscomprising also the two electroneutrality conditions for the respective phas-es and the n equations of conservation of mass for the different species. Sucha system is seldom solvable analytically, and numerical solutions have to beused. In more complicated systems where more than two ions are involved,the phase ratio can play a major role as shown by Kakiuchi [8].

2.6. Extra-thermodynamic Assumption

We have seen that we can measure the standard partition coefficient of asalt. In order to establish a scale of standard ionic partition coefficients, it isnecessary to impose an extra-thermodynamic assumption [4]. In this way, theGibbs energy of solvation of a salt can be written as the sum of standardGibbs energies of adsorption of ions, and hence the same applies to the stan-dard partition coefficients:

(Eqn. 16)

Many assumptions have been proposed, but only the most widely used,namely the so-called TATB assumption [9], is presented here. The basis of

ln ln lnP P PC A0

C0

A0

+ – + –= +


this assumption is to postulate that the standard Gibbs energy of transfer ofthe cation tetraphenylarsonium (TPA+) is equal to that of the anion tetraphe-nylborate (TPB–), and equal to half that of the salt tetraphenylarsonium tetra-phenylborate [10]:

(Eqn. 17)

Such a postulate is of course too simplistic to be fully correct. However,this does not matter as this assumption is just used to define an origin on thescale of standard Gibbs energies of transfer of ions between two phases, andconsequently on the scale of standard partition coefficients.

Using a different assumption will only shift the origin of the scale and allits values, but shall not change the relative difference between two differentions. In other words, (ln Pi

0 – ln Pj0) is independent of the scale used. Hence,

to obtain the standard partition coefficient of ionic species, it is usual to meas-ure by calorimetry or solubility the Gibbs energy of transfer of a series of saltswhich from a thermodynamic viewpoint can be considered as a neutral solute,and then to use an extra-thermodynamic assumption to deconvolute the re-spective contributions of the anions and of the cations.

3. Distribution of Acids and Bases

In order to simplify the presentation, we restrict here to the distribution ofa weak acid. General treatments for the case of di-acids, di-bases, or ampho-lytes have already been published [11].

3.1. Distribution of a Weak Acid

Let us consider the partition of a weak acid AH. The system can bedescribed by the following set of equations comprising the equality of theelectrochemical potentials of A– and H+;

(Eqn. 18)

(Eqn. 19)

the equality of the chemical potential of the acid AH,

(Eqn. 20)µ µα α β βAH0,

AH AH0,

AH+ = +RT a RT aln ln

µ φ µ φα α α β β βA0,

A A0,

A– – – –+ = +RT a F RT a Fln – ln –

µ φ µ φα α α β β βH0,

H H0,

H+ + + ++ = +RT a F RT a Fln – ln –

∆ ∆ ∆G G Gt,TPA0,w o

t,TPB0,w o

t,TPA TPB0,w o

+ – + –→ → →= = 1

2


and the acid-base equilibria in the two respective phases

(Eqn. 21)

(Eqn. 22)

In limiting the system to this set of equations, we neglect the presence ofthe hydroxide ions.

For this simple system, the Galvani potential difference is established asa simple distribution potential as that given by Eqn. 15.

The acidity constant in the phase is related to that of the phase by

(Eqn. 23)

It is interesting to note that the product of the partition coefficient of theanion and of the proton is equal to the product of the standard partition coef-ficient as the term relative to the Galvani potential difference cancels out.Eqn. 23 clearly shows that the pK of an acid in the phase can only be cal-culated if we know the standard partition coefficient of AH, A–, and, moreimportantly, that of H+.

Some authors define the distribution coefficient of an acid as

(Eqn. 24)

where x is the molar fraction of the neutral and ionized forms, respectively.Just as it is meaningless to define a partition coefficient of an ion, since it isGalvani-potential-dependent, the concept of distribution coefficient is equal-ly meaningless.

log logD x P x Pi ii

= +

∑N N

Ka a

aK

P P

PK

P P

Pa

A H

AHa

A H

AHa

A H

AH

– + – + – +ββ β

βα α= = ⋅ = ⋅0

0 0

0

µ µ µβ β β β β βH0,

H A0,

A AH0,

AH+ + – –+ + + = +RT a RT a RT aln ln ln

µ µ µα α α α α αH0,

H A0,

A AH0,

AH+ + – –+ + + = +RT a RT a RT aln ln ln


Fig. 3. Distribution of a weak acid between two phases

3.2. pH Titration in Two-Phase Systems

Some reports claim that it is possible to measure the log P of a neutralionizable molecule by titration [12] [13]. This is only possible if we assumethat the different ionic species do not partition into the organic phase (non-polar solvents such as alkanes). Indeed, in this case one only has to considerEqns. 20 and 21 taking for the aqueous phase and for the organic phase.For dilute solutions, an apparent pK value can be defined by:

(Eqn. 25)

where r is the volumic phase ratio (V0/Vw) and where, ctotAH = (nw

AH + n0AH)/Vw,

nAH being the number of AH molecules. The term in bracket is a constant, andit is therefore possible to evaluate the standard partition coefficient of the acidby measuring the apparent pK value. It is worth remembering here that thetwo-phase titration of a weak acid by a strong base yields a curve for whichthe apparent pK is shifted, but that the inflexion point of the shifted curvedoes not correspond to pKapp and that a curve-fitting routine should be usedto extract this value.

Taking into account the partition of the different ionic species, one canalso write for dilute solutions that

(Eqn. 26)

As seen above, the partition of an ionic species depends on the Galvanipotential difference established by the distribution of all the ions. Therefore,during the two-phase titration of a weak acid by a strong base, we continu-ously vary the partition of the different ions and consequently, the Galvanipotential difference also varies continuously. Hence, Kapp will depend on theGalvani potential difference, which depends on the other electrolytes and buf-fers used. Therefore, it is meaningless to measure Kapp if ions are allowed topartition into the organic phase.

It can be argued [13] [14] that using an excess of KCl (e.g., 0.15M) is suf-ficient to fix the Galvani potential difference, even if the ions are lipophilic.This point is examined here by way of an example.

Let us consider a lipophilic acid AH such that the associated base A– isalso lipophilic, i.e., A– has a negative standard Gibbs energy of transfer fromwater to oil (Gt, A–

0,wo < 0) and therefore a positive standard transfer potential(o

w0A– > 0), such that we always have o

w0A– >o

w0Cl–. To simplify, let us

assume that we use an excess of KCl (0.15M) in the aqueous phase and that

Ka a

a

c c

c

rP

rPK

rP

rPappa

w Aw

Hw

AHw

Atot

Hw

AHtot

AH0

A

AH0

A

– + – +

– –

= ≈+[ ]+[ ] =

+[ ]+[ ]

1

1

1

1

Ka a

a

c c

c

c c

crP K rPappa

w Aw

Hw

AHw

Aw

Hw

AHw

Aw

Hw

AHtot AH

0AH0– + – + – +

= ≈ = +[ ] = +[ ]1 1


we use HCl and KOH for the titration. At the beginning of the titration (Scheme 1), the pH is low, and most of the acid is in the organic phase and inits neutral form. As we add KOH, we shall first deprotonate the aqueous acid(Scheme 2). As the aqueous AH concentration is small, this bulk deprotona-tion is soon followed by interfacial deprotonation (Scheme 3). Of course, thisreaction is equivalent to a proton transfer from oil to water, and it must becounterbalanced either by an anion transfer from oil to water (e.g., Cl– or A–)or by a cation transfer from water to oil (e.g., K+). The most important con-sequence of these ion-transfer reactions is that the Galvani potential differ-ence which was slightly negative is becoming positive (see Fig. 2). As we addmore KOH, we completely displace the equilibrium to have KA in the organ-ic phase.

This simple example clearly demonstrates that having an excess of KCl inwater is not sufficient to always maintain a stable Galvani potential differ-ence, and that consequently it is not possible to measure the log P0 of an acid


Fig. 4. Schematic representation of the different species present in a two-phase titration. Thesize of the letters is proportional to the concentrations.

by two-phase titrations when the organic phase is polar enough to dissolve theionic species.

In the above example, if the anion A– is hydrophilic, most of the ionic spe-cies will be in the aqueous phase, and Eqn. 25 becomes then applicable. Inother words, such a method can only be used if rPA– is much smaller thanunity, keeping in mind that the phase ratio and the interfacial Galvani poten-tial difference contribute to this term. Also, it should clearly be stated that itis neither possible to measure the standard partition coefficient of an ion bytwo-phase titrations.

4. Interface between Two Immiscible Electrolyte Solutions

4.1. Polarized Liquid/Liquid Interfaces

By dissolving a hydrophilic salt in water and a lipophilic salt in the organ-ic phase, we have an Interface between Two Immiscible Electrolyte Solutions(ITIES) which can be polarized by an external circuit. From an experimentalviewpoint, this is done by using a 4-electrode potentiostat [15], which allowsthe potentiostatic control of the interfacial potential difference and which iscommercially available from many suppliers of electrochemical instrumenta-tion. As shown in Fig. 5, two reference electrodes control the Galvani poten-tial difference, and two counter-electrodes allow the passage of the current.The electrochemical cell to be used can be of many designs as reported in theliterature. A schematic design is given in Fig. 6.


Fig. 5. Schematic representation of an electrochemical cell and a 4-electrode potentiostat

The electrochemical cell includes a reservoir usually made of glass, twocounter-electrodes made of a noble-metal wire, e.g., platinum, and two ref-erence electrodes. The counter electrodes can be placed in compartmentsisolated from the main solution by a glass frit to avoid any redox reaction onthe platinum wires interfering with the study of ion transfer reactions at theliquid/liquid interface. The aqueous reference electrode can be a silver/sil-ver-chloride electrode used with or without a liquid junction. The organicreference is usually an ion-selective electrode for one of the ions of the sup-porting electrolyte, e.g., a tetrabutylammonium (TBA+) ion-selective elec-trode or a tetraphenylborate (TPB–) ion-selective electrode, which can,respectively, be represented by the following chains (see also [16] for furtherdetails)

TBA TPB TBA Cl excess NaClin the organic phase in water

AgCl Ag

TBA TPB NA TPB excess NaClin the organic phase in water

AgCl Ag

+ – + –

+ – + –

+

+


Fig. 6. Schematic representation of an electrochemical cell for the study of ion-transfer reactionsat liquid/liquid interfaces

The potential difference E obtained between the aqueous and the organicreference electrode depends on the nature of the reference electrodes used. Tocorrelate this experimental value to the Galvani potential-difference scale, thesimplest way is to study the transfer of a given ion, the standard transferpotential of which has been previously established (e.g., tetramethylammoni-um). In any case, we can always write

(Eqn. 27)

4.2. Voltammetry for Ion-Transfer Reactions at Liquid/Liquid Interfaces

As ion-transfer reactions are relatively fast compared to the mass trans-port of the ions to the interface (see Fig. 7), all the classical electroanalyticalmethodology can be transposed directly. Indeed, all electroanalytical tech-niques are based on current-potential responses obtained by solving the dif-fusion equations of the ion motion in the two adjacent phases with the Nernstequation and the equality of interfacial fluxes as boundary conditions.

Some of the electro-analytical techniques used to study ion-transfer re-actions across a liquid/liquid interface include polarography, normal-pulse

E = +∆ow constantφ


Fig. 7. Mass transport for an ion-transfer reaction. Diffusion – Transfer – Diffusion.

voltammetry, ac voltammetry, differential-pulse voltammetry, and of coursecyclic voltammetry [17–21]. Cyclic voltammetry is perhaps the most ubiqui-tous method when thermodynamic data or mechanistic information arerequired. For this reason, we restrict ourselves to a brief description of thistechnique for the study of ion-transfer reactions.

The gist of cyclic voltammetry is to sweep the applied potential from aninitial value (where the ion of interest is in one phase only and where no ion-transfer reaction occurs) to a final potential value at least 100 mV past thestandard transfer potential of the ion under study, and to return back to theinitial value. The current response to this linear variation of the potential isproportional to a dimensionless current, as shown in Fig. 8.

As the potential increases and approaches the standard transfer potential,the current increases as the flux of ions across the interface increases. Thisflux reaches a maximum value at the so-called peak potential and starts todecrease. Upon reversal of the sweep potential, the current continues todecrease before changing sign. At this point, we have a reversal of the ionic


Fig. 8. Dimensionless cyclic voltammogram where the dimensionless current c is plotted as afunction of the potential for the forward scan (top curve) and the return scan (bottom curve)

flux. This reverse current increases up to a maximum value, the so-calledreverse peak, and then decreases slowly to zero.The main characteristics of the dimensionless current response are:– The forward peak value is equal to 0.4463. – The position of the peaks is independent of the sweep rate.– The separation between the forward-peak value located at E1/2 + 28/zi mVand the backward peak located at E1/2 + 28/zi mV is therefore 59/zi mV. Thus,cyclic voltammetry is a well-suited tool to study ion-transfer reactions, asmonovalent ions yield a 59 mV separation, whereas divalent ions yield28 mV and trivalent 20 mV. This is clearly illustrated in Fig. 9 for the trans-fer of the monovalent and the divalent forms of N-methylephedrine and tri-metazidine, respectively.– The potential corresponding to the mid-point between the forward and thereverse peaks E1/2 is called the half-wave transfer potential and is related to


Fig. 9. Typical cyclic voltammograms obtained at the water/1,2-dichloroethane interface forthe transfer of: A) the monobase N-methylephedrine at pH 1.9; B) the dicationic form of tri-metazidine at pH 1.1. The potential scan rate is 10, 30, 50, 80, and 100 mV s–1; the forwardscan (FWD) is from left to right (upper part of the curves) and the reverse scan (REV) fromright to left. The data are already transposed from the applied potential scale to the absoluteGalvani potential scale (reproduced with permission from [26], Copyrights 1999 Wiley-VCH

for A) and from [27], Copyrights 1999 Plenum Publishing Corporation for B)).

the standard transfer potential by

(Eqn. 28)

where E0′ is the formal transfer potential, which is related to the standardtransfer potential E0 by

(Eqn. 29)

It should be noted that the interfacial concentrations of the diffusing spe-cies are equal at the half-wave potential.– If the rate of ion transfer is not fast enough, we observe a separation of thepeaks as the sweep rate increases.

By measuring the half-wave transfer potential of a given ion, the formaltransfer potential can be calculated knowing that the ratio of the diffusioncoefficient is (according to Walden’s rule) inversely proportional to the ratioof the viscosities of the two adjacent phases. Using either the Debye-Hückeltheory or by repeating the measurements at different supporting-electrolyteconcentrations, i.e., at different ionic strengths, one can estimate the standardtransfer potential from which one obtains the standard transfer potential onthe absolute scale of Galvani potential difference o

w.The current measured is directly proportional to this dimensionless cur-

rent and to the square root of both the sweep rate and the diffusion coeffi-cient of the ion to the interface [22], such that the peak current Ip is given by

(Eqn. 30)

This equation is known as the Randles-Sevcik equation, and it explainswhy it is usual in cyclic voltammetry to record a scan-rate-dependence and toplot the peak current as a function of the sweep rate. If the signal is due to anion-transfer reaction, this plot should be linear and the slope can be used tocalculate either the bulk concentration or the diffusion coefficient.

4.3. Voltammetry for Acid-Base Reactions at Liquid/Liquid Interfaces

Another very interesting application of cyclic voltammetry is the study ofinterfacial acid-base reactions. Again, for simplicity, we shall limit this pres-entation to a monoacid AH.

I z F Acz F D

RTp i ii i= 0 4463.ν

E E RTz Fi

i

i

′ = +

0 0 lnγγ

w

o

E E RTz F

DDi

i

i1 2

0/ ln= +

′o

w


In fact, from an electrochemical viewpoint, an interfacial acid-base reac-tion can be studied as a proton-transfer reaction. In this respect, the Nernstequation to consider is that relative to the proton transfer:

(Eqn. 31)

By introducing the acidity constant in the above equation, we can substi-tute the activity of the proton in the organic phase to obtain

(Eqn. 32)

From a mass-transport viewpoint, the proton-transfer reaction is limitedby the diffusion of both the acid and the base in the organic phase. In this way,this type of interfacial acid-base reactions behaves as redox reactions on asolid electrode where the oxidized species collect electrons upon reduction.

When using cyclic voltammetry to study proton-transfer reactions, thehalf-wave potential corresponds to the condition

(Eqn. 33)

Combining Eqns. 27, 28 and 29 with Eqns. 32 and 33, we see that theexperimentally measured half-wave potential for proton-transfer reactions

c cAHo

Ao

–=

∆ ∆ow

ow

H0 a

oAHo

Ao

Hw+

– +

φ φ= +

RTF

K aa a

ln

∆ ∆ow

ow

H0 H

o

Hw+

+

+

φ φ= +

RTF

a

aln


Fig. 10. Acid-base reaction at a liquid/liquid interface. The interfacial deprotonation of a lipo-philic acid is equivalent to a proton transfer from the organic to the aqueous phase. The inter-facial protonation of a lipophilic base is equivalent to the transfer of a proton from water to the

organic phase.

resulting from an interfacial acid-base reaction is pH-dependent.

(Eqn. 34)

This type of behavior has been observed experimentally in different cases(see [23] for a review).

5. Ionic Partition Diagrams

We have just seen above that even for the simple case of a monoacid AHpartitioning between two phases, a wide range of situations exist. To clarifythe complexity of such a system, we proposed a few years ago [24] to presentthe data as ionic partition diagrams based on the concept of the Pourbaix dia-gram as illustrated in Fig. 11.

Let us consider the simple case of a monoacid AH. At high pH, the anion-ic form is predominant. At Galvani potential differences more positive thanthe standard transfer potential, the anion is mainly in the aqueous phase andat Galvani potential differences smaller than the standard transfer potentialmainly in the organic phase. The separation line between these two zones isgiven by the Nernst equation for the anion.

(Eqn. 35)∆ ∆ow

ow

A0 A

o

Aw–

–

–

φ φ=

– lnRT

F

a

a

∆ ∆ow

ow

H0

ao

+ pHφ φ1 2 10/ lnln

= +

+′ RT

FK RT

F


Fig. 11. Ionic partition diagram for a hydrophilic monoacid

The line drawn to separate the two zones represents therefore an equi-concentration line, i.e., the geometrical locus where the two adjacent specieshave equal concentrations.

If the acid is hydrophilic, the neutral form will be mainly in the aqueousphase, and the separation line between the acid and the base is simply givenby

(Eqn. 36)

Finally, the separation line between the neutral acid in water and the basein the organic phase can be obtained by including the acidity constant toEqn. 35

(Eqn. 37)

From this equation, it is clear that this separation line is pH-dependent.If the acid is lipophilic, it is better to present the diagram so as to consid-

er the neutral form AH in the organic phase.Eqn. 35 remains as the equi-concentration line between the aqueous and

the organic anions. Regarding the border line between the aqueous anion andthe neutral acid in the organic phase, we have to consider the following equa-tion

(Eqn. 38)

from which we obtain the equation for the separation line

(Eqn. 39)

Finally, the separation line for the two species in the organic phase can beobtained by introducing the standard partition coefficient of the neutral acidin Eqn. 38 to obtain

(Eqn. 40)

Again, this border line is pH dependent.In the case of lipophilic acids, cyclic voltammetry experiments where the

half-wave potentials are measured at different pH values allow to draw thelines associated to Eqns. 35 and 40. We can therefore directly obtain both thestandard partition coefficient of the neutral form and the standard ionic parti-tion coefficient of the anionic forms. To our knowledge, electrochemicalmethods such as cyclic voltammetry are the only methods able to gather thisdual information.

∆ ∆ow

ow

A0 a

w

AH0

Ao

Hw

AHo–

– +φ φ=

+

– ln lnRT

KP

RTa a

a

pH = p aw

AH0K P+ log

Ka a

a

a a

aPa

w Aw

Hw

AHw

Aw

Hw

AHo AH

0– + – += =

∆ ∆ow

ow

A0

aw A

oHw

AHw–

– +φ φ= [ ]+

– ln lnRT K RT

a a

a

pH = p awK



Fig. 12. Ionic partition diagram for a lipophilic monoacid

Fig. 13. Ionic partition diagram of quinidine in water/DCE at 21°. HQH22+, HQH+, HQ, and

Q– stand for the doubly protonated, singly protonated, neutral, and deprotonated species,respectively. The dashed lines are the theoretical equi-concentration lines between two adjacentspecies. The figure also shows the mechanisms of the transfer reactions resulting from the pas-sage from one predominance domain to the other upon a change of Galvani potential across theinterface or of aqueous pH. The three dissociation constants of quinidine are 4.43, 8.66 (meas-ured by titration), and 17.00 (estimated value), while the standard transfer potentials deter-mined by cyclic voltammetry are 162, 80, and 253 mV for HQH2

2+, HQH+ and Q–, respectively.(Reprinted with permission from [24]. Copyright 1996 American Chemical Society)

Of course, drug molecules can have more than one acid or basic groups,and more complicated diagrams can be obtained as shown in Fig. 13 for thedibasic drug quinidine. A complete description of the systems and moleculesstudied to date, as well as detailed explanations about the lipophilicitydescriptors that can be deduced from electrochemical measurements of parti-tion coefficients, can be found in recent reviews [23] [25].

6. Conclusions

In this chapter, we have emphasized the difference between the ionic par-tition coefficient and the standard ionic partition coefficient. The value of theformer depends upon the other electrolytes and buffers present that contrib-ute to the establishment of the Galvani potential difference. By contrast, thevalue of the latter is an intrinsic property of the ion as it is related to the stan-dard Gibbs energy of transfer between two ideal solutions.

We have also shown how cyclic voltammetry can be used to calculate thestandard ionic partition coefficient from the measurement of the half-wavepotential for ion-transfer reactions. In particular, cyclic voltammetry can beused to study interfacial acid-base reactions.

Finally, we have shown that the complexity of the partition of drugs withacidic or basic properties can be easily presented with the help of ionic parti-tion diagrams, which are zone diagrams based on Galvani potential differencevs. pH plots.

The authors would like to thank the Swiss National Science Foundation for financial sup-port, as well as their colleagues Prof. B. Testa and Dr. P.-A. Carrupt from the Institut de ChimiePharmaceutique de l’Université de Lausanne for stimulating discussions. Laboratoired’Electrochimie is part of the European network ODRELLI (Organisation, Dynamics andReactivity at Electrified Liquid/Liquid Interfaces).

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Electrochemistry’, Eds. J. O. M. Bockris, B. Conway, R. White, Plenum Press, NewYork, 1993, Vol. 25, pp. 1–62.

[19] P. Vanysek, Electrochim. Acta 1995, 40, 2841.[20] M. Senda, Y. Yamamoto, ‘Amperometric Ion-Selective Electrode Sensors’, in ‘Liquid-

Liquid Interfaces. Theory and Methods’, Eds. A. G. Volkov, D. W. Deamer, CRC Press,Boca Raton, 1996, pp. 277–293.

[21] F. Reymond, D. Fermin, H. J. Lee, H. H. Girault, ‘Electrochemistry at Liquid/LiquidInterfaces: Methodology and Potential Applications’, Electrochim. Acta, in press.

[22] A. J. Bard, L. R. Faulkner, ‘Electrochemical Methods: Fundamentals and Applications’,Wiley, New York, 1980.

[23] F. Reymond, ‘Transfer Mechanisms and Lipophilicity of Ionisable Drugs’, in ‘LiquidInterfaces in Chemical, Biological and Pharmaceutical Applications’, Ed. A. Volkov,Dekker, New York, submitted.

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[26] F. Reymond, P.-A. Carrupt, B. Testa, H. H. Girault, Chem.-Eur. J. 1999, 5, 39.[27] F. Reymond, G. Steyaert, P.-A. Carrupt, D. Morin, J.-P. Tillement, H. H. Girault, B.

Testa, Pharm. Res. 1999, 16, 616.


Biolipid pKa Values and the Lipophilicity of Ampholytes and Ion Pairs

by Robert A. Scherrer

3M Pharmaceuticals, 3M Center 270-2S-06, St. Paul, MN 55144-1000, USA;e-mail: [email protected]

1. Introduction

1.1. Scope

This chapter covers three seemingly diverse topics on the partitioning ofacids and bases, but a key theme runs through them. That theme is consider-ation of ionization in the lipid phase, along with the partitioning of ion pairs.I believe this markedly enhances the understanding of lipophilicity and opensup new paths for the measurement of data. The first topic deals with tradition-al partitioning concepts. The octanol pKa (pKa, OCT) is calculated from theaqueous pKa and partition coefficients. The second topic describes a methodfor directly measuring the octanol pKa (pKa″) by titration in octanol. The thirdtopic describes a new classification of ampholytes based on their dissociationconstants in water and octanol.

A brief overview of additional topics provides a hint of a much broaderworld of ion pairs when ionization and partitioning are linked. Biochemistry isrich in chemistry involving ion pairs, but we are only beginning to appreciatethe subtleties of the way Mother Nature uses them. I will point out some of thelatter while discussing features that favor ion-pair formation, ways to estimatethese values from ionization constants, and applications to Hansch analysis.

1.2. The Concept of pKa in Octanol and Membranes

The concept of a pKa in octanol or a membrane becomes much simplerwhen one uses the pH of the aqueous phase in equilibrium as the effective pHof the system. The octanol pKa (or pKa, OCT), then, is the pH in the aqueousphase at which the concentration of ionized and neutral species in the octanol


phase is equal. A detailed discussion by Avdeef [1] appeared in theProceedings of the first symposium of this series. The same broad definitionapplies to membranes, but here the ionized species may be paired with amembrane counter ion, rather than a small inorganic ion. As shown by Comerand Tam in another chapter of this volume (see p. 275), and as will be seenhere, both the aqueous and lipid phase pKa values can be read off of log D vs.pH profiles. They occur at the upper and lower inflections, respectively,where the slope equals one-half.

2. The Thermodynamic Cycle of Ionization and Partitioning

The value of using octanol pKa is that it allows completion of a cycle ofequilibria of ionization and partitioning, as depicted on the quadrant diagramfor an acid (Fig. 1). Each macro-equilibrium is associated with a Gibbs freeenergy determined by its equilibrium constant, in accord with Eqn. 1. The netfree-energy change around the cycle is zero. This means the differencebetween log PN and log PI, or diff (log PN – I), is the same as the differencebetween the aqueous and octanol pKa values (Eqn. 2). This valuable relation-ship will be used frequently throughout this work.

G = –2.30 RT log Keq (Eqn. 1)

| pKa, OCT – pKa | = | diff (log PN – I) | (Eqn. 2)


Fig. 1. This quadrant diagram represents the four macro-equilibria for the partitioning of anionizable compound between octanol and water. Applying the Gibbs free energy equation allowsone to see that the net free-energy change around the cycle is zero. If any three equilibria areknown, the fourth can be calculated. It also means that the difference between the logs of thepartition coefficients is the same as the difference between the octanol and aqueous pKavalues.

Both log PI and pKa, OCT are dependent on the concentration of counter-ion in the aqueous phase. Physiological concentrations of counter ion (0.15 M

NaCl or KCl) will have the most biological relevance. Ion-pair partitioning into membranes differs from partitioning into octa-

nol in one important aspect. The ion pair can enter into a second equilibriumwith the phospholipid which results in a new ion pair and regeneration of thefirst counter ion (Eqn. 3). This is why there is little response to counter-ionconcentrations in membranes [2].

(R1O)(R2O)OPO–

RNH3+ + Cl– a RNH3

+Cl– u RNH3+(R1O) (R2O)OPO–+ C1–

U(water) (membrane) (membrane)

(Eqn. 3)

3. A Basis for Selectively Affecting the Free Energy of Ion Pairs

Referring again to the quadrant diagram (Fig. 1), if one could selectivelylower the free energy of the A–Na+ ion-pair species in octanol, the log PA

would increase. It would also cause the ionization of HAoct to increase by thesame degree. To selectively lower the free energy of A–Na+, one must makea change in structure that has a greater influence on the stability of the anionin one phase over the other. (The term ‘stability’ is used in reference to theGibbs free energy. The lower the free energy of a compound, the more stableit is; the more equilibria are shifted toward its formation.) Adding a -hydroxy group to an aliphatic acid is one way. In octanol, this group can coordinate with the close sodium of the ion pair, or act as an anchor for awater bridge to coordinate with the sodium. The -hydroxy group does littlefor the aqueous stability of the carboxylate because the counter ion and thecarboxylate are essentially independent in their shells of water. The more theion pair is stabilized, the closer log PA gets to log P. That difference, diff(log PN–A), is the best measure of the stability of the ion pair portion of themolecule.

4. When the Ion Pair May Be the Active Species: The Case of (Hydroxymethyl)glutaryl-Coenzyme A (HMG-CoA)Reductase Inhibitors (Statins)

The HMG-CoA reductase inhibitors are in a class of compounds in whichthe ionized species may well be the ‘active form’. These cholesterol-loweringdrugs (the statins) make up a multibillion dollar market. Pravastatin (1) illus-trates this class. They all contain the 3,5-dihydroxyhexanoic acid fragment


(some dosed as the lactone).


Table 1. Partition and Ionization Properties of the Carboxylic-Acid Form of Various Statins a)

Statin P b) PA b) diff (log PN–A) c) pKa pKa,OCTd)

(–COOH)

Simvastatin 29500 68.40 2.63 4.2 6.83Lovastatin 11000 32.70 2.52 4.2 6.72Mevastatin 4000 8.90 2.65 4.2 6.85Pravastatin (1) 152 0.35 2.64 4.2 6.64

a) Based on [3]. b) The partition coefficients of the neutral and ion pair forms were calculatedfrom distribution coefficients at several pH values. c) The difference between the log P of theneutral species and the log P of the carboxylate ion pair. d) The aqueous pH at which the statinin octanol is half-ionized.

4.1. Presumptive Evidence for the Active Form of the Statins

Serajuddin et al. [3] report distribution coefficients for four statins over arange of pH values. From their data, one can calculate the partition coeffi-cients for the ion pairs and the diff (log PN–A) (Table 1). Adding the latter to the pKa (Eqn. 2) gives pKa,OCT. If these were simple aliphatic acids, the diff(log PN–A) would be of the order of 4.1 instead of the 2.6 reported in Table 1.It appears that additional stabilization is present in the ion pair, the magnitudeof which suggests that more than one hydroxy group is involved. A pKa, OCT

of 6.7 means that, at pH 7.4, the compound in the octanol phase will be 80%ionized. In membranes, this percentage would be even higher, inviting thesuggestion that the ion pair is the active form.

4.2. When You Can’t Prove It by QSAR

Table 1 illustrates a problem for anyone attempting a QSAR analysis. Theratio of ion pair to neutral species in octanol is about the same for each mem-ber. Although these examples cover a good range of log P, any correlationwith log PA or log DA would be statistically identical to a correlation with

log P or log D. As much as one might suspect that the ionized species is theactive form, it cannot be proven with these compounds, or a dozen more likethem. How would one go about modifying the statins, or any other series, fora QSAR analysis, or to just maximize the ionized form at the active site?Some characteristics associated with enhanced ion-pair partitioning are listedin Table 2. Understanding and quantifying that partitioning process has beena primary objective of our work in this area [4–7].

4.3. A Requirement for Identifying the Active Species by QSAR

The limitation discussed for the statin series above regarding QSAR illus-trates, of course, the general principle that to analyze for the influence of aparameter, it has to be varied within the series. This brings us to the follow-ing active-species rule:

For a successful Hansch analysis to distinguish the ionized fromthe neutral species as the active form, the diff (log PN–I) or itsequivalent, pKa,OCT – pKa, must be varied within the series.

5. Four Ways to Modify a Series to Meet the QSAR ‘Active-Species Rule’

One can change the diff (log PN–I) of a series by modifying the primarycharacteristics favoring ion-pair formation listed in Table 2. Each one will bediscussed in turn.

5.1. Add or Remove Heteroatoms that Can Interact with the Ion Pair

Changing the number of heteroatoms within coordination distance, orwater-bridging distance, of the ions is probably the most common method forselectively altering the relative stability of an ion pair in a biolipid phase. The


Table 2. Characteristics of Acids or Bases Favoring Ion-Pair Formation

They are molecules…

1) … containing heteroatoms able to coordinate with one or both ions of the ion pair,or to act as an anchor for a water bridge to the ions.

2) … belonging to a chemical class with an inherently low diff (log PN–I).3) … tending to be a stronger acid (or base?).4) … having a minimum of steric hindrance to hydration/solvation of the ions.

beauty in this approach is in the variety of possibilities and that the effects arecumulative. An extreme example is the sodium ionophore monensin (2),which has two hydroxy groups and four ether atoms potentially able to con-tribute to the stabilization of its ion pair [8]. Hydroxy groups can stabilizeboth carboxylate and ammonium ions [5]. There is little quantitative data onthe contribution of particular groups toward the stabilization of a given classof ion pair.


5.2. Change the Chemical Class of the Ionizing Group

Tetrazoles, -diketones, sulfonamides, sulfonanilides, sulfonylureas, anddiarylphosphates are examples of acids with diff (log PN–I) values differingfrom carboxylic acids [5] [6]. A few examples are found in Table 3 and Sect.7.6.1. Aliphatic tetrazoles are an interesting class. They are weaker acids thancarboxylic acids, but they also have a lower diff (log PN–I), so there is a sim-ilar percentage of ionized form in biolipid phases [6].

5.3. Change the Aqueous pKa

Some day, I believe, there will be a general rule stating that the more acid-ic or basic a compound, the greater the solubility of its ion pair in relation tothe parent compound. Or, more succinctly, the smaller will be its diff (log PN–I).So far, this ‘pKa effect’ is known for phenols and benzoic acids (Sect. 7.1 and7.5).

5.4. Change the Steric Hindrance of Solvation of the Ion Pair

Interfering with the accessibility of water to an ion pair in a biolipid envi-ronment is one of the most powerful ways to reduce its stability (raise its freeenergy). It is the same explanation given for why tertiary amines are lessbasic than secondary amines, but the effect is magnified in octanol. This isanother area where compilation of quantitative data would be desirable.

Examples of steric effects on amines are seen in Sect. 6 and 7.6.3. Stericinfluences are also seen with acids. ,-Dimethyl groups on a phenoxyacet-ic acid increase its diff (log PN–A′′) by 0.89 [5].

6. Steric Factors in Ion-Pair Partitioning Revealed by Octanol and Membrane pKa Values

Sometimes looking at data from the perspective of octanol pKa values canbring out useful observations. Some nice work on a series of N-benzyl-N-alkyl amines [9] provides insight into the relative importance of steric factorsin octanol and membranes (Fig. 2). The pKa, OCT declines in the series byabout 0.1 pKa unit for each increase in the alkyl group from C2 to C7. Theliposome pKa, on the other hand, falls off rapidly from C1 to C4, by almost a


Fig. 2. Steric hindrance to ion-pair formation with phospholipid counter-ion in this series ofalkyl amines [9] becomes apparent when the pKa,MEM of the amine is plotted. The pKa,MEM isthe aqueous-phase pH when the amine in the membrane is half protonated. The gradual decrease

in pKa,OCT with alkyl size may indicate hindrance of solvation.

full pKa unit, and then declines more slowly, as in octanol. These membranedata indicate there is steric hindrance to close contact with the large phospho-lipid counter-ion, (reaching a maximum at butyl). The changes in octanol aremuch more gradual and may represent increasing hindrance to solvation ofthe amine-hydrochloride ion pair. In liposomes, the change in diff (log PN–C)with alkyl size is probably large enough to differentiate between the cationand neutral species as the active form by QSAR (if the series had biologicalactivity in a membrane). The range of pKa, MEM will be even greater if pri-mary and tertiary amines are included.

Miyazaki et al. [10] report that for a series of tertiary amine analogs of tet-racaine in dimyristylphosphatidylcholine, the diff (log PN–C) is about 1–1.5units. By contrast, the diff (log PN–C) for a primary amine, (4-phenylbutyl)-amine, is only 0.29 [2]. The same disparity between primary and tertiaryamines is seen in octanol where the diff (log PN–C) for a primary amine is 1.5units lower than the diff (log PN–C) for its N,N-diethyl analog (Table 3). Togeneralize, one can say that in octanol, primary amines are more basic than


Table 3. Determination of diff (log PN–I″) a) by Single-Phase Titrations in Octanol and Comparison with Differences of Measured Partition Coefficients

Compound pKab) pKa″ c) pKa″ – pKa

d) Measured Differencesame as diff (log PN–I) e) Col. 5 –diff (log PN–I″) Col. 4

ACIDSBenzoic acid 4.19 7.58 3.38 (4.04) f) 0.66Hexanoic acid 4.88 8.22 3.34 (4.08) g) 0.742-HO-Butyric acid 4.22 6.12 1.903-HO-Butyric acid 4.52 6.45 1.93N-Benzolyglycine 3.80 6.21 2.415-Phenyltetrazole 4.38 6.60 2.22 2.90 h) 0.68Diphenylphosphate 1.36 2.66 1.302-Nitrophenol 7.23 9.83 2.60 3.36 i) 0.762,6-Dinitrophenol 3.71 5.55 1.84 2.58 i) 0.74Monensin (1) (4.4) j) 3.88 k) –0.5 l)

BASESTriethylamine 10.75 7.48 3.53Hexylamine 10.64 8.63 2.01Propranolol 9.45 7.32 2.13 2.59 m) 0.46

a) The difference between log P and the log P of the ion pair calculated from its pKa″ usingEqn. 9. b) Values from the literature. c) Half-neutralization potentials in octanol, cf. [5][6],std. error ca. ±0.03. d) See Eqn. 9. e) log P minus log P of the ion pair. f) Calculated from -values; 0.1 M Na+. g) Calculated from aliphatic fragment values; 0.1M Na+. h) cf. [6]. i) cf. [11].j) Estimated. k) The sodium salt was acidified with triflic acid prior to titration. l) Value dependsdirectly on the accuracy of the estimated pKa.

m) cf. [1].

tertiary amines due to steric factors, and this difference is magnified in mem-branes. Acids generally exhibit a smaller difference between octanol andmembrane pKa values [2].

7. Direct Measurement of Ionization Constants (pKa″) by Titrationin Octanol

The quadrant diagram (Fig. 1) tells us that if one knows the log P of acompound and its octanol and aqueous pKa values, the partition coefficient ofthe ion pair can be calculated. Some years ago, we decided to try to measurethe half-neutralization potential of acids directly in water-saturated octanol bytitration with 0.1 N NaOH in isopropanol/methanol 4 :1. We hoped that rela-tive values in a series would be correct, even if electrodes were not designedfor use in octanol. In fact, the results are reproducible, usually < ±0.05, andseem well behaved. We label the half-neutralization potentials obtained, pKa″(called pKa double prime). The titration and analysis of 13 phenols and 22benzoic acids was published in 1984 [4]. The reason for bringing these uphere is that recent data by Escher and Schwarzenbach [11] now strongly sup-port the predictive value of the direct titrations in octanol. In addition, newcorrelations combining the phenols and benzoic acids into single equationsfor each class were derived by Magee [12].

7.1. Titration of Phenols

For a diverse set of 13 phenols ranging in pKa from 3.5 to 10.1, we find [7]:

pKa″ = 1.16 (±0.02) pKa + 0.28 (±0.07) I-o-Cl + 1.16 (±0.19)

r2 = 0.995, s = 0.19, n = 13, F = 1135 (Eqn. 4)

where I-o-Cl is an indicator term equal to 1 for each Cl in a 2- or 6-position(P = 0.002 for this term).

The coefficient of the pKa term in Eqn. 4 is greater than one. This meansthat the more acidic the phenol, the less the difference between pKa and pKa″values and the less the diff (log PN–A) (Eqn. 2). Looked at another way, pKa

is a more important factor for ion stability in lipid media than in water. Wewill see that this applies to benzoic acids as well (Sect. 7.5).

The I-o-Cl indicator probably accounts for steric hindrance of solvationby the halogen (item 4 in Table 2). It means that 2,6-dichlorophenols areweaker acids in octanol than otherwise expected by 0.5 pKa units. (And as acorollary, their sodium salts have a 0.5 lower log PA.) Interestingly, no spe-cial term is needed for ortho-nitro, or even 2,6-dinitro substitution.


7.2. Support for pKa″ by diff (log PN–A) Values

Escher and Schwarzenbach [11] measured the log P and log PA for 24chloro- and nitrophenols with pKa values ranging from 3.7 to 8.6. From Eqn.2, pKa, OCT can be calculated for these phenols. A simple analysis shows thatpKa, OCT is correlated with the aqueous pKa (Eqn. 5):

pKa, OCT = 1.19 (±0.06) pKa + 2.15 (±0.35)

r2 = 0.95, s = 0.42, n = 23 (omit 4-Cl), F = 442 (Eqn. 5)

Eqn. 5 is significantly improved by the addition of an I-o-Cl indicator term(Eqn. 6) (P = 0.000 for all terms). The relation between observed and calcu-lated pKa, OCT is plotted in Fig. 3:

pKa, OCT = 1.19 (±0.04) pKa + 0.33 (±0.08) I-o-Cl + 1.92 (±0.27)

r2 = 0.976, s = 0.32, n = 23 (omit 4-Cl), F = 401 (Eqn. 6)


Fig. 3. A correlation derived from log P and log PA values for a series of phenols [11]. I-o-Clis an indicator term with a value of 1 for each chlorine ortho to the OH of the phenol, other-wise it is 0. A prediction of octanol pKa is essentially a prediction of log PA, through Eqn. 2.

Although five of the thirteen phenols used for Eqn. 4 are different fromthose used for Eqn. 6, the same terms are required for each. In fact, the coef-ficients are statistically identical (compare below). The only difference is inthe constant, where the pKa″ values are 0.76 too low. This value could be usedas a conversion factor to be added to pKa″ to convert it to pKa, OCT. The lastcolumn of Table 3 shows essentially the same 0.7 difference for five acidswhich have been titrated in octanol and where log PA has been independent-ly measured. Note that the two nitrophenols, differing in pKa by 3.5 units,require the same correction. The difference between the two methods couldrelate to the behavior of electrodes in octanol, the isopropanol and methanolin the titrant, and/or to differences in concentration of counter-ion betweenthe two methods. We feel this strongly supports further use of titrations inoctanol. Some details of the titration method are published [4]. A later varia-tion [5] uses NaOH in octanol/methanol 3:1 as a titrant.

A direct comparison of Eqn. 4 and 6 is shown below:

pKa″ = 1.16 (±0.02) pKa + 0.28 (±0.07) I-o-Cl + 1.16 (±0.19) (Eqn. 4)

pKa, OCT = 1.19 (±0.04) pKa + 0.33 (±0.08) I-o-Cl + 1.92 (±0.27) (Eqn. 6)

7.3. A General Equation for Predicting the log PA of Phenols

Eqn. 4 or 6 should be useful for predicting the log PA of a wide range of phenols. They illustrate the potential quality of equations that may bedeveloped in the future to predict octanol pKa values from aqueous pKa

values, as long as one knows of any special indicator terms that may apply.The pKa, OCT, in turn, allows calculation of the partition coefficients of ionpairs through Eqn. 2. It is surprising that ortho-nitro-, -methyl, -sec-butyl,and -t-butyl groups did not require special treatment. Darvas et al. [13] de-rived a relationship for calculating phenol log PA from log P and pKa, basedon the data of Escher and Schwarzenbach [11], but the details are not dis-closed.

Eqns. 4 and 6 indicate that two phenols with the same log P, but with pKa

values of 4 and 9, will have ion pairs differing in lipophilicity by a log unit.This could be enough to meet the requirement for an effective QSAR to iden-tify the active species (especially with some 2,6-dichlorophenols included tofurther disrupt the correlation of log PN and log PA). We will see this is thecase in Sect. 8.


7.4. Calculating pKa, MEM from pKa, OCT

The publication by Escher and Schwarzenbach [11] also provides valu-able data on the partitioning of phenols and their ion pairs into liposomes andbacterial cell membranes. The authors conclude that diff (log PN–A)LIPOSOME

is more suitable than diff (log PN–A)OCT for modeling phenolate-ion partition-ing into bacterial membranes. However, there is another way to look at theirdata. The pKa,OCT and liposomal pKa,MEM are highly correlated (Eqn. 7), andin fact, differ only by a constant. This implies that octanol should be a goodsolvent for measuring the partition coefficients of acidic compounds, even ifthe biological system of interest is in membranes. Basic compounds are moresensitive than acids to steric factors in membranes (Sect. 6), so the correlationmay not hold for them. If reliable pKa, MEM values can be calculated from pKa, OCT (Eqn. 7), then these in turn should lead to reliable log PA

MEM valuesthrough Eqn. 2.

pKa, MEM = 0.99 (±0.04) pKa, OCT – 2.22 (±0.36) (Eqn. 7)

r2 = 0.98, s = 0.33, n = 19, F = 687

7.5. A General Equation for Predicting the log PA of Benzoic Acids

Twenty-two benzoic acids were titrated in octanol in the same way asdescribed for the phenols [4]. The pKa″ obtained were correlated with theiraqueous pKa values by dividing the acids into three series. These original cor-relation equations [4] were recently combined into one (Eqn. 8) [12], and theplot of the result is shown in Fig. 4. Eqn. 8 is interesting as much for the indi-cator terms required, and those not required, as for its predictive value. Twoindicator variables are required. Each chlorine ortho to a carboxy groupreduces its ionizability in octanol by 0.6 units, but, surprisingly, ortho-hydroxy groups require no special treatment. The influence of ortho-substit-uents on the pKa of benzoic acids in DMSO/water mixtures has been attribut-ed to steric hindrance of solvation [14]. The second indicator term is for each3- or 5-nitro group of a salicylic acid. Each nitro group dramatically stabiliz-es the ion pair by 1.2 pKa units. Especially interesting, since the phenol groupis not ionized at the pKa of the carboxylic acid. No special terms are neededfor simple nitrobenzoic acids.

pKa″ = 1.25 (±0.06) pKa + 0.0.60 (±0.08) I-o-Cl (Eqn. 8)–1.18 (±0.09) I-cNO2sal + 2.47 (±0.17)

r2 = 0.987, s = 0.17, n = 22, F = 467


As with the phenols, the coefficient of the pKa term is >1, so the moreacidic an acid, the greater the octanol solubility of its ion pair in relation tothe neutral form. Eqn. 8 predicts that salicylic acid, pKa 2.98, will have a diff (log PN–A) that is 0.30 lower than for benzoic acid, pKa 4.20.

It seems reasonable to make a preliminary assumption that Eqn. 8 can beused to calculate pKa, OCT from pKa values by the addition of the conversionfactor 0.66 taken from Table 3. Provided there are no unusual acids thatrequire additional indicator terms, predictions of log PA within a few tenthsof a log unit of measured values would be the hope.

7.6. A Survey of Titrations in Octanol (pKa″) and Comparisons with Alternative Determinations

7.6.1. Acids

Direct determination of pKa″ by titration in octanol, rather than the indi-rect determination of pKa, OCT through Eqn. 2, provides a rapid screening


Fig. 4. A plot of the octanol pKa″ (double prime) for a series of benzoic acids, calculated fromthe equation shown, vs. the measured pKa″ by titration in octanol (a one-phase system). TheI-o-Cl is an indicator term with the value of 1 for each chlorine adjacent to the carboxy group,otherwise it is 0. The other indicator term is 1 for each nitro group ortho or para to the 2-OHof a salicylic acid, or 0. This equation should allow prediction of the partition coefficients of

benzoic-acid sodium salts. See text for details.

method for examining factors that affect the stability of ion pairs in octanol.Some selected results taken from our earlier work [5] [6] are presented inTable 3. With the first two compounds as reference points, one can see featuresthat help stabilize, or solubilize, ion pairs by comparing the diff (log PN–I″)terms. Both - and -hydroxy groups stabilize a carboxylate ion pair substan-tially, and to about the same degree. N-Benzoylglycine shows that an amidegroup can stabilize an ion pair by about 0.9 log units. Monensin (2), a sodiumionophore, shows up as a highly stabilized ion pair. The tetrazole and phos-phate are examples of other classes of acids with their own characteristic diff (log PN–A) values (factor 2 in Table 2). The phosphate stands out byforming a very stable ion pair. Could this be why Mother Nature uses phos-phate chemistry so commonly?

7.6.2. A Conversion Factor for pKa″ to pKa, OCT

Table 3 shows a comparison of diff (log PN–A) calculated from titrations inoctanol and calculated from measured partition coefficients for five acids. Thedifference seems to be fairly consistent at 0.7 log units for acids. This couldrepresent cumulative adjustments for differences in counter-ion concentration,electrode function in octanol, and any effects of ca. 1.5% isopropanol/metha-nol in the octanol at the half-neutralization point. This consistent differenceencourages us that only a simple conversion factor may be needed for a giventitration procedure. Converting pKa″ to pKa, OCT allows calculation of log PA.

7.6.3. Bases

Triethylamine and hexylamine (Table 3) show the magnitude of stericinfluences on diff (log PN–C). Primary amines are more basic in octanol thanthe corresponding N,N-diethyl analog by over one pKa unit. Propranolol isincluded to allow a comparison of pKa with a measured diff (log PN–C). Thetitrant for amines is 0.1 N HCl in octanol (2% water), titrated into octanolcontaining 3% water [5]. The lower-than-saturation water level is required toprevent phase separation during some titrations.

7.6.4. The Advantage of Measuring a Difference in pKa Values Rather than a Difference in Log P

3-Hydroxybutyric acid (Table 3) has a CLOGP of –0.64, so the log P ofits potassium ion pair would be extremely difficult to measure by a two-phase


titration, or most other means. On the other hand, there was no problem meas-uring a pKa″ of 6.45, and subtracting the aqueous pKa of 4.52.

8. A Hansch Analysis for Active Species: Uncoupling Oxidative Phosphorylation

Probably the most important term in a Hansch equation is the one thatquantifies an agent at its site of action, usually log P or log D. If the activeform is an ion pair, one should use an appropriate term, such as log P I or logDI. Uncoupling oxidative phosphorylation is proposed to occur as a result ofan agent cycling between an inner and outer membrane wall, as an ion pair inone direction and a protonated species in the other, to reduce a cells protongradient. (Or perhaps it cycles sodium into the cell and potassium out.)Escher et al. studied uncoupling in natural photosynthetic membranes [15][16]. Their proposed model requires the cycling of two species, the phenox-ide ion pair and a 1:1 phenoxide/phenol heterodimer.

8.1. It’s the Ion Pair!

We used the new Eqn. 4 to re-examine our earlier work on uncoupling [4],to provide Eqn. 10. The coefficients of Eqn. 10 are the same as previouslyobtained. The correlation is now more meaningful, though, since the directtitration approach seems to be validated by the Escher and Schwarzenbachdata. The 13 titrated phenols of Eqn. 4 were taken from the 23 examined foruncoupling activity by Stockdale and Selwyn [17]. The missing pKa″ were cal-culated using Eqn. 4. (The indicator for 2,6-diBr was assumed to be the sameas for I-2,6-diCl.) The log PA″ values were then calculated from Eqn. 9, avariation of Eqn. 2. These in turn were converted to the fractional distributionvalues, DA″ and DN″. (DA″ is the concentration of the phenol anion in octa-nol, divided by the total concentration of all phenol species in the waterphase.) Eqn. 10 was the best correlation found. The resulting correlation ishighly significant with a single variable, log DA″.

(pKa″ – pKa) = diff (log PN–A″) (Eqn. 9)

log 1/C = 0.614 (±0.041) log DA″ + 4.37 (±0.070) (Eqn. 10)

r2 = 0.919, s = 0.30, n = 22 (omitting 2-Cl), F = 227


8.2. Mechanism of Uncoupling

No alternative to Eqn. 10 with a single term gave an r2 higher than 80%.A second term, log DN″, for the fractional distribution of the unionized phe-nol, was added to test the proposal of Escher et al. [15] [16] for a phenol/phenoxide heterodimer contribution to uncoupling. However, the log DN″ termhad a coefficient of –0.03 and entered with a P value of 0.46. This impliesthat the uncoupling activity, in this instance, is dependent almost solely on theconcentration of ion pair in the biolipid phase.

The best alternative equation, provided by Hansch [18], is Eqn. 11. Itrequires two terms, CLOGP and –:

log 1/C = 0.952 (±0.162) CLOGP + 0.915 (±0.182) – + 0.664 (±0.464)

r2 = 0.942, s = 0.279, n = 20 (omitting 3 phenols) (Eqn. 11)

9. Classification of Ampholytes by their Aqueous and Octanol pKa Values:The Four Classes of Ampholytes

A recent review of ampholytes [19] inspired us to look at this group ofcompounds in terms of their aqueous and octanol pKa values. The result is, I believe, a classification system that is easy to use and less prone to misas-signment. It also simplifies plotting species-distribution profiles for the octa-nol phase. The classifications are generated by listing the ionizable groups(A, acidic; B, basic), in order of increasing pKa, first in water and then in octa-nol. Three sequences of relative pKa values are possible, BA-BA, AB-BA andAB-AB. The AB phases will be the ones containing zwitterions. The AB-ABclass has to be further subdivided, as explained below.

The physical analogy for the source of these combinations is seen in Fig.5, in which the log D vs. pH profiles are plotted for pairs of acids and basesas if each ionizable group were independent. These begin with the weakestacid and base in Fig. 5A. As the strength of the acidic and basic groups isincreased, their curves shift towards each other until they cross, (Fig. 5B), andthen pass each other, (Fig. 5C).

The curves in Fig. 5 are labeled with their aqueous and octanol pKa

values. The aqueous pKa values are always seen at the upper inflections. InFigure 5B, for example, the ionizable groups would form an AB-BA ampho-lyte. In this case, since group A has a lower pKa than B, if they were the ioniz-able groups of an ampholyte, it would be a zwitterion in water at the iso-electric point. In octanol, though, because of the usual relation that acids andbases are weaker in octanol, there will only be unionized species at the iso-electric point. The third category in this continuum, Fig. 5C, contains zwitter-



Fig. 5. Hypothetical lipophilicity profiles for ampholytes if each ionizable group, in turn, couldbe suppressed. The progression is from a weak acid and base in panel A, to a strong acid and basein panel C. The panels illustrate the origin of the three broad classes of ampholytes. The aqueouspKa values are marked A and B, and the pKa,OCT marked A′ and B′ on the profiles. An ampholytewill form a zwitterion in any phase in which the pKa of the acid is lower than the pKa of the base.A) An ampholyte with these pKa values will contain no zwitterion at any pH. B) This class willhave zwitterion in the aqueous phase only. C) Ampholytes in this class will form zwitterions in

both phases. This class must be further divided into two groups as described in the text.

ion in both phases at the isoelectric point. This category has to be divided intotwo sub classes. When the ionizable groups can intramolecularly compensateeach other, there is a dramatic consequence. The A′ and B′ inflections areshifted outward to become more acidic and more basic than in water. We callthese ‘internally-compensated ampholytes’. The two sub-classes are differen-tiated by the subscript ic or nc, depending on whether they are internally com-pensated or not compensated. The free energy change associated with ‘inter-nal compensation’ can be viewed as the net result of two micro equilibria:partitioning a double ion pair into octanol, and converting it to a zwitterionand sodium chloride.

10. Analysis of Specific Ampholytes

10.1. BA-BA Ampholytes: No Zwitterion in Either Phase

This is the simplest class of ampholyte because there is essentially nointeraction between the two ionizable groups. For this class, just assigning theBA order to the aqueous ionization constants automatically defines the class.The order in octanol will have to be the same, as can be seen in Fig. 5A.Examples in this class include m-aminophenol, and o-, m-, and p-aminoben-zoic acids.

10.2. AB-BA Ampholytes: Zwitterion in the Aqueous Phase Only: The Case of Labetalol

Labetalol (3) is an aminophenol with pKa values of 7.4 and 9.4. On itslipophilicity profile (Fig. 6 [19]), one can see the inflections that occur at itsaqueous pKa values, A and B. The octanol pKa values are at the lower inflec-tions at about 5 and 11. The exact value at B′ is calculated by subtracting diff(log PN–C) from the pKa at A. The octanol pKa values are 5.4 and 10.7. Thesecan also be seen to occur at the intersections of log DN with log DC andlog DA. The first step in assigning ionizable groups to these values is toassume the usual pattern: the acid will have a higher pKa in octanol and thebase a lower pKa. That would mean, in this case, that the octanol pKa is 10.7,3.3 units higher than in water. This is reasonable. The pKa for a simple phe-nol is about 4. The assigned octanol pKa values are in the wrong order to pro-duce zwitterions in the octanol phase. The species distributions for this com-pound in water and octanol are shown in Fig. 7A and B.



Fig. 6. The lipophilicity profile of labetalol (3) [19]. The assignment of the aqueous and octan-ol pKa values is indicated (see text). This is an AB-BA ampholyte. The intersection of log PN

and log PC occurs at the pH where the octanol concentration of the two species is equal. Bydefinition, this is the octanol pKa of the cation.


Fig. 7. Distribution of the ionized and neutral forms of labetalol (3) in water and octanol arecalculated from the pKa values in each phase. A) In water at pH 7.4, a mixture of cation andzwitterion is present. B) In octanol, the neutral species is dominant over a broad pH range,

including pH 7.4.

10.3. AB-ABnc Ampholytes: Zwitterion in Both Phases: The Case of Acrivastine

Two H1-receptor antagonists, acrivastine (4) and cetirizine (5), provide aconvenient duo to compare the two subclasses of AB-AB ampholytes.Acrivastine is a member of the not-internally-compensated class. Its lipophi-licity profile (Fig. 8) does not have the classical U-shape only because thesecond nitrogen (pyridine) is also protonated below pH 4 [19]. Above pH 6,one can identify the inflection for the aqueous pKa of the pyrrolidine at 9.55.The adjacent inflection is at 8.65 (9.55 – [log PA (ca. 0.9) – log PZ (0.01)]).This must be the octanol pKa of the pyrrolidine ring, which is a weaker basein octanol. The acrivastine zwitterion is a double ion pair at physiological pH.It does not cross the blood-brain barrier [20] and is a non-sedating antihista-mine. It can be seen in Fig. 9A and B that the zwitterion exists in octanol overa narrower pH range than in water. The pKa 3.9 group was omitted from theprofile to avoid masking this general property.


Fig. 8. Lipophilicity profile of acrivastine (4) in octanol/buffer [19]. Because there are twobasic groups on acrivastine, it does not have a U-shaped curve, but the aqueous pKa at 9.55, andthe calculated octanol pKa at about 8.65 fit the pattern for a non-compensated zwitterion

(a weaker base in octanol than in water).

Fig. 9. Species distribution plots for acrivastine (4) are shown without the influence of theweaker base to show the classical pattern for an AB-ABnc ampholyte. The zwitterion in theoctanol phase (panel B) exists over a much narrower pH range than the zwitterion in the aque-

ous phase (panel A).

10.4. AB-ABic Ampholytes: Zwitterions More Acidic and More Basic in Octanol than in Water

10.4.1. Cetirizine, an AB-ABic Ampholyte with Unusual BiologicalProperties

Cetirizine (5) is also an H1-histamine antagonist. Its partitioning andpharmacokinetic behavior have been reported [19] [21], along with thelipophilicity profile seen in Fig. 10. Inflections at 2.91 and 8.0 correspond tothe acidic and the more basic pKa, respectively. The lowest pKa, 2.19, belongsto a weakly basic nitrogen. The latter would be part of a simple BA-BAampholyte. To simplify the discussion, we will assume this weak amine isnever protonated. The value of log PC, following this assumption, has beenestimated to be 1.0 [21]. The inflection marked B′ at about pH 10 cannot bedue to the carboxy group. That would require a difference in pKa betweenwater and octanol of 7 units, which is much too high. The pKa, OCT, calculat-


Fig. 10. The lipophilicity profiles of cetirizine (5) and hydroxyzine (6) in octanol/buffer [21].Hydroxyzine behaves as a typical amine in that it has a lower pKa in octanol than in water byabout 2.5 pKa units. The profile of the AB-ABic ampholyte, cetirizine shows the opposite shiftin pKa. It is a stronger base in octanol than in water by 1.7 pKa units. The difference betweenthe octanol pKa values represents a large energy of stabilization in cetirizine that should be

manifest in its physical properties.

ed to be 9.69 (Eqn. 2), must belong to the amine. That means the amine ismore basic in octanol than in water. The species distribution plots in Fig. 11Aand B show that, in contrast to acrivastine, the zwitterion exists over a broaderpH range in octanol than in water.

Cetirizine (5) has some unusual biological properties. It has a very lowvolume of distribution in humans, 0.4 l, which is even less than the exchange-able water (0.6 l), and is highly bound to serum proteins. It is non-sedating,so was presumed to not penetrate the BBB [20]. In fact, far from it, cetirizinedoes get into the CNS, but it exits the brain faster than deuterated water [21].It is non-sedating because it exits faster than it enters and so has a low equi-librium concentration there. In this light, it is interesting to speculate on howcetirizine might bind to proteins, including transport proteins. Presumably itbinds with the charge inward, buried away from the water phase. This would


Fig. 11. Species distribution plots for cetirizine (5) in A) water and B) octanol. The zwitterionexists over a much broader pH range in octanol than it does in water. The difference of 7 pKaunits between the acidic and basic octanol pKa means there is no reasonable possibility ofunionized neutral species being present in the octanol. In these plots, the influence of the weak-

er base is ignored.

take advantage of the 5.3 kcal/mol energy shift illustrated above. With thecharge buried, it would not offer as much hindrance to passing through theBBB.

10.4.2. Cetirizine vs. Hydroxyzine: the Free-Energy Benefit of Internal Compensation

It is informative to compare cetirizine (5) with its direct –OH for –CO2Hanalog, hydroxyzine (6). Fig. 10, based on a literature plot [21], illustrates thestriking differences in the octanol/water distribution profiles. On the hydrox-yzine curve, one can pick out the more basic pKa at 7.52, the log PN 3.55, andlog PC 0.99 (values from STAN [22a]). The octanol pKa at 4.96 is lower thanthe aqueous pKa by 2.6 units. By contrast, the octanol pKa of the correspond-ing nitrogen in cetirizine is higher by 1.3 units. The net shift of 3.9 pKa units(neglecting the slight difference in aqueous pKa values) is equivalent to 5.3kcal/mol (from the Gibbs equation, Eqn. 1).


10.4.3. Dipeptides and Tripeptides

Di- and tripeptides are important classes of agents that belong to the inter-nally-compensated AB-AB ampholytes. An example taken from the STANcompilation [22b] is Trp-Phe (7). Its lipophilicity profile (Fig. 12) is a littledifferent from those most commonly seen in that the cationic species is verysensitive to counter-ion concentration and partitions to a greater extent thanthe zwitterion from physiological saline. The pKa values at 3.18 and 7.30 aremarked on the lipophilicity profile. The octanol pKa values (the inflections atlower log D values than the aqueous values) are calculated to be 3.8 and 9.5.It is unrealistic for the 9.5 value to be due to the carboxy-group ionization. Ifthat were the case, this would be an AB-BA ampholyte, and the octanol couldcontain no zwitterion. The amino group is more basic in octanol by 2.2 pKa

units, while the carboxy group falls into a normal pattern of being a weakeracid by 0.6 units. Interestingly, this same pattern is seen for a tripeptide, Phe-

Phe-Phe, in Fig. 12 (from the STAN collection [23]). The tripeptide must forma 10-membered internally-compensated zwitterion ring in octanol.


10.4.4. Azapropazone: Internal Compensation by Resonance

Azapropazone (8) is an ampholyte with two resonance-stabilized chargesin its zwitterionic form. It is not obvious from the structure if this zwitterionwill partition into octanol without counter ions. The answer is seen in itslipophilicity profile (Fig. 13) [19]. It shows that azapropazone is more acidicand more basic in octanol than in water, therefore it must be an AB-ABic

ampholyte in which the charges are internally compensated. From Eqn. 2,using pKa 6.9, log PZ 1.7, and log PA –0.2, the octanol pKa is 8.8. Thatincrease in pKa of 1.9 units is greater than that noted above for cetirizine (5),so -bond charge neutralization can be highly effective.

Fig. 12. The lipophilicity profile of Trp-Phe (7) and Phe-Phe-Phe in octanol/0.15 M KCl [23].The aqueous and octanol pKa values are marked on the profile curves. Both compounds arestronger bases in octanol than in water, therefore these are internally compensated AB-ABic

ampholytes. The tripeptide zwitterion must exist as a 10-membered ring in octanol.

10.4.5. Piroxicam: H-Bond-Supported Internal Compensation

It is surprising to find that piroxicam (9) forms an AB-ABic zwitterion.The difference between its acidic and basic pKa values is only 2.74 units [24](pKa extrapolated from methanol-water, 0.15 M KCl), or 3.60 units by UV[25]. Piroxicam is difficult to work with because of its low water solubility.Using the lipophilicity profile (Fig. 14) and pKa data from the STAN publica-tion [24], piroxicam has pKa 2.33 and 5.07, and log PZ 1.98, log PC 0.96, andlog PA –0.38. The octanol pKa values are 1.31 and 7.43 (from Eqn. 2). Withthat range there should be zwitterion and no neutral species present in octa-nol at the isoelectric point. The pyridine nitrogen is more than 2 units morebasic in octanol than in water. There must be a very efficient conformationstabilizing the zwitterion. Tsai et al. [26] propose charge delocalization


Fig. 13. The octanol/buffer lipophilicity profile of azapropazone (8) [19]. It must belong to theclass of AB-ABic zwitterions. The octanol pKa of the basic group is shifted to higher pKa by1.5 pKa units, greater than for cetirizine. This indicates that internal compensation by resonance

can be highly effective in stabilizing an ion pair.

through an arrangement as in compound 9 containing two internal hydrogenbonds.

From the above calculation that the octanol pKa is 7.43, there would be52% zwitterion present in octanol at pH 7.4. This is close to the value meas-ured by Tsai et al. [26] by first-derivative UV-spectral analysis. They found33% zwitterion present at pH 7.4. The latter implies an octanol pKa of 7.1 forthe pyridine. It is additional confirmation that the pyridine is more basic inoctanol than in water.

11. Augmentation of pKa, BIOLIPID through Intramolecular Associations

There are examples of pKa shifts analogous to those observed in AB-ABic

zwitterions that occur between ionizable groups on different molecules. Theformation of ion pairs in receptor pockets includes some dramatic examples.

11.1. Receptor-Site Binding

11.1.1. Binding to the Aspartic Proteases Plasmepsin II and HIV-1

Xie et al. [27] recently reported their study of an aspartic protease,Plasmepsin II, from malarial parasites. The two aspartic acid carboxy groups


Fig. 14. Lipophilicity profile of piroxicam (9) (0.15 M KCl) [24]. The aqueous pKa values areonly 2.7 units apart and would not ordinarily be expected to form a zwitterion in octanol. Anassignment other than AB-ABic is not reasonable due to the large shift of pKa in octanol. The

large shift indicates special features must be stabilizing the zwitterion in octanol.

in the active site have identical pKa values of 4.7. But when the transition-state inhibitor, pepstatin, binds to this enzyme, the pKa values shift. One goesto 6.5, typical of a carboxy group in a low dielectric medium; the other shiftsto a pKa of 3.0 with formation of an ion pair. That proton-transfer processaccounts for 40% of the binding energy at pH 5 [27]. The differentiation ofthe carboxy groups conveniently allows one to act as a proton donor and theother as a proton acceptor.

A similar finding was clearly demonstrated by Smith et al. [28]. They used13C-NMR to determine the aspartate pKa values in the catalytic site of HIV-1protease with and without substrate. They found that the aspartates, which arechemically equivalent in the unbound state, are differentiated in the presenceof substrate. One carboxy group remains ionized and the other protonatedover the pH range 2.5 to 6.5. The ionized aspartate must be participating asan ion pair.

11.1.2. E. Coli D-Alanine:D-Alanine Ligase

E. coli D-alanine:D-alanine ligase is an early enzyme in the sequence lead-ing to the peptidoglycan polymer responsible for the tensile strength of bac-terial cell walls. Carlson et al. [29] have worked out a detailed pathway forthe coupling of the two alanines. Of interest here are the pKa changes theyfind, based on quantum-mechanical calculations. When the second alaninebinds, its amine group is shifted 5.6 pKa units more alkaline, and the carbox-ylic acid is shifted 8.7 pKa units more acidic! The carboxy group ionizationis enhanced by the presence of magnesium ions.

11.2. Hydrophobic Ion Pairs (HIP)

Hydrophobic ion pairing was recently reviewed by Meyer and Manning[29]. It is a fascinating topic for anyone interested in ion pairs. This processappears to involve the phenomenon of enhanced acidity and basicity in organ-ic media. The fact that the dodecyl ion pair of a basic drug or peptide has itspartition coefficient increased 2 to 4 magnitudes, is equivalent to its pKa

being increased 2 to 4 units. The hydrophobic tail of the counter ion maycreate a lipid environment that can stabilize an intramolecular ion pair in away analogous to AB-ABic ampholytes in octanol. HIP have application inenhancing the solubility of enzymes in organic solvents with retention ofactivity, and many other fields [30].


12. Glucuronides and Other Examples of Mother Nature’s Low-EnergyIon Pairs

One can see from Fig. 15 that glucuronide salts have characteristic fea-tures of stabilized ion pairs (Table 2). They are highly acidic and have twoether oxygens and a hydroxy group that can associate with, and stabilize, thecounter-ion in a low-dielectric environment. This is reinforced by the recentreport of Kuehl and Christensen [31] on the high sensitivity of glucuronidepartitioning to counter-ion concentration. Let us look more specifically attheir properties in Fig. 15. If one had a primary alcohol with a log P of 2.3, itwould form a glucuronide with log P 1.0 [32]. Against physiological saline,the glucuronide ion pair would have log P 0.0, following the work of Avdeefon the -glucuronide of 4-methylumbelliferol [33]. With a pKa of 3.0 [32], itspKa, OCT would be 4.0. This is enough information to fill in a quadrant dia-gram with the relative concentration of each species (Fig. 15). (The quadrantshave the same designation as in Fig. 1.) It appears that the physical proper-ties, the chemistry, the biology, the partitioning, the metabolism, and the


Fig. 15. An example of one of Mother Nature’s low-energy ion pairs that should exist predom-inantly as the anion in all biological phases

excretion of the glucuronide are overwhelmingly the properties, chemistry,biology, partitioning, metabolism, and excretion of the ionized form.

There are other examples in nature of what appear to be compounds thatwould form stabilized ion pairs. They include cyclic-AMP, hexose phos-phates, phosphoinositol and glucosamine, as simple examples. These shouldbe considered for the possibility that their chemistry, passage through mem-branes, and location are due to the properties of the ion pair.

13. Conclusion

I am struck by the thought that it is not what we now know, and can con-clude about this subject, but how much we still don’t know. It almost seemslike another world opens up when we allow ions into our thinking of biolipidphases. Questions arise about the possibility that peptides that can form C- orN-terminal internally compensated ion pairs may have special properties,e.g., C-terminal lysines, or N-terminal glutamates. Would the histidine imid-azole stabilize C- or N-terminal ion pairs? What can we learn from AB-ABic

ion pairs about intermolecular interactions in a receptor or folded protein?How do norepinephrine (primary amine with a -hydroxy group) and N-methylephedrine (tertiary amine with an -methyl and a -hydroxy group)compare in phospholipid binding? Does that effect their biological proper-ties?

On the mechanical side of measuring properties, I now feel it is importantthat direct titrations in octanol be worked out on a modern automatic titratorso they will be available and be used.

I hope that interest has been raised for the potential of QSAR to identifythe active species in a series. Then, one last area to mention is the possibilityof looking at hydrophobic ion pairs in terms of their pKa values. And I won-der if phospholipids can form hydrophobic ion pairs.

The author is grateful for many interactions over the years with F. H. Clarke, ChemClarkeInc., and for the use of his programs for the determination of pKa and partition coefficients.These were indispensable for work carried out over the past 20 years.

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Recent Advances in Reversed-Phase-HPLCTechniques to Determine Lipophilicity

by Chisako Yamagami

Kobe Pharmaceutical University, Motoyamakita-machi, Higashinada-ku, Kobe, 658-8558,Japan; Fax: +81 78 435 20 80; e-mail: [email protected]

1. Introduction

The logarithm of the octanol/water partition coefficient, log P, has beenwidely used as a measure of lipophilicity of organic compounds and hasplayed an important role in structure-activity relationship studies [1–3].Although the conventional procedure for measurement of log P is the shake-flask method, this method is not usually applicable to highly lipophilic com-pounds (log P > 3.5) because of extremely low concentrations of the testcompounds in the aqueous phase. To solve this problem, procedures such asre-extraction of the aqueous phase before measurement of the concentrationsand the use of radiolabeled solutes, have been employed to obtain satisfacto-ry responses [4] [5]. Accordingly, we attempted to develop a new experimen-tal method to determine experimentally log P values for highly lipophiliccompounds by application of HPLC column-switching techniques. Thismethod has been applied to measurements of log P for diarylpyrazines withpotent anti-platelet aggregation activity [6].

In addition, development of useful procedures to estimate log P valuesbefore synthesis is also of special significance in QSAR studies. Prediction oflog P for heterocyclic compounds is particularly difficult because an impor-tant contribution of H-bonding is involved in log P [7–10]. Electronic inter-actions between ring heteroatom(s) and substituents modify their mutual H-bonding properties and can result in variation of the value from one solutesystem to another [9–11]. Most frequently used parameters to describe suchH-bond effects are discrete-type indicator variables. These are useful andconvenient when used for a group of substituents with comparable H-bond-ing abilities [11] [12]. However, in using such discrete-type parameters, thecompounds to be analyzed need to be classified into sub-categories accordingto appropriate criteria settled for each series [12]. Therefore, a pre-established


individual scale for each substituent would be desirable. Although Abrahamand co-workers have constructed a large number of H-bond acceptor anddonor parameters by using correlation analysis of physicochemical parame-ters [13–15], available data are limited because they are derived from experi-mental data. Accordingly, we have developed a new H-bond acceptor scale[16], SHA, by calculations with the use of the COSMO (conductor-likescreening model) method [17].

The log P values are also estimated by retention factors, log k′, derivedfrom RP-HPLC. We have studied systematically the relationship between logP and log k′ for monosubstituted heteroaromatic series by using the above-mentioned SHA parameter; the optimum HPLC conditions to predict log P arereported and discussed below.

2. Methods

The partition coefficients of compounds whose log P values are lowerthan about 3.5 were measured by a conventional shake-flask method at 25o.The concentrations of the solutes in both the water and octanol (or organic)phases were determined by HPLC [18]. The log P values of highly lipophil-ic compounds (log P > 3.5) were measured by using a newly developedHPLC column-switching method [6].

The retention factor, k′, was obtained from the retention times accordingto the well-know relation k′ = (tR – t0)/t0, where t0 and tR are the retentiontimes of an unretained compound and of the analyte. A commercial CapcellPak C18 (AG or SG type) was used without further treatment. As eluents,MeOH-buffer (pH 7.4, 0.01M) solutions of different methanol percentageswere used except for the pyridine series where MeOH-buffer (pH 9.2, 0.01M)solutions were used. Diazines yielded the equivalent results at pH 7.4 and 9.2.

A new scale of hydrogen-acceptability, SHA, was defined for monosubsti-tuted (di)azine series [16], on the basis of the heat of formation under variousdielectric environments calculated by the COSMO method incorporated inthe MOPAC 93 program package [19]. All calculations were performed withan Anchor II modeling system [19]. The heat of formation, Hf, of the mini-mum-energy conformation of a given compound (Ar-X) was calculated forthe gaseous phase and also for several dielectric () environments with theAM1 [20] Hamiltonian and by using the eps command to perform theCOSMO method. In practice, the calculation was done at five values of ,from = 1 (gas) to = 78.4 (water). The Hf values calculated at five valuesfor Ar-X were plotted against the corresponding Hf values for the unsubstitut-ed compound (Ar-H), giving a straight line. The slope of this line is expectedto indicate how well the substituent X can be stabilized by a polar solvent.


Therefore, the slope of this line was defined as SHA for the X substituent [16].As clearly shown by the definition, the SHA value for H (unsubstituted com-pound) is always 1 and that for substituent X changes depending on the skeletal system. The SHA parameter thus obtained was used in the analysis ofa series of compounds belonging to the same skeletal structure.

3. Heterocyclic Log P: Does the Additivity Rule Hold?

To investigate the partition behavior of heterocyclic compounds, wemeasured the log P values of monosubstituted pyridines (2PY), diazines (PR,PM, and PD), thiophenes (TH), and furans (FR) (Fig. 1). Their values,defined by = log PAr-X – log PAr-H for X substituent, are summarized inTable 1. The variation in values between series is attributable to the mutu-al effects of electronic interactions between the substituent and the ring het-eroatom(s) on their H-bonding abilities [9] [10]. As clearly shown in Fig. 2,these effects are more evident in diazine- values than in those for the otherseries. Accordingly, our studies on heterocyclic log P have been predomi-nantly devoted to log P values for the diazine series.

To estimate log P values by calculation, the most widely used methodol-ogy is based on an additive-constitutive, free-energy-related property of log P[1] [2]. Thus, for polysubstituted benzenes where important steric effects arenot involved, log P is estimated by Eqn. 1, where the first term represents thelog P of benzene and the second the summation of values for all substitu-ents attached to the benzene ring.

log P = a log PH + Xi (Eqn. 1)

In order to examine whether the additivity assumed by Eqn. 1 is effectivefor predicting log P values for diazines, we measured log P of disubstitutedpyrazines with various combinations of substituents given in Table 1 andcompared the results with those calculated from Eqn. 1 [10]. The results con-form to the principle of additivity by providing acceptable first-order predic-


Fig. 1. Monosubstituted heteroaromatic compounds examined


Tabl

e 1.

Va

lues

for

Var

ious

Aro

mat

ic S

erie

sa )

Seri

esb )

PhX

c )2P

YP

RP

MP

DT

HF

Rd )

log

PH

log

PH

log

PH

log

PH

=–0

.44

log

PH

=–0

.73

log

PH

= 1

.89

log

PH

= 1

.34

= 2

.13

= 0

.65

=–0

.26

2PM

4PM

5PM

3PD

4PD

2TH

3TH

2FR

3FR

H0.

000.

000.

000.

000.

000.

000.

000.

000.

000.

000.

000.

00F

0.14

0.19

0.55

0.46

0.41

Cl

0.71

0.62

0.96

0.80

0.91

0.91

0.83

0.80

0.66

Br

0.86

0.73

1.19

0.94

1.10

0.95

0.84

0.84

Me

0.56

0.46

0.47

0.39

0.39

0.45

0.38

0.41

0.58

0.56

0.51

Et

1.02

0.95

0.95

1.12

1.01

e )1.

06M

eO–0

.02

0.69

0.99

0.67

0.98

0.51

0.81

0.42

0.24

0.10

EtO

0.38

1.16

1.54

1.18

1.41

1.00

1.36

PrO

1.73

2.10

MeS

0.61

1.06

1.43

1.45

NM

e 20.

181.

001.

191.

511.

020.

901.

020.

64C

N–0

.57

–0.2

50.

250.

52f )

0.36

0.02

f )0.

10–0

.55

–0.3

8A

c–0

.55

0.18

0.46

–0.6

2–0

.63

–0.8

2N

O2

–0.2

8–0

.31

–0.2

6e )

CO

2Me

–0.0

1–0

.22

f )0.

03–0

.27

0.17

0.47

0.30

0.46

–0.0

6–0

.13

–0.3

4–0

.06

a )C

O2E

t0.

510.

220.

540.

130.

960.

960.

500.

430.

180.

44C

O2P

r1.

121.

141.

02a )

CO

NM

e 2–1

.51

–1.1

0–0

.54

–1.1

0–1

.35

–0.9

3–1

.10

Ph1.

962.

00f )

2.32

f )C

ON

HM

e–1

.23

a )–0

.02

–1.0

5–1

.08

–1.1

1a )

–1.0

0a )

CO

NH

Et

–0.8

5a )

0.39

–0.7

1–0

.71

–0.7

3a )

–0.6

2a )

CO

NH

Pr–0

.41

a )0.

89–0

.21

–0.2

4–0

.24

a )C

ON

H2

–1.4

9–0

.50

–0.2

4–0

.76

–0.2

4–0

.48

0.00

–0.2

3–1

.41

–1.3

9–1

.45

–1.2

5N

HA

c–0

.97

–0.1

30.

23–0

.37

0.47

0.22

0.32

NH

2–1

.23

–0.1

70.

210.

240.

190.

20

a )Tak

en f

rom

our

pre

viou

s w

orks

[9]

[29]

[30]

unl

ess

othe

rwis

e in

dica

ted.

b ) Fo

r st

ruct

ures

and

abb

revi

atio

ns, s

ee F

ig. 1

.c ) Fr

om [

2] u

nles

s ot

herw

ise

indi

-ca

ted.

d ) Fr

om [

38]

unle

ss o

ther

wis

e in

dica

ted.

e ) U

npub

lishe

d re

sults

. f ) Fr

om [

37].

tion for log P for the majority of the 68 compounds examined (Fig. 3). Thisis due to the use of pyrazine- values (PR); use of benzene- values leads toa substantial underestimation of log P values. Significant deviations, largerthan 0.2, are shown by 18 compounds. Careful scrutiny suggests that each ofthese deviations can largely be attributed to electronic and/or steric interac-tions between the substituents.

It would be of interest to know if additivity of PR remains valid for high-ly lipophilic pyrazines. We therefore attempted to measure log P values for aseries of diarylpyrazines by our column-switching method [6]. When PR val-ues were available, we also calculated log Padd values according to Eqn. 2 andcompared them with the experimental values (Table 2).

log Padd = log P (pyrazine) + (R2, R3, R5, R6) (Eqn. 2)

It is noteworthy that all the observed log P values were much lower thanlog Padd. This feature was more significant in 2,3-diarylpyrazines as shownby comparing the log P values of compounds 4, 23, and 24. Steric hindrancebetween the ortho-diphenyl rings would cause resonance inhibition, decreas-


Fig. 2. Comparison of values for various heteroaromatic series. PhX: values for mono-substituted benzenes. ArX: values for heteroaromatic series. For symbols, see Fig. 1. The

straight line represents the unity line.

ing the degree of conjugation and consequently reducing hydrophobicitybelow expectations for a molecule with full delocalization of -electrons. Theadditivity rule (Eqn. 2) overestimated the log P value of compound 3 by 1.2and that of compound 14 by 1.3, suggesting that the steric hindrance betweenthe two vicinal benzene rings makes a negative contribution of about –1.2 tolog P (ortho-diaryl effect).

In contrast, we may expect normal substituent effects where the substitu-ent is not subject to significant steric effects, as in the case of 5-substituted2,3-diarylpyrazines. Addition of log P values for 3 or 14 and PR values forthe R5 substituent give estimates (log Ppred) in fairly good agreement withexperimental values (Table 2). These results indicate the utility of pyrazine-


Fig. 3. Relationship between observed and calculated log P (log Padd) values for disubstitutedpyrazines. Pyrazine- : calculated by log P (pyrazine) + PR (X,Y). Benzene- : calculated

by log P (pyrazine) + PhX (X,Y). The straight line represents the unity line.

values in providing reasonable estimates of log P even for highly lipophil-ic pyrazines where severe steric effects are not involved. Estimates of log Pfor PR from Eqn. 1 require pyrazine- values for substituents attached to apyrazine ring. This indicates that knowledge of values for each series isessential in predicting log P values by calculations especially in (di)azinesystems.


Table 2. Observed and Predicted Log P Values for (Di)arylpyrazines a)

R2 R3 R5 R6 log Pobsd. log Padd.b) log Ppred.

c)

1 Ph H H H 2.072 4-MeO-Ph d) H H H 2.243 Ph Ph H H 3.19 4.44 Ph Ph Me H 3.52 4.9 3.75 Ph Ph MeO H 4.21 5.4 4.26 Ph Ph EtO H 4.73 5.9 4.77 Ph Ph Cl H 4.05 5.4 4.28 Ph Ph i-Pr H 4.719 4-Me-Ph 4-Me-Ph H H 4.10

10 4-F-Ph 4-F-Ph Me H 3.7511 4-CN-Ph 4-CN-Ph Me H 2.5212 Ph Ph 2-MeO-Bn e) H 5.1813 Ph Ph 3-MeO-Bn e) H 5.2014 4-MeO-Ph 4-MeO-Ph H H 3.42 4.715 4-MeO-Ph 4-MeO-Ph Me H 3.66 5.2 3.916 4-MeO-Ph 4-MeO-Ph Et H 4.22 5.7 4.417 4-MeO-Ph 4-MeO-Ph Cl H 4.30 5.7 4.418 4-MeO-Ph 4-MeO-Ph MeO H 4.47 5.7 4.419 4-MeO-Ph 4-MeO-Ph EtO H 5.00 6.3 5.020 4-MeO-Ph 4-MeO-Ph CN H 3.70 5.0 3.721 4-MeO-Ph 4-MeO-Ph COOMe H 3.41 4.8 3.522 4-MeO-Ph 4-MeO-Ph Me Me 4.10 5.223 Ph Me Ph H 4.28 4.924 Ph Me H Ph 4.29 4.925 Ph Et Ph Et 4.94 6.326 Ph Et H Ph 4.66 5.427 Ph Et Et Ph 4.83 6.3

a) From [6]. b) Sum of log P of pyrazine (–0.26) and PR (R2, R3, R5, R6). c) For compounds4–7, log P (3) + PR (R5), and for 15–21, log P (14) + PR (R5). d) 4-X-Ph refers to 4-X-substituted phenyl. e) 2-MeO-Bn and 3-MeO-Bn refer to 2-MeO-benzyl and 3-MeO-benzyl,respectively.

4. Prediction of Log P from RP-HPLC Retention Factors

Log P values can also be estimated from the retention factors, log k′,derived from chromatographic retention times. Most investigators have used anODS column as the stationary phase and MeOH/water solutions as the mobilephase. Although this method is convenient and frequently used, and extensiveinvestigations for finding optimum conditions have been reported [21–25],standard procedures to estimate log P do not seem to be established. One of themost important factors hampering the utilization of this method is H-bondingarising in polar solutes [21–30]. It is well known that an alkyl-bonded station-ary phase often discriminates among H-bond acceptors, donors, and non-H-bonders. Since such an effect depends on the composition of the mobile phase,many investigators have used log kW values (log k′ values at 100% waterobtained by extrapolation of plot of log k′ against methanol content) as a lipo-philicity parameter [21][22][31][32]. Braumann has argued that log kW isidentical with log P [21]. We also examined the validity of the log kW approachfor various monosubstituted aromatic compounds by linear extrapolation usingthe data for 30–70% methanol concentrations [26–30]. As shown in Table 3,while most compounds gave log kW values close to log P, substituents such asCO2R , CONMe2 and Ac, gave log kW larger than log P by 0.2–0.3, indicatingthat the prediction by the log kW approach may lead to overestimated log P val-ues. It should be noted that these deviant substituents have larger SHA values(Table 4) than others, and suggests that the log kW approach fails to estimate logP values for strong H-bond acceptors. Moreover, the extrapolation methodaffects log kW; in our experience, linear extrapolation from the range of medi-um methanol concentrations appears favorable. For very polar solutes, the pro-cedure for estimating t0 can be another factor affecting log kW values, though,as demonstrated by Minick and co-workers [22], this factor appears to cause noproblem in deriving log kW higher than 1. Despite the convenience of the logkW approach where there is no need to know the log P values for any trial com-pounds, limitations of its application should be taken into account.

Some investigators have reported that isocratic log k′ values, rather than logkW, are correlated better with log P [33][34]. We also have found that eluentscontaining around 50% MeOH are effective to predict log P values [8][26–30]. To gain an insight into the retention behavior as a function of themobile-phase composition, we measured log k′ for 2PY, PR, PM, and THderivatives with eluents containing 15, 30, 50 and 70 % (v/v) MeOH (M15,M30, M50 and M70, respectively), and analyzed quantitatively the relationshipbetween log P and log k′ in terms of the new hydrogen-acceptor scale, SHA,(Table 4). We found that log k′ is can be expressed by Eqn. 3 for each eluent[16]:

log k′ = a log P + I + sSHA + const. (Eqn. 3)


where I represents Charton’s inductive electronic substituent constant [35].The results are given in Table 5, and a typical example is shown in Fig. 4. Itwas generally observed that the correlation was more direct at M50, and thatthe contributions of the two correction terms, I and SHA, increased in morewater-rich eluents. As the methanol content approaches zero, the plot of logk′ vs. log P shows that substituents with large SHA values, such as CO2R andCONMe2, give larger positive deviations from the line formed by non-H-bonders (Fig. 4). This is the reason why the log kW procedure overestimatesthe log P value of strong H-bond acceptors.


Table 3. Comparison between Log P and Log kW for Monosubstituted Aromatic Compounds a)

Compound log P log kWb) Compound log P log kW

b)

PhX c) TH d)H 2.13 2.04 H 1.89 1.79F 2.27 2.22 2-Cl 2.69 2.66Cl 2.84 2.77 2-Br 2.84 2.81Br 2.99 2.94 2-Me 2.47 2.42Me 2.69 2.65 2-Et 3.01 3.01Et 3.15 3.21 2-MeO 2.13 2.17MeO 2.11 2.12 2-CN 1.34 1.60EtO 2.51 2.60 2-Ac 1.27 1.60CN 1.56 1.65 2-NO2 1.58 1.71Ac 1.58 1.77 2-CO2Me 1.83 2.18CO2Me 2.12 2.34 2-CO2Et 2.39 2.74CO2Et 2.64 2.90 2- CO2Pr 3.01 3.35

2-CONMe2 0.79 1.50PR 3-Cl 2.55 2.48

H –0.26 –0.21 3-Br 2.73 2.64Cl 0.7 0.72 3-Me 2.45 2.38Me 0.21 0.32 3-Ac 1.26 1.58Et 0.69 0.89 3-CO2Me 1.76 2.13MeO 0.73 0.89 3-CO2Et 2.32 2.68EtO 1.28 1.48 3-CO2Pr 3.03 3.27PrO 1.84 2.14NMe2 0.93 1.20 FR e)CN –0.01 0.21 H 1.34 1.23Ac 0.20 0.54 2-Me 1.85 1.83CO2Me –0.23 0.42 2-Et 2.40 2.46CO2Et 0.28 0.98 2-MeO 1.44 1.55Ph 2.06 2.28 2-Ac 0.52 0.90

2-CO2Me 1.01 1.382-CO2Et 1.52 1.932-CONMe2 0.41 1.003-CO2Me 1.28 1.523-CO2Et 1.78 2.073-CO2Pr 2.36 2.66

a) For structures of series PR, TH, and FR, see Fig. 1. b) Calculated by the linear extrapola-tion using the data for 30–70% MeOH. c) From [26]. d) From [30]. e) From [27] [29].


Table 4. H-Accepting Scale, SHA, for Each Series a)

2PY b) PR b) PM b) TH

Substituent SHA Substituent SHA Substituent SHA Substituent SHA

H 1.00 H 1.00 H 1.00 H 1.00F 1.06 F 0.99 2-F 1.02 2-Cl 1.06Cl 1.03 Cl 0.97 2-Cl 0.99 2-Br 1.25Br 1.10 Me 0.96 2-Br 1.02 2-Me 0.94Me 0.96 Et 0.91 2-Me 0.92 2-OMe 1.59Et 0.88 OMe 1.02 2-OMe 1.15 2-CN 1.94OMe 1.07 OEt 1.00 2-OEt 1.14 2-Ac 2.73OEt 1.05 OPr 0.99 2-SMe 0.96 2-CO2Me 2.93OPr 1.03 SMe 0.96 2-NMe2 0.94 2-CONMe2 3.22SMe 0.98 NMe2 1.09 2-CN 1.13 3-Cl 1.24NMe2 1.15 CN 1.21 2-CO2Me 1.51 3-Br 1.44CN 1.34 Ac 1.31 2-CO2Et 1.49 3-Me 0.98Ac 1.42 CO2Me 1.62 5-F 0.95 3-Ac 2.87CO2Me 1.92 CO2Et 1.60 5-Cl 0.95 3-CO2Me 3.15CO2Et 1.89 CONMe2 1.78 5-Br 1.01 3-CONMe2 3.43CONMe2 2.19 5-Me 0.97

5-OMe 1.155-OEt 1.125-CN 1.255-CO2Me 1.535-CO2Et 1.50

a) For structures of series, see Fig. 1. b) From [16]

Fig. 4. Relationships between log P and log k′ for monosubstituted pyrazines. M15, M30, M50and M70 represent eluents containing 15, 30, 50 and 70% MeOH, respectively. Closed

circles: H, Alkyl, Halogen, OR, SMe, NMe2, CN, Ac. Open circles: CO2R, CONMe2.

The log k′ values at 50% MeOH, log k′M50, are plotted against log P forcompounds in Table 3 in comparison with the log kW (Fig. 5), clearly demon-strating that log k′M50 provides a better correlation than log kW regardless ofthe skeletal structure. In order to verify the predictive ability of the M50 elu-ent over a wider range of log P, we measured log k′M50 for lipophilic pyra-zines (see Table 2) and for some typical monosubstituted pyrazines. A plot ofresultant k′M50 values against log P shows good linearity (Fig. 6). It is ofinterest to find that a simple, linear equation can cover a range of lipophilic-ity of six log units, including sterically hindered conjugated systems. On theother hand, it was impossible to obtain log kW for such lipophilic compoundsbecause retention times were too long to be measured with water-rich eluents.As the measurement of log k′ is easier and faster than that of log P, particu-

Table 5. Analyses of log k′ for Monosubstituted Heteroaromatic Series using Eqn. 3

regression coefficients

Eluent a) log P I SHA const. n b) r c) s d) F e)

2PY f)M15 0.783 –0.643 0.470 –0.149 15 0.992 0.058 216.2M30 0.702 –0.469 0.260 –0.290 15 0.995 0.045 343.1M50 0.592 –0.235 0.133g –0.669 15 0.994 0.047 332.9

PR f)M15 0.889 –0.494 0.809 –0.559 15 0.998 0.034 973.6M30 0.781 –0.220 0.451 –0.579 15 0.998 0.032 996.5M50 0.581 –0.511 15 0.990 0.059 635.4M70 0.489 –0.884 15 0.986 0.052 414.2

2PM f)M15 0.843 –0.686 1.129 –0.734 12 0.995 0.043 272.8M30 0.720 –0.357 0.616 –0.664 12 0.996 0.035 343.7M50 0.509 –0.467 12 0.987 0.049 362.9M70 0.413 –0.747 12 0.991 0.033 537.5

5PM f)M15 0.917 –0.472 0.585 –0.163 10 0.993 0.053 147.8M30 0.810 –0.285 0.349 –0.353 10 0.996 0.034 266.5M50 0.642 –0.412 10 0.981 0.052 209.1M50 0.639 0.160 –0.595 10 0.991 0.039 191.1M70 0.491 –0.726 10 0.976 0.046 158.6

THM15 0.726 0.189 –0.242 18 0.977 0.098 154.7M30 0.702 0.068 –0.344 20 0.992 0.066 548.9M50 0.578 –0.045 –0.452 20 0.994 0.055 656.1M70 0.440 –0.090 –0.664 20 0.991 0.053 483.8

a) The numbers after M represent %-MeOH by volume. b) Number of compounds used for cor-relations. c) Correlation coefficients. d) Standard deviations. e) Values of the ratio betweenregression and residual variances. f) From [16].


larly for very hydrophobic compounds, the log k′M50 parameter could beexpected to be a powerful tool for predicting the log P value.

5. A Model for Chromatographic Partition

In the HPLC method, one deals with the partition of solutes between analkyl-bonded stationary phase (hydrocarbon layer) and a mobile phase.However, differences in properties between stationary phases and octanol,and also between aqueous methanol and pure water would restrict the use ofretention data as an alternative to log P. To examine the difference betweenthe two partitioning systems, we attempted to simulate the HPLC system by

Fig. 5. Plots of log kW and log k′M50 vs. log P for compounds in Table 3


use of a batch-like equilibrium between octane and aqueous methanol, log PO/M-W, which might be expected to resemble the chromatographicsystem more closely than the octanol-water system (Fig. 7). We measured thelog PO/M-W values for monosubstituted pyrazines using various compositionsof aqueous methanol as the polar phase. Preliminary measurements of themethanol content in octane and octane content in aqueous methanol at equi-librium showed that penetration into the other phase was almost negligible at

Fig. 6. Relationship between log P and log k′ for variously substituted pyrazines [6]. Opensymbols: (di)arylpyrazines in Table 2. Closed symbols: monosubstituted pyrazines and poly-

alkylpyrazines.


methanol concentrations <70 %. Plots of these results (log PO/M-W) against%-MeOH demonstrate that while log PO/M-W decreases almost linearly withan increase in methanol content from 5 to 70%, the decrease in log k′ is non-linear over the same range (Fig. 8). These results suggest that the log k′parameter is mainly influenced by the factors that control partition betweenthe corresponding bulk solvents, but is also affected by additional interactionsspecific to the HPLC system.

The log P values measured for the octane/M50 partitioning system, logPO/M50, are plotted against log k′M50 in Fig. 9. Surprisingly, log k′M50 wasfound to correlate better with octanol-water log P than log PO/M50. Next, inorder to make the chromatographic system resemble more closely the octa-nol-water partitioning system, a small quantity of octanol was added to theeluents [22]. Typical results (Fig. 10, [36]) show that addition of octanol

Fig. 7. A model simulating the HPLC system by bulk-solvent partitioning between octane andaqueous MeOH

Fig. 8. Comparison between the HPLC system and bulk-solvent partitioning for monosubsti-tuted pyrazines. Left panel: dependence of log PO/M-W on the MeOH concentration by volumein the aqueous phase. Right panel: dependence of log k′ on the MeOH concentration in the

mobile phase.


Fig. 9. Comparison between HPLC and bulk-solvent partitioning for monosubstituted pyraz-ines. Log Poct: log P for octanol/water; log PO/M50: log P for octane/M50 (M50: MeOH/H2O

50:50); log k′M50: log k′ with M50 eluent.

Fig. 10. Effect of octanol added to mobile phase on log k′ for monosubstituted pyrazines.Log k′M15(oct-0.25) (k′M50(oct-0.25)): log k′ measured with the M15(M50) eluent prepared withMeOH containing 0.25% (v/v) octanol. log k′M15 (k′M50): log k′ obtained with M15 (M50) eluent

without octanol. Solid lines represent the unity line.


makes all the solutes less retentive, that is, log k′ values become smaller. Thisacceleration effect was increased with decreasing methanol concentrationsand also with strong H-bond acceptors such as CO2R and CONMe2. Such anoctanol effect was minimal at 50% MeOH, suggesting that the chromato-graphic system with eluents containing about 50% MeOH has propertiesmore similar to the octanol-water partitioning system than to eluents of othercompositions.

All the results described above indicate that the complexity of the reten-tion process in RP-HPLC is not perfectly modeled by the corresponding bulk-solvent partition. The stationary phase is known to be enriched in the organ-ic solvent component. Therefore, the dynamic solvated stationary phase couldbe expected to demonstrate some alcohol-like characteristics. The findingthat the octanol effect on log k′ was minimal at 50% MeOH allows us to spec-ulate that an HPLC system with M50 eluent most closely resembles the octa-nol-water partitioning system. This would account for the better correlationof log P with log k′M50 than log k′ obtained from other eluent compositions.

6. Conclusion

Our systematic studies of the log P values of heteroaromatic compoundshave demonstrated that values vary with the parent skeleton due to H-bond-ing effects, indicating the necessity of selecting appropriate values of whenthe principle of additivity is used to estimate log P. Our new H-acceptorscale, SHA, would be useful to express such H-bonding property of solutes,though the applicability of this parameter to other aromatic H-bond acceptingsolutes should be further examined. It is, however, encouraging that the SHA

parameter can also very adequately describe the relationship between octa-nol/water log P (log Poct) and chloroform/water log P (log PCL) for 2PY, PRand PM [37]:

log PCL = a log Poct + s SHA + const. (Eqn. 4)

When predicting log P from retention parameters obtained by RP-HPLC,we have to accept that the log kw approach tends to provide overestimated logP values for strong H-acceptors having large SHA values. In such a case, itwould be safer to use log k′ obtained from the M50 eluent. Care must betaken, however, in treating amphiprotic compounds by the HPLC method.When alkyl-bonded stationary phases are used, plots of log k′ against log Pfor amphiprotics often give separate lines parallel to that of non-amphiproticsolutes [25–30]. This can be ascribed to the greater ability of octanol, com-pared to the stationary phase, to function as a H-bond acceptor. An effectiveprocedure for treating amphiprotic compounds remains to be established.


Although our discussion has concentrated mainly on optimum eluent con-ditions, the choice of the stationary phase is also an important factor forobtaining reliable log P values. Our preliminary results, using recently devel-oped C18 stationary phases with complete end capping, including a UG-typeCapcell Pak C18 column, showed that while the convenience of using the logk′M50 parameter remains valid, correlation between log P and log k′ is some-what inferior than in the present case. This suggests that trace amounts of freesilanols effectively make the stationary phase more alike octanol than hydro-carbons in H-bonding environments. More work is needed to find optimalstationary-phase structures.

REFERENCES

[1] C. Hansch, T. Fujita, J. Am. Chem. Soc. 1964, 86, 1616.[2] C. Hansch, A. Leo, ‘Exploring QSAR-Fundamentals and Applications in Chemistry and

Biology’, American Chemical Society, Washington, DC, 1995, pp. 97–543.[3] V. Pliska, B. Testa, H. van de Waterbeemd (Eds.), ‘Lipophilicity in Drug Action and

Toxicology’, VCH, Weinheim, 1996.[4] S. C. Schimmel, R. L. Garnas, J. M. Patrick, Jr., J. C. Moore, J. Agric. Food. Chem. 1983,

31, 104.[5] P. Camilleri, S. A. Watts, J. A. Boraston, J. Chem. Soc., Perkin Trans. 2 1988, 1699.[6] C. Yamagami, K. Araki, K. Ohnishi, K. Hanasato, H. Inaba, M. Aono, A. Ohta,

J. Pharm. Sci. 1999, 88, 1299.[7] S. J. Lewis, M. S. Mirrlees, P. J. Taylor, Quant. Struct.-Act. Relat. 1983, 2, 100.[8] C. Yamagami, T. Fujita, in ‘Classical and Three-Dimensional QSAR in Agrochemistry’,

C. Hansch, T. Fujita (Eds.), American Chemical Society, Washington DC, 1995, pp. 36–47.

[9] C. Yamagami, N. Takao, T. Fujita, Quant. Struct.-Act. Relat. 1990, 9, 313. [10] C. Yamagami, N. Takao, T. Fujita, J. Pharm. Sci. 1991, 80, 772. [11] T. Fujita, T. Nishioka, M. Nakajima, J. Med. Chem. 1977, 20, 1071.[12] C. Yamagami, N. Takao, T. Fujita, J. Pharm. Sci. 1993, 82, 155. [13] M. H. Abraham, P. L. Grellier, D. V. Prior, P. P. Duce, J. J. Morris, P. J. Taylor, J. Chem.

Soc., Perkin Trans. 2 1989, 699. [14] M. H. Abraham, Chem. Soc. Rev. 1993, 22, 73. [15] M. H. Abraham, J. Phys. Org. Chem. 1993, 6, 660.[16] C.Yamagami, K. Kawase, T. Fujita, Quant. Struct.-Act. Relat. 1999, 18, 26. [17] A. Klamt, G. Schüürmann, J. Chem. Soc., Perkin Trans. 2 1993, 799. [18] C. Yamagami, N. Takao, Chem. Pharm. Bull. 1993, 41, 694.[19] J. J. P. Stewart, Fujitsu Ltd., Tokyo.[20] M. J. S. Dewar, E. G. Zoebisch, E. F. Healy, J. J. P. Stewart, J. Am. Chem. Soc. 1985,

107, 3902.[21] T. Braumann, J. Chromatogr. 1986, 373, 191.[22] D. J. Minick, J. H. Frenz, M. A. Patrick, D. A. Brent, J. Med. Chem. 1988, 31, 1923. [23] W. J. Lambert, J. Chromatogr. A 1993, 656, 469. [24] M. H. Abraham, H. S. Chadha, R. A. E. Leitao, R. C. Mitchell, W. J. Lambert, R.

Kaliszan, A. Nasal, P. Haber, J. Chromatogr A 1997, 766, 35.[25] H. Terada, Quant. Struct.-Act. Relat. 1986, 5, 81.[26] C. Yamagami, T. Ogura, N. Takao, J. Chromatogr. 1990, 514, 123.[27] C. Yamagami, N. Takao, Chem. Pharm. Bull. 1992, 40, 925.[28] C. Yamagami, M. Yokota, N. Takao, Chem. Pharm. Bull. 1994, 42, 907.[29] C. Yamagami, M. Yokota, N. Takao, J. Chromatogr. A 1994, 662, 49.


[30] C. Yamagami, Y. Masaki, Chem. Pharm. Bull. 1995, 43, 2238.[31] J. E. Garst, J. Pharm. Sci. 1984, 73, 1623. [32] W. E. Hammers, G. J. Meurs, C. L. de Ligny, J. Chromatogr. 1982, 247, 1.[33] A. Bechalany, A. Tsantili-Kakoulidou, N. El Tayar, B. Testa, J. Chromatogr. 1991, 541,

221.[34] M. Kuchar, V. Rejholec, E. Kraus, V. Miller, V. Rábek, J. Chromatogr. 1983, 280, 279.[35] M. Charton, Prog. Phys. Org. Chem. 1981, 13, 119.[36] C. Yamagami, K. Iwasaki, A. Ishikawa, Chem. Pharm. Bull. 1997, 45, 1653.[37] C. Yamagami, T. Fujita, J. Pharm. Sci., in press.[38] J. Bradshaw, P. J. Taylor, Quant. Struct.-Act. Relat. 1989, 8, 279.


Liposome/Water Partitioning: Theory, Techniques, and Applications

by Stefanie D. Krämer

Biopharmacy, Dept. of Applied BioSciences, Federal Institute of Technology ETH, CH-8057 Zürich, Switzerland; Tel.: +41 1 635 60 41; Fax: +41 1 635 68 82;


1. Introduction

Liposomes have been used for several decades as model membranes tostudy solute/biological membrane interactions. The field of applications isunlimited. Which in vivo process does not include the interaction of a com-pound with a lipid bilayer? Liposomes have for example been used to inves-tigate the macroscopic partitioning of solutes between lipid membranes andaqueous phases [1–5], the localization of solutes within the bilayer [6] [7], thetranslocation rates of solutes between the two lipid leaflets [8], and micro-scopic affinity parameters such as charge-charge interactions between lipidsand solutes or proteins [9]. The partitioning behavior of solutes between lip-osomal membranes and aqueous phases provides information on their affin-ity to biological membranes and on their in vivo pharmacokinetic behavior ingeneral.

The liposomal partitioning system is increasingly employed as an alterna-tive to the n-octanol/buffer system, which is traditionally used for the estima-tion of pharmacokinetic processes such as intestinal absorption or blood-brain-barrier passage. In this chapter, some of the theoretical and technicalaspects of the liposomal partitioning system are discussed, and examples aregiven how this partitioning system has been applied to estimate and interpretbiological processes.

2. Partition Coefficients

The partition coefficient as described by Nernst in 1891 [10] is the con-centration ratio of a solute between a lipophilic and an aqueous phase at equi-


librium (Eqn. 1):

(Eqn. 1)

where [S] is the molar concentration of the solute in the lipophilic phase andthe aqueous (aq) phase, respectively. Here, one must distinguish between thetrue partition coefficient P and the apparent partition coefficient or distribu-tion coefficient D. P is the partition coefficient of a single molecular species,i.e., a defined ionization species existing as monomer in both phases. Soluteconcentrations are low so that activity coefficients equal 1.0 in both phases.P is therefore independent of experimental conditions such as solute concen-tration and pH.

P cannot always be measured directly in partition experiments. Theexperimentally determined concentration ratio is D, which considers theentire solute, regardless of the different molecular species that contribute tothe value. D is therefore dependent on pH and other variables, e.g., soluteconcentration, if they deviate from the Nernst conditions. For protonable anddeprotonable compounds, D at a particular pH can be calculated from the Pvalues of the present ionization species according to Eqn. 2:

(Eqn. 2)

where i is the molar fraction of species i (see Appendix B). This functionalso allows to estimate the P values from curve fitting of a data set of D val-ues at different pH (see later).

The density of the lipids in the vesicle bilayer has to be known to express[S]lipophilic phase in the liposome membrane. Data are available for several lip-ids under defined conditions (see, e.g., [11] [12]) but have to be determined orestimated for lipids and conditions beyond the published selection. To com-pare different systems, it is therefore more convenient to use the lipid stan-dardized molar partition coefficients Pmolar and Dmolar. The molar ratio ofsolute to lipid within the membrane replaces the solute concentration in thelipophilic phase as shown in Eqn. 3:

(Eqn. 3)

where [S]L denotes the concentration of solute associated with the membraneregarding the volume of the total system, i.e., membrane plus aqueous phase.[L] is the lipid concentration in the total system. The unit of the molar parti-tion coefficient is [M

–1]. As the Mr of most phospholipids is around 800 and

DmolarL

aq

[S][S] [L]

=⋅

D Pn

i

= ⋅=∑( )i i

1

Partition CoefficientS]

[S]lipophilic phase

aq=

[


the density of phospholipids arranged in lipid bilayers is approximately 1.0[11] [12], the factor between D and Dmolar or P and Pmolar is ~1/0.8 [mol lip-ids/liter lipids] as shown in Eqn. 4:

D ≈ 1.25 [M] · Dmolar [M–1] (Eqn. 4)

3. Liposomes

3.1. Physical Description

Lipid-bilayer vesicles have been introduced around 1960 [13] and havesince then been used as models for biological membranes in countless stud-ies. Lipid bilayers contain amphiphilic lipid molecules, typically phospholip-ids, with their hydrophobic acyl chains assembled in the bilayer core and thepolar headgroups facing towards the water phases inside and outside the ves-icle. The vesicle can consist of a single lipid bilayer or of several concentricbilayers.

Unilamellar vesicles are preferred in partition studies for several reasons.Biological membranes are generally unilamellar. A system with predominant-ly unilamellar vesicles with a homogenous size distribution is better definedthan a heterogeneous mix of multilamellar vesicles. The equilibration time ofsolute partitioning between the bilayer(s) and the aqueous phase is shorter inunilamellar vesicles [14]. Lipid bilayers in multilamellar vesicles are notalways fully hydrated, and the conditions at the bilayer/bilayer interfaces in amultilamellar vesicle are different from the bilayer/aqueous interfaces of uni-lamellar vesicles.

The bilayer thickness is around 4 nm and is determined by the length andarrangement of the lipid acyl chains [11] [12]. The vesicle diameter dependson the preparation method and conditions and on the lipid composition. Inpartition studies, large vesicles (large unilamellar vesicles, LUVs) are pre-ferred to avoid curvature effects on the distribution of the lipids and solutesbetween the two lipid layers (see Sect. 3.4.2). A typical size is around 100 nm[15–19]. However, smaller vesicles allow higher lipid concentrations, whichis sometimes inevitable in partition studies (see Sect. 3.4.2). Small unilamel-lar vesicles (SUVs) can have diameters as small as 25 to 30 nm [20] [21].

3.2. Choice of Lipids

Liposomes can be prepared from all kind of lipids and mixtures thereof(for a review, see [22]). However, not all lipids are able to form stable lipo-


somes. While phosphatidylcholine (PC), phosphatidylinositol (PI) and phos-phatidylserine (PS) form vesicles by themselves, phosphatidylethanolamine(PE), for example, only forms homogenous vesicles under certain conditionsbut can be mixed with other lipids to form stable liposomes. Cholesterol(Chol) can be incorporated into liposomes at Chol/lipid ratios above 1 [23],but does not form vesicles alone. The major lipids in biological membranesare PC, PE, PS, Chol and cholesteryl esters (CE), sphingomyelin (Sph), PI,free fatty acids (FA), and triglycerides (TG). PC, PE, Chol, CE, Sph, and TGare net neutral under physiological conditions, while FA are partly deproto-nated [17], and PS and PI are negatively charged at pH 7.4.

It is interesting to note that the amino-phospholipids and negativelycharged lipids are predominantly (PE) or exclusively (PS, PI) located in theinner plasma-membrane leaflet of mammalian cells, while Sph is enriched inthe outer leaflet, where it is associated with Chol, PC, and some of the PEpool [24–27].

The steric arrangement of the lipids in the bilayer is temperature-depen-dent. The major conformations are the gel state at low temperatures and theliquid-crystal state at higher temperatures. In membranes consisting in onelipid species, the transition happens within a narrow temperature range andcan be followed by microcalorimetric analysis (for a review, see [12]). Thetemperature at which this transition occurs is the main transition temperatureTc. Biological membranes are generally in the liquid-crystal state.Phospholipids of natural sources have a low Tc due to their high amount ofunsaturated acyl chains. Egg PC, for example, has a Tc between –15 and–8°C [28]. Liposome preparation from lipids of natural sources as well aspartition studies with such liposomes are therefore carried out above Tc whenworking at 25 or 37°C. When using synthetic lipids with saturated acylchains, Tc can be relatively high, even above 37°C, and attention has to bepaid to the temperature at which liposomes are prepared and partition experi-ments are performed.

3.3. Liposome Preparation Techniques

There is a large variety of techniques for the preparation of liposomes [12][22]. Liposomes in partition studies ideally are unilamellar, have a reprodu-cible size distribution and are free of impurities such as detergents or residualsolvents which could affect the membrane characteristics and the solute affin-ity to the membrane.

For stability and solubility reasons, lipids are generally stored in high pur-ity solvents, e.g., chloroform/methanol, at –20°C or lower in the dark. Beforethe lipids are dispersed in water, the solvents are evaporated, and the lipids


are dried under an oxygen-free atmosphere, e.g., nitrogen or argon. Low tem-perature prevents degradation and the evaporation of lipids, but it shouldremain above Tc. The spreading of the lipids over a large surface, for exam-ple in a round flask, has the advantages that the lipids dry within a shortertime and that the redispersion in the water phase is faster and more efficientin forming liposomes. The surface area can also be enhanced by the additionof glass beads to the lipid solution before drying. Special care should be takenif the sample contains lipids with unsaturated acyl chains. The large surfacearea makes them even more susceptible to oxidation and hydrolysis.

3.3.1. Preparation of Multilamellar Vesicles (MLVs)

When water, saline, or buffer is added to such a lipid film, the lipid layersin the film get hydrated and swell. The flask is shaken so that lipid layers canpeel off and spontaneously form multilamellar vesicles. To complete thehydration of all lamellae in these vesicles, several freeze-thaw cycles are usu-ally carried out after rehydration, for instance in isopropanol cooled withsolid CO2 and a 37°C water bath. This also guarantees a more homogenousdistribution of solutes if they were added during the preparation. MLVs forma heterogeneous population with diameters in the m range. They are thestarting material for a further refinement by other methods.

3.3.2. Preparation of Large Unilamellar Vesicles (LUVs) by the Extrusion Method

In our partition studies, we found the extrusion method described by Hopeet al. [29] to be the most convenient one to prepare LUVs. The MLV prepar-ation is forced several times through membrane filters with defined poresizes. The pore size and the lipid composition determine the mean diameterof the vesicles. The extrusion is usually carried out by means of a stainless-steel extruder, which allows the passage of the sample through the porousmembrane under pressure from a nitrogen cylinder. For small volumes andlow lipid concentrations, two syringes connected to a membrane holder or adisposable sterile filter can replace an extruder [20] [30]. The passage throughthe pores breaks down large vesicles up into several smaller ones, whereby adecrease in the number of lamellae can be observed. After 5 to 10 passages,a single liposome population of predominantly unilamellar vesicles with anarrow size distribution is found [29].


3.3.3. Preparation of Small Unilamellar Vesicles (SUVs) by the Sonication Method

A common technique to prepare small unilamellar vesicles (SUVs) is thedisintegration of the MLVs by a sonication probe or an ultrasonic bath [31].Because of the disruptive character of the sonication method, the MLVs donot need to be homogenously hydrated. An ultrasonic probe provides enoughenergy to prepare liposomes as small as 25 nm in a small volume. The highenergy applied by the probe may cause degradation of the lipids if the sam-ple is not appropriately cooled. However, the temperature should be above theTc of the lipids. Traces of titanium from the probe can also induce degrada-tion. Besides the SUVs, sonicated samples contain residual large vesicles.The populations are separated either by column chromatography withSepharose® or by ultracentrifugation. Larger vesicles can also be prepared inan ultrasonic bath, which allows larger sample volumes, does not overheat thesample, and induces less oxidation.

3.3.4. Preparation of Unilamellar Vesicles by Detergent Dialysis

The preparation of liposomes by detergent dialysis is based on a differentprinciple than the two methods described above [32]. The lipid film from thelipid solution is prepared in the presence of a suitable detergent with a highcritical micelle concentration (CMC), e.g., cholate. The lipid/detergent film,which is transparent under these conditions, is dissolved in saline or buffer.The lipid/detergent mixture forms micelles instead of vesicles. The micellepreparation is dialyzed until no further decrease in the detergent concentra-tion is observed. Upon removal of the detergent from the micelles, the lipidsstart to form unilamellar liposomes with a homogenous size distribution. Thefinal diameter depends on lipid composition and dialysis conditions. Typicaldiameter and residual detergent in egg PC liposomes prepared in 10 mM phos-phate-buffered 130 mM saline at pH 7.4 using cholate in an initial molarlipid/cholate ratio of 0.6 are 70 nm ± 20 and 1 cholate molecule per 700 lipidmolecules, respectively [15].

3.4. Characteristics of Liposomes in Partition Studies

3.4.1. Lamellarity

As discussed above, liposomes in partition studies should be predomi-nantly unilamellar. The average number of lipid bilayers per vesicle in lipo-


some preparations can be estimated from freeze-fracture electron micro-graphs, from measurements of the entrapped aqueous volume, or from thedetermination of the lipid ratio facing the bulk aqueous phase (for methods,see, e.g., [22]).

3.4.2. Size Distribution

The distribution of lipids and solutes between the outer and inner lipidleaflet of liposomes depends on the diameter of the liposomes [33][34].Especially in small vesicles, the steric arrangement and electrostatic forcesbetween lipid headgroups and charged solutes in the membrane will changewith membrane curvature and hence with vesicle diameter. In large vesicles,the tension of the membrane is much lower, and steric and electrostatic forces are similar in the two leaflets. Large vesicles are therefore preferred inpartition studies. A narrow size distribution guarantees reproducible data andindicates that the vesicles have not undergone aggregation or fusion (see Sect. 3.4.4.). Balon et al. [21] compared the partitioning of diclofenac andpropranolol in systems containing liposomes of 32 nm and 100 nm diametersby the potentiometric titration method (see Sect. 6.2). The diameter had noinfluence on the partitioning of neutral compounds. However, the partitioningof the ionized solutes was about half a log unit lower with the small vesiclesthan with the larger vesicles. Further studies have shown that the liposometype, i.e., size and lamellarity, influences the partitioning of n-alkyl p-amino-benzoates [35].

The size distribution of a liposome preparation can be analyzed fromfreeze-fracture or negative-staining electron micrographs. The most conven-ient method is the estimation of the size distribution from photon-correlationspectroscopy (laser light scattering) [36].

3.4.3. Lipid Concentration and Composition

Knowledge of the exact lipid concentration [L] in the partition experimentis essential to calculate the partition coefficients (Eqns. 1 and 3). If the prep-aration contains only PC, the choline concentration can be determined enzy-matically [37] [38] with a commercially available kit (MPR2, BoehringerMannheim, Germany).

If the preparation is made from a mixture of different lipids, the lipidcomposition needs to be analyzed in the final liposome preparation. Commonanalytical methods for the quantification of lipid classes are HPLC in combi-nation with a light-scatter (mass) detector [16] and TLC. Quantification from


TLC plates is most accurate in combination with the phosphate quantificationafter extraction of the phospholipids from the plate and hydrolysis of theextracts [22]. The quantification of phosphorus gives molar concentrations,which is of advantage for the calculation of Dmolar, but it only applies forphospholipids [39].

An easier method for the analysis of the lipid composition, although lessaccurate, is the scanning of the TLC plates after staining or charring the lip-ids [39] [40] and estimating lipid amounts in the spots from density analysis.This method also allows the determination of lipids without phosphorusgroups, but has to be accompanied by the exact quantification of at least oneof the lipids.

3.4.4. Physical and Chemical Stability of Liposomes and Lipids

Liposome preparations are of limited physical and chemical stability. Wefound that egg-PC LUVs can be stored for about 2 weeks at 4° for use in par-tition experiments. The liposomes can undergo several degradation processes.Uncharged liposomes especially tend to aggregate whereas liposomes con-taining charged lipids repel each other and reduce the risk of aggregation.Aggregation and fusion can be monitored by size-distribution measurements.

Both the physical stability of vesicles and the chemical stability of lipidsneed to be verified. Phospholipids undergo hydrolysis in aqueous systems.Hydrolysis is lowest at neutral pH but increases towards higher and lower pHvalues [41]. In equilibrium-dialysis experiments where egg-PC liposomeswere exposed to 37°C for 5 hours, no hydrolysis products and no changes insize distribution were detected between pH 2 and 10.5 [15] [16]. Hydrolysisproducts can be separated and quantified by HPLC and TLC (see Sect. 3.4.3).

Unsaturated acyl chains are particularly susceptible to oxidation and per-oxidation. However, under the experimental conditions of the various tech-niques described here, no evidence occurred that the results were affected byoxidation or peroxidation of the lipids (see, e.g., [21]). Several methods for thequantification of lipid peroxides can be found in the literature (e.g., [42–45]).

3.4.5. Zetapotential of Liposomes

Zetapotential () measurements are of interest if the liposomes containnet-charged lipids or membrane-associated charged compounds. The valuegives information on the pH-dependent ionization state of the lipids andallows the estimation of the local pH at the hydrodynamic shear plane of thevesicles, and, under certain conditions, also at the vesicle surface [46] (see


Sect. 7.2). The is determined by photon-correlation spectroscopy (laser-light scattering) from the velocity of the vesicles in saline or buffer in anelectric field (e.g., [47]).

Fig. 1 shows the pH-dependent of several liposome preparations withdifferent lipid compositions [16] [17] [19]. Under the chosen conditions, i.e.,0.23 M ionic strength, values are ~ 0 at the pH where the lipids are net neu-tral, and turn negative with the deprotonation of oleic acid (OA) and PEtowards higher pH values. PC-Liposomes have a negative in the pH rangewhere hydrolysis, i.e., the production of negatively charged lipids, occurs. PIis negatively charged over the whole pH range above pH 3.

Sects. 5.2 and 7.2 show how and the surface potential , respectively,influence the partitioning of solutes. Two effects have to be considered in par-tition studies: the solute-induced -dependent repulsion of ionized solutefrom the membrane surface at low lipid/solute ratios m in the membrane (seeSect. 5.2) and the -dependent pH shift in the vicinity of a charged lipidmembrane (see Sect. 7.2).


Fig. 1. Zetapotentials () of liposomes with different lipid compositions. The electrophoreticmobilities of the extruded unilamellar 70–100 nm large vesicles were measured at 37°C bymeans of a Malvern Zetasizer 3 in a universal buffer solution with an ionic strength of 0.23 M

containing ~0.2 M Na+ and mainly Cl– as anions. Values were calculated using the Helmholtz-Smoluchowski equation.

3.4.6. Proton Exchange between the Inner and Outer Aqueous Phases

Partition-coefficient calculations from experimental data are based on theassumption that the aqueous phases inside and outside the vesicles are equal.However, a pH gradient between the vesicle lumen and the outer aqueousphase could lead to a misinterpretation of the data, depending on the lipoph-ilicity and acidic or basic characteristics of the solute, the lipid concentration,and the average size of the liposomes.

Pauletti and Wunderli-Allenspach [15] have shown that the proton equi-librium between the inner and outer aqueous phases of detergent-dialysis eggPC LUVs is reached within 5 minutes at 37°C. The pH in the inner aqueousphase was estimated from an entrapped hydrophilic pH-sensitive fluorescenthigh-Mr compound. The liposomes were therefore prepared in the presence of fluorescein-isothiocyanate-labeled dextran (FD, Mr ~ 40 kDa), and non-entrapped FD was subsequently removed by Sephadex® G-100 gel filtration.The pH inside the liposomes was estimated from the fluorescence ratio ofentrapped FD at two different wavelengths and compared to the bulk pHmeasured with a microelectrode.

4. Arrangement of Lipophilic Solutes in the Liposomal Membranes

The preferred location of a solute in the liposome system depends on itslipophilicity in general and on its dipolarity/polarizability, its hydrogen-bond-ing capabilities, and its molecular volume in particular. Polar lipophilic com-pounds behave like the lipids themselves. They intercalate between the lipidsin the two lipid layers of the membrane with their polar and/or hydrogen-bonding groups in the vicinity of the polar lipid headgroups and their hydro-phobic groups, e.g., aromatic ring systems, in the acyl chain region of themembrane. Non-polar compounds, as for example amiodarone, apparentlyprefer the pure acyl-chain region of the membrane and localize in the centerof the lipid bilayer [6] [48–51].

5. Maximal Solute and Lipid Concentrations in the Liposomal Partition System

5.1. Maximal Concentrations Independent of Solute Ionization

The maximal solute concentration that remains without influence on par-titioning can be determined experimentally by comparing D or Dmolar over awide solute concentration range or by using the Langmuir adsorption iso-


therm where the concentration of the membrane-associated solute is plottedagainst the aqueous solute concentration. D or Dmolar is independent of soluteconcentration in the linear range.

Sometimes it is convenient to estimate rather than measure the maximalsolute concentration which does not influence Dmolar. An approximate valueof D is needed in this case. The principle is the same as for the interpretationof protein-binding data, with the liposome corresponding to the protein mole-cule and Ln as the binding site for one solute S; Ln can be one or more (n) lip-ids. Dmolar as expressed in Eqn. 3 is related to the association constant of a 1:1SLn complex. Under Nernst conditions Eqn. 3 can be rewritten as Eqn. 5which combines Dmolar and the association constant Ka of the SLn 1:1 com-plex:

(Eqn. 5)

Eqn. 5 is closely related to the Langmuir absorption isotherm (e.g., [52]).Under ideal conditions, where [SL]n K [L], Eqns. 3 and 5 reveal the samevalue, since [SLn] equals [S]L, and [L]non-assoc is not significantly differentfrom [L]. At higher [SL]n, [L]non-assoc is smaller than [L]. Comparison ofEqns. 3 and 5 shows that Dmolar depends on the solute concentration in thiscase. From the comparison of Ka with Dmolar, it is also clear that when morethan one liposomes are involved in the SLn complex, Dmolar depends on thelipid concentration because (n · [L]non-assoc) appears in the second or a higherorder in the association equation but not in Eqn. 3. Accordingly, Dmolar

depends on solute concentration when the solute is present as di- or oligom-er in one of the phases.

Based on the above considerations, the accuracy of Dmolar can be estimat-ed from the lipid/solute ratio (m) in the membrane. At m = 100 or 10, [L]non-assoc is 99% or 90% of the total lipid concentration, and Dmolar is there-fore 1 or 10% lower than the intrinsic, i.e., the solute-concentration-indepen-dent, Dmolar. For simplicity, a 1:1 solute/lipid complex is assumed in thisapproximation, i.e., the maximal theoretical number of solutes in the mem-brane equals the number of lipids, which is a very vague estimate but can beextrapolated from experimental data using the Langmuir absorption isotherm.The ratio m can be calculated from Dmolar, the total solute concentration, andthe lipid concentration, as shown in Eqn. 6 (see Appendix A):

(Eqn. 6)

These estimations assume activity coefficients of 1.0 for the solute in theaqueous phase and no influence of the high solute concentrations on themembrane characteristics. If, for analytical reasons, the solute concentration

m [L][S]

[L] 1[S]L

molar

molar tot= = ⋅ +

⋅DD

DKn

nmolar

a

aq non-assoc

[SLS] [L]

= =⋅

][


is relatively high, the lipid concentration should be sufficiently high to resultin an acceptable m and therefore accurate partition coefficients. However,unlike the conditions in bulk biphasic systems, the lipid concentration is lim-ited in the liposome/buffer system. To get an idea on the maximal lipid con-centration in a liposome preparation, liposome geometry and membranethickness have to be considered. For a membrane thickness of 4 nm [11] [12]and a spherical liposome with a diameter of 100 nm, the volume of the lipidsaccounts for about 20% of the total vesicle volume. Of course, the vesicle canbe squeezed into a smaller volume but this would mean that the surfaces ofthe liposomes are in very close contact to each other and that part of the mem-brane is under strong tension due to the high curvature. This certainly wouldaffect partitioning. To offer an estimate, the highest lipid concentration atwhich homogeneous unilamellar liposomes with a diameter of 100 nm cantheoretically exist as spheres (hexagonal closed-packed) is ~150 mg · ml–1(corresponding to ~200 mm PC). However, when working with lipid concen-trations above 10 mg · ml–1, it is recommended to verify that the high lipo-some density does not affect partitioning.

In some studies, smaller liposomes have been used in order to allow highlipid concentrations [21]. The problem, which can occur from smaller lipo-somes due to their high membrane curvature, was discussed in Sect. 3.4.2. Fora neutral compound with, for example, log Pmolar = 0.5, the maximal soluteconcentration for an accurate log D determination in the PC-liposome/buffersystem containing 10 mg PC per ml (m = 20, i.e., D = 0.95 · P, [L] = 13 mM)can be estimated from Eqn. 7 which is based on Eqn. 6:

(Eqn. 7)

From [S]tot, [L] and Pmolar, the concentrations in the two phases can beestimated as shown in Eqns. 8 and 9. This can be helpful to choose the ana-lytical methods according to their sensitivity.

(Eqn. 8)

[S]aq = [S]tot – [S]L = 15.81 mM (Eqn. 9)

5.2. Maximal Concentrations for Ionized Solutes

The partitioning of charged solutes follows the same rules as described in Sect. 5.1. In addition, effects from electrostatic solute-solute repulsion at

[S][L] [S]

[L] 110 [ ] 0.013[ ] 0.01646[ ]

10 [ ] 0.013[ ] 10.65 mL

molar tot

molar

0.5 1

0.5 1M M M

M MM= ⋅ ⋅

⋅ += ⋅ ⋅

⋅ +=

−

−P

P

[S][L] 1

m10 [ ] 0.013[ ] 1

20 10 [ ]16.46 mtot

molar

molar

0.5 –1

0.5 1M M

MM= ⋅ +

⋅= ⋅ +

⋅=−

PP


low lipid/solute ratios in the membrane must be taken into account. Thesimplest model to estimate electrostatic effects at the membrane surface is theGouy-Chapman diffuse double-layer theory [52–54]. It is stressed here thatthe following considerations are only approximations in order to estimate thelimits and the accuracy of log D determinations of ionizable solutes. For moreaccurate calculations, the reader is referred to the reviews cited above.However, it has been shown in several studies [55–57] that solute-concen-tration-dependent partitioning of ionized compounds in the liposome/buf-fer system can be satisfactorily described based on the Gouy-Chapmantheory.

The prerequisite is that the activity coefficient of the solute ions is 1.0 inthe aqueous phase [58] and that no other relevant membrane characteristicsare altered by the associated solute apart from the surface-charge density. Atphysiological electrolyte concentrations, the minimal lipid/charged soluteratio yielding accurate Dmolar values is higher than the minimal lipid/neutralsolute ratio (see Sect. 5.1.). However, since P of the ionized species is oftenlower than P of the neutral species, the lipid/charged solute ratio is not nec-essarily the limiting ratio. The lipid/charged solute ratios resulting in logDmolar values within tolerated error limits can be estimated from theBoltzmann relation as shown in Eqns. 10 and 11:

(Eqn. 10)

(Eqn. 11)

where [S]( )L, [S]( )aq and Dmolar( ) are the membrane-associated and aque-ous solute concentrations and Dmolar, respectively, under conditions, whichlead to the build up of . The term z is the valence of the ion, F the Faradayconstant (96485 C · mol–1), R the gas constant (8.31 J · mol–1 · K–1) and T theabsolute temperature in Kelvin (K). From Eqns. 10 and 11, the minimal m(m0.95) to result in a Dmolar of the ionized solute, which is not lower than 0.95 · Pmolar of the ionized solute can be estimated as follows:

(Eqn. 12)

must therefore be lower than 0.00137 V (Eqn. 13).

(Eqn. 13)| |Ψ ≤ ⋅⋅

ln 0.95 RTz F

e–

.z F⋅ ⋅

≥

RT 0 95

D Dz FRT

molar molar e( )

–

= ⋅⋅ ⋅

[S][S]

[S][S] e( )L

aq

aqL

= ⋅ ⋅⋅ ⋅

( )

– z FRT


According to the Gouy-Chapman theory, depends on the surface chargedensity [C · m–2] as shown in Eqn. 14:

(Eqn. 14)

with c as the electrolyte concentration [M] (c replaces the term 0.5 · (zi2 · ci)

in the Gouy-Chapman equation in case of a symmetrical monovalent electro-lyte, such as NaCl), 0 the permittivity of vacuum (8.8542 · 10–12 C · V–1 · m–1),and r the dielectricity constant of the aqueous phase, i.e., 78. Insertion ofEqn. 13 into Eqn. 14 results in a surface charge density ^ 0.00137 C · m–2

at c = 0.2 M and T = 310 K to result in a Dmolar value of the charged solutewhich equals 0.95 · Pmolar of the charged solute. Note that the values for and are incidentally equal in this example. is the product of the numberof charges s per area A [m2] and the elementary charge e (1.6022 ·10–19 C)(Eqn. 15):

(Eqn. 15)

The number of charges per area must therefore be lower than 8.55 ·1015 m–2

as follows from Eqn. 16:

(Eqn. 16)

As an estimate, the membrane-surface area occupied by one solute is setequal to the area occupied by one lipid, i.e., A/l = 7·10–19 m2 [11]. The num-ber of lipids l per A in presence of the solute is therefore (Eqn. 17):

(Eqn. 17)

The lipid/solute ratio m for charged solutes must therefore be higher than 166at c = 0.2 M and K = 310 K (Eqn. 18):

(Eqn. 18)

For c = 0.1 M, m0.95 is 235, and for c = 0.01 M it equals 746. At T = 298 andc = 0.2 M, m0.95 is 163.

If ion pairing occurs, is lower than calculated in Eqn. 15 where s is setequal the number of ionized solute per area. s and therewith m decrease withion pairing. It has been demonstrated that ion pairing is negligible in the lipo-some/aqueous system [59]. The high dielectricity coefficient in the vicinity ofthe polar headgroups appears to offer a favorable environment for the chargedgroups of the solute so that ion pairing is not of advantage for the partition-

m l ss

= −

l sA− = ⋅ −1 42 1018 2. m

sA e= σ

= ⋅s eA

σ ε ε ψ= ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅

80002

c RTz F

RT0 r sinh


ing into the lipid bilayer. This is of course different in bulk solvent/buffersystems where ion pairs are by far more likely to partition into the organicphase than net-charged compounds [59].

It should also be kept in mind that the pH where 50% of the solutes in themembrane are deprotonated/protonated (pH (m 0.5)) does not correspond tothe aqueous pKa. The ionization grade of the solute in the membrane can becalculated according to Eqn. 19:

pH(m 0.5) = pKa + log PSH – log PS (Eqn. 19)

where log PSH is the log P of the protonated solute and log PS the log P of thedeprotonated solute.

Dmolar of propranolol in partition systems with mixed lipid liposomesdecreased above solute/lipid ratios (1/m) 0.01 to 0.1 as expected from thetheoretical considerations [16].

6. Methods to Determine Partitioning

Several methods have been described to determine the partition coeffi-cients of neutral and charged solutes between liposomal membranes and an aqueous phase. Only a small selection will be discussed here, startingwith the standard technique, i.e., the equilibrium-dialysis technique.Methods such as microcalorimetry, fluorescence spectrometry, electron paramagnetic resonance spectroscopy (EPR), NMR (see chapter by Frut-tero in this volume, p. 465) and other spectroscopic techniques will not bedescribed here.

6.1. Equilibrium Dialysis

The equilibrium-dialysis technique is a standard technique because itallows the determination of partition coefficients under equilibrium condi-tions and guarantees a sufficiently high lipid/solute ratio within the mem-brane so that partitioning is not affected by the solute concentration (seeSects. 5.1 and 5.2). However, this is only true if radiolabeled compounds areavailable or if another analytical method can be applied, which has a highsensitivity and allows to determine the concentrations in the aqueous phaseand liposome suspension.

The principle of the equilibrium-dialysis technique is shown in Fig. 2. Adialysis cell consists of two half-cells which are separated by a semipermeablemembrane. Liposome suspension containing the solute is added into one halfand buffer into the other half. Kinetic studies have revealed that the equilibra-


tion of the solute between the two compartments separated with a cellulosemembrane with an Mr cutoff of 10 000 is reached within 5 hours at 37°C.After this time, the distribution of the solute propranolol between the twolipid layers of egg PC-liposomes was equilibrated [19]. However, the time toreach equilibrium in equilibrium dialysis depends on the characteristics of thesolute, the lipids, and the dialysis system and may take longer than describedhere.

Once the equilibrium is reached, the two half-cells are emptied, and thepH is measured immediately using a microelectrode. Samples are taken forthe determination of the solute and the lipid concentration in both cell halves.The lipid concentration in the buffer side should be zero and has only to bechecked in a few representative samples. The partition coefficient D or Dmolar

is calculated as shown in Eqns. 20 and 21 based on the mass balance in theliposome-containing chamber (lipos chamber) [15] [19]:

(Eqn. 20)Dmolarlipos chamber

buffer chamber

m

lipos chamber

1[L]

[S]

[S]V

V1= ⋅ + −


Fig. 2. Equilibrium-dialysis cell for partition studies under ideal conditions in the lipo-some/aqueous system. Solute concentrations are measured at equilibrium in both compart-

ments to allow the precise calculation of partition coefficients.

(Eqn. 21)

where Vm is the estimated volume of the lipid membrane, approximately 1.0 ml per gram lipids (see Sect. 2).

6.2. Potentiometric Titration

An upcoming technique for the determination of liposome/aqueous parti-tioning is the potentiometric titration of an ionizable compound in the lipo-some/saline system [14]. The development of sophisticated potentiometrictitration instruments and of computer programs to interpret the titration datahas generated broad interest [21] [60].

The theory is described by Comer and Tam elsewhere in this volume (see p. 275). The log P values of all ionization species of a compound are deter-mined from potentiometric titration at different lipid/saline ratios. Log D isthen calculated from the log P values according to Eqn. 2. This method isfaster and less labor-intensive than equilibrium dialysis, and no radiolabeledcompound is required.

The lipophilicity range, which can be accurately determined by this meth-od, is smaller than the range accessible by equilibrium dialysis. Nernst con-ditions are difficult to fulfill in some cases due to the relatively high soluteconcentration needed for the titrations. However, limited deviations in themeasured log D or log P values are often acceptable. For example, a devia-tion of 0.2 from the intrinsic log Dmolar value, i.e., the concentration-indepen-dent log Dmolar, may be tolerated in screening programs. A value of 0.2 logunits corresponds to 37% of the intrinsic Dmolar value. A reduction of Dmolar

by 37% (Dmolar = 0.63 · intrinsic Dmolar) corresponds to m0.63 between 17 and2.7, depending on the ionization state of the compound, i.e., 100% chargedand 100% neutral in the membrane, respectively (see Sect. 5.1 and 5.2).However, the high solute amount in the membrane at m = 2.7 certainly altersthe membrane characteristics.

How the titrated apparent pKa (pKaapp) develops with increasing lipid/saline

volume ratios (r) can be shown in a simulation (Fig. 3) [61]. At low r, pKaapp

equals pKa, whereas at high r, pKaapp levels off at pH (m 0.5) (see also Eqn. 19).

From a plot of the pKaapp – pKa difference at different log r and the function

shown in Eqn. 22, the log P values of all ionization species can be fitted [61].

pK aapp – pKa = log (PSH · r + 1) – log (PS · r + 1) (Eqn. 22)

This plot also gives an estimate of the lipophilicity range, which can beanalyzed by this technique. The lowest r is given by the sensitivity of the elec-

D = ⋅ −

+

V

[Vlipos chamber

m ]

[S]

[S]1 1lipos chamber

buffer chamber


trode, i.e., the lowest possible solute concentration, and m. The highest r cor-responds to the maximal lipid concentration as discussed under Sect. 5.1. Forthe determination of the log P values of all ionization species, at least one ofthe studied r should be higher than the reciprocal lower P value, and at leastone analyzed r should lie between the two reciprocal P values (see Fig. 3)[61].

6.3. Immobilized Liposomes in Chromatography Columns

Beigi et al. [62] described the immobilization of liposomes of differentmembrane compositions in agarose-dextran gel beads in chromatography col-umns. Log D of the solutes was calculated from their retention times in thechromatography column.


Fig. 3. Dependence of the titrated pKaapp of a solute in a biphasic system from the logarithmic

lipid/aqueous volume ratio r. Every titrated protonable group of a molecule generates one sig-moid. The log P values of the solute can be fitted from the differences between pKa

app and pKaof the single protonable groups at different log r. Titrations at log r between the two

–log P values and above the minus value of the lower log P are needed for an accurate fit.

6.4. Ultracentrifugation and Ultrafiltration

Alternative techniques to determine partitioning between liposomal mem-branes and the aqueous phase include phase separation by ultracentrifugation[63] [64] or ultrafiltration. The solute concentration is generally determinedonly in the aqueous phase, and the concentration in the membranes is estimat-ed from difference calculations. These methods do not request extra equip-ment like equilibrium dialysis or potentiometric titration. Their disadvantag-es are that the partition equilibrium is disturbed by the centrifugation or fil-tration and that compound adsorption to the tube walls or the filter materialcannot always be accounted for if only the aqueous concentration of the com-pound is determined.

7. Quantitative Analysis of Partition Data

7.1. D as a Function of pH

As shown in Eqn. 2, D follows the Henderson-Hasselbalch functions ofthe solute species multiplied with P of the single species. If not only thesolute but also membrane lipids alter their ionization state within the studiedpH range, the lipophilicity profiles (D/pH plots) are the sum of all different Pvalues of the solute species at the different membrane-ionization states, mul-tiplied by the molar fractions of the solute species and the membrane states(Eqn. 23) [16] [17] [19]:

(Eqn. 23)

where i are the different ionization species of the solute, and j the states ofionization of the membrane. The term i is the molar fraction of the solutespecies i, and j is the molar fraction of the ionization state j of the mem-brane. Pij is the true partition coefficient of the solute species i at the mem-brane state j. The equations to calculate of mono- and diprotic moleculesare given in Appendix B. D and P in Eqn. 23 can be replaced with Dmolar andPmolar.

The D/pH plots of different acids and bases in the egg PC-liposome/buf-fer system [18], as well as the partitioning of propranolol in a system contain-ing liposomes with different lipid compositions and pH-dependent surfacecharges (see later, [16] [17] [19]), have been successfully fitted using thisfunction.

Dnn

= ⋅ ⋅

==

∑∑ α αi j ij

ji

P( )11


7.2. pH Corrections from Zetapotential Measurementsfor Charged Liposomes

The inflection points of the function shown in Eqn. 23 equal the pH of thebulk aqueous phase at which 50% of the solute in the aqueous phase (i = 0.5)or 50% of a specific membrane lipid (j = 0.5), respectively, are protonated.However, equilibrium dialysis partition data from systems containing nega-tively charged lipids revealed inflection points for the protonation of propran-olol, which were several tenth of a log unit higher than the titrated aqueouspKa.

This difference was assigned to the difference between the bulk pH,which was measured by a pH electrode, and the pH in the vicinity of thecharged membrane. According to the electrochemical double-layer theory,negative charges at the membrane surface lead to an accumulation of posi-tively charged ions in the aqueous phase adjacent to the membrane surface toneutralize the membrane charges. Within this aqueous layer, the proton con-centration is likewise enhanced, resulting in a lower local pH compared to thebulk pH. The local pH in the vicinity of the membrane can be estimated fromeither the calculated surface-electrical potential of the membrane or from themeasured zetapotential of the liposomes using Eqn. 24, which is based onthe Boltzmann relation. This equation was used to calculate the pKa of mem-branous lipids from titrated apparent pKa values [65]:

(Eqn. 24)

It was shown that in 1 mM electrolyte, does not deviate significantlyfrom up to a value of 60 mV [46]. Without knowledge of the exact lipidpKa (pH (m 0.5)) in the system and the association constants of electrolyteions to charged lipids, the calculation of at pH values in the range of thelipid pKa and at physiological electrolyte concentrations (e.g., 0.2 M) isimpossible. The zetapotentials were therefore used to estimate the pH in thevicinity of the membrane.

The resulting fits of propranolol partition data in different liposome sys-tems determined by equilibrium dialysis are shown in Fig. 4. The D values ascalculated according to Eqn. 21 were fitted using functions based on Eqn. 23and the estimated difference between the bulk pH and the pH at the shearplane of the vesicles (Eqn. 24).

The surface potential of PC/OA (76/24 mol/mol), PC/PI (7/3), and PIvesicles were estimated for membranes with totally deprotonated OA and PI,respectively, to allow the direct comparison with at pH 10. For the esti-mations, association constants of 0.7 M

–1 were assumed between Na+ and thenegatively charged PI and OA. This value has been found for the association

pH(at membrane) – pH(bulk) =ln 10

FRT

⋅⋅Ψ


of Na+ to PS [47]. The resulting estimated values were up to twice as highas the measured (unpublished results).

In the pure PI-liposome system, where the difference between and esti-mated is highest, curve fitting based on and , respectively, revealed sig-nificantly different partition parameters. However, the fit was better, i.e., 2

was significantly lower, when the pH was corrected for (Eqn. 24) than whencorrected for (data not shown). This indicates that -measurements allowto estimate the pH in the vicinity of the membrane, which is relevant forsolute partitioning, and that there is no need to calculate surface potentials.

8. Factors Influencing Liposome/Water Partitioning

8.1. Influence of Charged Lipids on the Membrane Affinity of Solutes

As mentioned above, we have studied the influence of several chargedlipids on the partitioning of the -receptor-blocking drug propranolol in the


Fig. 4. Partitioning of (RS)-[4-3H]propranolol between liposomes consisting of different lip-ids and buffer. Partition experiments were performed at 37°C by equilibrium dialysis in asystem containing extruded unilamellar liposomes (1–2 mg lipid/ml) with mean diametersbetween 70 and 100 nm in a universal buffer solution with a ionic strength of 0.23 M and phys-iological osmolality. Solute/lipid ratios were 10–6. The dotted line indicates the partitioningprofile of propranolol in the egg PC liposome system. The bars show the pH-dependent chargeof propranolol and the lipids in the membrane (black bars: negatively charged lipids; hatched

bars: positively charged propranolol).

equilibrium-dialysis system [16] [17] [19]. Propranolol has basic characteris-tics due to its secondary amine group with a pKa of 9.24 at 37° [15].

In Fig. 4, the partition profiles of propranolol in different liposome/buffersystems containing acidic lipids [16] [17] [19] are compared with its behaviorin the PC-liposome/buffer system [15]. The negatively charged lipids exert astrong attraction on the positively charged propranolol (indicated by the barsin Fig. 4). The experimental data were fitted as described under Sects. 7.1 and7.2. The curve fitting with Eqn. 23 revealed that the charged lipid headgroupshad no significant influence on the partitioning of the neutral propranolol buthad a relevant effect on the membrane affinity of the cationic solute.

From the fitted parameters, not only P of the two ionization species ofpropranolol in the different ionization sates of the membranes, but also thepH(m 0.5) values of the lipids in the membranes could be estimated. Theyare in good agreement with other published values [65] [66]. The fittedpH(m 0.5) values of membrane OA in the different systems were between7.5 and 7.8 after the correction for according to Eqn. 24. Based on the par-tition results with propranolol in the liposome system containing free OA(as shown in Fig. 4), it can be expected that the presence of free FA in bio-logical membranes leads to a pH-sensitive membrane affinity of cationiccompounds in the physiological pH range. A small shift in pH alters D con-siderably.

The interpretation of the partitioning of propranolol in the PC/PE-lipo-some system may be difficult without the use of Eqn. 23 to fit the experimen-tal data over an appropriate pH range. From a first sight on the D/pH plot, it may be concluded that the net neutral PE but not the deprotonated lipidenhances the membrane affinity of neutral propranolol. Curve fitting usingEqn. 23 revealed, that the data can also be fitted using a log P of the neutralpropranolol for the net neutral membrane which is in the same range as log Pfor the PC membrane. Consistently, log P of the protonated propranolol in thenegatively charged PC/PE membrane is relatively high in this case. In the pHregion between the pKa of propranolol and pH(m 0.5) of PE a certain amountof both PE (j) and propranolol (i) are ionized, apparently leading to the rel-atively high partition coefficients between the two pKa values.

In some of the D/pH plots, e.g., PC, PI and PC/PI, a further inflection wasobserved around physiological pH. For a good fit, a lipid pKa in this pHregion had to be included in the fit functions for the PC/PI and PI liposomesystems. The fitted pH(m 0.5) corresponds to the fitted pH(m 0.5) of thefree OA in the PC/OA system. The membranes contained a few percent of FA,as confirmed in TLC analysis (data not shown). However, the influence onthe fitted P values is negligible. A protonable group was found in the same pHregion in titration experiments with PC-liposomes [21].


8.2. Influence of Cholesterol on the Membrane Affinity of Solutes

Many studies have shown that physiological amounts of cholesterol lowerthe membrane affinity of solutes [21] [64] [67–69]. Studies on the affinity ofethanol to membranes containing cholesterol led to a better understanding onthe influence of cholesterol on solute partitioning [70]. In the phospholipidmembranes, cholesterol acts as space-filler in the acyl chain region of the first~10 C atoms adjacent to the lipid headgroups, increasing the packing densityand decreasing the freedom of mobility in this range [22] [71]. Cholesterolpreferentially assembles with lipids containing saturated acyl chains formingthe so called rafts [72].

8.3. Influence of Ionic Strength on the Partitioning of Neutral and Ionized Solutes

It has been shown using net-neutral liposomes that the electrolyte concen-tration has a significant influence on the partitioning of ionized compoundsbut not on the partitioning of neutral solutes [55] [59]. A constant ionicstrength is therefore crucial in partition experiments, especially in studieswith ionizable compounds. The ionic strength can have different effects onthe partitioning of ionized solutes.

At relatively high solute concentrations, the ionized compound in themembrane builds up an electrical potential (see Sect. 5.2). According to theGouy-Chapman theory, depends on the electrolyte concentration (see Eqn. 14). At low electrolyte concentrations, the surface charges have a repel-ling effect on the charged compound, so that D decreases with increasingsolute concentration. At high electrolyte concentration, the surface chargesare shielded by the counter-ions so that the effect is smaller and D is constantover a larger concentration range (see Sect. 5.2). This phenomenon can beneglected if partition experiments are carried out under Nernst conditions,e.g., using equilibrium dialysis and radiolabeled solute.

The other effect of high electrolyte concentrations is the formation of ionpairs with ionized solute and/or with charged lipids in the membrane (see alsoSect. 5.2). Due to their electroneutrality, solute ion pairs may have a higheraffinity for lipid membranes than the ionized solute compound. However, asalready discussed in Sect. 5.2, the formation of ion pairs in liposome parti-tioning is not always significant [59].


9. Significance of Liposome/Aqueous Partitioningfor the Interpretation and Prediction of in Vivo Processes

An all-integrating model was published for the calculation of permeationthrough vesicular lipid membranes based on partition coefficients, rate con-stants for permeation, and the vesicle characteristics [73]. Liposomal partitioncoefficients with PC liposomes or liposomes consisting of a lipid mixture aremore and more used to estimate in vivo absorption and barrier passage.Although the partition coefficient of a solute between the lipid membrane andthe aqueous phase is only one equilibrium constant in the permeation processacross the cell barrier, it gives a useful estimate for passive permeation acrossin vivo barriers or cell models thereof [60] [62]. The correlations between theliposomal partition coefficients and the physiological barrier permeationallows the estimation of in vivo barrier passage. As with most non-cellularmodels, prediction is only possible for compounds absorbed by passive diffu-sion through the cell membranes.

However, the liposomal partition system is also applied to interpret pro-tein-driven transport processes. Romsicki and Sharom [74] have uncovered arelationship between the liposomal partition coefficients of multidrug-resis-tance P-glycoprotein (P-gp) substrates and their P-gp binding constants. Thisfinding substantiates the hypothesis that P-gp binds its substrates out of thelipid bilayer rather than out of the aqueous phase.

The proton-uncoupling effect of phenols and other ionizable environmen-tal pollutants with intrinsic toxicity could be modeled in the liposomal parti-tioning system [75]. The uncoupling activity of different compounds wasrelated to their partitioning characteristics, acidity, steric effects, and chargedistribution within the compound.

As described above, negatively charged lipids can enhance the membraneaffinity of positively charged solutes. In the cell plasma membrane, negative-ly charged lipids are predominantly located in the inner leaflet. It is thereforeexpected that positively charged solutes also accumulate in the inner mem-brane leaflet if no other mechanism works against it. The consequences ofthis can be multiple. It was shown, for example, that solutes in the inner plas-ma-membrane leaflet of polarized cells, e.g., epithelial or endothelial cells,can move freely around the whole cell, whereas solutes in the outer leafletstay either apical or basolateral [76]. This may be of significance for the bar-rier permeation of solutes. Another mechanism, which may depend on thepreferred location of a solute in the membrane, is the recognition of mem-brane-associated solutes by membrane proteins, such as P-gp or other effluxproteins [9].


10. Conclusion

The liposome/aqueous partition system used to investigate drug/lipid-membrane interactions goes far beyond the traditional octanol/buffer system.Liposomes are a one-to-one model of the lipid bilayer of biological mem-branes. Partition results directly reflect the affinity of a compound to the lipidbilayer of a cell.

The membrane affinity of solutes depends on the type of lipid headgroupsof the membrane. In particular, charged lipids significantly alter the partitionprofiles of ionizable solutes compared to their behavior in net-neutral mem-brane systems. This can only be modeled with the liposomal partition system.The liposomal system allows the quantification of the affinity of compoundsto different membrane domains in the cell. However, the system is more sus-ceptible to experimental conditions than solvent/buffer systems. Liposomeshave a limited stability, and Nernst conditions cannot always be met in parti-tion experiments. The octanol/buffer system and other solvent/buffer systemswill not be replaced by the liposomal system. They give additional informa-tion about the behavior of the solute in the membrane region between thepolar surfaces of the lipid bilayer. They are also less time-consuming and lessexpensive than liposomal partition studies. The lipophilicity range, which iscovered by solvent/buffer systems, is larger than the range covered by liposo-mal partition systems, and Nernst conditions are better achievable in sol-vent/buffer systems. Both the liposomal and the octanol/aqueous systems canbe used to estimate in vivo absorption [60] [77].

Bernard Testa is gratefully acknowledged for the careful reading of the manuscript.

Appendix

A) Estimation of the lipid/solute ratio m in the membrane

(Eqn. 25)

[S]L = Dmolar · [S]aq · [L] (Eqn. 26)

[S]aq is calculated from the mass balance

[S]tot · Vtot = [S]L · Vtot + [S]aq · Vtot (Eqn. 27)

[S]L is replaced by Eqn. 26

[S]tot · Vtot = [S]aq · Vtot · (Dmolar · [L] + 1) (Eqn. 28)

m [L][S]L

=


(Eqn. 29)

[S]L is calculated from Eqns. 26 and 30

(Eqn. 30)

and replaced in Eqn. 25

(Eqn. 31)

B) Calculation of the molar fractions of the ionization species of mono- and diprotic molecules.

Monoprotic:

(Eqn. 32)

(< pKa) is the molar fraction of the compound that predominates at pH val-ues lower than its pKa, i.e., the protonated species, (> pKa) is the molar frac-tion of the deprotonated species, i.e., the species that predominates abovepKa.

Diprotic:

(Eqn. 33)

pKa1 and pKa2 are the lower and the higher macroscopic pKa of the com-pound

(Eqn. 34)

(pKa1/pKa2) is the molar fraction of the compound predominating betweenthe two pKa values. It does not distinguish between the two microscopic spe-cies.

(Eqn. 35)α ( p ) 1010 10 10a1

p p

2pH –pH p p p

a1 a2

a1 a1 a2> =

+ +− −

− − − −KK K

K K K

α (p /pa1 a2

pH p

2pH pH p p p

a1

a1 a1 a2K K

K

K K K) =+ +

− −

− − − − −10

10 10 10

α ( p ) 1010 10 10a1

2pH

–2pH –pH–p –p –pa1 a1 a2< =

+ +−

K K K K

α

α α

( ;

( ) – ( )

< =+

> = < =+

p )

p p

a pH–p

a a p –pH

a

a

K

K K

K

K

11 10

1 11 10

m[L] 1[S]

molar

molar tot= ⋅ +

⋅DD

[S][L] [S]

[L] 1Lmolar tot

molar= ⋅ ⋅

⋅ +D

D

[S][S]

[L] 1aqtot

molar=

⋅ +D


REFERENCES

[1] J. A. Rogers, S. S. Davis, Biochim. Biophys. Acta 1980, 598, 392.[2] G. V.Betageri, J. A. Rogers, Pharm. Res. 1989, 6, 399.[3] Y. W. Choi, J. A. Rogers, J. Pharm. Sci. 1991, 80, 757.[4] L. Ma, C. Ramachandran, N. D. Weiner, J. Pharm. Sci. 1992, 81, 1104.[5] J. A. Rogers, W. C. Young, Pharm. Res. 1993, 10, 913.[6] L. G. Herbette, M. Trumbore, D. W. Chester, A. M. Katz, J. Mol. Cell. Cardiol. 1988, 20,

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1993.[13] A. D. Bangham, M. M. Standish, J. C. Watkins, J. Mol. Biol. 1965, 13, 238.[14] A. Avdeef, K. J. Box, J. E. A. Comer, C. Hibbert, K. Y. Tam, Pharm. Res. 1998, 15, 209.[15] G. M. Pauletti, H. Wunderli-Allenspach, Eur. J. Pharm. Sci. 1994, 1, 273.[16] S. D. Krämer, H. Wunderli-Allenspach, Pharm. Res. 1996, 13, 1851.[17] S. D. Krämer, C. Jakits-Deiser, H. Wunderli-Allenspach, Pharm. Res. 1997, 14, 827.[18] C. Ottiger, H. Wunderli-Allenspach, Eur. J. Pharm. Sci. 1997, 5, 223.[19] S. D. Krämer, A. Braun, C. Jakits-Deiser, H. Wunderli-Allenspach, Pharm. Res. 1998,

15, 739.[20] H. Wunderli-Allenspach, M. Günthert, S. Ott, Biochemistry 1993, 32, 900.[21] K. Balon, B. U. Riebesehl, B. W. Muller, J. Pharm. Sci. 1999, 88, 802.[22] R. R. C. New (Ed.), ‘Liposomes: a Practical Approach’, IRL Press, Oxford, 1990.[23] J. Y. Huang, J. T. Buboltz, G. W. Feigenson, Biochim. Biophys. Acta 1999, 1417, 89.[24] M. S. Bretscher, Nature New Biol. 1972, 236, 11.[25] J. L. Whatmore, D. Allan, Biochim. Biophys. Acta 1994, 1192, 88.[26] D. Allan, Mol. Mem. Biol. 1996, 13, 81.[27] E. M. Bevers, P. Comfurius, D. W. C. Dekkers, M. Harmsma, R. F. A. Zwaal, Biol.

Chem. 1998, 379, 973.[28] D. Marsh (Ed.), ‘Handbook of Lipid Bilayers’, CRC Press, Boston, 1990.[29] M. J. Hope, M. B. Bally, G. Webb, P. R. Cullis, Biochim. Biophys. Acta 1985, 812, 55.[30] L. D. Mayer, M. J. Hope, P. R. Cullis, Biochim. Biophys. Acta 1986, 858, 161.[31] C. Huang, Biochemistry 1969, 8, 344.[32] O. Zumbühl, H. G. Weder, Biochim. Biophys. Acta 1981, 640, 252.[33] J. A. Berden, R. W. Barker, G. K. Radda, Biochim. Biophys. Acta 1974, 375, 186.[34] J. Bramhall, Biochemistry 1986, 25,[35] L. Ma, C. Ramachandran, N. D. Weiner, Int. J. Pharm. 1991, 77, 127.[36] N. Ostrowsky, Chem. Phys. Lipids 1993, 64, 45.[37] M. Takayama, S. Itoh, T. Nagasaki, I. Tanimizu, Clin. Chim. Acta 1977, 79, 93.[38] A. Nagayasu, T. Shimooka, H. Kiwada, Biochim. Biophys. Acta 1994, 1194, 12.[39] J. B. C. Findlay, W. H. Evans (Eds.), ‘Biological Membranes: a Practical Approach’, IRL

Press, Oxford, 1987.[40] W. W. Christie (Ed.), ‘Lipid Analysis: Isolation, Separation, Identification, and Structural

Analysis of Lipids’, Pergamon Press, Oxford, 1982.[41] M. Grit, D. J. A. Crommelin, Chem. Phys. Lipids 1993, 64, 3.[42] R. A. Klein, Biochim. Biophys. Acta 1970, 210, 486.[43] Z. Y. Jiang, J. V. Hunt, S. P. Wolff, Anal. Biochem. 1992, 202 , 384.[44] T. Hiramitsu, T. Arimoto, T. Ito, M. Nakano, Adv. Exp. Med. Biol. 1994, 366,[45] S. Tokumaru, H. Iguchi, S. Kojo, Mech. Ageing Develop. 1996, 86, 67.[46] R. C. MacDonald, A. D. Bangham, J. Mem. Biol. 1972, 7, 29.[47] M. Eisenberg, T. Gresalfi, T. Riccio, S. McLaughlin, Biochemistry 1979, 18, 5213.[48] L. Herbette, A. M. Katz, J. M. Sturtevant, Mol. Pharmacol. 1983, 24, 259.


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1998, 87, 599.[60] K. Balon, B. U. Riebesehl, B. W. Muller, Pharm. Res. 1999, 16, 882.[61] S. D. Krämer, J. C. Gautier, P. Saudemon, Pharm. Res. 1998, 15, 1310.[62] F. Beigi, I. Gottschalk, H. C. Lagerquist, L. Haneskog, E. Brekkan, Y. Zhang, T.

Osterberg, P. Lundahl, Int. J. Pharm. 1998, 164, 129.[63] Y. Katz, J. M. Diamond, J. Mem. Biol. 1974, 17, 69.[64] G. V. Betageri, J. A. Rogers, Int. J. Pharm. 1988, 46, 95.[65] F. C. Tsui, D. M. Ojcius, W. L. Hubbell, Biophys. J. 1986, 49, 459.[66] R. Lieckfeldt, J. Villalain, J. C. Gomez-Fernandez, G. Lee, Pharm. Res. 1995, 12, 1614.[67] J. B. A. Custodio, L. M. Almeida, V. M. C. Madeira, Biochem. Biophys. Res. Comm.

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Importance of the Mobile Phase in ImmobilizedArtificial Membrane Chromatography

by Kimberly L. Morse* and Charles Pidgeon

Admetric Biochem Inc., 300 Putnam Avenue, Cambridge, MA 02139; Phone: (617) 868-2222;Fax: (617) 868-2358; e-mail: [email protected], [email protected]

1. Immobilized Artificial Membrane (IAM) Chromatography

All pharmaceutical compounds interact directly with cellular membranes.Fig. 1 illustrates the distribution process of drug molecules from the time theyare administered until they reach their target site. For compounds adminis-tered orally, the first barrier to be crossed is the gastrointestinal cell mem-brane. Many compounds are absorbed across the intestinal mucosa into theinterstitial fluid by passive diffusion. Determining the permeation of a com-pound through the gastrointestinal membrane is both experimentally difficultand time-consuming when done in animals. As a result, several in vitro mod-els have been developed to predict the permeation of compounds: octanol-water partitioning, reversed-phase (C18) chromatography, liposomes, Caco-2cells, and immobilized artificial membranes [2–8].

Immobilized artificial membranes (IAMs) consist of monolayers of phos-pholipids covalently immobilized to a silica surface. Fig. 2 shows the struc-tures of five IAM surfaces: phosphatidylcholine (PC), phosphatidyletha-nolamine (PE), phosphatidylserine (PS), sphingomyelin (SM), and choles-terol (CL). These IAM silica surfaces are used as the stationary phase in

Fig. 1. The distribution process of drug molecules from compound administration to the targetcell [1]



Fig.

2.St

ruct

ure

of i

mm

obil

ized

art

ific

ial

mem

bran

e(I

AM

)st

atio

nary

pha

ses

high-performance liquid chromatography (HPLC). The ability of a drugmolecule to partition into a fluid membrane is modeled by IAM chromato-graphy [2–8].

The retention time of a compound on an IAM column correlates with thecompound’s equilibrium membrane-partition coefficient (Km) as shown in thefollowing equations.

(Eqn. 1)

k is the capacity factor of the drug molecule, tr is the retention time of thedrug molecule, and t0 is the dead time or void volume of the IAM column.

(Eqn. 2)

Vs is the volume of the IAM interphase created by the immobilized phos-pholipids, Vm is the total volume of solvent within the IAM column, KIAM isthe equilibrium IAM-partition coefficient, and is the phase ratio (Vs/Vm).Previous work has shown that KIAM correlates with Km determined in lipo-somes [2–8].

One of the most important parameters in IAM chromatography is thechoice of mobile phase. While the solid-phase surface in IAM chromatogra-phy models the phospholipid bilayer, the mobile phase models the aqueousenvironment surrounding the cells. In order to model this physiological envi-ronment, phosphate-buffered saline (PBS) is used as the mobile phase. ThepH (pH =7.4) and the salt concentrations (0.027 M KCl and 0.137 M NaCl) ofthis buffer correspond to the physiological properties of blood. Unfortunately,100% PBS cannot be used as the mobile phase in IAM chromatography,because most compounds do not elute from the IAM columns. As a result,typically 15% (v/v) acetonitrile (ACN) is added to the PBS buffer in order todecrease the retention times of the compounds. For most drug molecules, theretention time in ACN/PBS 15:85 is less than one hour. The addition of ace-tonitrile to the mobile phase raises an important question: How closely doesthe ACN/PBS 15:85 mobile phase mimic the physiological environment sur-rounding cells? This chapter focuses on characterizing acetonitrile-watermixtures (solvent-solvent interactions) and their effect on the physicochemi-cal properties of drug molecules (solute-solvent interactions).

k K K VV

s

mIAM IAM

kt t

t r – 0

0


2. Characterization of Acetonitrile-Water Mixtures:Solvent-Solvent Interactions

2.1. Structural Regions of Acetonitrile-Water Mixtures

When acetonitrile-water mixtures are used as mobile phases in HPLC,they typically are assumed to be homogeneous mixtures exhibiting an evendistribution of acetonitrile and water molecules. However, closer examinationof the structural features of such mixtures reveals the assumption of homoge-neity is incorrect. Acetonitrile-water mixtures exhibit microheterogeneity inwhich each solvent molecule is preferentially surrounded by molecules of thesame kind, without the existence of a liquid phase separation [9].

Acetonitrile-water mixtures consist of three distinct regions of structuralcomposition defined by the weight percent (w/w) of acetonitrile (x): 1) 0 < x< 30; 2) 30 < x < 85; and 3) 85 < x <100. In the first region or water-rich re-gion, the mixture maintains the structural features of a 100% aqueous solu-tion. The acetonitrile molecules occupy the cavities between the individualwater molecules without disrupting the overall structure. When the weightpercent of acetonitrile exceeds 30%, the cavities of the water structure can nolonger accommodate the acetonitrile molecules, thus causing a disruption inthe water structure. In this second region, there are clusters of individualwater molecules and individual acetonitrile molecules surrounded by regionsin which the water and acetonitrile clusters are in close proximity to eachother. In addition, the occurrence of phase separation of acetonitrile-watermixtures at 38 mol-% acetonitrile (272 K) supports the existence of micro-heterogeneity in this region. When the weight percent of acetonitrile exceeds85%, very few water clusters exists. In this region, the water molecules slight-ly disrupt the dipole-dipole interactions of neat acetonitrile through the for-mation of hydrogen bonds (CH3CN…HOH and CH3CN…HOH…NCCH3)[9–12].

2.2. The Kamlet-Taft Solvatochromic Parameters

The three structural regions of acetonitrile-water mixtures can be charac-terized by the Kamlet-Taft solvatochromic parameters , , and *. Mea-sures the hydrogen-bond donor capability of the solvent. measures thehydrogen-bond acceptor or electron-pair donating capability of the solvent.* measures the mixed solvent’s polarizability and polarity. The Kamlet-Taftequation is given below:

XYZ = (XYZ)0 + a + b + s* (Eqn. 3)


where XYZ is a free-energy-related property such as pH or pK, (XYZ)0 is thevalue of the property in a hypothetical solvent for which = =* = 0, anda, b, and s are the susceptibilities of the property to changes in , , and [10] [11] [13].

The solvatochromic parameters for both neat solvents and binary solventmixtures are experimentally determined by measuring the wavenumber () ofthe lowest energy-absorption peak of indicator compounds sensitive to either, , or . Table 1 shows both the indicators and their relevant coefficientsused to determine each solvatochromic parameter. Indicators 1–5 are used todetermine the polarity/polarizability parameter * according to the followingequation

(Eqn. 4)

where i designates the indicator and 0(i) and s(i) are the intercept and slope,respectively, for a series of solvents. The value of * obtained from these fiveindicators is averaged to give * for the given solvent system. Indicators 6–8are used to determine the hydrogen-bond acceptor parameter . for indica-tors 6 and 7 is determined according to the following equation

(Eqn. 5)

where 0(i) and s(i) are the intercept and slope, respectively, for a series ofsolvents and * is the previously established value from indicators 1–5. Sinceindicator 8 is not sensitive to the properties determined by *, the following

= + +0 ( ) ( ) * ( )I s i b i

*( )

( ) – ( )( )

ii i

s i= 0


Table 1. Structures and Regression Coefficients of Indicator Used in the Determination of Sol-vatochromic Parameters [13]

No. Indicator 0 s a b

1 p-NO2C6H4NEt2 27.52 –3.1822 m-NO2C6H4NEt2 25.52 –2.2143 p-NO2C6H4OMe 34.12 –2.3434 p-NO2CH=CHC6H4OMe 29.96 –2.2505 p-NO2C6H4Et 37.67 –2.2596 p-NO2C6H4NH2 31.10 –3.14 –2.797 p-NO2C6H4OH 35.05 –1.64 –2.888 Cu(acac)(tmen) a) 18.82 2.719 Fe(phen)2(CN)2

b) 15.64 1.21 2.66 –0.7710 Ph3PyPh2C6H4O c) 10.53 4.662 5.075 0.727

a) Cu(acac)(tmen) = ((N, N, N, N-tetramethylethylenediamino)acetylacetononato)copper(II) per-chlorate. b) Fe(phen)2(CN)2 = cis-dicyanobis(1,10-phenanthroline)iron(II). c) Ph3PyPh2C6H4O =2,6-diphenyl-4-(2,4,6-triphenylpyridinium-1-yl)phenolate.

equation is used to calculate for this indicator

(Eqn. 6)

where 0(8) and b(8) are the intercept and slope, respectively, for a series ofsolvents. The value of obtained from these three indicators is averaged togive for the given solvent system. Indicators 9 and 10 are used to determinethe hydrogen-bond donor parameter according to the following equation

(Eqn. 7)

where 0(i) and s(i) are the intercept and slope, respectively, for a series ofsolvents, * is the previously established value from indicators 1–5, and isthe previously established value from indicators 6–8. The value of obtainedfrom these two indicators is averaged to give for the given solvent system[9][13].

Table 2 contains the solvatochromic parameters for binary mixtures ofacetonitrile and water ranging from 0 to 100% (w/w) acetonitrile. The data forthe three solvatochromic parameters was fit to the following equations:

(Eqn. 8)

(Eqn. 9) = +1 12 1 648 3 5602 030 0 670

. – . .– . – .

x xx x

CH CN CH CN2

CH CN3

CH CN4

3 3

3 3

* . – . . 1 13 0 767 0 380x xCH CN CH CN2

3 3

0 ( ) ( ) * ( ) ( )I s i b i a i

( ) – ( )( )

8 88

0

b


%-ACN (w/w) *

0 1.14 1.13 0.585.5 1.12 1.07 0.59

10.0 1.10 1.03 0.5916.3 1.07 0.98 0.6020.7 1.08 0.97 0.6125.0 1.03 0.94 0.6130.0 1.01 0.92 0.6136.4 0.97 0.90 0.6140.0 0.97 0.91 0.6150.0 0.92 0.90 0.6160.0 0.87 0.90 0.6069.4 0.85 0.90 0.5970.0 0.84 0.89 0.5977.2 0.82 0.87 0.5983.9 0.80 0.83 0.5990.0 0.77 0.75 0.57

100 0.73 0.31 0.47

Table 2. SolvatochromicParameters for Acetonit-rile-Water Mixtures (w/w)[9–11]

(Eqn. 10)

where xCH3CN is the mole fraction of acetonitrile. The parameter * is de-scribed by a parabolic function, while the parameters and are describedby fourth-degree polynomials [9].

Fig. 3 contains a plot of each solvation parameter as a function of weight-%acetonitrile. The value of * decreases gradually over the acetonitrile range,indicating a linear relationship between the polarity/polarizibility of the sol-vent and acetonitrile concentration. This linear relationship indicates that *is not related to the previously mentioned three structural regions of acetoni-trile-water mixtures. The hydrogen-bond donor parameter, , does exhibitthree distinct regions across the acetonitrile concentration gradient: 1) 0 to30% ACN; 2) 30 to 80% ACN; and 3) 80 to 100% ACN. In the second region,the existence of microheterogeneity decreases the ability of the water mole-cules to donate a hydrogen atom for the formation of a hydrogen bond.However, the values are still considerably higher than in the third region inwhich water is hydrogen-bonded to acetonitrile. Both water and acetonitrile

β =+0 53 1 009 3 724

5 218 2 654. – . – .

. – .x x

x xCH CN CH CN

2

CH CN3

CH CN4

3 3

3 3


Fig. 3. Solvatochromic parameters *, , and as a function of weight-% acetonitrile for aceto-nitrile-water mixtures [9–11]

exhibit similar values: 0.58 and 0.47, respectively. Interestingly, the val-ues of acetonitrile-water mixtures do not fall in between these two values, butrather exhibit slightly larger values from approximately 20 to 90% acetoni-trile. The larger values indicate that water molecules at the surface of awater cluster have an increased capability to donate an electron pair, sincethese molecules are hydrogen-bonded to fewer other water molecules.Acetonitrile-water mixtures are the only aqueous aprotic-solvent systems inwhich is larger than that of either pure solvents. Other aqueous aprotic-sol-vent systems exhibit intermediate values. The higher values provide addi-tional evidence for the existence of microheterogeneity in acetonitrile-waterbinary systems [9] [13].

2.3. Determination of the pH of Acetonitrile-Water Mixtures

Typically, the pH of organic-aqueous solvents is determined under theassumption that the pH of the aqueous component is equal to the pH of themixed solvent. However, the organic co-solvent substantially influences thepH of organic-aqueous solvents due to medium effects. The pH of organic-aqueous solvents is determined using a potentiometric sensor according to theoperational definition of pH as shown below.

(Eqn. 11)

pH(x) is the pH of the unknown solution, pH(s) is the pH of standard buffersolution prepared in the same solvent mixture as the unknown, Ex and Es arethe electromotive force (e.m.f.) measurements in Cell A for the unknownsolution (x) and the standard solution (s), and g = log(RT/F). The pH(s) val-ues of buffer solutions containing 0 to 100% acetonitrile are available fromthe National Institute of Standards and Technology (NIST). The pH(s) valuesfor KHtartrate, KH2citrate, phosphate buffer, KHphthalate, and acetate bufferin acetonitrile are shown in Table 3 [9] [13].

Fig. 4 contains a plot of the pH of each buffer as a function of wt-% ace-tonitrile. The variation in pH with percent acetonitrile is approximately line-ar over the range of 0 to 80% for each of the buffers. However, as seen forKHtartrate, KH2citrate, and KHphthalate, the pH values are considerablylower than expected based on their pH values in 100% acetonitrile. For exam-

Cell A

referenceelectrode

saltbridge

unknown solution at pH(x) orstandard buffer solution at pH(s)

glasselectrode

pH(x) = pH(s) +(E – E

gs x)


ple, the pH of KHtartrate is 5.7 at 80% compared to 17.8 at 100%. Thisdecrease in pH is attributed to preferential solvation by one of the two sol-vents, i.e., water or acetonitrile. Preferential solvation occurs when solutemolecules, in this case H+ ions, interact more strongly with one solvent than


Table 3. pH Values of Standard Reference Buffers in Acetonitrile [9] [13]

%-ACN (w/w) a)

Reference Standard 0 10 30 40 50 70 100

KHtartrate 3.557 3.802 4.325 4.570 4.852 5.723 17.79KH2citrate 3.776 3.994 4.470 4.702 4.995 5.610 16.48KHphthalate 4.008 4.318 5.015 5.346 5.644 6.428 16.82Acetate buffer 4.644 4.898 5.532 5.875 6.275 – –Phosphate buffer 6.865 7.149 7.604 7.667 8.002 – –

a) The pH of acetate and phosphate buffer could not be determined at >70% ACN due to in-solubility. The composition of the five buffers was as follows: 1) KHtartrate: saturated solutionat 25°; 2) KH2citrate: 0.05 mol kg–1 potassium dihydrogen citrate; 3) Khphthalate: 0.05 molkg–1 potassium hydrogen phthalate; 4) acetate buffer: 0.1 M acetic acid, 0.1 M sodium acetate; 5) phosphate buffer: 0.025 mol kg–1 potassium dihydrogen phosphate, 0.025 mol kg–1 sodiumhydrogen phosphate.

Fig. 4. pH as a function of weight-% acetonitrile for five standard buffers [9] [13]

the other. Since the acetonitrile-water mixtures exhibit lower than expectedpH(s) values, the preferred solvent is water. The local environment of the H+

ions tends to favor water over acetonitrile, which leads to a pH(s) more sim-ilar to a 100% aqueous solution than a 100% acetonitrile solution.Preferential solvation of H+ ions by water is possible due to the microhetero-geneity that exists in the 30 to 80% (w/w) acetonitrile region. The existingwater clusters in this region surround the H+ ions, leading to a lower thanexpected pH for the bulk solution [11][14].

The standard pH(s) values of the buffers in Table 3 can be describedKamlet-Taft solvatochromic parameters, as shown in Eqn. 12.

pH = (pH)0 + a + b + s* (Eqn. 12)

Multiple-regression analysis of the pH(s) values of Table 3 and the corre-sponding solvatochromic parameters of Table 2 results in the following best-fit equations for the five buffers.

KHtartrate: pH(s) = 9.19 – 8.97* + 3.35 + 1.71 (Eqn. 13)

KH2citrate: pH(s) = 9.34 – 7.16* + 1.89 + 0.97 (Eqn. 14)

KHphthalate: pH(s) = 12.79 – 8.25* + 0.49 + 0.15 (Eqn. 15)

Acetate buffer: pH(s) = 12.87 – 8.43* + 1.11 + 0.26 (Eqn. 16)

Phosphate buffer: pH(s) = 11.18 – 4.92* + 0.62 + 1.25 (Eqn. 17)

The pH(s) of these buffers in any binary mixture of acetonitrile-water can bedetermined using Eqns. 13–17. Accurate determinations of pH(s) values areextremely important for the standardization of potentiometric sensors [11].For example, by standardizing the potentiometric sensor with a phosphatebuffer described in Eqn. 17, the pH of the phosphate buffer used in IAMchromatography can be accurately adjusted to 7.4.

3. Characterization of Solute-Solvent Interactions in Acetonitrile-Water Mixtures

3.1. Standard Free Energy of Dissociation (pKa)

The previous section described how the addition of acetonitrile to aque-ous buffers affects both the structural organization of the water molecules andthe pH of the buffer systems. This section focuses on the effects that aceton-itrile has on the physiochemical properties of solute molecules analyzed byHPLC. Specifically, the effect of acetonitrile on the solute’s standard freeenergy of dissociation needs to be considered, since the chromatographic



retention of a compound partially is determined by its degree of ionization.The acid and base dissociation constants (Ka and Kb) of a compound dependon two terms: 1) an electrostatic term; and 2) a nonelectrostatic term whichdescribes solute-solvent interactions.

The Born equation describes changes in the pKa/pKb values (pK) ofcompounds due to electrostatic effects.

(Eqn. 18)

In this equation, n is related to the number of charged species in ionizationprocess (n = 2 for ionization process HA s H+ + B– and n = 0 for the ioniza-tion process HA+ s H+ + A), r is the common radius of all the ions, and isthe dielectric constant of the solvent. Table 4 contains the dielectric constants() of acetonitrile-water mixtures. The dielectric constant decreases withincreasing mole fractions (x) of acetonitrile, according to the following equa-tion [10] [15]:

–1 = 1.26 x 10–2 + 1.73 x 10–2x (Eqn. 19)

Eqn. 18 reveals that changes in pK due to electrostatic effects occur onlywhen the ionization process results in a change in the total number of charges.For example, the dissociation of a carboxylic acid (RCOOH s H+ + RCOO–)results in a change in the number of charges, while the dissociation of a proto-nated amine (RNH3

+ s H+ + RNH2) does not result in a change in the numberof charges.

Table 5 contains the pKa values of seven peptides in acetonitrile-watermixtures from 0 to 50% ACN. For the peptides in Table 5, pKa1 is for the dis-sociation of the carboxylic acids, pKa2 is for the dissociation of the protonat-ed amines, and pKa3 is for the dissociation of the hydroxy group on Tyr. ThepKa1 and pKa3 values for all of the peptides increase as the percent acetoni-trile increases. For these two ionization processes, a change in the number ofcharges occurs, and electrostatic interactions dominate the standard free ener-gy of dissociation. The decrease in due to the addition of acetonitrile caus-

p 0.0218Kn

r=

121 6 1.–

Table 4. Dielectric Constants of Aceto-nitrile-Water Mixtures at 25° [10] %-ACN (w/w)

0 78.365.5 76.68

10.0 75.0116.3 72.2925.0 68.0650.0 55.44


Tabl

e5.

pKa

Valu

es o

f P

epti

des

in A

ceto

nitr

ile-

Wat

er M

ixtu

res

from

0 t

o 50

% A

ceto

nitr

ile

[15]

Gly

-Gly

Gly

-Gly

-Gly

Tyr-

Gly

-Gly

Gly

-Gly

-Val

Gly

-Gly

-Ile

Gly

-Gly

-Phe

Ala

-Leu

-Gly

%-A

CN

pKa1

pKa2

pKa1

pKa2

pKa1

pKa2

pKa3

pKa1

pKa2

pKa1

pKa2

pKa1

pKa2

pKa1

pKa2

03.

148.

043.

307.

96–

––

–8.

12–

8.09

–8.

04–

–5.

543.

188.

223.

508.

073.

457.

5110

.17

3.47

8.11

3.54

8.12

3.21

8.02

3.57

8.03

10.0

03.

308.

303.

468.

093.

467.

4510

.51

3.54

8.08

3.55

8.09

3.33

8.02

3.62

8.12

16.3

03.

498.

343.

588.

213.

667.

5210

.52

3.73

8.06

3.79

8.17

3.59

8.11

3.78

8.23

25.0

33.

678.

393.

878.

173.

877.

5411

.09

3.97

8.12

4.00

8.14

3.82

8.09

4.00

8.18

50.0

04.

288.

504.

428.

324.

447.

6511

.93

4.68

8.51

4.81

8.39

4.51

8.26

4.64

8.36

es the term (–1 – 0.0128) in Eqn. 18 to increase, thus leading to an increasein pK [10] [15].

A change in the number of charges does not occur in the dissociation pro-cesses of protonated amines (Table 5, pKa2). The n in Eqn. 18 equals 0, andtherefore pK equals 0. In this case, changes in the pKa2 values with increas-ing concentrations of acetonitrile are due to solute-solvent interactions andnot electrostatic effects. At low percents of acetonitrile (< 30%), solute-sol-vent interactions have little effect on the pKa values since the structure ofwater remains intact. For example, the change in the pKa2 value of Tyr-Gly-Gly from 0 to 25% ACN is 0.03 compared to 0.42 for pKa1 where electrostat-ic effects are dominating. At higher acetonitrile percentages (> 30%), micro-heterogeneity exists in the mixture structure, leading to changes in the pKa

values due to solute-solvent interactions. The change in pKa2 from 25 to 50%for Tyr-Gly-Gly is 0.09 which is three times larger than the change observedfrom 0 to 25% [10][15].

The Kamlet-Taft equation is used to explain the variation in the pKa val-ues in different acetonitrile-water mixtures, as shown in Eqn. 20.

pK = (pK)0 + a + b + s* (Eqn. 20)

Multiple-regression analysis of the pK values of Table 5 and the correspond-ing solvatochromic parameters of Table 2 results in the best-fit equations forthe seven peptides. The equations for pKa1 and pKa2 of Gly-Gly are givenbelow [10][15].

pK1 = 14.21 – 5.02* – 1.36 – 6.71 (Eqn. 21)

pK2 = 13.02 – 0.45* – 1.62 – 4.34 (Eqn. 22)

3.2. Capacity Factors

The most commonly used mobile phase to determine capacity factors (k)in IAM chromatography is ACN/PBS 15:85 (v/v). Unfortunately, all com-pounds do not elute from the IAM columns within 1 h in this mobile phase.As a result, some compounds are analyzed using a larger amount of aceto-nitrile (i.e., 20 and 30%). In order to determine the capacity factor at 15%ACN, a plot of log k vs. %-acetonitrile is generated. By fitting the data to alinear equation, the capacity factor at 15% ACN can be calculated. Unfortu-nately, the log k vs. %-acetonitrile plots are not linear over the entire aceto-nitrile range. For example, Table 6 contains the log kvalues determined on areversed-phase C18 column for seven peptides in mobile phases ranging from3–40% ACN (v/v). Fig. 5 shows a representative plot of log k vs. %-ACN.The correlation coefficient (r) for this peptide is 0.898 [10][15].


Fig. 5 shows that there are two lines with different slopes which intersectat approximately 25% (v/v) in the log k vs. %-acetonitrile plot. These two dis-tinct regions can be explained in terms of the structural organization of watermolecules in acetonitrile-water mixtures. As discussed previously, micro-heterogeneity begins to exist in acetonitrile-water mixtures at 30% (w/w)


Table 6. Logarithms of Capacity Factors for Seven Peptides in Different Acetonitrile-WaterMixtures [16]

%-ACN Gly-Gly Gly-Gly-Gly Tyr-Gly-Gly Gly-Gly-Val Gly-Gly-Ile Gly-Gly-Phe Ala-Leu-Gly(w/w)

3 –0.389 –0.345 0.695 0.662 1.190 – 1.0425 –0.396 –0.367 0.505 0.586 1.102 1.453 0.8867 –0.403 –0.379 0.301 0.452 0.934 1.258 0.711

10 –0.425 –0.425 0.073 0.254 0.684 1.007 0.47512.5 –0.442 –0.449 –0.097 0.106 0.510 0.821 0.29915 –0.475 –0.482 –0.156 –0.005 0.352 0.624 0.16020 –0.518 –0.534 –0.246 –0.186 0.078 0.319 –0.06025 –0.553 –0.571 –0.380 –0.261 –0.106 0.064 –0.19830 –0.573 –0.602 –0.436 –0.338 –0.231 –0.115 –0.27640 –0.597 –0.640 –0.496 –0.464 –0.351 –0.296 –0.351

Fig. 5. Log k vs. %-Acetonitrile (v/v) for Gly-Gly-Ile [16]

ACN. Therefore, it is not surprising that the plots of log k vs. %-acetonitrileexhibit two regions.

The variation in log k values in different acetonitrile-water mixtures canbe explained by the Kamlet-Taft equation.

log k = (log k)0 + a + b + s* (Eqn. 23)

Multiple-regression analysis of the log k values in Table 6 and the corre-sponding solvatochromic parameters of Table 7 results in the best-fit equa-tions for the seven peptides. Table 8 contains the best-fit equations, the cor-responding correlation coefficient (r1), and the correlation coefficient (r2)from the log kvs. %-acetonitrile. Fitting the data using the solvatochromicparameters *, , and significantly improves the correlation coefficient[10] [15]. For example, r1 equals 0.996 compared to r2 of 0.947 for the pep-tide Gly-Gly-Val.


Table 7. Solvatochromic Param-eters for Acetonitrile-Water Mix-tures (v/v) [16][17]

%-ACN (v/v) *

3.0 1.17 1.24 0.435.0 1.17 1.21 0.417.0 1.16 1.19 0.39

10.0 1.15 1.16 0.3712.5 1.15 1.14 0.3615.0 1.14 1.12 0.3520.0 1.11 1.08 0.3425.0 1.09 1.04 0.3430.0 1.06 1.01 0.3540.0 1.00 0.95 0.38

Table 8. Relationship between Log k and the Solvatochromic Parameters [16] [17]

Peptide Equation r1a) r2

b)(Kamlet-Taft) (log k vs. %-ACN)

Gly-Gly log k = –1.76 + 0.62* + 0.38 + 0.46 0.987 0.978Gly-Gly-Gly log k = –2.02 + 0.46* + 0.71 + 0.60 0.996 0.961Tyr-Gly-Gly log k = –3.40 – 3.56* + 5.45 + 3.47 0.995 0.898Gly-Gly-Val log k = –6.53 + 2.61* + 1.61 + 5.10 0.996 0.947Gly-Gly-Ile log k = –8.93 + 3.92* + 2.31 + 6.39 0.997 0.959Gly-Gly-Phe log k = –7.38 – 1.42* + 7.38 + 3.86 0.999 0.968Ala-Leu-Gly log k = –6.85 + 1.08* + 3.32 + 5.95 0.997 0.934

a) Correlation coefficient for the Kamlet-Taft equation. b) Correlation coefficient for the plotsof log k vs. %-ACN (v/v).

4. Conclusion

The chemical space occupied by typical drug-discovery compounds islarge. For instance, hydrophobicity (ClogP) can vary 104–105 fold. Thisrange of hydrophobicity in drug-discovery compounds indicates that com-pounds of interest to medicinal chemists will also have a wide range of mem-brane-binding constants. Based on ca. 500 commercial drugs and multipleIAM surfaces, the range of membrane-binding constants measured atAdmetric Biochem varies >106 fold in compounds that elicit pharmacologi-cal activity. To cover this range of membrane-binding constants is difficult orimpossible using fluid membrane systems because some compounds arequantitatively bound, whereas others have virtually no binding.

Two problems have to be overcome to measure a wide range of mem-brane-binding constants: 1) the immobilization of the phospholipids, whichimparts chemical stability to the membrane, so that 2) organic solvents in thesolution bathing the membrane allow virtually all compounds to elicit somesolubility. Limited solubility is needed in order to measure equilibrium bind-ing constants. IAMs provide a solution to the first problem. IAMs are bothmechanically stable and stable in the presence of organic solvents, detergents,and other harsh mobile-phase conditions that would destroy any type of fluidmembrane. We have established the utility of IAMs in measuring membranebinding properties for the purposes of predicting biologically relevant phe-nomena [2–8]. The work described in this chapter provides the critical guide-lines for establishing mobile-phase conditions that are acceptable for measur-ing membrane-binding constants of medicinally interesting compounds. Inparticular, mobile phases with >30% ACN should not be used because of thedisruption of water structure that occurs when acetonitrile concentrationsexceed this value. This work thus provides relevant experimental conditionswhen developing membrane models for drug discovery.

REFERENCES

[1] J. Kehrer, in ‘ACS Short Course: Toxicology: Principles and Applications’, Ed. J. Brown,1999.

[2] H. Liu, S. Ong, L. Glunz, C. Pidgeon, Anal. Chem. 1995, 67, 3350.[3] S. Ong, H. Liu, X. Qiu, G. Bhat, C. Pidgeon, Anal. Chem. 1995, 67, 755.[4] S. Ong, H. Liu, C. Pidgeon, J. Chromatogr. A 1996, 728, 113.[5] C. Pidgeon, ‘Immobilized Artificial Membranes’, in U.S. Patent Application, U.S.A.,

1990.[6] C. Pidgeon, ‘Method for Solid Phase Membrane Mimetics’, in U.S. Patent Application,

U.S.A., 1990.[7] C. Pidgeon, R. Markovich, M. D. Liu, T. J. Holzer, R. M. Novak, K. A. Keyer, J. Biol.

Chem. 1993, 268, 7773.[8] C. Pidgeon, S. Ong, H. Liu, X. Qiu, M. Pidgeon, A. Dantzig, J. Munroe, W. J. Hornback,

J. S. Kasher, L. Glunz, T. Szczerba, J. Med. Chem. 1995, 38, 590.


[9] Y. Marcus and Y. Migron, J. Phys. Chem. 1991, 95, 400.[10] J. Barbosa, S. Hernandex-Cassou, V. Sanz-Nebot, I. Toro, J. Peptide Res. 1997, 50, 14.[11] J. Barbosa, V. Sanz-Nebot, J. Chem. Soc., Faraday Trans. 1994, 90, 3278.[12] H. Kovacs, A. Laaksonen, J. Am. Chem. Soc. 1991, 113, 5596.[13] Y. Migron, Y. Marcus, J. Chem. Soc., Faraday Trans. 1991, 87, 1339.[14] J. Barbosa, R. Berges, V. Sanz-Nebot, I. Toro, Anal. Chim. Acta. 1999, 389, 43.[15] J. Barbosa, D. Barron, S. Buti, Acta Chem. Scand. 1997, 51, 1078.[16] J. Barbosa, V. Sanz-Nebot, I. Toro, J. Chromatogr. A 1996, 725, 249.[17] J. Barbosa, R. Berges, V. Sanz-Nebot, J. Chromatogr. A 1996, 719, 27.


High-Throughput Artificial MembranePermeability Studies in Early Lead Discovery

and Development

by Manfred Kansy*, Holger Fischer, Krystyna Kratzat, Frank Senner,Björn Wagner, and Isabelle Parrilla

F. Hoffmann-La Roche Ltd., Pharma Research, Molecular Structure Research, Molecular Properties, CH-4070 Basel, Switzerland; Fax: +41 61 688 74 08;


1. Introduction

With the rise of combinatorial chemistry and the possibility to producelarge collections of individual compound sets, the number of new compoundsto be characterized as potential drug candidates increases. Additionally, suc-cessful application of high-throughput technologies in biological screeningdemonstrates that lead identification itself is often not the rate-limiting step indrug development. Factors with strong impact on the bioavailability of apotential drug candidate like, for example, solubility, absorption, partitioning,or biodegradation are equally important.

The gastrointestinal absorption of an orally administered drug for exam-ple is one of the key factors for its bioavailability. Therefore, significant inter-est in the development of simple high-throughput in vitro models for the pre-diction of human intestinal drug absorption exists. Several in vitro permeabil-ity-measurement methods have been developed in the last decade, includingbiological cell layers (Caco-2, MDCK cells, etc.)[1][2]. Fig. 1 depicts therelationship between the fraction absorbed in human and the derived perme-abilities in different cell lines. Usually, a steep sigmoidal or parabolic rela-tionship between permeability and fraction absorbed is observed. This com-plicates the correct prediction of human absorbability for compound entitieswith borderline permeability.

The majority of known drugs are absorbed by passive diffusion processes.However, the introduction of new biological measurement systems hasallowed an increasing number of actively transported drugs to be identified.Active transport mechanisms are for example described for a small number of


compounds such as amino acids, small peptides, monosaccharides, relativelystrong organic acids and bases, vitamins, and cortisone derivatives [3][4].

Several molecular properties are known to influence passive absorptionprocesses. These include solubility, partition coefficients, size parametersoften represented by the molecular weight, ionization (pKa), and hydrogenbonding. Although these parameters have shown to be useful in the predic-tion of passive permeation processes, some restriction in the availability ofthese descriptors can hinder their usage. Octanol/water partition coefficients(log P) can easily be calculated using well-known programs. But these valuessometimes do not reflect the correct measurement results if newly synthe-sized compound classes have to be calculated. Fragment values and factorscorrecting for intramolecular hydrogen bonding, necessary for a precise cal-culation, are missing or incorrect so that the log P calculation might be impre-cise. The calculation of pKa values as a precondition for the determination of


Fig. 1. Relationship between in vitro permeabilities and human absorption rates described inthe literature [1][2]. A and C: Caco-2-derived data. B: MDCK-derived data (adapted from [1]

and [2]).

apparent partition coefficients (log D) and solubilities is not as established aslog P calculations.

2. Artificial Membranes

The use of artificial membranes to investigate passive permeation pro-cesses has a long history going back more than 30 years [5–24]. Mueller andco-workers [5–7] prepared thin membranes (<100 Å) using phospholipidsand organic solvents. Although solvent-free membrane systems have beendescribed, the addition of organic solvents is necessary to prepare stable blacklipid membranes. Different organic modifiers such as decane, dodecane, andhexadecane have been studied for their influence on membrane stability andtheir ability to mimic permeation processes. Mueller [5–7], Cass and Finkel-stein [8–10], White [13][14][16][17][25–27], Gutknecht [19][20][28–34],and Xiang and Anderson [23][24][35–38] have given excellent overviews onthe preparation and the use of artificial membranes in permeation measure-ments.

Thompson [39][40] and co-workers could show by electrochemical mea-surements that extremely stable bilayers, so called micro-BLM (‘Black LipidMembranes’), could be formed on supported filter material. Additionally,photoinduced potential differences on filter-supported membranes formedfrom dodecane/lecithin and bacteriorhodopsin [41–44] gave further supportfor the formation of stable bilayers on filter support.

The mass transport through an artificial membrane or living cell

systems can in principle be described by Fick’s law (Eqn. 1). In this equation,D describes the diffusion coefficient of the solute, A the diffusion area, P themembrane/water partition coefficient of the solute, h the thickness of themembrane and Cdonor and Cacceptor the concentration in the donor and theacceptor phase. The permeation coefficient Papp can be derived from themembrane/water partition coefficient P of the solute, the diffusion coefficientDand the thickness of the membrane layer h (Eqn. 1).

(Eqn. 1)

Unfortunately, the drug membrane/water partition coefficient, the diffu-sion coefficient, and the influence of pH on absorption are often unknown.Furthermore, the membrane-partition coefficient can be different in compar-ison to octanol/water partition coefficients [45–59], often used as a surrogatefor membrane-partition coefficients. Fick’s first law as described by Eqn. 1 isvalid for sink conditions. Although current technologies for the determination

dd

Adonor acceptor app donor acceptor

Ct

D A Ph

C C P A C C= ⋅ ⋅ ⋅ − = ⋅ ⋅ −˜

( ) ( )

dd

ACt


of apparent permeabilities try to establish sink conditions, this is hardlyachievable for more lipophilic compounds in the standard setup for perme-ability measurements. Wils and co-workers [60], Krämer [61], and Sawada[62] found a bilinear relationship between lipophilicity and permeability inCaco-2 cell and MDCK permeability determinations. Sawada [62] couldshow that current technologies for permeability measurements do not suffi-ciently correct for sink conditions if lipophilic compounds have to be charac-terized. Palm and co-workers [63] recently described a calculation methodfor the correction for non-sink conditions. This procedure seems to be moresuitable for compounds with high permeability coefficients, but it does notcompletely describe the combined effects of non-sink conditions, lipophilicmembrane retention, permeability, flux, and ionization in in vitro permeabil-ity measurements.

In a recent article [64], we have described the use of artificial membranesas an alternative to the established cell-based permeability-measurementassays. Artificial systems have the advantage to be robust and easily adapt-able to the needs of high-throughput screening in transcellular drug-absorp-tion prediction. In combination with cell-based assays, they allow a clear dif-ferentiation between active and passive transport processes. In the currentpaper, we will discuss results obtained by this assay for a larger set of morethan 100 known drugs. Additionally, we give a more detailed description ofthe method.

3. Description of the Method

A 96-well microtiter-plate (Dynatech M29 A 655170), in the followingreferenced as acceptor plate, is completely filled with aqueous buffer solu-tions (pH 6.5) using a TECAN Genesis 100 or 150 robotic system. Each wellis filled with exactly 410 l of the buffer solution. A multiscreen-filterplate(Millipore MAP 4510), in the following referenced as the donor-plate, isimpregnated with exactly 4 l of a membrane-forming solution. The mem-brane-forming solution is prepared by solving 10% (w/v) of 3-sn-phosphati-dylcholine and 0.5% (w/v) of cholesterol in dodecane. After impregnation, theacceptor plate (see above) is covered with the donor plate. Thus, a sandwichconstruction is formed as depicted in Fig. 2.

Transport studies are started by the transfer of 100 l of a 500 M com-pound solution, prepared from a 10 mM DMSO stock solution, on top of thedonor-plate. Buffers are prepared with and without addition of 0.5% of gly-cocholic acid. Thus, the influence of natural components of bile liquid on sol-ubility and permeability should be simulated. 0.05 M 2-Hydroxy-3-morpho-linopropanesulfonic acid (MOPSO) pH 6.5 or 0.05 M phosphate buffer pH


7.4 are used in the preparation of the diluted solutions. The maximum DMSOcontent of the measurement solutions is 5%.

Compound concentrations are determined by UV spectroscopy using amicrotiter-plate-reader SPECTRAMAX 190. In general, measurements areperformed at 240–600 nm in 4-nm increment steps. After processing, 150 lare transferred from the acceptor compartment to a quartz microtiter-plate.The UV maximum of a reference solution with defined concentration isselected for the calculation of the so-called PAMPA (parallel artificial mem-brane-permeation assay) flux rates [64]. Reference solutions are generatedfrom the 10 mM DMSO solutions. The concentration of the reference solutionis 100 Molar (equilibrium condition for the assay) with a maximum DMSOcontent of 50%. High DMSO concentrations might be necessary to keep com-pounds with low solubility in solution. DMSO can be replaced by acetonitrilewithout affecting measurements in case compounds with UV maxima below240 nm are of interest.

4. Novel Results and Insights from PAMPA

4.1. Background

In a recent publication [64], we have shown that artificial systems enablethe fast determination of passive-permeation properties of drugs. The first


Fig. 2. Parallel Artificial Membrane-Permeation Assay (PAMPA) sandwich. Pure dioleoyl-phosphatidylcholine (DOPC) and diphytamoylphosphatidylcholine (DPhPC) are used in thepreparation of the artificial membranes [10][14–16][25][91]. Phosphatidylcholine (98%) has asolubility of 1–2 % in dodecane, whereas DPhPC has a high solubility. Results with DPhPCwere not significantly different from those obtained with lecithin under the same conditions.

Therefore, pure lecithin was used in the measurement of the described series.


Fig. 3. Comparison of the fraction absorbed in human, effective permeabilities in human andPAMPA permeabilities (flux) for a set of 12 drugs described by Winiwarter [65] and Walter [66]

studies were performed on a relatively small data set. An extension of ourstudies should, however, allow the examination of relationships betweenmolecular properties and permeability. More than 100 known drugs werecharacterized in our high-throughput screen (HTS) for passive permeability(PAMPA). In contrast to our earlier studies, all results described in this paperwere obtained by a completely automated procedure. In a direct comparisonwith human permeabilities described by Winiwarter [65] and Walter [66], nomajor differences in the classification of a small subset of 12 compoundscould be found (see Fig. 3 and Table 1). Actively transported drugs, (e.g.,L-DOPA) as well as compounds with known paracellular transport route,show differences in human permeability studies. Compounds with molecularweight below 200 are considered to be absorbed via pore diffusion [67–71].Conductivity measurements have shown that pore diffusion, comparable tothe paracellular route in vivo, is hardly possible in the current setup ofPAMPA. Transendothelial electrical resistance (TEER) values were above300 cm2 under the described conditions for more than 15 hours.

The application of our assay to more diverse compounds made a modifi-cation of the measurement procedure necessary. A few more lipophilic com-pounds precipitated in simple buffer solution, even though the DMSO contentwas 5%. Bile salts are known to influence bioavailability of poorly water-sol-uble drugs by enhancing the rate of dissolution and solubility [72].

Glycocholic acid (GC) [73], a component of bile liquid, is an excellentsolubility enhancer, and was therefore chosen as solubilizer in our studies ata concentration near the critical micelle concentration (CMC; 0.5%). Most ofthe more lipophilic compounds were solubilized by the addition of GC.Nevertheless the observed permeabilities were only slightly altered by the


Table 1. Comparison of Human Intestinal Permeabilities [65] [66] and Artificial Membrane Flux Values. s.d.: Standard deviation; Flag: Indicates values below the detection limit.

ID Name Peff human s.d. % Human Flag PAMPA(10–4 cm/sec) Peff absorption Flux [%]

1 antipyrine 4.5 2.5 100 262 atenolol 0.2 0.2 45 113 carbamazepine 4.3 2.7 724 furosemide 0.05 0.04 40 75 hydrochlorothiazide 0.04 0.05 90 < 56 ketoprofen 8.4 3.3 100 387 L-DOPA 3.4 2.6 100 < 58 metoprolole 1.3 1 100 359 naproxen 8.3 4.8 100 89

10 propranolol 2.9 2.2 95 6611 ranitidine 0.4 50 712 verapamil 6.7 2.9 100 68

addition of glycocholic acid for the majority of the examined compounds, asshown in Fig. 4. It is known that interaction with bile salts do not automati-cally lead to improved absorption rates, due to solubilization effects [72].Formation of bile-salt complexes with decreased absorption profile isdescribed for a number of drugs [74]. Therefore, the differences obtained inthe observed flux rates with and without glycocholic acid might be related toimproved solubilization as well as the formation of poorly absorbable drug-bile complexes. The in vivo absorption of rifampin, for example, is known tobe negatively influence by food intake [75]. This is correctly described by thedifferences in the observed PAMPA flux rates (see Table 2).

4.2. Effects of Lipophilicity and Polar Surface Area

The development of a new HTS for the determination of lipophilicityallowed the fast measurement of distribution coefficients (log D) beside fluxvalues. Fig. 5 shows a bilinear/parabolic relationship between our results andthe derived log D values in octanol/water. Bilinear relationships betweenoctanol/water partition coefficients and permeabilities have been describedfor Caco-2-cell-derived permeabilities [60–62] and in vivo absorption-rate


Fig. 4. Comparison of flux values determined under addition and exclusion of glycocholic acid. Solute concentration was 500 M in both cases.


Table 2. Complete Overview on Measurement and Calculation Results a)

Row Name ClogP Lt/Gt Log D Flag Flux 1 Prec. Flag Flux 2 Prec. Hydro- TotalID Flag pH 7.4 flux1 flag flux2 flag philic surface

log D flux1 flux2 surface areaarea [Å] in [Å]

1 acetaminophen 0.494 0.38 < 10.2 no 10.6 no 36.7 169.02 acyclovir –2.066 < –1.00 < 8.7 no < 9.7 no 99.8 233.83 alprazolam 2.783 2.08 73.4 no 84.6 no 38.7 297.14 alprenolol 2.652 64.2 no 29.9 299.15 amitriptyline 4.641 > 52.2 no 3.3 306.66 antipyrine 0.414 0.26 26.4 no 40.6 no 20.7 220.87 aspirin 1.023 < 5.6 no 51.2 191.18 atenolol –0.109 –0.44 10.9 no 67.9 297.39 atropine 1.319 36.8 no 42.4 364.8

10 betamethasone 1.485 2.10 68.6 no 71.2 no 52.3 318.311 caffeine –0.057 0.06 42.9 no 43.9 no 50.9 217.812 carbamazepine 1.98 1.45 71.7 no 84.5 no 37.3 246.013 cefixime 0.37 –0.79 < 5.0 no < 5.8 no 147.8 418.914 cefoxitin –0.101 –0.60 < 12.7 no 97.9 392.615 ceftriaxone –0.763 –0.63 < 5.5 no 170.4 504.916 chlorambucil 3.421 0.61 19.4 no 34.5 no 32.7 295.417 chloramphenicol 0.687 67.6 no 74.1 289.318 chloroquine 4.839 0.89 46.6 no 27.5 339.219 chlorpropamide 2.35 44.6 no 56.7 252.320 chlorprothixene 5.356 3.71 18.3 yes 11.8 no 3.3 315.121 chlorthalidone 0.322 0.78 6.2 no 4.1 272.422 cimetidine 0.351 < 5.0 no 77.9 281.023 clonazepam 2.696 2.45 55.5 no 54.4 no 68.0 290.424 cocaine 2.719 1.07 84.9 no 35.1 311.425 codeine 0.817 0.35 47.0 no 30.9 274.126 corticosterone 1.163 1.91 92.7 no 91.3 no 45.6 315.727 coumarin 1.412 1.44 90.0 no 91.4 no 19.7 165.628 dapsone 1.068 0.68 54.2 no 54.6 no 53.2 233.929 diazepam 3.288 2.87 47.9 no 48.2 no 25.4 278.630 diflunisal 4.392 0.37 38.9 no 24.0 no 36.5 248.531 dihydrocodeine 1.301 38.4 no 31.7 268.932 diltiazem 3.408 2.36 75.6 no 79.3 no 43.6 408.233 doxorubicin –1.451 –0.33 < 5.7 no < 6.5 no 139.6 488.734 ethinylestradiol 3.474 > 3.00 < 13.0 yes 23.0 299.035 etilefrine 0.441 –0.23 < 13.4 no 42.0 211.136 etofylline –0.887 –0.27 < 9.3 no < 11.1 no 59.8 251.137 famotidine –0.112 –0.44 < 6.7 no < 10.7 no 115.2 297.138 felbamate –0.285 45.5 no 74.8 268.939 fluocortolone 1.661 2.10 62.9 no 69.0 no 45.3 337.640 furosemide 1.239 –0.49 < 7.0 no < 5.6 no 75.4 288.541 griseofulvin 1.762 2.23 62.2 no 58.5 no 41.5 336.242 heptastigmine 2.209 0.17 25.8 no 35.2 298.443 hydrochlorothiazide –0.399 0.04 < 5.0 no < 5.9 no 36.8 115.244 hydrocortisone 0.537 1.46 91.5 no 94.7 no 52.0 325.845 hydroflumethiazide –0.251 0.31 < 5.2 no < 5.4 no 77.6 241.246 imipramine 4.413 2.62 66.2 no 53.7 no 6.4 311.447 indomethacin 4.18 0.85 70.3 no 61.9 no 44.1 349.748 isoproterenol 0.153 < 11.9 no 49.5 237.149 ketoprofen 2.761 0.02 38 no 31.4 no 24.4 178.050 lidocaine 1.983 70.2 no 15.3 266.051 lorazepam 3.515 2.39 47.7 no 44.3 no 43.1 280.852 mebendazole 3.057 3.28 < 6.0 yes 71.9 316.8


Table 2. (cont.)



53 metergoline 3.996 > 3.50 30.0 no 32.1 441.655 methylprednisolone 1.642 2.21 72.4 no 69.9 no 50.5 329.856 methysergide 2.021 2.13 43.1 no 34.4 no 40.1 378.257 metoclopramide 2.032 0.41 50.7 no 45.9 no 51.4 327.258 metolazone 2.023 1.84 70.1 no 62.3 314.359 metoprolol 1.196 0.02 35 no 36.9 314.760 metronidazole –0.703 14.5 no 65.3 175.061 midazolam 3.7 3.10 40.2 no 36.3 no 23.5 304.362 molsidomine 0.19 14.1 no 17.5 no 75.6 280.763 morphine 0.242 0.12 24.8 no 38.9 242.964 naloxone –0.044 1.09 54.9 no 41.0 288.265 naproxen 2.816 0.31 89 no 72.0 no 26.7 215.666 nitrazepam 2.633 2.23 59.0 no 69.6 no 66.9 282.067 nitrendipine 2.962 > 3.50 < 10.3 yes < 12.0 no 71.5 329.968 nitrofurantoin –0.467 –0.26 27.9 no 41.3 no 114.2 253.069 nordazepam 3.334 3.01 34.8 no 35.8 no 34.5 261.870 norfloxacin 1.496 –0.46 8.1 no < 5.6 no71 omeprazole 2.531 2.15 45.2 yes 59.8 353.872 oxazepam 3.452 2.49 47.0 no 51.9 no 46.0 273.073 oxprenolol-HCl 1.692 0.21 44.8 no 20.9 203.274 papaverine 3.223 2.93 61.7 no 30.6 no 35.9 354.175 pentamidine 2.308 –0.19 < 5.0 no < 5.0 no 94.0 381.276 pentoxifylline 0.099 0.33 27.7 no 29.2 no 61.9 307.277 phenobarbital 1.365 75.6 no 51.4 205.878 phenylbutazone 3.165 0.47 53.4 no 78.6 no 32.0 334.179 phenytoin 2.085 41.0 yes 38.4 230.980 pindolol 1.671 –0.08 45.5 no 57.4 no 49.6 286.381 practolol 0.755 –0.69 < 9.6 no < 10.5 no 64.8 297.482 prednisolone 1.123 1.83 80.3 no 83.0 no 54.1 326.183 prednisone 0.721 1.44 75.8 no 78.5 no 53.5 323.184 primidone 1.737 32.3 no 44.6 194.185 probenecid 3.371 –0.07 < 14.3 no 22.6 no 20.7 147.386 procainamide 1.227 –0.36 28.3 no 28.7 no 25.6 136.887 promethazine 4.646 2.93 25.2 no 20.4 no 6.4 290.888 propoxyphene 4.128 80.3 no 13.4 318.389 propranolol 2.753 1.13 65.9 no 35.0 292.790 propylthiouracil 0.73 23.7 no 27.3 no 41.4 189.391 proquazone 3.646 3.21 60.1 no 57.3 no 25.0 295.892 proxyphylline –0.579 –0.07 11.0 no 11.1 no 64.1 253.193 pyrimethamine 3.379 2.44 62.0 no 69.7 no 60.5 245.794 quinidine 2.931 2.41 86.3 no no 12.2 179.395 ranitidine 1.327 –0.53 7.2 no 9.0 no 66.8 327.196 rifampin 2.351 0.98 55.0 yes 31.1 yes 135.0 798.797 saccharin 0.518 < –1.00 < 5.0 no 43.0 167.498 salicylic acid 2.187 –0.67 < 5.0 no 36.6 155.499 scopolamine –0.195 26.5 no 40.2 270.6

100 sulfadiazine 0.138 –0.60 < 5.6 no 9.8 no 69.7 234.1101 sulfinpyrazone 1.435 –0.07 < 5.0 no < 5.0 no 43.9 409.2102 sulfisoxazole 0.275 –0.56 < 5.0 no < 5.0 no 72.3 254.1103 sulindac 2.807 0.12 14.0 no 15.1 no 33.3 337.6104 sulpiride 1.114 –0.28 < 5.0 no 47.2 221.9105 suprofen 2.538 –0.30 13.2 no 17.3 no 19.0 139.4


Table 2. (cont.)



106 tacrine 3.454 0.34 54.3 no 41.1 no 29.8 214.9107 terbutaline 0.482 –0.05 8.8 no 10.0 no 55.5 234.5108 testosterone 3.219 3.19 79.3 no 79.7 no 23.8 293.7109 tetracycline –1.861 < –1.00 18.1 no 17.5 no 46.4 178.5110 theophylline –0.064 0.13 12.2 no 12.4 no 63.5 195.2111 trimethoprim 0.802 0.50 43.2 no 56.2 no 81.9 308.9112 verapamil 3.706 2.56 67.8 no 60.9 no 54.2 490.7113 warfarin 2.785 0.78 70.5 no 70.8 no 37.4 298.1114 zolpidem 2.819 2.50 67.4 no 62.1 no 26.9 344.3

a) CLOGP Values were calculated with version 4.51, Daylight Inc., Irvine CA. The octanol partition coefficients describedin this paper are derived by a newly developed miniaturized high-throughput shake-flask method (unpublished procedure)developed in our laboratory. The correlation between our measurement results and literature log Doctanol values has beenshown recently [87]. Results below the detection limit and/or precipitation are marked by a “< >” flag or yes/no in the cor-responding columns. Flux1: PAMPA Flux without addition of glycocolic-acid. Flux2: PAMPA Flux under addition of glyco-colic acid (0.5%). Molecular surfaces values are calculated using our in-house modeling tool MOLOC [88], considering oneconformer derived by the CORINA 1.7 software package. Similar calculations have been described recently [89][90].

Fig. 5. Depiction of PAMPA flux vs. lipophilicity at pH 7.4 under consideration of the polar surface/total surface ratio (2D representation)

constants [76–78]. Kubinyi [79] has given an excellent overview in thedescription of drug-absorption and -distribution processes by bilinear models.

Hydrophilic surface area is known to be interrelated with the fractionabsorbed in human [80–84], 120–140 Å2 being an approximate borderlinebetween compounds with high and reduced membrane permeability. Recently,Stenberg and co-workers [83][85] noted that the balance between polar sur-face area (PSA) and non-polar surface area (NPSA) might be important inpermeability prediction. Therefore, we used the ratio of hydrophilic vs. totalsurface area to assess the influence of H-bonding on flux values, taking lipo-philicity into account (see Fig. 5). The use of hydrophilic/total surface arearatios simplifies the comparison in structurally diverse compound collections.In compounds with comparable lipophilicity, lower flux values correspond tohigher surface ratios. A 3D plot of observed flux values, lipophilicity, andpolar surface area (Fig. 6) demonstrates that lipophilicity governs the per-meability of the compounds, whereas PSA makes a minor contribution to theflux values. Note, however, that most of the compounds have a polar surfacearea <120 Å2. Therefore, the observed bilinear relationship cannot be assumedto be associated with hydrophilicity expressed by calculated surfaces ratios.

4.3. Combining Ionization, Membrane Retention, and Back-Flux in Fick’s First Law

The decrease in flux values of compounds with higher lipophilicity (log DpH7.4 > 2.5) might be partly related to reduced solubility and variablesink conditions as described by Sawada [86], as well as drug retention in the

Fig. 6. Relationship between flux, lipophilicity, and polar surface area (PSA) (3D-representation)


membrane. Here, we describe the relationship between lipophilicity, per-meability, observed flux, and membrane binding under non-sink conditionsfor highly lipophilic compounds. Sink conditions can hardly be achieved forlipophilic compounds in most currently used membrane-permeation assays(i.e., PAMPA, Caco-2), since back-flux from the acceptor to the donor com-partment has to be completely abolished. However, permeation constants canbe corrected if one takes into account in Fick’s first law (Eqn. 2)a) the back-flux [63], see Eqn. 2:

(Eqn. 2)

b) the amount of compound sticking in the membrane (Eqn. 3):

Xb = Kapp · Ceq (Eqn. 3)

and c) the effect of pH and pKa on Papp and Kapp as exemplified for amonoprotic base in Eqns. 4 and 5, respectively.

P0 = Papp · (1 + 10pKa – pH) (Eqn. 4)

K0 = Kapp · (1 + 10pKa – pH) (Eqn. 5)

where CA and CD, CD0are the concentrations in the acceptor and donor

(donor at time 0) compartment, respectively, VA and VD the correspondingvolumes, A the area of the filter, Papp the apparent and P0 the intrinsic perme-ability constant of the neutral species, Xb corresponds to the amount of boundcompound, Ceq to the concentration in equilibrium, and Kapp to the apparent,and K0 to the intrinsic binding constant of the uncharged species.

Combining Eqns. 3, 4 and 5 with Eqn. 2 after solving the differential equa-tion leads to Eqn. 6:

(Eqn. 6)

Under steady-state conditions (t→∞) and taking the octanol/water distribu-tion coefficient (log D) as a first and rough estimation of membrane bindingKapp, one gets Eqn. 7, which in accordance with the experimentally derivedvalues (Fig. 7).

(Eqn. 7)CC

ab

Dc D

A

D0

= ′ + ′ ⋅log

log

CC

a e

KK

e

PA

V V

K

K

PA

V V

K

K

A

D

t

p pH

p pH

t0

p a pHA D

a

a

p a pHA Db c

=

⋅ −

⋅ + + ⋅+

⋅ −

+−

−

−+

−

−

−

1

1 101 10

1

0

0

1 101 1

0

0 1 101 1

∂C tt

A PCp t C t

VA

appA

Ad( ) ( ) ( )= ⋅ ⋅ −


Although log D turns out to describe roughly the flux of a compound, the datascattering in Fig. 7 implies that log D is not the optimal descriptor for a moreprecise depiction of flux values. Membrane-binding constants are expected tobe more suitable in describing the permeation process as recently reported byBalon and co-worker [57][58]. The effect of permeability and pH on the fluxof a basic compound is depicted in Fig. 8. Maximal flux can be observed atpH 7.4 for compounds with P0 larger than 3.2 · 10–4 cm/s.

The flux decreases at higher pH values due to an increase in log D (mem-brane retention) as well as at lower pH due to a decrease of log D. Theseresults should allow the introduction of correction parameters for lipophiliccompounds in in vitro permeability measurements. Thus, improved predic-tions for highly lipophilic compounds, generally excluded from permeabilitymeasurements due to solubility and detection problems, should be easily pos-sible in the future.

Fig. 7. PAMPA Values ((CA/C0)100) as a function of octanol-water partition coefficient (log D)at pH 7.4 depicted for 126 reference compounds with various absorption properties. The solidline was calculated according to Eqn. 7. 100% Flux refers to the maximum possible concentra-tion in the acceptor compartment, which is equal to the concentration in the donor compartment.


Fig. 8. Simulated relationship between intrinsic permeability, pH and PAMPA flux for basiccompounds with a pKa of 9.5 and a log D of 1.5 in our artificial permeation assay

5. Conclusions

The integration of methodologies for adequately estimating pharmacolog-ically relevant compound properties in the early planning and developmentphase of drug molecules is of major importance in pharmaceutical industrytoday. Methods allowing the reproducible determination of such propertieswith small compound quantities are of major interest.

In the current paper, we describe a new method for high-throughputpermeability screening, using artificial membranes. Artificial systems havethe advantage to be robust and easily adaptable to the needs of high-through-put systems in transcellular drug-absorption prediction. Measurement of~100 compounds a day in triplicate is possible. Lipophilicity is the major fac-tor governing the observed permeabilities. Hydrogen bonding expressed byPSA seems to have minor influence on the observed flux values. Consideringthe effect of back-flux, ionization, membrane retention, and intrinsic perme-abilities, we were able to derive a mathematical model to describe the


observed flux values. These findings can be applied easily to the developmentof improved tools for membrane-permeability prediction, especially for high-ly lipophilic compounds with strong membrane retention.

Possible modifications of the described method (PAMPA), e.g., exchangeof the used solvent systems (polar to apolar), might be a step on the way tohigh-throughput assays for the description of hydrogen-bonding strength inthe near future.

We thank Alex Avdeef, pION Inc., for stimulating discussions on high-throughput measure-ments ofphysicochemical properties (pC-HTS) and Daniel Bur for critical comments during thepreparation of this manuscript.

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NMR Spectroscopy for the Study of Drug-Phospholipid Interactions

by Roberta Fruttero

Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Giuria 9, I-10125 Torino, Italy; Fax: +39 011 670 76 87; e-mail: [email protected]

1. Membrane Functions and Properties

Biological membranes are a complex mixture of lipids, sterols, and pro-teins, each of them carrying out specific functions for the maintenance of thecell. They also play an important role in drug transport, distribution, action,selectivity, and toxicity. The phospholipid matrix includes a hydrophobicapolar region (hydrophobic core) consisting of saturated or unsaturated fatty-acid esters and a polar hydrophilic region composed of neutral, positively ornegatively charged headgroups. Due to their amphipathic character, phos-pholipids have a strong tendency to aggregate spontaneously to form usuallylamellar bilayer structures, making the preparation of artificial membranesvery easy.

The physical and functional properties of natural and artificial membraneshave been the subject of extensive studies, and a variety of books and reviewshave been published (for examples, see [1–4]).

Important aspects of biomembranes are the asymmetric distribution ofphospholipids in the two leaflets of the bilayer, the surface tension and the sur-face curvature and the presence of cholesterol, Ca++ ions, and multiple types ofintegrated proteins. Another important property of the membrane phospholipidbilayers is their polymorphism, which means that they can exist in a variety ofdifferent defined physical organisations (gel-phase, L; liquid-crystallinephase, L; or fluid state, HII), depending on their constituents and on the degreeof hydration, temperature, pressure, ionic strength, and pH. Generally, underphysiologically relevant conditions, most membrane lipids exist as bilayers,usually in the liquid-crystalline phase. Finally, the heterogeneous lateral organ-isation in the lipid-bilayer assembly causes the formation of different domainswhich support collective and co-operative phenomena. The resultant microhet-erogeneity is of utmost importance for many membrane functions.


All of these features contribute together to make biological membraneshighly structured fluids, both in space and in time, giving them peculiar struc-tural, dynamic, and functional properties [2].

2. How Can Drugs Perturb Membrane Organization?

The complex organization of membranes can be perturbed by drug mole-cules which can interact with either the polar headgroups or the apolar hydro-carbon chains, or both, depending on their structures. It is widely known that thepharmacological effects of ‘non-specific’drugs, such as general anaesthetics forinstance, are due to their binding to lipid membranes, thus altering their fluidity,curvature, and cooperativity functions [5][6]. Change in the membrane fluiditymay in turn affect the conformation and the function of membrane-spanningproteins or enzymes, with subsequent pharmacological effects. In contrast, themolecular mechanism of a large number of drugs involves specific receptor-based interactions with the embedded proteins. Whereas charged, water-solublemolecules (like most endogenous ligands) are thought to bind to an extracellu-lar portion of the plasma-membrane receptor, there is a quantity of experimen-tal evidence which shows that highly lipophilic drugs need to interact favorablywith the lipid matrix in order to gain access to their specific receptors.

Finally, a large number of drugs belonging to a wide variety of pharmaco-logical classes, such as cardioactive compounds, local anaesthetics, antima-larials, tricyclic antidepressants, antihistamines, etc. possess cationic amphi-philic properties. They contain a lipophilic portion, generally provided by anaromatic ring system, and a hydrophilic side chain with a nitrogen predomi-nantly protonated at physiological pH. Their structural properties may havean important impact on the interaction with amphipathic membrane compo-nents and may strongly influence the pharmacokinetic and the pharmacody-namic behavior of such drugs [7–10]. In this respect, a general model hasbeen discussed by Herbette and co-workers [11] [12].

On the basis of these remarks, it is evident that the consequence of drug-membrane interaction on transport, distribution, accumulation, efficacy, andresistance cannot be explained sufficiently by the partition coefficient inorganic bulk solvents with invariant properties throughout. For these reasons,natural membranes or phospholipid preparations have been proposed as a bet-ter model system for drug-partition studies [7] [13] [14]. It has been demon-strated, in fact, that the partition coefficient of ionized ligands in lipid mem-branes is significantly higher than the partition coefficient in octanol, as ion-ized species strongly associate with the zwitterionic phospholipid by meansof electrostatic interactions, the degree of the interaction depending on thecomposition of the membranes [11] [13–17].


Furthermore, the process of interaction may affect the drug molecules indifferent ways. Effects which may arise from ligand-membrane interactionare summarized in Table 1.

3. NMR Spectroscopy as a Powerful Tool for Identificationand Quantification of Drug-Phospholipid Interaction

Among other physical methods (Table 2), nuclear magnetic resonancespectroscopy (NMR) has proven to be an excellent tool in the investigation ofthe structure, dynamics, and properties of membranes as well as the changesarising from their interaction with ligands or drugs [18] [19]. Despite the factthat NMR is the least sensitive of the spectroscopic techniques, it presents afew important advantages over the other methods. In particular, it allows usto observe both partners at the atomic level, it offers much insight into thetime-scale at which the interaction occurs, and, virtually, does not perturb themonitored system.

Since the pioneering studies by Chapman and co-workers in the 1960ies[20], an incredible number of methods have been developed, and a huge num-ber of papers reporting applications of NMR spectroscopy for the investiga-tion of both model and natural membranes have appeared in the literature.Interestingly, the majority of these studies were carried out by spectroscopistsand biophysicists because of the high level of specialization required.Nevertheless, as well as with most sophisticated techniques and instrumenta-


Table 1. Possible Events during Drug-Membrane Interactions

• Membrane may prevent or limit drug diffusion to the active site

• Membrane may bind drug (accumulation)

• Membrane may lead to conformational changes in drug

Drug may induce conformational changes in phospholipid acyl chains

Drug may increase membrane surface

Drug may change thickness, fluidity, cooperativity, potential, and hydration of membrane

Table 2. Methods for Studying Drug-Membrane Interactions

• Circular Dichroism (CD) • Differential Scanning Calorimetry (DSC)

• Electron Spin Resonance (ESR) • Fluorescence

• Fourier-transform infrared spectrometry • High-Presssure Liquid Chromatography(FT-IR) (HPLC)

• Molecular modeling • X-Ray

tion, other routinely applicable NMR methods are available even to non-spe-cialized researchers such as pharmaceutical and medicinal chemists. Theycould represent a powerful and informative aid to the methods recently devel-oped to describe partitioning and localization of drugs in anisotropic systemssuch as, for example, HPLC and potentiometric methods [21] [15].

The present review focuses on the information content of the NMRresponse arising from the most important techniques. A brief comment will bedevoted to those methods which require dedicated instrumentation or exper-tise. Much attention will be paid to methods and applications of greater inter-est for pharmaceutical and medicinal chemists, in particular in unravelling thecomplex mechanisms governing the partitioning, localization and conforma-tion of ionizable and amphiphilic ligands in a membrane environment.

4. Description of the NMR Phenomenon and Its MeasurableParameters [22] [23]

The nuclear spin (I) of magnetically active nuclei has a magnetic moment() associated with it. When a sample is placed in a magnetic field Bo, thenuclei in the molecule tend to align with the direction of the field and to rotateabout it. Different nuclei will precess with different frequencies (0), accord-ing to their nuclear spin ( = I/), the frequency magnitude being of the orderof 106 Hz (Eqn. 1)

(Eqn. 1)

When the system is disturbed from equilibrium by a pulse of radio fre-quency, it is possible to monitor the response of the system as a function of timeand to collect and store the data in a computer. The frequency spectrum is gen-erated mathematically in the computer using a Fourier transformation whichconverts the time-domain data into the classical frequency-domain spectrum.

The observable response in NMR is a spectral line characterized by anumber of parameters. Basically they include:

− The ‘chemical shift’ ( [ppm]) which defines the position on a frequen-cy scale of the signal of the observed nucleus. It depends on the mag-netic properties of the nucleus and on its chemical environment.

− The ‘intensity’ of the signal which reflects the number of nuclei in eachenvironment.

− The ‘spin coupling’ (J, ), i.e., the spacing in Hz between splittedlines. It is induced by the presence of neighbouring spins and containsinformation on both the electronic structure and the conformation ofthe molecule.

0

0

2= B


− The ‘spin-lattice relaxation rates’ (T1–1) and the ‘spin-spin relaxation

rates’ (T2–1), defining the rate (s–1) at which the nuclei come back to

the equilibrium after a radio-frequency pulse. They provide kineticinformation on rate processes, including the rate of molecular motion.

− The ‘NOE effect’ (, Nuclear Overhauser Enhancement), which is thechange in the signal intensity of the observed nucleus, as result of aselective perturbation of other spins spatially close to it. It mainly pro-vides information on intra and intermolecular distances.

All of these parameters reflect, to different degrees, the structural anddynamic features of both ligand and phospholipids. During the interaction,one or more parameters can change, providing information on the molecularportion which is directly involved in the interaction and, possibly, on thetime-scale at which it occurs.

5. Description of the Most Important NMR Methods for Studying Drug-Phospholipid Interaction

1H (proton), 2H (deuteron), 13C (carbon), 31P (phosphorus), and 19F (flu-orine) are magnetically active nuclei of special interest for carrying out stud-ies in solution and in the solid state. In the case of 31P, the NMR probe isalready found in lipids, while in others, isotopic enrichment at specific posi-tion in the lipid molecules is always (2H) or sometimes (13C) required.

Basically, two different approaches are feasible depending on the infor-mation being sought and on the available experimental equipment. A firstapproach involves the use of ‘wide-line’ experiments carried out on crystals,unsonicated phospholipid bilayers, or large (>50 nm diameter) lipid vesiclesin the gel or liquid-crystalline phase. Alternatively, ‘high-resolution’ NMRexperiments on 1H, 13C, 31P, and 19F can be performed on aqueous disper-sions of phospholipids using small-size vesicles or micelles.

5.1. Wide-Line Experiments

In this area, a great contribution to the development of both the theoreti-cal background and experimental techniques is undoubtedly represented bystudies carried out by Seelig and co-workers [24] [25]. The analysis of param-eters such as 31P chemical shift anisotropy and 2H quadrupolar splitting, pro-viding a link between the head-group moiety and the glycerol backbone of thelipid molecule, have been widely applied to the study of the location of bin-ding sites, and the variation in the order and in the molecular dynamics of lip-ids during drug binding.


31P Wide-line experiments require the measurement of the chemical shiftanisotropy (CSA, ) of the 31P-atoms of the phosphate groups in crystalsamples or unsonicated phospholipid dispersions and natural membranes.The CSA originates from the fact that the magnetic field experienced by thephosphorus nucleus depends on the orientation of the lipid’s phosphategroups with respect to that of the applied magnetic field. In the case of lipidsin a solid state or in liquid- or gel-crystalline phases, the bilayer domains arenot oriented homogeneously, but distributed at random, and the absence ofmotion or even the limited motion of the phosphate segments does not aver-age the chemical shifts corresponding to the different orientations. Thus, thespectrum, also referred to as powder-type spectrum, results in a broad line(having of kHz) with different shapes and intensities, depending on thephysical state of the lipids and on the experimental conditions, due to super-imposition of resonances. Typical 31P-NMR spectra for polymorphic phasesof phospholipids in bilayers and hexagonal phase HII are represented in Fig. 1A and Fig. 1B, respectively [26]. The distance between the two edgesof the asymmetric signals (low-frequency and high-frequency shoulders) isthe measured parameter. Its value depends on the average orientation ofthe phospholipid head groups relative to the normal plane of the bilayer andalso to the molecular motion of the lipid molecules. Sonication of bilayer dis-persions to yield small vesicles or micelles, which rapidly tumble in solution,gives rise to narrow spectra (Fig. 1C).

As membrane-active compounds often change the average orientation ofthe lipid headgroups as well as the phase-transition temperature of the lipidsfrom liquid-crystalline to gel phase, the CSA is a sensitive parameter toaccount for these changes. As an example, the effects of the four cationic anti-malarials chloroquine, quinacrine, quinine, and mefloquine (cf. 1, 2, 3, and 4,respectively, in Fig. 2) on 31P-NMR spectra of aqueous dispersions of dipal-mitoylphosphatidylcholine (DPPC) are discussed in [27].

31P-CSA Studies have been often applied in combination with 2H-NMRspectroscopy, a powerful technique which provides complementary informa-tion.

2H-NMR Spectroscopy requires the introduction of deuterons into specificpositions, either into the phospholipid molecules (Fig. 3A) or into the drugmolecules. In the case of specifically labelled positions, the assignments of theresonances are straightforward, and the interpretation is particularly simple. Thistechnique has been extensively used as a probe to investigate chain packing inlipid bilayers of model and natural membranes [24][28] as well as the effects ofmembrane-perturbing drugs as in the case of antimalarials [29], or to explore thelocation of local anaesthetics [30–33] and calcium-channel antagonists [34].

The 2H-NMR spectrum of selectively deuterated phosphatidylcholine in agiven segment of the fatty-acyl chain or of the head group is shown as an


example in Fig. 3B [30]. The separation between the two emerging lines isthe most important parameter which can be read off from the spectrum and iscalled ‘residual deuterium quadrupole splitting’, Q. Its value (Eqn. 2) isequal to

(Eqn. 2)

where e2 · q · (Q/h) is the static quadrupole coupling constant of a deuteron(170 kHz for an aliphatic C–D bond) and SCD is the derived ‘order parame-

∆νQ2

CD34

= ⋅ ⋅ ⋅

⋅e q Q

hS


Fig. 1. Typical 31P-NMR spectra for polymorphic phases of phospholipids in bilayers (A), hex-agonal HII (B), and small vesicles (C) (adapted from [24])

ter’ within the limits –1/2 < SCD < 1. It is taken to measure the ‘average’ orderof the PPL bilayer. Quadrupolar splitting, Q, collapses at the magic angle = 54.74°, cos = 1/3. Using fatty acid labelled with deuterium in differentpositions, it is possible to obtain the order parameter as a function of its posi-tion.

In a perfectly ordered bilayer, e.g., with all bonds in a trans conformation,S = 1. Quadrupolar splitting and the derived order parameter are used todescribe the average orientation and fluctuation of the C–D bond vector withrespect to a fixed symmetry axis. The presence of drugs in the bilayer, e.g.,the local anaesthetic tetracaine (5 in Fig. 2), affecting the degree of order at a given labelled position of the acyl chain, is indicated by a decrease in thequadrupolar splitting (Fig. 3B, left side). The relative variation of this param-eter at different positions of the fatty-acyl chain and of the head group


Fig. 2. Structural formulae of the compounds cited in the text. Chloroquine (1), quinacrine (2),quinine (3), mefloquine (4), tetracaine (5), substituted benzylalkylamines (6), (4-methylben-

zyl)alkylamines (7–13), cetirizine (14), hydroxyzine (15).

(Fig. 3B, right side), plotted as a function of the amount of tetracaine eitherat pH 5 or pH 9, gives a precise indication of the possible membrane locationof the charged and uncharged forms of the molecule. Analysis of 2H-NMRspectra performed on perdeuterated lipids, despite the fact that it is more com-plex due to superimposition of the quadrupolar splittings of all the deuterons,gives, in turn, an overall view of the drug-induced changes on the bilayer, asdepicted in Fig 4. The more intense shoulders at the edges of the 2H spectrumare due to the deuterons in the region of high and constant order, i.e., in thefirst ten segments of the acyl chain. The inner, more intense lines are due tothe terminal methyl groups characterized by a higher degree of motion. Thepresence of tetracaine modifies the order-parameter profile resulting in adecrease in intensity of the shoulders, with a build-up of intensity at frequen-cies corresponding to smaller order parameters, thus suggesting that insertionof tetracaine into the DPPC bilayer leads to a decrease in the extent of the


Fig. 3. Effects of the interaction between tetracaine hydrochloride (TTC; 5) and selectivelydeuterated dipalmitoylphosphatidylcholine (DPPC). A) Structure of deuterated DPPC. B) 2H-NMR spectra of selectively deuterated phosphatidylcholine at the given position of the acylchain (left side) and of the headgroup (right side) and relative changes induced by an increas-

ing amount of 5 at pH 5.5 (from [28]).

∆νQ2

CD34

= ⋅ ⋅ ⋅

⋅e q Q

hS

region of high and constant order, with an overall disordering effect on theacyl chains. From these and other 31P-NMR studies, a schematic representa-tion of the possible membrane location of the charged and uncharged formshas been suggested [30].


Fig. 4. 2H-NMR Spectra of perdeuterated phosphatidylcholine (DPPC-d62) multilamellar dis-persions and changes induced by an increasing amount of tetracaine hydrochloride (TTC; 5)

at pH 5.5 (from [28])

5.2. High-Resolution NMR Experiments

An alternative approach to the investigation of drug-phospholipid interac-tions involves detecting NMR spectra using aqueous dispersions of small-sizelipid vesicles. Under the conditions of fast exchange between free and boundstate, the observed NMR responses (chemical shift, spin coupling, relaxationrates, etc.) are time-averaged. The spectral lines are still narrow, thus simpli-fying the approach. 1H, 13C, and 31P are nuclei of great interest in this contexteither because they constitute the backbone of drug molecules and phospho-lipids or because they have spin = 1⁄2 which allows an easier interpretation ofthe spectra. 19F is another naturally abundant spin = 1⁄2 nucleus typicallypresent in fluorinated anaesthetics.

Many aspects of the interaction between drugs and membranes can bemonitored by the use of high-resolution NMR parameters. Interesting appli-cations have been reported in relation to: 1) study of location, mobility, andconformation of drugs in the presence of lipid bilayers [35–37]; 2) character-ization of drug-liposomal dispersions in drug-targeting studies [38–40]; and3) study of permeation and trans-membrane transport [41] [42]. Regarding thelatter, a well-suited method consists in recording the spectra of drug-liposo-mal preparations in the presence or absence of ions such as praseodymium(Pr3+) [41] or manganese (Mn2+) [42]. These ions do not cross the vesiclemembrane and, due to their paramagnetic properties, induce dramatic chang-es (i.e., marked paramagnetic shifts or strong broadening of the signals) in themolecules located in the extravesicular milieu. Therefore, only the reso-nances of molecules located in the intravesicular milieu are observed. Theliterature mentioned is recommended for further details.

5.3. Relaxation Rates as Quantitative Monitors of Drug-PhospholipidInteractions and Their Use as Molecular Descriptor of Lipophilicity

Highly informative NMR descriptors, useful for quantifying drug-mem-brane interactions, are spin-lattice (T1

–1) and spin-spin (T2–1) relaxation rates,

observable in 1H- or in 13C-NMR spectra. These parameters define the rate atwhich the spin system comes back to equilibrium, after the radio-frequencypulse, by interacting with the surrounding environment. Changes in T1

–1andT2

–1 can be related to a decrease in the rotational freedom of small moleculesin the presence of a ‘receptor’ with which they can interact [23] [43]. Adirectly measurable parameter is the linewidth at half peak height (1/2),which is proportional to T2

–1 according to Eqn. 3.

(Eqn. 3)∆ν π1 2/ = ⋅T2–1


Under the experimental conditions used, the assumption holds that T1

–1 = T2–1 and that a rapid exchange occurs between free and liposome-bound

ligand as expressed by Eqn. 4.

(Eqn. 4)

where is the fraction of bound ligand, T2–1

bound is the proton (carbon) relax-ation rate for the liposome-bound ligand, T2

–1free is the proton relaxation rate

for the free ligand, and T2–1

obs is the observed relaxation rate, representing aweighted average of the free and bound species of the ligand. The chosendrug/phospholipid concentration ratio allows the observation of the drug-pro-ton signals without interference by phospholipid-proton signals. Expressed asa function of the drug/phopspholipid ratio, 1/2 can be used to quantify thedegree of interaction, provided that no other factors produce signal broaden-ing (Fig. 5A). The different slope (NMR slope, Fig. 5B) indicates a differentrotational freedom of the observed portion of the molecule, thus pointing todifferent degrees of interaction with phospholipids.

The possibility of monitoring the behavior of virtually every atom in themolecule renders this method very useful in obtaining information on whichportions of the drug molecule are involved in the binding, although a directcomparison of the degree of interaction is only possible for structurally relat-ed drugs where identical spin systems are monitored. The use of NMR relax-ation rates to quantify drug-phospholipid interaction has been widelyexplored by Seydel and co-workers on a large number of cationic amphiphil-ic drugs and validated by other appropriate methods [9] [44].

An illustrative example of the method is found in studies carried out on ahomologous series of amphiphilic benzylalkylamines (7–13 in Fig. 2). Thesemolecules have provided a simple but meaningful model either for explicitphospholipid-interaction mechanisms [45] or for validating the resultsobtained by the pH-metric method when investigating partitioning in andinteractions with zwitterionic liposomes [46]. In this latter study, the lipophil-ic profile of homologous amphiphilic (4-methylbenzyl)alkylamines (MBAA7–13) was studied in isotropic (octanol/water) and anisotropic (lipo-somes/water) systems. In the former, as expected, a linear dependence of thedistribution coefficient determined at pH 7.5, log Doct

7.5 (pH at which the mole-cules are present in their protonated form), on the alkyl-chain length wasfound, while a bilinear dependence was observed in the case of the distribu-tion in liposomes (log Dlip

7.5) (Fig. 6A and 6B). The fact that the change inrelaxation times, measured at pD 7.5 and expressed as log (NMR slope),appeared linearly related with log Dlip

7.5 (Fig. 6C) indicates that the twodescriptors encode the same interaction forces which control the partitioningof these molecules in phospholipids.

T T T2 obs–1

2 free–1

2 bound–1(1 –= ⋅ + ⋅α α)



Fig. 5. Interaction of (4-methylbenzyl)alkylamines with liposomes. A) Linewidth broadeningof the benzylic CH2 protons of (4-methylbenzyl)ethylamine (8) and of (4-methylbenzyl)butyl-amine (10), respectively, as a function of egg-phosphatidylcholine concentration. B) Change in [Hz], for the CH2 protons of Fig. 5A as a function of increasing quantity of phosphatidyl-

choline [mg ml–1] (full circles for 8 and empty circles for 10).

A possible interpretation is that, for shorter N-alkyl homologues (cf. 7–9),strong electrostatic forces dominate the interaction and determine an electro-static surface binding between the positively charged nitrogen and the negative-ly charged phospholipid headgroups. In contrast, the partitioning of the mole-cules with longer chains (cf. 11–13) is additionally controlled by hydrophobicanchoring into the phospholipid core, which becomes stronger and stronger asthe chain length increases. This effect is best accounted for by assuming thatbenzylamines with a chain longer than n = 4 bind to phospholipids in a foldedconformation reinforcing the interaction. Conformational analysis [46] [47]and NOE experiments [47] seem to confirm this behavior and provide a pos-sible model for the membrane allocation of these amphiphilic molecules. Thebreak point seen at compound 10 might suggest that the competition betweenelectrostatic and hydrophobic forces weakens the overall interaction.

Interestingly, the biological action induced by the benzylalkylamines, asevaluated by their inhibitory effects on the Ca2+-channel currents (pIC50)[48], is linearly related to their distribution coefficient in octanol (log Doct

7.5)


Fig. 6. Relation between the lipophilicity of benzylalkylamines 7–13 and other properties. A)Variation of the logarithm of the distribution coefficient at pH 7.5 in an octanol/water system(log Doct

7.5) as a function of the number of methylene groups (n). B) Variation of the logarithmof the distribution coefficient at pH 7.5 in a liposome/water system (log Dlip

7.5) as a function ofthe number of methylene groups (n). C) Relation between the logarithm of the NMR slope andthe logarithm of distribution coefficient at pH 7.5 in a liposome/water system (log Dlip

7.5).D) Relation between the negative logarithm of the inhibitory effects of the Ca2+-channel cur-rents (pIC50) and the logarithm of the distribution coefficient at pH 7.5 in an octanol/water

system (log Doct7.5).


(Fig. 6D). This finding could be explained by admitting that the inhibition ofchannel current caused by these molecules is due to their interaction with thesurface of the channel proteins which is directly in contact with the aqueousmedium, an interaction which would only depend on the bulk lipophilicityand would not be mediated by partitioning in the lipid matrix.

The use of proton-NMR relaxation rates as molecular descriptors of theinteraction with phospholipids has been recently applied to the study of thelipophilicity behavior of the zwitterionic antihistamine cetirizine (14 inFig. 2). The compound is a well-known marketed 1H-receptor antagonist dis-playing synergistic pharmacodynamic properties, low CNS effects, and beingdevoid of cardiotoxicity. The ionization and lipophilicity properties of thisdrug in isotropic systems like octanol/water and dodecane/water have beenrecently investigated and related with pharmacokinetic properties [49]. Thecomplexity underlying the whole picture called for an investigation of its par-titioning in anisotropic liposomes.

Two isolated singlet signals, i.e., the benzhydryl CH and the CH2 close tothe carboxylic-acid function, were selected as suitable probes to check thedependence of the line broadening (1/2) on the increasing liposome concen-tration in a pH range between 3–9. Outside of this range, experiments havebeen performed but the results are not totally reliable as a change in ioniza-tion pattern of the phosphate groups may occur, thus destroying the integrityof the vesicles. The basic drug hydroxyzine (15 in Fig. 2), a first-generationantihistamine and the metabolic precursor of cetirizine, was also studied forcomparison. In Fig. 7A, the dependence of the log (NMR slope) of the ben-zhydrilic CH proton as a function of pD, is reported together with the sameparameter obtained for the benzhydrylic CH of the hydroxyzine. A directcomparison of the degree of interaction of the two drugs with phopholipids ispossible since identical spin systems are observed.

It appears that positively charged cetirizine and hydroxyzine display simi-lar behavior, whereas, approaching neutral pD an opposite trend is observedimplying that different mechanisms become involved in the partitioning of thetwo drugs with phospholipids. The different electronic species which character-ize the two antihistamines in the pH range 4–7 play indeed a key role in deter-mining their partitioning properties. The low degree of interaction evidencedfor cetirizine (which is in its zwitterionic form [49]), suggests that weak elec-trostatic surface interactions with polar headgroups of phospholipids dominateits partitioning in liposomes. In contrast, the large amount of neutral hydroxy-zine could be responsible for its high partitioning in the phospholipid core.

The flexibility of cetirizine is another feature to be taken into accountwhen interpreting the results. In fact, two main classes of conformers of zwit-terionic cetirizine, namely folded and extended ones, were identified havingdifferent estimated lipophilicity [49]. Folded conformers displayed higher

virtual log P values (i.e., 1.3) in comparison with the extended ones (i.e., 0.3).The distribution profile in the octanol/water system (Fig. 7C) evidences abell-shaped curve having a plateau in the zwitterionic pH range (log D7.4

oct = 1.5),suggesting that folded, more lipophilic conformers govern the partitioningbehavior of cetirizine in this solvent. The contrary is true when the distribu-tion profile in liposomes is considered, as was also confirmed by equilibrium-dialysis experiments (Fig. 7D) [50]. Finally, an even weaker interaction withliposomes is evidenced for cetirizine by monitoring, in the zwitterionic pHrange, the CH2 protons close to the carboxylic-acid function (Fig. 7B). Theanomalous line-broadening observed at pH 2 could tentatively be related tothe particular electronic state of either phospholipid headgroups or cetirizineitself, as well as to the position of the CH2 group within the molecule.

Taken together, these results suggest that anisotropic liposomes couldinduce in cetirizine a prevalence of more polar, extended conformers or that,regardless of the presence of different conformers, dominating electrostaticsurface interactions globally decrease the capacity of cetirizine to partition inphosphatidylcholine.


Fig. 7. pH-Lipophilicity profiles of the anthistamines cetirizine (14) and hydroxyzine (15) inliposome/water and in octanol/water systems, respectively, and their relation with changes in [Hz] (log (NMR slope)) of the given nuclei. A) Variation of log (NMR slope) of cetirizine(empty circles) and hydroxyzine (full circles) benzhydrylic CH with pD. B) Variation oflog (NMR slope) of cetirizine CH2–COO(H) with pD. C) Distribution profiles in an octanol/water system of cetirizine (solid line) and hydroxyzine (dashed line) (from [49]). D) Dis-tribution profiles in the liposome/water system of cetirizine (empty circles) and hydroxyzine

(full circles) determined by dialysis (from [50]).

6. Conclusion

NMR Spectroscopy proves to be a powerful tool to investigate the inter-action of drugs with a complex biological matrix like phospholipids withoutinfluencing the state of the membrane or drugs. It allows not only to monitorthe location of the ligand and the resulting changes in both partners, but alsoto quantify the degree of the interaction itself.

The choice of the most suitable NMR techniques strongly depends on theinformation sought. Methods involving ‘wide-line’ 31P- and 2H-NMR spec-troscopy are very informative, well developed and codified, although materi-als and instrumentation are not always available in medicinal and pharmaceu-tical chemistry laboratories. In particular, relaxation-rates measurementsoffer advantages to monitor, virtually for each molecular portion, the interac-tion of the ligand with the target and to evidence possible drug-conformation-al changes induced by the interaction. Besides these advantages, experimen-tal conditions differing from other methods and the impossibility to directlycompare different spin-systems are the main limiting factors in determiningquantitative relationships between traditional lipophilicity descriptors andNMR parameters. Nevertheless, relaxation rates, once the methods are optim-ized and validated, could represent a new informative molecular descriptor ofthe partitioning in membranes.

R. F. is indebted to Joachim Seydel of the Experimental Biology and Medicine Institute ofBorstel for his important contribution to the discussion and for giving bibliographic material.Thanks are also due to Bernard Testa and Pierre-Alain Carrupt of the University of Lausanneand to Alberto Gasco of the University of Turin for stimulating discussions.

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[14] R. P. Austin, A. M.Davis, C. N. Manners, J. Pharm. Sci. 1995, 84, 1180.[15] U. Hellwich, R. Schubert, Biochem. Pharmacol. 1995, 49, 511.[16] C. Ottiger, H. Wunderli-Allenspach, Eur. J. Phar. Sci. 1997, 5, 223. [17] A. Avdeef, K. J. Box, J. E. A. Comer, C. Hibbert, K. Y. Tam, Pharm. Res. 1998, 15, 209.[18] H. Hauser , I. Pascher, R. H. Pearson, S. Sundell, Biochem. Biophys. Acta, 1981, 650, 21.[19] J. K. Seydel, in ‘NMR Spectroscopy in Drug Development and Analysis’, Eds. U.

Holzgrabe, I. Wawer, B. Diehl, Wiley-VCH, Weinheim, 1999, pp. 173–229.[20] D. Chapman, Ann. N. Y. Acad. Sci. 1966, 137, 745.[21] S. Ong, H.Liu, X. Qiu, G. Bhat, C. Pidgeon, Anal. Chem. 1995, 67, 755.[22] H. Günther, ‘NMR Spectroscopy: Basic Principles, Concepts and Application in

Chemistry’, 2nd Edn., Wiley, New York, 1992.[23] D. J. Craig, ‘NMR in Drug Design’, 1996, CRC Series in Analytical Biotechnology.[24] J. Seelig, Biochim. Biophys. Acta 1978, 515, 105.[25] a) J. Seelig, Quart. Rev. Biophys 1977, 10, 353; b) J. Seelig, P. MacDonald, Acc. Chem.

Res. 1987, 20, 221.[26] A. Watts, P. J. R. Spooner, Chem. Phys. Lipids 1991, 57, 195.[27] R. Zidovetzki, I. W. Sherman, A. Atiya, H. de Boeck, Mol. Biochem. Parasitol. 1989, 35,

199.[28] N. Boden, S. A. Jones, F. Sixl, Biochemistry 1991, 30, 2146.[29] R. Zidovetzki, I. W. Sherman, M. Cardenas, D. B. Borchardt, Biochem. Pharmacol.

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Fujii, T. Nakagawa, Biophys. J. 1996, 71, 1191.[34] Bäuerle, J. Seelig, Biochemistry 1991, 30, 7203.[35] A. Saran, S. Srivastava, V. M. Kulkarni, E. Coutinho, Ind. J. Biochem. Biophys. 1992, 29,

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Part V. Computational Strategies

Virtual Screening of Molecular Properties: A Comparison of Log P Calculators

Mark E. Duban, Mark G. Bures, Jerry DeLazzer,and Yvonne C. Martin*

Quantitative Structure-Absorption RelationshipsHan van de Waterbeemd

Hydrogen Bonding: The Last Mystery in Drug Design?Hugo Kubinyi

Molecular Hydrogen-Bonding Potentials (MHBPs) in Structure-Permeation Relations

Giulia Caron*, Sébastien Rey, Giuseppe Ermondi,Patrizia Crivori, Patrick Gaillard, Pierre-Alain Carrupt,and Bernard Testa

VolSurf and Its Application in Structure-Disposition RelationshipsGabriele Cruciani*, Sara Clementi, Patrizia Crivori,Pierre-Alain Carrupt, and Bernard Testa

Molecular-Modeling Approaches to Predict Metabolism and ToxicityAntonius M. ter Laak* and Nico P. E. Vermeulen


Virtual Screening of Molecular Properties:A Comparison of Log P Calculators

by Mark E. Duban, Mark G. Bures, Jerry DeLazzer,and Yvonne C. Martin*1)

Pharmaceutical Products Division, Abbott Laboratories, Abbott Park IL 60064-6100, USA;Tel.: 847 937 53 62; Fax: 847 937 26 25; e-mail: [email protected]

1. Background: Statement of the Problem

The recent remarkable automation in compound synthesis and combina-torial chemistry has changed the strategy of drug discovery: No longer arecompounds made and tested in single-digit quantities, but rather in sets ofthousands [1]. This paradigm shift has changed the practice of computer-assisted drug designing as well. Rather than designing the optimum com-pound from existing structure-activity relationships, computational chemistsare now asked to select subsets of compounds from those offered for purchaseor from virtual combinatorial libraries. If the selected compounds are intend-ed for general screening, then there is no specific biological information toguide the selection. Because it has been calculated that there might be 10180

possible drug molecules [2], there is a consensus that wise selection is notonly possible, but necessary.

The first approach to compound selection and combinatorial librarydesign was to emphasize molecular diversity [3][4]. Considering that all ofchemistry is based on the similarity principle, that similar molecules havesimilar properties, it makes sense to select compounds for these libraries thatare as different from each other as possible. Compounds are usually describedby the types of substructures that they contain when doing molecular-diver-sity analysis [5]. These substructures are fast and easy to calculate and areavailable for every structure. Additionally, compounds that are close in this150–1296 dimensional property space look similar to chemists; that is, theyare analogues of each other. Others take their cue from molecular modeling

1) Current address: D-07CE, AP8B/2, Abbott Diagnostics Division, Abbott Laboratories,Abbott Park IL 60064-6100, USA.


and describe molecules by the types of pharmacophores that they contain [5][6].

Many scientists have worked to perfect the diversity selection strategies[7]: does one emphasize purely selecting the most diverse subset or selectingcompounds that are ‘different enough’ from each other?

Diversity selection based on substructures or pharmacophore features alsoignores some of the lessons learned from medicinal chemistry: e.g., octa-nol/water log P must be considered. For example, a compound must be solu-ble in order to be biologically active, and there is a correlation between solu-bility and octanol/water log P [8][9]. Additionally, very often changes in theoctanol/water log P are correlated with changes in biological potency [10].For this reason, we investigated the physicochemical information content ofthe molecular descriptors used in diversity analysis. We found that thedescriptors and clustering methods that perform best at grouping biological-ly similar compounds together also perform best at using neighbors to esti-mate values of traditional QSAR descriptors such as octanol/water log P,pKa, molecular weight, and MolconnX descriptors [11]. For example, if onepredicts log P from compounds with a mean similarity of 0.85 for MACCSsubstructural descriptors, the result is that 8500 of the 8651 1994 starlist com-pounds are predicted with an r.m.s. error of 0.75. To include the whole star-list, similarities of 0.70 must be used. For the same number of compounds,clustering performed only slightly worse than CLOGP v4.41. In a similarway, 7000 of the 8416 compounds with measured pKa values can be predict-ed with an r.m.s. error of 1.6, and 650 of the 762 compounds with measuredcyclohexane/water log P values can be predicted with an r.m.s. error of 0.82.Accordingly, the substructural descriptors contain indirect information abouttraditional physical properties. However, because this relationship is indirect,substructural descriptors cannot be used to eliminate compounds from furtherexamination based on physical properties.

One could consider using traditional physical properties such as log P andpKa directly for diversity selection. When selecting compounds from vendorlibraries, these properties are not typically used in the diversity analysisbecause there are only a few relevant properties with the result that the com-pounds are not differentiated from each other. For example, we showed thatfor a diverse set of 1650 compounds, only three dimensions explain 89% ofthe variance in the correlation matrix of 1, 2, 3, flexibility , numberof rotatable bonds, surface area, volume, CMR, number of H-bond acceptors,and number of H-bond donors [11]. We concluded that the property space thatis readily calculated is spanned by only three dimensions. Another way tolook at the same problem is to note that compounds with the same log P oftenhave very different substructure and pharmacophore features — althoughthey are considered similar in log P, they are dissimilar in other measures.


Table 1 shows examples of compounds that have essentially identical log P*(preferred log P value) and pKa values, making it clear that more descriptorsare needed to differentiate diverse compounds [12].

Because combinatorial libraries are designed around a common core, only their substituents contribute to differences in physical properties. Ac-cordingly, diversity selection can consider the relative steric and pharmaco-phore properties along with log P or Hansch-Fujita and pKa or Hammett orTaft . For example, statistical experimental design procedures have beenused to design diverse combinatorial libraries [13].

Even though log P cannot be used for diversity selection, it has becomeincreasingly clear that compounds with extreme values of log P should bediscarded before the diversity analysis is performed [14][15]. However, theserious problem with using log P as a descriptor for compound selection isthat many programs, particularly the ones thought to be most accurate, can-not calculate values for a substantial subset of the data. This study wasprompted by our observation that only 40% of the Abbott compounds wereprocessed with error code 0, a good estimate, using CLOGP v4.51. In fact,15% of the compounds in the Abbott collection could not be processed byCLOGP because of a missing fragment. Although, in principle, we could pro-vide estimates for these missing fragments, we discovered that 8248 uniquefragments were missing for the Abbott compounds. When purchasing com-pounds for high-throughput screening, we are especially interested in com-pounds for which there is no fragment value because these very compounds


Table 1. Pairs of Compounds with Similar Log P* and pKa Values [12]

NO. Structure 1 Log P* pKa No. Structure 2 Log P* pKa

1 1.47 4.61 2 1.47 4.60

3 1.65 6.99 4 1.65 6.95

5 2.0 8.40 6 2.0 8.44

7 2.15 8.11 8 2.14 8.11

are different from compounds with measured log P values; presumably, thesecompounds are those that are most different from compounds already studiedextensively.

This communication describes our evaluation, conducted in late 1997 andearly 1998, of the log P calculators available to us [16]. We investigated boththe coverage of compounds and the accuracy of the calculations. In manycases, we worked closely with the database and program providers to under-stand and improve the results. The lessons learned apply to the calculation ofother physical properties as well as to efforts to devise all-encompassingmodels for biological properties, that is, to all virtual property calculationmethods.

2. Raw Comparison of Log P Programs

Table 2 lists the programs that we tested. Many were kindly provided tous specifically for this evaluation. We compared the performance of the pro-grams on the log P* values collected in the Medchem97 database [17].

To explore how much the values for individual compounds might deviate,we compared the log P* values in Medchem97 with those in the Syracusedatabase: there are 7787 compounds in common. The statistics of the fit of thevalues between the two databases show an excellent correlation, R2 = 0.993,s = 0.13. This is presumably because the workers at Syracuse accepted thelog P* values available at the time they started their work as the best to use.However, the log P values for 87 compounds deviate by more than 0.5 logunits, 27 deviate by more than 1.0 log units, and 6 deviate by more than 2.0 log units. The deviations are approximately evenly distributed to the pos-itive and negative directions.

Some structures were lost in converting the Medchem97 smiles into theinput format expected by the program. Of 9853, 49 or 0.5% were lost in con-verting to sdf format via Tripos dbtranslate [33], and 202 or 2% were lost con-verting to Sybyl mol2 via CONCORD [34]. Although the absolute numbersof compounds lost is small, the loss of molecular diversity is probably great-er because the programs fail on structures the programmer has never seen. Weeliminated from the set of 9853 any compounds with metals or silicon: thisbrought the number for testing to 9392.

Table 3 lists the numbers of compounds that each program can process.Recall that, in general, these are structures with published log P values,hence, we would expect that all structures could be handled. This expectationwas nearly met by all programs.

Because each program can calculate values for different compounds, weneeded to be sure that we made a fair comparison of their accuracies. It is pos-


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sible that those compounds for which a log P was not calculated by a partic-ular program were more difficult to calculate than the average. Fig. 1 showsthat this is not the case. In this figure, we have plotted the average absolutedeviation of the calculated log P from log P* for two sets of compounds: onthe x-axis is the deviation for all compounds that the program could calculate,and on the y-axis is the deviation for only those compounds that most pro-grams can calculate. The correlation is striking. As suspected, the deviationsfor the consensus compounds are usually lower than for all of the compoundsa program can calculate. However, the difference is ca. 0.03 log units, aninsignificant amount. In the remainder of this report, we present the results onthe consensus compounds of thirteen of the programs with 8570 of the 9392compounds.

Table 3 also summarizes the results of the calculations. Because the num-ber of compounds that a program cannot process may be an important factorin deciding which to use, we examined this factor. The number of compounds(of 9392) that could not be handled by the program, including the 114 forwhich CLOGP reports a missing fragment value, varied from 1 to >3000. In


Fig. 1. Comparison of the calculation results for different subsets of compounds for each program

a similar manner, there was a large difference in the accuracy of the calcula-tions: the mean absolute deviation between observed and calculated valuesvaries from 0.36 to 0.98.

3. Discussion

The first caveat to bear in mind when considering these results is that theyare two years old – in particular, these are old versions of the programs. Mosthave improved since then. For example, there is now a version of CLOGPthat estimates the values for missing fragments [35], XLOGP v2.0 claimsimproved precision [36], ALOGP has been updated by two groups [37] [38],the ChemDraw program was just in development, etc.

The second caveat is that ALOGP, MLOGP, and Rekker/CompuDrugProLogP were programmed from a literature description. As such, they mightnot accurately reflect the precision of the original program and do not reflectimprovements which were made in the algorithm but not published in detail.For example, the Rekker fragment method has been implemented byCompuDrug and also in a contributed SPL for Sybyl: for the 7410 compoundsthat both can calculate the mean absolute error for the former is 0.605, andthat of the latter is 0.673; the maximum absolute errors are 5.79 and 14.84,respectively. Because neither of the programmers is the original author of thealgorithm, it might be anticipated that the true error of the Rekker method islower than that of either of these implementations.

The third caveat is that HINT and ALOGP were not developed as methodsfor calculating log P, but rather for displaying hydrophobicity on 3D struc-tures. The authors might have needed to make compromises in the accuracy ofthe overall calculation in order to properly represent the relative hydrophobic-ity of different parts of the molecules. For example, how does one decide howto partition the hydrophobicity into the individual atoms of a functional group?

A major problem in interpreting these results is that we do not knowwhich log P values served as training sets to develop the various algorithmsand which represent true tests of their predictivities: It is not fair to confusethe accuracy of fitting with the accuracy of prediction. For example, becausethe log P* value was selected for the Medchem97 database by the same per-son who developed the rules for CLOGP, one should not be surprised that, ofthe programs tested, CLOGP provides the closest fit between calculated andobserved log P values. It is not clear that the same result would be found forcompounds outside the Medchem97 dataset. Table 3 lists an estimate of thenumber of molecules considered in developing each algorithm.

An issue of larger significance to the long-term prospects for a good log Pcalculation algorithm is the intrinsic accuracy of the data used both to de-



velop the methods and in this test. Recall that there has been some discus-sion on which is the most reliable log P value for certain compounds. Table 4 lists some examples, selected by browsing Medchem00, that suggestto us that there is less accuracy in log P values than is commonly assumed.Values frequently differ by as much as 0.2 log units and sometimes evenmore.

Complicating the experimental data further is the observation that themeasured pKa values of a compound differ even more dramatically than log P

Table 4. Examples of Compounds for which More than One Octanol/Water Log P Value IsReported in Medchem00 [12]

No. Structure CLOGP Log P* Other log P values

Log P1 Log P2 Log P3

9 2.768 2.87 2.64

10 4.926 5.18 4.0 5.15

11 2.018 1.64 1.50 1.52 1.54

12 2.741 2.70 2.47

13 4.90 4.81 4.73 4.98

14 –1.380 –0.96 –1.00 –0.89 –0.85

15 1.878 1.98 1.59 1.60 1.69

16 2.196 2.05 1.88 1.98 1.99

17 2.051 2.07 1.89 1.94 1.98

18 3.639 3.59 3.55 3.66

19 3.339 3.39 3.23 3.62 3.76

20 –0.916 –0.55 –0.46 –0.56


values (see Table 5). Moreover, there is not even good agreement of log Dmeasurements made in different labs at the same pH. This suggests that, at aminimum, care must be taken when considering log P values calculated byextrapolating a measured log D value to a pH at which the neutral form pre-dominates. Hence, it is not only important to know which molecules wereused in the training set, but also which log P value was used.

A more subtle issue is how to deal with tautomers. Most programs pro-vide different estimates for the different tautomers of the same molecule.Which is correct? Were the correct tautomers used for the original trainingset? Of course, structural changes can affect the ratio of tautomers, and different tautomers may be present in the two phases of the partitioning sys-tem.

Table 5. Examples of Compounds for which More than One Octanol/Water Log P andAqueous pKa Value Is Reported in Medchem00 [12]

No. Structure CLOGP Log P* Other log P values pKa Values

13 4.90 4.81 (See also Table 4) 9.10, 9.01,pH 7.4: 2.85, 2.88, 3.10, 9.11, 8.863.18;pH 6.0: 1.58, 2.27

21 –0.109 0.16 0.16, 0.17, 0.22, 0.23, 9.32, 9.54,0.27, 0.18 9.60, 9.70

pH 7.4: –2.00, –2.00, –1.94,–1.94, –1.92, –1.82, –1.80,–1.78, –1.74, –1.74, –1.70,–1.64, –1.61, –1.60, –1.42,–1.40, –1.03, –1.29

22 3.101 2.70 2.52, 2.82 5.0, 5.10,5.15, 4.90

23 2.63 2.67 2.18, 2.58 8.82, 9.95

24 0.318 0.32 0.27, 0.28 8.96, 9.11,9.17

25 3.254 2.26 2.26, 2.37, 2.36, 2.39, 7.25, 7.63,2.48, 2.48, 2.56, 2.56 7.72, 7.84,

7.86, 7.87,pH 7.4: 1.28, 1.28, 1.34, 7.89, 7.90,1.38, 1.63, 1.65, 1.73, 1.84, 7.90, 7.96,2.04 8.01, 8.05


A nastier problem is how to handle compounds that exist only as a zwit-terion in water. Because log P is defined as the partitioning of the neutralform, what is the solution?

Does the performance of these programs preclude their use? At best, thepredictions of log P of 87 of the 8570 compounds deviated from experimen-tal by more than 2 log units: this is outside experimental error. At worst, morethan 10% deviated by this much. The best programs produced a calculatedvalue that was more than 4 log units off for at least one ‘easy-to-calculate’compound; the worst had a deviation of 9 log units. Clearly, there is an oppor-tunity for new approaches to the calculation of log P. Additionally, usersshould remember to check their calculations with similar known moleculeswith measured log P values. Often, we calculate log P values with bothBiobyte CLOGP and KowWin32, retrieve similar compounds with measuredvalues, and compare the observed and calculated log P values for these com-pounds to decide which program is more accurate for the type of compoundat hand.

Others have compared log P calculation programs. For example, an earlystudy examined 36 drugs and concluded: ‘the ultimate goal for faultless log Pcalculations, although within reach, has not yet been fully realized’ [39].Somewhat later, the same authors compared four calculation procedures on90 simple organic structures and 48 drugs (-blockers, class-I antiarrhyth-mics, and neuroleptics). They concluded that ‘all four tested calculation pro-cedures have their own restrictions’ [40]. A later study used the same data-base to compare 14 calculation procedures and found that ‘the predictivepower of the calculation procedures is significantly better for simple organicmolecules than for chemically heterogeneous drug structures’ [41]. Othershave added the results of their calculation method to this comparison [18][36]. A reparameterized ALOGP algorithm was compared with CLOGP overthe whole starlist. It was found that CLOGP gives better predictions for mole-cules with 1–20 atoms, that the methods were comparable for structures with21–45 atoms (in contrast to our results with ALOGP as programmed inTSAR), and that ALOGP has better predictivity for molecules with more than45 atoms [37]. As part of an extensive review, the log P values of 145 com-pounds were compared with calculations from QLOGP, CLOGP, KOWWIN,ACD/Log P, KLOGP, and ProLogP/Rekker [42]. In contrast to the results pre-sented here, the r.m.s. errors were in the order ACD < KOWIN < QlogP <CLOGP. We do not know if this represents differences in the selection of theexperimental value correlated or the compound set analyzed. Guanine deriv-atives were a problem for all tested programs [43].

Each of these evaluations, including our own, has limitations in both thelog P values used for comparison and in the number of programs considered.We suggest that a competition be held in which companies provide the struc-


tures of compounds for which they have reliable log P values, and the authorsof the programs provide estimates of the log P values. An independent panelwould collect the results and publish them for consideration of the wholecommunity. To be a fair test, the dataset should include a variety of mole-cules, some similar to those already measured, and some quite different. Thepanel would be responsible for collecting the structures and announcing thecompetition when there is a diverse and large enough set.

We wondered if the experiences with log P calculations extend to othercalculation procedures. Indeed, many errors of >3 log units were found whencalculating pKa values with the ACD program and water solubilities witheither Yalkowsky’s equation or that of Howard and Meylan (data not shown).We conclude that any molecular property calculation that claims to apply toall molecules must be tested with a large variety of structures different fromthose of the training set. The more the molecular descriptors reflect the inter-molecular processes involved in determining the target property, the morelikely is the calculation to predict beyond its training set. These results high-light the difficulty of obtaining a reliable, general model to predict such inter-esting properties as absorption of compounds from the gastrointestinal tractor even water solubility of compounds, particularly electrolytes.

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Research Studies Press, Letchworth, 1987.[4] D. B. Turner, S. M. Tyrrell, P. Willett, J. Chem. Inf. Computer Sci. 1997, 37, 18.[5] R. D. Brown, Persp. Drug Disc. Design 1997, 5, 31.[6] R. A. Lewis, J. S. Mason, I. M. Mclay, J. Chem. Inf. Computer Sci. 1997, 37, 599.[7] M. G. Bures, Y. C. Martin, Current Opinion in Chemical Biology 1998, 2, 376.[8] S. H. Yalkowsky, S. C. Valvani, J. Pharm. Sci. 1980, 69, 912.[9] W. M. Meylan, P. H. Howard, R. S. Boethling, Env. Tox. Chem. 1996, 15, 100.

[10] C. Hansch, A. Leo, ‘Exploring QSAR: Fundamentals and Applications in Chemistry andBiology’, American Chemical Society, Washington DC, 1995.

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A Program for the Rapid Generation of High Quality Approximate 3-DimensionalMolecular Structures’, The University of Texas at Austin and Tripos Associates, St. Louis,Missouri.

[35] A. Leo, D. Hoekman, Persp. Drug Disc. Design 2000, 18, 19.[36] R. Wang, Y. Gao, L. Lai, Persp. Drug Disc. Design 2000, 19, 47.[37] A. K. Ghose, V. N. Viswanadhan, J. J. Wendoloski, J. Phys. Chem. 1998, 102, 3762.[38] S. A. Wildman, G. M. Crippen, J. Chem. Inf. Computer Sci. 1999, 39, 868.[39] R. F. Rekker, A. M. ter Laak, R. Mannhold, Quant. Struct.-Act. Relat. 1993, 12, 152.[40] R. Mannhold, R. F. Rekker, C. Sonntag, A. M. ter Laak, K. Dross, E. E. Polymeropoulos,

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Quantitative Structure-AbsorptionRelationships

by Han van de Waterbeemd

Pfizer Global Research and Development, Department of Pharmacokinetics, Dynamics andMetabolism, Sandwich, Kent CT13 9NJ, UK; Fax: +44 130 465 64 33;


1. Introduction

The preferred route of administration of many drugs is orally once ortwice per day. A reliable estimate of the oral absorption potential of a newcompound in humans is therefore an important contribution to the guidanceof drug-discovery projects. The gastrointestinal tract can be seen as a physi-cal and biochemical barrier to oral drug absorption. Our knowledge of phys-iological barriers (epithelia and endothelia) has increased considerably butstill is far from complete [1]. Several in vitro systems based on cell-monolay-er cultures such as the Caco-2 [2] or MDCK [3] cell line or so-called Ussingchambers are in use for oral absorption evaluation (see article by Borchardtet al. in this volume, p. 117).

A different approach consists in attempting to estimate oral absorptionusing calculated and experimental molecular properties and fitting these to amathematical model using appropriate statistical tools. In this chapter, wewill discuss a number of such computational approaches. First, we willpresent briefly current views on what distinguishes a drug from a non-drug.

Further, some of the current limitations in making reliable absorption pre-dictions will be discussed. These include: use of inadequate and often too-small data sets, lack of understanding of the role of molecular flexibility,insufficient insight on the role of transporter proteins and gut-wall metaboliz-ing enzymes, and a further need for refined descriptors.

2. Drugs vs. Non-Drugs

The design of good combinatorial libraries containing ‘drug-like’ mole-cules has been addressed in the literature by several groups [4–6]. In several


approaches [7–11], collections of drugs such as the World Drug Index (WDI)have been compared to catalogues of common chemicals such as theAvailable Chemicals Directory (ACD). Such comparisons are partly biasedsince the latter database still contains compounds that never were tested onpharmacodynamic targets. Another, but possibly minor, concern is that theused classifiers reflect the characteristics of drugs existing today and not offuture novel classes. The studies presented below were all able to predict ca.80% of the compounds correctly as either drug or non-drug.

Gillet et al. [8] used a set of seven one-dimensional (1D) descriptors (log P,Mr, number of hydrogen-bond donors and acceptors, number of rotatablebonds, aromatic density, and the topological kappa index reflecting the degreeof branching) to compare the SPRESI (presumed inactives) and WDI (drugs)database. A genetic algorithm (GA) was used for the calculation of optimalweights for the properties.

Ajay et al. [9] compared the Comprehensive Medicinal Chemistry (CMC)database with the ACD using 1D and 2D parameters and Bayesian neural net-works to predict drug-likeness. Around 90% of the CMC compounds wereclassified correctly. A comparable approach was used by Sadowski andKubinyi [10] using a back-propagation neural network. The molecular descrip-tors considered qualified 83% of the ACD and 77% of the WDI correctly.

Ghose et al. [12] defined the following consensus definition of a drug-likemolecule (covering more than 80% of the compounds):

• an organic compound having a calculated log P between –0.4 and 5.6• molar refractivity between 40 and 139• molecular weight between 160 and 480• total number of atoms between 20 and 70.

Lipinski and colleagues [13] examined the WDI, and, based on the distribu-tion of a number of key properties, defined the ‘rule-of-five’, which states thatpoor absorption is likely when a compound has a Mr > 500, number of H-bonddonors is >5 and H-bond acceptors >10, and calculated log P > 5. Compoundswhich are absorbed via an active transport mechanism may form exceptions tothese rules. The rule-of-five can be seen as a qualitative absorption/permeabil-ity predictor [1] and should not be used as a quantitative predictor [14].

3. Prediction of Human Intestinal Absorption from ExperimentalProperties

3.1. Prediction from Caco-2 Flux

One approach to predict human intestinal absorption is to use Caco-2 cell-permeability data. A more indirect method consists in predicting Caco-2 flux


from physicochemical properties. Artursson and Karlsson [15] observed a poorlinear correlation between Caco-2 flux and log D values. However, the relation-ship between permeability and lipophilicity is considered to be a sigmoidal one[16] [17]. Using the human absorption data compiled by Yee [18] [19], a sig-moidal correlation between Caco-2 data and octanol/water distribution coeffi-cients (log D values) can be observed, assuming compounds with low Mr (Mr

below 200) and high Mr (Mr above 500) to be exceptions. Low-Mr compoundsmay use the paracellular pathway to cross a membrane and are potential candi-dates for active transport mechanisms. High-Mr compounds have poorer mem-brane-diffusion characteristics and may be more susceptible to interactions withP-glycoprotein, both limiting membrane permeation and absorption.

Yazdanian et al. [20] studied relationships between Caco-2 permeabilitycoefficients and various lipophilicity scales including distribution coefficientsin octanol, hexadecane, and propyleneglycol dipelargonate (PGDP). No sim-ple relationships were found for a set of 51 structurally diverse low-molecu-lar-weight compounds in any of the solvents used. Furthermore, log D val-ues, from the difference between log Dhex and either log Doct or log DPGDP,as a measure for hydrogen bonding, did not yield significant correlations withCaco-2 permeability.

Camenisch et al. [21] have critically analyzed the processes of passivemembrane crossing and added the aqueous-pore pathway to previously estab-lished models. Most of the old membrane models developed in the 1970s usepartition coefficients as the key descriptor. It was demonstrated that molecu-lar size should be considered as an additional factor to lipophilicity. Theseauthors suggested that the relationship between lipophilicity and Caco-2 fluxcan thus be described by a set of sigmoidal curves. This study underlines howcomplex these relationships are and why it is so difficult to find good corre-lations using a single-property approach.

3.2. IAM, ILC, and Liposomes

Other authors looked at immobilized artificial membranes (IAM) [22](see article by Morse and Pidgeon in this volume, p. 429), immobilized lipo-some chromatography (ILC) [23], or binding to liposomes [24] as an alterna-tive measure for lipophilicity. These systems are potentially a better mimic ofmembranes than octanol. A new absorption-potential parameter has been sug-gested, as calculated from liposome-distribution data and the solubility-doseratio, which shows an excellent sigmoidal relationship with human passiveintestinal absorption (Eqn. 1) [24].

APSUV = log (distribution × solubility × V/dose) (Eqn. 1)


Here, APSUV is the absorption potential measured from the distribution insmall unilamellar vesicles (SUV) at pH 6.8, solubility was measured at pH6.8 in simulated intestinal fluid, V is the volume of intestinal fluid, and doseis a mean single oral dose. Liposome partitioning is only partly correlatedwith octanol/water distribution.

Sugawara et al. [25] have measured hydrogen-bonding ability as the dif-ference in partition coefficients of drugs at pH 6.0 between polar (diethylether and chloroform) and nonpolar (isooctane) solvents. Adsorption of drugsto a cation-exchange resin was used as an index of what they called electric-ity (polarity). Permeation rates across a silicon or ethylene vinyl acetate arti-ficial membrane was combined with the above descriptors using multiplelinear regression and gives reasonable correlations (r = 0.88) with in situ sin-gle-pass rat-perfusion data. This approach needs further exploration withhuman data.

4. Computed Molecular Properties

Many molecular and fragmental descriptors have been used in QSARstudies. In QSAbR (Quantitative Structure-Absorption Relationships) orQSPeR (Quantitative Structure-Permeation Relationships) studies, some ofthese appear to be appropriate for permeability prediction. In the modelsbelow, it will appear that a small subset of descriptors emerges in variousforms as mostly influencing permeation and absorption. These include partic-ularly descriptions of lipophilicity, molecular size, and hydrogen bonding[26].

A combination of experimental log D values in octanol/water at pH 5.5,6.5, and 7.4 and several computed properties using partial least squares (PLS)and multiple linear regression (MLR) models was suggested to predict pas-sive intestinal membrane diffusion [27]. The MLR models are simple linearmodels not reflecting the generally observed sigmoidal shape, and thus havesome limitations.

A novel numerical molecular representation, called the molecular hash-key, has been proposed representing molecular surface properties [28].Neural-network-based hashkey models show potential to predict molecularproperties such as log P, and ADME properties such as intestinal absorption.However, in their present form, the results are preliminary, and larger datasets should be examined.

The approaches above do not take into account that there may be differ-ences in absorption along the intestine. These differences may be related todifferences in expression of P-glycoprotein and CYP3A4, two absorption-limiting factors. Otherwise, differences may be related to solubility and intes-


tinal pH. Calculated three-dimensional solubility parameters appear to beable to distinguish between drugs that are absorbed along the whole gastroin-testinal tract and those absorbed in the upper part [29]. This may be a prom-ising tool in designing appropriate dosage forms. However, we will not dis-cuss further the challenges around understanding the SAR for gut-wall effluxand metabolism.

5. Computational Absorption Models

5.1. Estimation of Caco-2 Permeability

Permeability across Caco-2 monolayers is often used as a surrogate forhuman intestinal absorption. Prediction of Caco-2 permeability using com-puted molecular properties has been studied by several groups using multiplelinear regression (MLR), principal component analysis (PCA), cluster analy-sis, and partial least squares (PLS) [30] [31]. Significant, simple MLR equa-tions can be derived combining a size and H-bond descriptor, such as in Eqn. 2 [30]. Interestingly, this equation contains apparently no lipophilicityterm such as log P or log D.

log Pe = 0.008 (±0.002) Mr – 0.043 (±0.008) PSA– 5.165 (±0.605)

n = 17; r = 0.833 (Eqn. 2)

In this equation, Pe is the permeability constant across Caco-2 cells, PSA thepolar surface area and MW the molecular weight of the compounds.

5.2. ADAPT Descriptors

Over the years, the group of Jurs has developed a range of moleculardescriptors which have been implemented in the ADAPT software and usedto predict properties such as water solubility, and recently, Wessel et al.studied human intestinal absorption [32]. From a larger pool of descriptors, aneural network model selected six key descriptors. Of these six descriptors,three encode for size (cube-root of gravitational index), shape (SHDW-6: nor-malized 2D projection of molecule on yz-plane) and flexibility (NSB: num-ber of single bonds), while the three others are related to hydrogen-bondingproperties (CHDH-1: charge on donatable hydrogen atoms, SCAA-2: surfacearea × charge of hydrogen-bond acceptor atoms, SAAA-2: surface of hydro-gen-bond acceptor atoms). A 16% r.m.s. error was observed in an external testset.


5.3. MolSurf

The program MolSurf, described elsewhere in detail, offers a number of descriptors related to physicochemical properties such as lipophilicity,polarity, polarizability, and hydrogen bonding. Their relevance for predictingoral absorption was investigated by Norinder et al. [33] using partial leastsquares (PLS). Good statistical models were obtained revealing that proper-ties associated with hydrogen bonding had the largest impact on absorptionand should be kept to a minimum.

5.4. The HYBOT Approach

Based on experimental thermodynamic data, H-bond-donor and -acceptordescriptors have been developed which have been correlated to permeabilityand absorption data [34]. It was concluded that both H-bond-donor and -acceptor effects, often in combination with a steric descriptor, are importantphysicochemical properties for permeation processes. However, due to thefrequently observed intercorrelation between donor and acceptor, only themore significant one can be used in MLR equations. Obviously, this problemcan be avoided using other statistical tools such as PLS and neural networks.It may also be more sensible to use the combined acceptor plus donor term[30].

5.5. Polar Surface Area

A simple measure of hydrogen-bonding capacity is polar surface area,summing the fractional contributions to surface area of all nitrogen and oxy-gen atoms [35]. This was used to predict passage of the blood-brain barrier[35–37], flux across a Caco-2 monolayer [30] (Eqn. 2), and human intestinalabsorption [38] [39]. The physical explanation is that polar groups areinvolved in desolvation when they move from an aqueous extracellular envi-ronment to the more lipophilic interior of membranes. PSA thus represents atleast part of the energy involved in membrane transport.

The method developed by van de Waterbeemd and Kansy [35] is based on a single minimum-energy conformation. Palm et al. [38] have taken intoaccount conformational flexibility and coined a dynamic PSA, in which aBoltzmann-weighted average PSA is computed. Clark [7] [39] has demon-strated that PSA calculated for a single minimum-energy conformation is inmost cases sufficient to produce a good sigmoidal relationship to intestinalabsorption, differing very little from the dynamic PSA described above (see


Fig. 1). Poorly absorbed compounds have been identified as those with a PSA>140 Å2. Considering more compounds, considerably more scatter wasfound than Fig. 1 may suggest [39]. This is partly due to the fact that manycompounds do not show simple passive diffusion only, but are affected byactive carriers, efflux mechanisms involving P-glycoprotein (P-gp) and othertransporter proteins, and gut-wall metabolism. A further refinement in thePSA approach is expected to come from taking into account the strength ofthe hydrogen bonds, which in principle already is the basis of the HYBOTapproach (see Sect. 5.4).

Using effective permeability data in humans, Winiwarter et al. [27] stud-ied relationships with polar surface area (and several other descriptors) usingthe MOLCAD module within SYBYL, based on a single minimum-energyconformation. For the 13 passively transported compounds in their data set, alinear correlation coefficient with PSA was obtained of r2 = 0.76. A plot ofthese data shows that the trend is possibly sigmoidal, however with somescatter. Even more scatter is observed when all compounds in the study (n = 22) are plotted against PSA (see Fig. 2). Clearly, PSA alone is insuffi-cient to account for effective permeability or absorption. In this case, the cut-off for poor absorption seems to be at lower PSA values around 100 Å2. Thismay be due to a scaling difference between methods using in [27] and [39].The compounds identified in Fig. 2, glucose, L-DOPA, and amoxicillin, are


Fig. 1. Sigmoidal relationship between human intestinal absorption and single-conformationPSA [38] [39]

believed to have active uptake mechanisms and thus are better absorbed thanpredicted by PSA. The following equations have been obtained [27]:

log Peff = – 0.01 PSA + 0.19 log D5.5 – 0.24 HBD – 2.88 (Eqn. 3)

n = 13; r2 = 0.93; q2 = 0.90

log Peff = – 0.01 PSA + 0.16 CLOGP – 0.24 HBD – 3.07 (Eqn. 4)

n = 13; r2 = 0.88; q2 = 0.85

log Peff = – 0.01 PSA – 0.28 HBD – 2.55 (Eqn. 5)

n = 13; r2 = 0.85; q2 = 0.82

In these equations, Peff is the in vivo permeability measured with a single-pass perfusion technique. Log D5.5 is the octanol/water distribution coeffi-cient measured at pH 5.5, believed by the authors to be the most relevantvalue for absorption and reflecting the pH in the unstirred mucus layer adja-cent to the intestinal wall. HBD is the number of hydrogens connected to N-and O-atoms, i.e., the total potential H-donating capacity. Since these modelsare based on only 13 compounds, the three-parameter Eqns. 3 and 4 have lim-ited statistical significance. No definitive conclusions can be drawn on the


Fig. 2. Relationship between effective permeability (Peff) in the human jejunum and PSA [27].The sigmoidal is inspired by Fig. 1 and roughly indicates the expected permeability for pas-

sively absorbed compounds.

role of a lipophilicity descriptor. The best result was obtained by combiningtwo H-bond descriptors (PSA and HBD). However, the partial correlationbetween PSA and HBD is 0.82. This sheds serious doubts on Eqn. 5, despitethe fact that it was derived using PLS. A larger data set is required to fullyexplore this approach.

The continuous variable PSA is correlated with simple count of H-bondsas exemplified in Fig. 3 using data from [27]. However, PSA is probably abetter reflection of H-bonding capacity, since it takes conformational behavi-or into account.

The membrane permeability (Caco-2) of three series of peptides andendothelin antagonists could be predicted by a theoretical model which takesboth the polar (PSAd) and non-polar (NPSAd) part of the dynamic molecularsurface area of the investigated molecules into consideration [14] [40]. Thethree peptide series were AcNH-X-phenetylamides, AcNH-X-D-Phe-NHMederivatives, and D-Phe-oligomers. Experimental log D (octanol/water) valuesgive a permeability rank order within series, but fail to combine the threeseries (Fig. 4). Possibly, some of the compounds are substrate for one or moretransporters present in Caco-2 cells, but this needs further investigation. Astrong correlation was found between log D and NPSAd (r2 = 0.96). A goodsigmoidal correlation was obtained when Papp (Caco-2 permeability) wasplotted against a linear combination of PSAd and NPSAd. Thus, this modelpredicts permeability based on a combination of hydrogen-bonding capacityand hydrophobicity. The latter is suggested to be related to the transport of a


Fig. 3. Comparison of two hydrogen-bonding descriptors: the discrete descriptor total H-bonding count vs. the continuous descriptor polar surface area (data from [27])

compound from the aqueous environment into the polar headgroup region ofthe membrane, while hydrogen bonding is detrimental to transport into thenon-polar interior of the membrane [40].

The percentage polar surface area (%PSA) was investigated as a furthersurface property [14]. However, this was unsuccessful. %PSA is a numberbetween 0 and 1 not related to a more fundamental phenomenon such as sol-vation energy in the case of PSA.

Using dynamic polar surface area as a descriptor, a linear relationship (r = 0.92) was found with brain penetration for 45 drugs [37], which is in con-trast to reported sigmoidal curves for oral absorption [38]. Brain penetrationdecreases with increasing polar surface area. Orally active drugs that aretransported by the transcellular route should not exceed a polar surface area(PSA) of about 120 Å2 [36] [37] and for good brain penetration should evenbe tailored to PSA < 100 Å2 [36] or even < 60–70 Å2 [37].

5.6. The PATQSAR System

Based on the topological DARC/PELCO methodology, a biophysicaldrug-absorption model named PATQSAR (Population Analysis by Topology-based QSAR) has been proposed [41]. The model considers the absorption


Fig. 4. Correlation between Caco-2 permeability and log D for three series of peptide deriv-atives [14] [40]

process from the intestinal lumen as the sum of two resistances in series,namely an aqueous diffusional barrier and a lipoidal membrane. Lipophilicityappears to play a major role in a sigmoidal relationship with absorption-rateconstants obtained from the in situ rat-gut technique.

5.7. VolSurf Parameters

The VolSurf descriptors are a set of descriptors related to surface proper-ties of a molecule and are calculated using a H2O and a DRY probe in the pro-gram GRID (see article by Cruciani et al. in this volume, p. 537). Thesedescriptors have been evaluated in correlations with human absorption [42][43]. A new descriptor called ‘integy moment’ was defined in analogy to thedipole moment and describes the distance of the center of mass to the bary-center of polar interaction sites at a given energy level. If the integy momentis high, a clear separation between polar and nonpolar parts of a molecule ispresent. Hydrophobicity and high integy moments are positively correlatedwith human intestinal absorption, whereas polarity and a high concentrationof polar interaction sites on the molecular surface are detrimental to absorp-tion.

5.8. GRID Calculations

By means of the program GRID, using a NH amide probe to explore thehydrogen-bond acceptor regions, a carbonyl probe to detect hydrogen-bonddonor areas, and the water probe to characterize both, hydrogen-bondingcapacity was quantified [44]. The water surface interaction map appears to be a good descriptor in the prediction of drug permeability, although no im-provement over previously reported methods was obtained.

6. Conclusion

A number of difficulties should not be overlooked when using computa-tional and physicochemical properties to assess human absorption. Some ofthe relevant properties such as log D and hydrogen bonding are in fact con-formation-dependent [45–47]. Therefore, most of the published relationshipscontain some degree of fuzziness. Further progress in the treatment of molec-ular states (conformation, ionization) and the development of more appropri-ate descriptors may results in better models. Current methods are only predic-tive for the passive-diffusion component of membrane transport (see Fig. 5).


Only when SARs between drugs and their interactions with transporter pro-teins [48] [49] and metabolizing enzymes can be accounted for, more sophis-ticated predictive absorption models will be within reach.

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[2] P. Artursson, K. Palm, K. Luthman, Adv. Drug Deliv. Rev. 1996, 22, 67.[3] J. D. Irvine, L. Takahashi, K. Lockhart, J. Cheong, J. W. Tolan, H. E. Selick, J. R. Grove,

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Fig. 5. A simplified view on membrane-transport components, consisting of passive diffusion,P-glycoprotein efflux, and CYP3A4 metabolism. A compound is entering the membrane at ratekin, transferred to the other side at rate km, and leaves the membrane at rate kout. Proteins suchas P-glycoprotein (P-gp) and CYP3A4 potentially are limiting effective transport, the firstthrough effluxing the compound back into the lumen, the second by metabolizing the com-pound. Other proteins, such as the oligopeptide transporter (PET) and the monocarboxylic-acidtransporter, or the anion-exchange transporter are involved in active uptake mechanisms[48] [49]. Most likely, the picture is more complicated and other (transporter) proteins will be

uncovered to play a role.

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[14] P. Sternberg, K. Luthman, H. Ellens, C. P. Lee, Ph. L. Smith, A. Lago, J. D. Elliott, P.Artursson, Pharm. Res. 1999, 16, 1520.

[15] P. Artursson, J. Karlsson, Biochem. Biophys. Res. Commun. 1991, 175, 880.[16] G. Camenisch, G. Folkers, H. van de Waterbeemd, Pharm. Acta Helv. 1996, 71, 309.[17] G. Camenisch, G. Folkers, H. van de Waterbeemd, Eur. J. Pharm. Sci. 1998, 6, 321.[18] S. Yee, Pharm. Res. 1997, 14, 763.[19] H. van de Waterbeemd, in ‘Methods for Assessing Oral Drug Absorption’, Eds. J.

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6, 313.[22] S. Ong, H. Liu, C. Pidgeon, J. Chromatogr. A 1996, 728, 113.[23] U. Norinder, T. Österberg, Perspect. Drug Disc. Des. 2000, 19, 1. [24] K. Balon, B. U. Riebesehl, B. W. Müller, Pharm. Res. 1999, 16, 882.[25] M. Sugawara, Y. Takekuma, H. Yamada, M. Kobayashi, K. Iseki, K. Miyazaki, J. Pharm.

Sci. 1998, 87, 960.[26] G. M. Pauletti, S. Gangwar, G. T. Knipp, M. M. Nerurkar, F. W. Okumu, K.Tamura, T. J.

Siahaan, R. T. Borchardt, J. Contr. Rel. 1996, 41, 3.[27] S. Winiwarter, N. M. Bonham, F. Ax, A. Hellberg, H. Lennernäs, A. Karlen, J. Med.

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Relat. 1996, 15, 480.[31] U. Norinder, T. Österberg, P. Artursson, Pharm. Res. 1997, 14, 1785.[32] M. D. Wessel, P. C. Jurs, J. W. Tolan, S. M. Muskal, J. Chem. Inf. Comput. Sci. 1998, 38,

726.[33] U. Norinder, T. Österberg, P. Artursson, Eur. J. Pharm. Sci. 1999, 8, 49.[34] O. A. Raevsky, K.-J. Schaper, Eur. J. Med. Chem. 1998, 31, 799.[35] H. van de Waterbeemd, M. Kansy, Chimia 1992, 46, 299.[36] H. van de Waterbeemd, G. Camenisch, G. Folkers, J. R. Chretien, O. A. Raevsky,

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Trouiller, C. Mercier, J. Pharm. Sci. 1999, 88, 3.[42] G. Cruciani, P. Crivori, P. A. Carrupt, B. Testa, Theochem 2000, 503, 17.[43] W. Guba, G. Cruciani, in ‘Molecular Modelling and Prediction of Bioreactivity’, K.

Gundertofte, F. S. Jorgensen (Eds.), Plenum. New York, 2000, pp. 89–94.[44] V. Segarra, M. Lopez, H. Ryder, J. M. Placios, Quant. Struct. Act. Relat. 1999, 18, 474.[45] B. Testa, P. A. Carrupt, Proceed. Int. Symp. Control. Rel. Bioact. Mater. 1998, 25, 83.[46] B. Testa, Med. Chem. Res. 1997, 7, 340.[47] B. Testa, Pharm. News 2000, 7, 13.[48] A. Tsuji, I. Tamai, Pharm. Res. 1996, 13, 963.[49] C. Y. Yang, A. H. Dantzig, C. Pidgeon, Pharm. Res. 1999, 16, 1331.


Hydrogen Bonding: The Last Mystery in Drug Design?

by Hugo Kubinyi

Combinatorial Chemistry and Molecular Modelling, ZHF/G – A 30, BASF AG,D-67056 Ludwigshafen, Germany; e-mail: [email protected]

1. Introduction

Life on earth depends on water, on hydrogen bonds, and on hydrophobicinteractions. DNA and proteins are held together in their defined three-dimen-sional structures primarily by hydrogen bonds. The double helix of DNA,RNA structures, peptide and protein secondary structures, like -helices, -sheets, - and -loops, and the tertiary structures of proteins are formed byhydrogen bonds (enthalpic contributions) and by hydrophobic contacts (pri-marily entropic contributions). With a few exceptions, e.g., the binding ofretinol to RBP and of some ligands to the aromatic-hydrocarbon (Ah) recep-tor, also the formation of ligand-protein complexes depends on hydrogenbonding.

In ligand binding, three different contributions arise from hydrogenbonds:

1) Orientation of the ligand by a binding partner, sometimes associated bya conformational distortion of the molecules.

2) Recognition of substrates, inhibitors, agonists, and antagonists; dif-ferentiation between sterically similar but chemically different ligands,e.g., the steroid hormones; recognition of nucleic-acid bases in DNAreplication and translation.

3) Affinity of ligands – the most important issue in ligand design.

Of course, hydrogen bonding also affects membrane transport, as well asthe distribution of drugs within the biological system; these issues areaddressed in many other chapters.

Whereas hydrophobic interactions between lipophilic surfaces of a ligandand hydrophobic areas of its binding site always contribute to affinity in apositive manner (sometimes reducing water solubility to such an extent that


unfavorable pharmacokinetic properties result), the influence of hydrogenbonding needs a more careful inspection. In ligand-protein complex forma-tion, surrounding water molecules compete with binding. Desolvation of thefree ligand and the binding site has to be taken into consideration. Binding ofthe ligand to its specific site will be favored if the energy of the hydrogenbonds in the complex and the entropy gain in releasing some bound watermolecules are more favorable than the free-energy contribution of the hydro-gen bonds between the binding partners, in their free state, and these watermolecules (Fig. 1) [1–4].

Several examples of this chapter illustrate the role of hydrogen bonds inligand binding and the unexpected affinity changes that occur after replace-ment of polar groups of ligands. Sometimes, such groups can be removedwithout loss in affinity, especially if a different conformation of the bindingsite changes the surface properties. Other examples demonstrate the influenceof hydrogen-bond patterns on the binding mode of ligands, the effect of thereplacement of water molecules within the binding site, and the ‘use’ of watermolecules by a ligand to enhance its affinity by several orders of magnitude.

2. Orientation and Conformation of Ligands in Their Binding Site

Ligands that interact by hydrogen bonds with certain functional groups ofa binding site can only bind in a certain orientation. The directionality ofhydrogen bonds, demanding optimum distances and angles, is well under-stood and supported by statistics from the Cambridge Structural Database(Fig. 2) [2][5–9].

However, there are distinct differences in the strength of such hydrogenbonds. In competitive situations (e.g., the sp2-oxygen atom vs. the sp3-oxy-gen atom in esters; nitrogen vs. oxygen atoms in methoxypyridines, oxazoles,and isoxazoles), hydrogen bonds are formed to sp2-oxygen atoms and to


Fig. 1. Solvation and desolvation in the formation of a ligand-protein complex

nitrogen atoms, respectively. A theoretical study and statistical analyses ofoxygen and nitrogen atoms as hydrogen-bond acceptors suggest that hydro-gen bonds to sp3-oxygen atoms that are directly linked to an sp2-carbon atom(like in esters, aromatic ethers, and furans) are rather rare [10]. The poorhydrogen-bond-formation capacity of such oxygen atoms is also reflected bya significant decrease of their polarity in going from aliphatic to araliphaticand to aromatic ethers R–O–R′ (Table 1) [11].

The significant contribution of hydrogen bonds to ligand orientation canbe demonstrated by the binding modes of dihydrofolate (DHF) and metho-trexate (MTX) to dihydrofolate reductase (DHFR) (Fig. 3). A different bind-ing mode of the DHF complex was predicted from the 3D structure of theMTX/DHFR complex [12] and later experimentally confirmed by the crystal-lographic 3D-structure determination of the DHF/DHFR complex [13].

In comparing the binding geometries of ligands, it makes quite a differ-ence whether substrates, inhibitors, allosteric effector molecules, receptoragonists, or antagonists are considered. A substrate of an enzyme has to beconverted into a product; thus, the enzyme will distort this ligand into thedirection of the transition-state geometry. A well-known example is the bind-ing of creatine and carbamoylsarcosin to creatinase, in conformations withnonplanar guanidino and urea systems [2]. The situation is different withenzyme inhibitors that most often bind with high affinity. Different ligandconformations may be observed in solution, in the crystal structure, and in the


Fig. 2. Geometry of a hydrogen bond. The distance N ···O is typically between 2.8 and 3.2 Åand the angle N–H···O is most often larger than 150°. The angle C = O···H has a much

broader range; typical values are between 100 and 180°.

Table 1. Lipophilicity Values of Hydrocarbons and Ethers (octanol/water log P* values;MedChem database)

Compound X= –CH2– X=–O– O / CH2

Et–X–Et 3.39 0.89 –2.50Phe–X–Et 3.72 2.51 –1.21Phe–X–Phe 4.14 4.21 +0.07

ligand-protein complex; however, if too much free energy of binding is wast-ed to distort the inhibitor and/or the protein into an energetically unfavorablegeometry, the affinity of the ligand and, correspondingly, its activity will besignificantly reduced.

3. Hydrogen Bonds and Ligand Recognition

Enzymes and receptors most often show high specificities for their sub-strates and agonists, respectively. Exceptions are some metabolic enzymes,e.g., the cytochromes that oxidize a large number of different drugs, and sometransporters. An oligopeptide-binding protein (OppA) from Salmonella typhi-murium, binding peptides of two to five amino-acid residues without regardto their sequence, shows a remarkable lack of substrate specificity. The OppAcrystal structure reveals that the ligands are completely buried within the pro-tein. The preference for short-chain peptides is achieved by utilizing thehydrogen bonds and the electrostatic potential of the main-chain functionalgroups; the rest of the binding pocket is filled with water molecules to accom-modate very different amino-acid side chains [14].

On the other hand, steroid-binding proteins show high specificity [15]. Allsteroid receptors have a conserved arginine in the binding site for the steroidring A. Receptors for 3-keto steroids have, in addition, a conserved gluta-mine, whereas only the oestrogen receptor has a glutamate in this position,which allows to accommodate the 3-hydroxy group of the aromatic ring A ofoestrone and its analogues (Fig. 4) [16][17].


Fig. 3. The chemical structures of DHF and MTX look very similar. However, a closer inspec-tion of the hydrogen-bond donor and acceptor patterns of both compounds shows that by a sim-ple atom-by-atom superposition of both molecules only three donor and acceptor functionspoint in the same direction (left; filled arrows indicate identical hydrogen-bond directions). Inthe other binding mode (right), DHF has six donor and acceptor functions in the same place

as in the MTX complex.

Crystallographic investigations of steroid-binding antibodies reveal thatsome 4,5-didehydro-steroids bind in a regular mode, with the 18- and 19-methyl groups pointing ‘upwards’; some 5-H analogues bind in a ‘bottom-up’ mode, where the whole steroid-ring system flips upside-down and themethyl groups point ‘downwards’ [15]. Superpositions of 3-keto-17-hydroxy-steroids and 3-hydroxy-17-ketosteroids, using a modified version of the com-puter program SEAL [18][19], indicate that these steroids may bind to thecorticosteroid-binding globulin in a different mode. Whereas all 3-keto-17-hydroxysteroids, including the 3,20-diketo-21-hydroxycorticosteroids, bind ina normal mode, the 3-hydroxy-17-ketosteroids most probably bind in a ‘head-to-tail’ mode, with the 17-keto group mimicking the 3-keto group and the 3-hydroxy group mimicking the 17-hydroxy group [20]. In these orientations,the overall shape of the analogues has a high degree of similarity. This resultclearly indicates the importance of hydrogen-bonding potentials in molecu-lar-superposition programs.

In drug design, it is most important to realize that ligands are recognizedby properties, not by their molecular structure. Analogues which exert simi-lar interactions with the functional groups of the binding site have similaraffinities, even if their chemical structures are quite different. Scytalone-de-hydratase inhibitors are just one example; salicylamides as well as quinazo-lines are subnanomolar inhibitors of this enzyme, despite the differences intheir chemical structures (Fig. 5) [21]. More examples are found in the liter-ature [3][22].


Fig. 4. Schematic representation of the hydrogen-bond network in ligand binding at the oestrogen and progesterone receptors (distances in Å)

4. Ligand Affinities: The Replacement of Polar Groupsby Nonpolar Groups

The affinities of ligands to their binding sites depend on many differentfactors [2][4][23] but only the influence of hydrogen bonds shall be dis-cussed here. From the very first investigations on tyrosyl-AMP binding totyrosyl-tRNA-synthase mutants, values of 2–6 kJ · mol–1 for neutral hydro-gen bonds and up to 20 kJ · mol–1 for ionic hydrogen bonds have repeatedlybeen reported in the literature, corresponding to an affinity enhancement byfactors of 2–15 and up to 3000, respectively (for a review, see [4]). However,there are many exceptions to these values and, in general, we have to under-stand that there is no clear relationship between the number of hydrogenbonds of a ligand to its binding site and the corresponding binding affinity [1][2].

The allosteric effector 2,3-diphosphoglycerate forms seven ionic hydro-gen bonds to human hemoglobin but, nevertheless, shows only millimolaraffinity [24]; on the other hand, the sulfate ion, forming seven neutral (!)hydrogen bonds to its binding site, has an affinity value Ki = 120 nM to thesulfate-binding protein of Salmonella typhimurium [25].

An extreme effect of a single ionic hydrogen bond has been observed ina Gln102Arg mutant of lactate dehydrogenase. Whereas the wild-typeenzyme has a selectivity for lactate, as compared to malate, of 1000 :1, it isconverted into a malate dehydrogenase on introduction of Arg102. Due to thenew ionic hydrogen bond between the malate -carboxylate group and thearginine side chain, the reaction rates are now 10 000 times faster for malatethan for lactate [26], resulting in a selectivity change of seven orders of mag-nitude!


Fig. 5. Salicylamides (R=–CH(CH3)C6H4–p-Br, Ki =0.14 nM) and quinazolines (R = –CH2–CH2–CH(C6H5)2; Ki = 0.15 nM) bind to scytalone dehydratase in a comparable manner. Thehydrogen bonds between the interacting groups are identical, despite the differences in the

chemical structures.

The influence of replacing an hydrogen bond has beenextensively studied in a series of thermolysin inhibitors (Fig. 6). The replace-ment of the -NH- group of the ligands by –O– reduces binding affinities by afactor of 1000, which has been explained by the lack of the hydrogen bond and a mutual repulsion of the two electronegative oxygenatoms. On the other hand, it has been predicted that –CH2– analogues shouldhave about the same affinities as the –NH– analogues, which turned out to betrue – one of the rare cases of a correct quantitative prediction from molecu-lar-modelling studies [27]. The reason for this surprising effect is that there isno hydrogen bond between –CH2– and O = C , but there is neither repulsionbetween these two groups nor a negative effect of desolvation of the –CH2–group in the ligand.

However, such structure-activity relationships cannot be transferred toother series. In some NEP 24.11 inhibitors (Fig. 6), the –O– and –NH– ana-logues show about the same affinities (IC50 = 1.6 and 10 nM), whereas the –CH2– and –S– analogues are much less active (IC50 = 1000 and 800 nM)[28]; in this case it has to be assumed that the binding partner of these func-tional groups is a donor/acceptor function, like serine, threonine, or tyrosinehydroxy groups, or the side chains of asparagine or glutamine.

Thrombin inhibitors are investigated because of their therapeutic poten-tial to prevent coagulation disorders. Several pairs of ligands, where either the–NH– or C = O groups that form hydrogen bonds to Gly216 of the enzymehave been replaced, show affinity differences that vary in an unpredictablemanner. If the –NH– group of N-methyl-D-phenylglycyl-Pro-ArgH is re-placed by –CH2– or if the NH2 group of D-Phe-Pro-Arg-H is eliminated,affinities are only reduced by a factor of 8 (Table 2) [29][30].

If, on the other hand, the ligand carbonyl groups that interact with theGly216 –NH– group are replaced by –CH2–, creating a more flexible mole-cule and an (additional) basic center, affinities are reduced by factors of400–10 000 [29–31]. If in a bicyclic imide inhibitor the carbonyl group that

NH ···O = C

NH ···O = C


Fig. 6. The binding mode of thermolysin inhibitors (X= –NH–, –O–, –CH2–; left) and the chemical structure of NEP 24.11 inhibitors (right)

interacts with Gly216 is reduced to –CH2–, affinity decreases only by a fac-tor of 4 [32].

Sometimes, it seems that exact shape similarity is more important foraffinity than the formation of hydrogen bonds. 2,4-Difluorotoluene, sterical-ly mimicking thymine, is a striking example. Although this analogue does notform hydrogen bonds, it codes specifically and efficiently for adenine inDNA replication, due to a perfect steric mimicry [33][34].

Some more problems in affinity predictions and in rational drug designarise from the flexibility of binding sites [2–4][22]. Neuraminidase of theinfluenza virus (common flu) cleaves sialic acid from cell-surface carbohy-drate chains to enable the virus to enter and leave the cell in which it is repro-duced. Already several years ago, the low-affinity ligand Neu5Ac2en (Fig. 7)was designed from a postulated transition state of the enzymatic reaction; inaddition to some other important interactions, this ligand forms hydrogenbonds between its glycerol side chain and Glu276 of the enzyme. A detailedanalysis of the binding site with the computer program GRID [35] showedthat basic groups in the 4-position should enhance affinity by interaction withtwo other acidic residues, Glu119 and Glu227. Indeed, Zanamivir (4-Guani-dino-Neu5Ac2en; Relenza®, Glaxo Wellcome, UK; Fig. 7) is a subnanomolarneuraminidase inhibitor that is systemically active after inhalation [36][37].However, by rearrangement of the Glu276 side chain, this area becomes ahydrophobic pocket that can accommodate a lipophilic side chain, as in theGilead Sciences neuraminidase inhibitor GS 4071 [38][39]; its prodrug


Table 2. Influence of the Modification of Polar Groups on Ligand Affinities

Pair of thrombin inhibitors Thrombin inhibition Change Ratio Ref.

N-Me-D-Phenylglycyl-Pro-Arg-H IC50 = 0.009 g/ml –NH– to2-Phenylbutyryl-Pro-Arg-H IC50 = 0.07 g/ml –CH2– 8 [29]

D-Phe-Pro-Arg-H IC50 = 49 nM –NH2 toPhenylpropionyl-Pro-Arg-H IC50 = 390 nM –H 8 [30]

3-Cyclohexylpropionyl-Pro-Arg-H IC50 = 0.15 M C = O to4-Cyclohexylbutyl-Pro-Arg-H IC50 = 56 M –CH2– 400 [30]

Boc-D-Phe-Pro-Arg-H IC50 = 0.028 g/ml C= O to3-Pheny-l,2-(Boc-NH)-propyl-Pro-Arg-H IC50 = 52 g/ml –CH2– 2 000 [29]

N-Acetyl,4-O-benzyl-Hyp-Arg-CMK IC50 = 0.9 nM C = O toN-Ethyl,4-O-benzyl-Hyp-Arg-CMK IC50 = 8600 nM –CH2– 10 000 [31]

Bicyclic ETH inhibitor, imide Ki = 0.5 M C = O toBicyclic ETH inhibitor, lactame Ki = 2.0 M –CH2– 4 [32]

Abbreviations: Arg-CMK=arginine chloromethylketone, Arg-H=arginine aldehyde, Boc= tert-butoxycarbonyl, Hyp=4-hydroxyproline, Me=methyl, Phe=phenylalanine, Pro=proline.

Oseltamivir (GS 4104; Tamiflu®, Hoffmann-La Roche, Switzerland; Fig. 7) isorally active.

5. Conserved Water Molecules in the Binding Site

Various ligand-protein 3D-structures demonstrate the importance of con-served water molecules in the ligand-binding site [3][22][40]. Most often,hydrogen-bond networks between the ligand and the protein include suchwater molecules (compare, e.g., Figs. 4 and 5). The attempt to replace suchwater molecules most often fails; reduced affinities result if the energy of de-solvation is larger than the energy contribution of the new hydrogen bond.Only one example for such an ‘unexpected’ lower affinity is the binding of amodified cyclosporin A, 2-(5-hydroxynorvaline)-cyclosporin A, to cyclophy-lin; the newly introduced –CH2OH group indeed replaces a conserved watermolecule in the binding pocket, but affinity is reduced by about one order ofmagnitude [41].

However, there are never rules without exceptions. In quinoline analoguesof scytalone-dehydratase inhibitors, one aromatic N-atom of the quinazolinering (Fig. 5; R = –CH2–CH2–CH(C6H5)2; Ki = 0.15 nM) is replaced by a C–Hmoiety (Fig. 8). Obviously, the aromatic hydrogen atom interferes stericallywith the conserved water molecule, and biological activity is reduced by


Fig. 7. The neuraminidase inhibitor Zanamivir (GG 167, Relenza®) was designed with thecomputer program GRID, starting from the weak transition-state inhibitor Neu5Ac2en (left).GS 4071 has a lipophilic ether group instead of the glycerol side chain of Zanamivir; the highaffinity results from a rearrangement of some amino acids within the binding pocket. Its ethyl

ester Oseltamivir (GS 4104, Tamiflu®) is an orally active drug.

about 3 orders of magnitude. On the other hand, exchange of this hydrogenatom by a cyano group leads to a replacement of the conserved water mole-cule (Fig. 8); and biological activity is enhanced by a factor of 30 000.Seemingly, the cyano group binds more strongly to Tyr30 and Tyr50 than thewater molecule because there is still a 20-fold affinity increase, as comparedto the quinazoline inhibitor (Fig. 5) [21]. An additional favorable entropiccontribution may arise from the release of the bound water molecule. Withrespect to the ‘two’ hydrogen bonds of the cyano nitrogen atom, one shouldconsider that crystallographic 3D-structures are a time-averaged reproduc-tions of reality.

A significant biological difference between two chemically highly similaranalogues is observed in cytidine-deaminase inhibitors. The natural productzebularine (Fig. 9) can add a conserved water molecule to become a highlypotent transition-state inhibitor with Ki = 1.2 pM. Its 3,4-dihydro analogue,with only two additional H-atoms, can neither add the conserved water mole-

Fig. 8. Replacement of one aromatic nitrogen of the quinazoline inhibitor (Fig. 5; R=–CH2–CH2–CH(C6H5)2; Ki = 0.15 nM) by –CH– leads to an overlap of the aromatic hydrogenatom with the conserved water molecule; biological activity is reduced by about 3 orders ofmagnitude. On the other hand, exchange of this disturbing hydrogen atom against a cyanogroup leads to a replacement of the conserved water molecule; biological activity is enhanced

by a factor of 30 000.

Fig. 9. The cytidine-deaminase inhibitor zebularine (left) ‘uses’a conserved water molecule tomimic the transition state of the enzymatic reaction (middle). 3,4-Dihydrozebularine (Ki = 30 M)can neither add this water molecule nor replace it; affinity is reduced by more than seven

orders of magnitude.


cule, nor replace it. Thus, the water molecule hinders binding, and affinity isreduced by more than 7 orders of magnitude to 30 M [42][43].

6. Conclusions

Significant progress has been achieved in the derivation of scoring func-tions for docking and de novo design of ligands [44][45]. However, consid-ering the examples presented here, it is not surprising that these affinity esti-mations still lack sufficient precision, especially with respect to the influenceof hydrogen bonding on ligand affinities. Despite all attempts to arrive at abetter understanding of the role of water and of hydrogen bonds in biologicalsystems, we are far from a satisfactory situation.

For rational drug design, all the necessary tools are available: we can gen-erate meaningful 3D-structures of ligands from scratch, we can convert theminto multiple low-energy conformations, we can calculate their steric, electro-static, and lipophilic properties, and we can dock them in a flexible manner intotheir binding sites (for reviews see, e.g., [46][47]). We can even constructligands in a combinatorial manner [47][48], but in the very end we fail becausewe do not have a sufficient understanding of all the individual enthalpy andentropy terms that are involved in desolvation, hydrogen-bond formation, andhydrophobic interactions. The challenge for the current decade will be to fur-ther understand and explain the mystery of hydrogen bonding. However, itmight take much longer than ten years until we will arrive at reliable scoringfunctions. Then, the way to tailor-made ligands seems to be straightforward.

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Molecular Hydrogen-Bonding Potentials(MHBPs) in Structure-Permeation Relations

by Giulia Caron*1), Sébastien Rey, Giuseppe Ermondi2),Patrizia Crivori, Patrick Gaillard, Pierre-Alain Carrupt,

and Bernard Testa

Institut de Chimie Thérapeutique, Section de Pharmacie, Université de Lausanne, CH-1015 Lausanne, Switzerland

1. Prediction of Drug Permeation:Experimental and Computational Tools

Drug molecules can cross cellular barriers either by moving across cells(transcellular transport) or by passing between cells (paracellular transport)[1]. Most drugs traverse cellular barriers by transcellular pathways whichinclude passive diffusion, carrier-mediated, and vesicular transport mecha-nisms [2].

Although tissue and in particular cell-culture models are the best predic-tors of drug absorption, it is evident and unfortunate that the closer the meth-od is to the in vivo situation, the more labor-intensive and material-consum-ing it is.

Physicochemical determinants of passive diffusion are for example thoseforming Lipinski’s ‘rule-of-5’ [3], an alert procedure which predicts that theabsorption or permeation of a compound is more likely to be problematicwhen it has a molecular mass >500, a calculated log P (CLOGP) >5, morethan 5 H-bond-donor groups and more than 10 H-bond-acceptor groups.

Physicochemical techniques and computational tools commonly adoptedto predict passive diffusion are summarized in Fig. 1. As a rule, molecularmass has less influence on absorption than lipophilicity and H-bondingcapacity.

1) Present address: Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino,Via Giuria 9, I-10125 Torino, Italy; e-mail: [email protected]

2) Present address: Dipartimento di Scienze Chimiche, Alimentari, Farmaceutiche e Farma-cologiche, Viale Ferrucci 33, I-28100 Novara, Italy


Lipophilicity (Fig. 1) is approximately correlated with passive transportacross cell membranes. Traditional experimental partitioning data (log P,log D, and chromatographic indices like log k) as well as novel descriptors [4]are easily determined both in isotropic and in anisotropic systems by theapplication of recent experimental developments such as potentiometry andcapillary electrophoresis. In addition, several methods to calculate log P arecurrently available [5]. Each of these methods has advantages and drawbacks,and the use of one or the other depends on the specific conditions. Amongthese methods, the Molecular Lipophilicity Potential (MLP) [6] is notewor-thy in that it can take intramolecular effects into account. Nevertheless,lipophilicity taken alone often fails to predict drug absorption, perhaps due to its complex nature and the (too) rich information it contains (Eqn. 1)[7]:

Lipophilicity = Hydrophobicity – Polarity (Eqn. 1)

In a first approximation, size and H-bonding capacity are the main com-ponents of hydrophobicity and polarity, respectively, explaining why the roleof H-bonding (HB) has often been considered in structure-absorption rela-tions [1][8–14].

Experimental HB molecular descriptors (Fig. 1) are , the H-bond-donoracidity, and , the H-bond-acceptor basicity. Abraham [15][16] and Raevsky[17] have compiled quantitative scales of H-bond acidity and basicity based


Fig. 1. Currently available tools to predict absorption

on lipophilicity and thermodynamic data. Both databases express the H-bond-donor and -acceptor properties of a moiety on a thermodynamic free-energyscale. The solvatochromic approach is the one most widely adopted; briefly,it is based on linear free-energy relationships (LFERs) using Eqn. 2, whereSP (a generic solution property) in this particular case is P, the partition coef-ficient in a given solvent system:

(Eqn. 2)

Various equations can be derived using data of many partitioning systems.For a given molecule, provided that R2 (the excess molar refraction) and Vx

(the McGowan molecular volume) can be calculated, the molecular H-bond-ing parameters that best describe the 2

, 2 and 2

(dipolarity/polariz-ability) terms are easily obtained by an iterative procedure.

Another well-known experimental descriptor of HB properties is the dif-ference between two different log P scales, generally octanol/water andalkane/water (log Poct-alk) [18][19]. Although various alkane/water systemshave been used to generate partitioning data (hexadecane/water, cyclohexane/water, dodecane/water etc.), there is no detectable difference between theirlog P scales as seen when comparing the respective solvatochromic equations[19].

Finally, to have an indirect estimation of HB properties ( and ) fromexperimental lipophilicity data and calculated molecular volume, El Tayaret al. [20] have calculated descriptors. Basically, oct (calculated from log Poct) is mainly correlated with , whereas alk (calculated from log Palk)is correlated with and .

Among theoretical approaches to account for the H-bonding capacity ofdrugs (Fig. 1), the simplest consists in counting the number of groups able toform H-bonds. However, such a straightforward method treats all hydrogenbonds as energetically equivalent and neglects conformational factors.

Standard force fields can also be used to determine H-bonding properties.GRID [21] in particular can be adapted by choosing the most suitable probes.The main limit of this approach lies in its parametrization which is conceivedto handle not only H-bonding forces but also steric and electrostatic ones.

MolSurf [22] is a generator of chemical descriptors from quantum-mechanically calculated energies of the valence electrons. Again, this is not atool specifically dedicated to the computation of H-bonding properties.

The polar surface area, defined as the fraction of the molecular surfaceoccupied by polar heteroatoms (nitrogen, oxygen, sulfur, and phosphorus)and connected hydrogen atoms, is considered to be a factor of H-bonding[23]. Palm et al. [24] recently proposed the dynamic polar surface area (cal-culated as a statistical average in which the polar surface area of each low-

logSP c r R s b vVH H Hx= + + + + +∑∑2 2 2 2


energy conformer was weighted by its probability of existence) as a good pre-dictor of intestinal absorption in humans. This method takes the 3D shape andflexibility of the drug molecule into account but does not distinguish betweendonor and acceptor properties.

2. The Need for a New Computational Tool:The Molecular Hydrogen-Bonding Potentials (MHBPs)

The MLP of Gaillard et al. [6] has been proposed as a routinely appli-cable computational tool available to pharmaceutical and medicinal chemistsfor the investigation of the 3D-lipophilic behavior of ligands. The popularitygained by this tool and the number of published applications [25–30] indi-cates that the use of molecular fields to investigate relationships between phy-sicochemical properties and permeation data can be successful. Molecular-surface properties are of great interest as predictors of drug absorptionbecause the molecular surface reflects the 3D shape of the molecule, and itsproperties determine how the compound is perceived by its environment.

To overcome the limitations of currently available tools in HB computing,new molecular fields (the Molecular Hydrogen-Bonding Potentials, MHBPs)have been created based on the experience gained with the MLP. Thus, a step-wise procedure (see below) comparable to the one used to calculate the MLPhas been adopted [6] to generate two MHBPs able to distinguish betweendonor (MHBPdo) and acceptor (MHBPac) H-bonding properties. In addition,because of the 3D nature of molecular fields, the MHBPs have been inter-faced with algorithms for conformational analysis.

3. Methodological Aspects

Two components, namely a fragmental system and a distance function,are needed to calculate the MLP [6], as expressed by Eqn. 3,

(Eqn. 3)

where k indicates a given point in space, i is a given molecular fragment, Nthe total number of fragments in the molecule, fi the lipophilic increment offragment i, fct a distance function, and dik the distance between fragment i andpoint k.

To calculate the MHBPs, a third component, namely an angular function,must be added to take into account the directionality of the H-bonds as report-

MLP dk ii

N

ikf fct= ⋅=∑

1( )


ed in Eqn. 4, where fi is the or value of atom i, f (U) the angular function,and k, N, dik and fct (dik) have the same meaning as in the MLP.

(Eqn. 4)

In the current version of the MHBPs, we use the donor and acceptor frag-mental systems derived from solvatochromic parameters (Eqn. 2), a Gaussiandistance function and an ad hoc angular function. Briefly, the angular func-tion describes the variation of the MHBPs at a given point k as a function ofthe distance between k and the axis of the H-bond. This distance is measuredby the angle U defined by a) the point in space, b) the polar atom (to whichare attached polar hydrogen or lone pair(s)), and c) the polar hydrogen (for a donor group) or the lone pair (for an acceptor group). The larger U, the smaller the MBHPs. The function used to describe this behavior is given byEqn. 5,

(Eqn. 5)

where max is set to 30° for a polar H and 60° for a polar lone pair. Details ofthe method are given in [31].

Clearly, the larger the values of MHBPs (donor MHBPdo and acceptorMHBPac), the stronger the HB.

In Fig. 2, the simple molecule N,N-dimethylbutylamine is taken as anexample to explain the stepwise procedure used for calculating MHBPac. Thepreliminary steps include :

A) Identification of the conformer on which the MHBP is calculated.Basically, two conformers can be chosen: a reasonably averaged 3D structure generated by the CONCORD (CONnectivity toCoORDinates) algorithm [32] and minimized by the Merck MolecularForce Field (MMFF94s), or a specific conformer obtained from aQuenched Molecular Dynamics (QMD) simulation.

B) Identification of polar hydrogens (defined as H-atoms in a polar moiety; i.e., none for N,N-dimethylbutylamine) and polar lone pairs(defined as free electrons associated with polar atoms; i.e., the nitro-gen lone pair for N,N-dimethylbutylamine) and their association withthe correct donor (here, f = 0) and acceptor fragmental value (heref = 0.73).

C ) Calculation of the grid, region, or molecular surface (called Surface ofH-bonds, SHB, and obtained by adding 1.8 Å to the center of theatoms) on which the potential will be computed.

f U U( )max

= ⋅

cos

90

MHBP d (k i iki

Nf fct f U= ⋅ ⋅

=∑ ( ) )

1


The subsequent steps involve:D) Calculation of the MHBPac (Eqn. 4) at each point k of the SHB and

summing up of all MHBPk (numerical result, here 93.59).E ) Representation by dots of the points k for which the MHBPac is

greater than 0 (graphical result).

The sensitivity of the MHBPs to take into account the 3D-molecularstructure is partly due to the fact that the contribution of polar hydrogens andpolar lone pairs is neglected when they are involved in intramolecular H-bonds. In particular, an intramolecular HB is identified by the algorithmwhen a polar atom and a polar hydrogen on different atoms are separated bya distance ranging from 1.6 to 2.4 Å and when the angle formed by the polarhydrogen, the polar atom, and the lone pair is in the range of 120°–180°.

The quinolone-derived antimicrobial ciprofloxacin is taken as an exampleto illustrate this feature. Fig. 3 shows the conformer for which the MHBPdo isminimal (MHBPdo

Min) (A) and the conformer for which the MHBPdo is maxi-


Fig. 2. Stepwise procedure to compute the Molecular Hydrogen-Bonding Potential (MHBP),applied to N,N-dimethylbutylamine whose HB-donor capacity is 0. Preliminary steps from A to

C and final steps from D to E.

mal (MHBPdoMax) (B). The structural difference between the two conformers is

due to the formation of an intramolecular HB between the carboxylic and thecarbonyl moieties. When this HB is present, the potential is minimal(MHBPdo

Min), whereas when it is absent, the potential is maximal (MHBPdoMax).

Finally, as shown in Fig. 2 and in contrast to Molecular ElectrostaticPotentials (MEPs) but in analogy with the MLP, the MHBPs are not generat-ed with a probe. On the contrary, all interactions with the molecular environ-ment are implicitly contained in the fragmental system. In addition, andbecause of the empirical nature of and , the MHBPs contain in implicitform the entropy component of the free energy of binding.

4. Validation of the MHBPs

4.1. Integration on a Given Surface

As a matter of principle and validation, the and values used as inputshould be recovered by integrating the MHBPdo and MHBPac on the SHB.Such an integration is obtained by summing up the values of the MHBPs ateach point of the surface. The linear relationships between the integratedMHBPs and and values show excellent correlations: r2 = 0.98 (n = 232)and 0.95 (n = 488) for MHBPdo and MHBPac, respectively (plot not shown).

An additional validation run is in progress; its aim is to reproduce theexperimental log P data (i.e., log Pdce) of an optimal set of compounds [33][34] using solvatochromic equations and back-calculated and parameters.


Fig. 3. Ciprofloxacin: effects of conformational variability on H-bond-donor properties(MHBPdo). Zones 1 and 2 represent the point of the surface where the MHBPdo is > 0: A) Thepresence of an intramolecular H-bond decreases the MHBPdo; B) The absence of intramo-

lecular H-bonds maximizes the MHBPdo.

4.2. Graphical Comparison with GRID

The GRID field [21][35] is one of the most widely used computationaltools to map molecular surfaces of drugs and macromolecules. The interac-tions recognized and accounted for in the GRID force field are steric, electro-static, and H-bonding. Different probes corresponding to different types ofinteraction can be used in the GRID program, thus, by changing the probe, var-ious specific interactions can be mimicked. In particular, the H-bond-donorand -acceptor properties of drugs can be characterized by using as probes acarbonyl group (O) and an amide function (N). Steric and electrostatic termscan be excluded with ad hoc settings. Interaction energies between a probeatom and the target molecule were calculated on each point of the SHB.

The results obtained with GRID fields and MHBPs have been comparedgraphically. Fig. 4 illustrates the H-bonding properties (donor and acceptor)


Fig. 4. MHBPs validated by GRID interaction energies: the case of atenolol. MHBPs are rep-resented in the upper part (acceptor potential on the left, donor on the right), interaction ener-gies calculated by GRID in the lower part (acceptor properties on the left, donor on the right).

The shaded regions indicate where the H-bond capacity is maximal.

of atenolol. The shaded zones indicate where the H-bond capacity is maxi-mal. Basically, the two methods yield comparable results except for the treat-ment of intramolecular H-bonding (for atenolol between the hydroxy and theether groups) whose contribution to the MHBPs is neglected (see above).Other small differences seem to reflect the specificity of MHBPs towards H-bonding properties compared to GRID (see above).

5. Applications

A number of studies have explored the role of MHBPs as determinants for drug permeation and will be published in due course. In this paper, rela-tionships between MHBPs and BBB permeation or oral absorption are re-ported.

5.1. MHBPs, log P and Brain Penetration

A seminal paper is the study of Young et al. [8], where the log Poct-cyc

parameter (log Poct – log Pcyclohexane) was shown for the first time to beinversely proportional to brain penetration expressed as log(Cbrain/Cblood).Since log Poct-cyc and MHBPs are both descriptors of HB properties, a cor-relation must exist between them and also between MHBPs and brain pene-tration. But because log P encodes both HB donor and acceptor properties(Eqn. 5) [19], the MHBPs must be expressed as MHBPtot (Eqn. 7), namelythe sum of MHBPdo and MHBPac averaged by the same relative weights as inEqn. 6.

(Eqn. 6)

(Eqn. 7)

In Fig. 5A, the relationship between MHBPsolv and log Poct-cyc is shownfor six compounds as used by Young et al. (clonidine, mepyramine, imipra-mine and three H2-receptor antagonists numbered as 3, 4, and 5 in the origi-nal paper [8]). Their brain penetration is plotted as a function of MHBPsolv inFig. 5B. A good linear relationship is found despite the limited number of

MHBP MHBP

MHBP

solv do

ac

=+

⋅

++

⋅

3 82

3 82 1 441 44

3 82 1 44

.

( . . ).

( . . )

log – . . .

. . .

P R

V

H

H HX

oct cyc

2 2

− = − ++ + −

0 04 0 25 0 68

3 82 1 44 0 832 2


compounds (Eqn. 8 and Eqn. 9),

n = 6; r2 = 0.92; s = 12.59; F = 46.77 (Eqn. 8)

n = 6; r2 = 0.90; s = 0.40; F = 34.50 (Eqn. 9)

where 95% confidence limits are given in parentheses, n is the number ofcompounds, r2 the squared correlation coefficient, s the standard deviationand F Fischer’s test. Work is in progress to extend the relationships to largerseries of compounds.

5.2. MHBPs in the Prediction of Oral Absorption

Using a set of drugs covering a wide range of values, we examine here the reliability of MHBPs in rationalizing intestinal absorption of drugs (ex-pressed as Abs%). The test set consists in the well-known series of 20 com-pounds already investigated by Palm et al. [24]: alprenolol, atenolol, cipro-floxacin, diazepam, forscarnet, lactulose, mannitol, metolazone, metoprolol,nordiazepam, olsalazine, oxazepam, oxprenolol, phenazone, pindolol, practo-lol, raffinose, sulphasalazine, sulpiride and tranexemic acid.

The correlation between absorbed fraction and MHBPs calculated on anaveraged 3D structure (donor and acceptor, MHBPdo

Con and MHBPacCon, respec-

tively) yielded the results shown in Figs. 6A and 6B. Whereas no correlationexists for MHBPac (Fig. 6B), a sigmoidal relationship is seen (Fig. 6A,r2 = 0.84) for MHBPdo. This finding indicates that the H-bond-donor capac-

log . ( . ) . ( . )C

Cbrain

bloodsolvMHBP

= − ± ⋅ + ±0 03 0 01 2 06 0 93

MHBPsolv oct cyc= ± ⋅ + ±−21 09 6 22 35 80 20 54. ( . ) log . ( . ) P


Fig. 5. BBB Permeation of 6 compounds expressed as log (Cbrain/Cblood) [8]. Linear relation-ship with log Poct-cyc; Linear relation with the MHBPsolv.

ity is a better predictor of absorption than the H-bond-acceptor capacity, inagreement with other data assigning a predominant role to the H-bond-donoracidity in influencing biomembrane permeation [9].

To check the relevance of drug flexibility to theoretical parameters relat-ed to absorption, a Quenched Molecular Dynamics (QMD) [36] study of thecomplete conformational space was performed for the series under study. TheMHBPs of all conformers so obtained (ranging in number from 20 to 40 percompound) were calculated. The difference between the largest(MHBPdo

Max or MHBPacMax) and the smallest (MHBPdo

Min or MHBPacMin) MHBP

values for each compound was taken as its MHBP range (donor range:MHBPdo

Max – MHBPdoMin; acceptor range: MHBPac

Max – MHBPacMin).

The absorption values (Abs%) plotted as a function of MHBPdo proper-ties are shown in Fig. 7. For each compound, the full circle represents theMHBPdo of the CONCORD-generated conformer (see above) and the hori-zontal bar the range in MHBPdo. As shown above (Fig. 6A), the sigmoid rep-resents the best statistical correlation (r2 = 0.86) between absorption andMHBPdo

Con, a correlation that holds true when MHBPdoMax is considered

(r2 = 0.85) but not for MHBPdoMin (r2 = 0.41; sigmoid not represented).

The relationships between the smaller and larger value of the MHBPac

(MHBPacMin and MHBPac

Max) and the MHBPac calculated on 3D structures gen-erated by the CONCORD algorithm [32] (MHBPac

Con) were also examined(plots not shown). The good linear relationships (r2 = 0.94 and r2 = 0.98,respectively) indicate that the influence of conformation on HB acceptorproperties is of minor relevance.

These findings demonstrate that the relevance of conformational flexibil-ity on intestinal absorption is negligible in this series of compounds. In order


Fig. 6. Correlation of MHBPs descriptors with human intestinal absorption (Abs%).Relationship between Abs% and the H-bond donor potential calculated on the conformationgenerated by the CONCORD algorithm and minimized by MMFF94s field including MMFF94formal atomic charges in order to remove initial high-energy interaction (MHBPdo

Con); Lack ofa relation between the Abs % and the H-bond acceptor potential calculated on the con-

formation generated by the CONCORD algorithm (MHBPacCon).

to lead to a more general result, this study should be extended to a larger num-ber of compounds with a greater conformational variability.

6. Conclusion and Perspectives

Molecular Hydrogen-Bonding Potentials (MHBPs) are new and promis-ing computational tools able to describe 3D H-bonding properties. Built by astepwise procedure comparable to the one used successfully to calculate theMolecular Lipophilicity Potential (MLP), MHBPs are complementary to theMLP as 3D-molecular descriptors useful in predicting drug permeation.

The large amount of information contained in 3D-molecular surfaces suchas the MHBPs requires a supplementary tool able to extract useful descrip-tors. A new procedure called VolSurf [37] should be able to compress the rel-evant information present in 3D maps into a few simple descriptors. Work isin progress to apply this chemiometric tool to the MHBPs.

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Fig. 7. Oral absorption data for 20 compounds, taken from [8][24] and expressed as Abs%,plotted as a function of MHBPdo

Con (full circles), MHBPdoMin (empty squares) and MHBPdo

Max (fullsquares). The horizontal lines connecting MHBPdo

Min and MHBPdoMax represent the MHHBdo

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V. Pliska, B. Testa and H. van de Waterbeemd, VCH Publishers, Weinheim, 1996, pp. 311–337.

[14] H. S. Chadha, M. H. Abraham, R. C. Mitchell, Bioorg. Med. Chem. Lett. 1994, 4, 2511.[15] M. H. Abraham, J. Phys. Org. Chem. 1993, 6, 660.[16] M. H. Abraham, H. S. Chadha, R. C. Mitchell, J. Pharm. Sci. 1994, 83, 1257.[17] O. A. Raevsky, in ‘Computer-Assisted Lead Finding and Optimization’, Eds. H. van de

Waterbeemd, B. Testa and G. Folkers, Verlag Helvetica Chimica Acta, Basel, 1997, pp. 369–378.

[18] N. El Tayar, R. S. Tsai, B. Testa, P. A. Carrupt, A. Leo, J. Pharm. Sci. 1991, 80, 590.[19] M. H. Abraham, H. S. Chadha, G. S. Whiting, R. C. Mitchell, J. Pharm. Sci. 1994, 83,

1085.[20] N. El Tayar, B. Testa, P. A. Carrupt, J. Phys. Chem. 1992, 96, 1455.[21] P. J. Goodford, J. Med. Chem. 1985, 28, 849.[22] P. Sjöberg, in ‘Computer-Assisted Lead Finding and Optimization’, Eds. H. van de

Waterbeemd, B. Testa and G. Folkers, Verlag Helvetica Chimica Acta, Basel, 1997, pp. 83–92.

[23] K. Palm, P. Artursson, K. Luthman, in ‘Computer-Assisted Lead Finding andOptimization’, Eds. H. van de Waterbeemd, B. Testa and G. Folkers, Verlag HelveticaChimica Acta, Basel, 1997, pp. 277–289.

[24] K. Palm, P. Stenberg, K. Luthman, P. Artursson, Pharm. Res. 1997, 14, 568.[25] K. Palm, K. Luthman, A. L .Ungell, P. Artursson, J. Pharm. Sci. 1996, 85, 32.[26] K. Palm, P. Stenberg, K. Luthman, P. Artursson, Pharm. Res. 1997, 14, 568.[27] K. Palm, K. Luthman, A. L. Ungell, G. Strandlund, F. Beigi, P. Lundhal, P. Artursson,

J. Med. Chem. 1998, 41, 5382.[28] L. H. Krarup, I. T. Christensen, L. Hovgaard, S. Frokjaer, Pharm. Res. 1998, 15, 972.[29] D. E. Clark, J. Pharm. Sci. 1999, 88, 807.[30] D. E. Clark, J. Pharm. Sci. 1999, 88, 815.[31] S. Rey, G. Caron, G. Ermondi, P. Gaillard, A. Pagliara, P. A. Carrupt, B.Testa, J.

Comput.-Assist. Molec. Design, in press.[32] CONCORD3.0.1. R. S. Pearlman, R. Balducci, A. Rusinko, J. M. Skell, K. M. Smith.

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[35] D. N. A. Boobbyer, P. J. Goodford, P. M. McWhinnie, R. C. Wade, J. Med. Chem. 1989,32, 1083.

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503, 17.


VolSurf and Its Application in Structure-Disposition Relationships

by Gabriele Cruciani*a), Sara Clementia), Patrizia Crivorib),Pierre-Alain Carruptb), and Bernard Testab)

a) Laboratory for Chemometrics, University of Perugia, Via Elce di Sotto 10, I-06123 Perugia, Italy; tel.: +39 07 55 85 55 50; fax: +39 07 54 56 46;

e-mail: [email protected];b) Institute of Medicinal Chemistry, BEP, University of Lausanne,

CH-1015 Lausanne-Dorigny, Switzerland

1. Introduction

Pharmacokinetics deals with the absorption, distribution, biotranforma-tion, and excretion of drugs. These factors, coupled with dosage, determinethe concentration of a drug at its sites of action and influence the intensity andduration of its effects.

Various principles of physicochemistry, biochemistry, and enzymology,and many models dealing with active and passive transfer and with mem-brane permeation are readily applied to the understanding of this complexaspect of pharmacology [1]. The models require a relevant description ofmolecular and biological properties. However, to obtain useful parameters ofpartitioning and membrane permeation is not an easy task. A large number ofdescriptors have been developed, all of which have limitations in terms of rel-evance, interpretability, or speed of calculation. Here, we present calculatedmolecular properties from 3D molecular fields of interaction energies as anovel approach to correlate 3D molecular structures with pharmacokineticand physicochemical properties.

2. Molecular Interaction Fields (MIFs)

A molecular field may be viewed as a 3D matrix, the elements of which (called grid nodes) are the attractive and repulsive forces, mapped by color cod-ing, between an interacting partner and a target (macro)molecule. Since theinformation contained in 3D molecular fields is related to the interacting molec-


ular partners, the amount of information in MIFs is generally greater than inone-dimensional and two-dimensional computed molecular descriptors.

The interaction of compounds with biological membranes is caused bysurface properties such as shape, electrostatic forces, H-bonding capacity, andhydrophobicity. This explains why the GRID force field [2][3], which uses apotential based on the total energy of interaction (the sum of Lennard-Jones,H-bonding, and electrostatic terms) between a target molecule and a probe, isadequate to characterize putative polar and hydrophobic interaction sitesaround target molecules. The water probe is used to simulate solvation-desol-vation processes, whereas the hydrophobic probe (called DRY in the GRIDprogram) and the carbonyl probe (called O) are being used to simulate drug-membrane interactions.

The DRY probe specifically generates the hydrophobic energy [4], whichis computed at each grid point as

Eentropy + ELJ – EHB

where Eentropy is the ideal entropic component of the hydrophobic effect in anaqueous environment, ELJ measures the induction and dispersion interactionsoccurring between any pair of molecules, and EHB measures the H-bondinginteractions between water molecules and polar groups on the target surface.

Although 3D molecular fields usually contain a large amount of relevantinformation, its extraction and use are not straightforward. Specialized toolsare required to help the user extract useful descriptors from images of 3Dmolecular interaction fields and to link experimental observations withmolecular structures.

3. Calculation of Molecular Descriptors: the VolSurf Procedure

3D Molecular fields can automatically be converted into simpler molecu-lar descriptors using a procedure called VolSurf [5][6]. The method is simpleto apply and is specifically designed to produce descriptors related to phar-macokinetic properties, starting from 3D molecular field maps. In the stan-dard procedure, interaction fields with a water probe and a hydrophobic probeare calculated around the investigated molecules. However, other grid mapsproduced by different probes or by different molecular mechanics or semiem-pirical methods can also be used.

The basic concept of VolSurf is to extract the information present in 3Dmolecular field maps into a few quantitative numerical descriptors which areeasy to understand and interpret. Molecular recognition is achieved using animage-analysis software, but the image-compression process is performed byadding external chemical knowledge. VolSurf does this by selecting the most


appropriate descriptors and parametrization according to the type of 3D mapsunder study.

The molecular descriptors so obtained are compiled in Table 1. They referto molecular size and shape, to size and shape of hydrophilic and hydropho-bic regions, and to the balance between them. Hydrogen bonding, amphiphil-ic moments, and critical packing parameters are other useful descriptors. TheVolSurf descriptors have been presented and explained in detail elsewhere[6]. It is important to note that VolSurf descriptors can be obtained for small,medium, and large molecules, as well as for biopolymers such as DNAsequences, peptides, and proteins.

A large number of papers have been published which describe variousalgorithms to compute molecular descriptors related to molecular surfacesand volumes [7]. The originality of VolSurf resides in the fact that surfaces,volumes, and other related descriptors can be obtained directly from 3Dmolecular fields without the use of complex algorithms of trigonometric pro-jections, recursive generations, and tessellation.

All in all, VolSurf has the advantage of producing descriptors using the3D information embedded in any map. Not all the information can be trans-ferred from 3D to these new molecular descriptors, but practical examples do


Table 1. Definition of VolSurf Descriptors [6]. Descriptors 1–4 refer to molecular size andshape, 5–34 to molecular hydrophilic regions, 35–42 to molecular hydrophobic regions, and

51–56 are mixed descriptors.

Numbering Definition

1 Total volume (computed at 0.25 kcal/mol)2 Total surface (computed at 0.25 kcal/mol)3 Rugosity = total volume (Vtot) / total surface (Stot)4 Globularity (Stot/Se; Se = surface area of equivalent sphere with volume =Vtot)

5–12 Volumes (V–) of the interactions with the H2O probe at 8 energy levels (–0.2, –0.5, –1.0, –2.0, –3.0, –4.0, –5.0, –6.0 kcal/mol)

13–20 Integy moment: proportional to distance between barycenter of Stot and V–(at the above energy levels)

21–28 Capacity: V–/Stot (at the above energy levels)29–34 Energy minima interactions, with water probe, and distances between the

energy minima

35–42 Volumes (V–) of the interactions with the DRY probe at 8 energy levels (–0.2, –0.4, –0.6, 0.8, –1.0, –1.2, –1.4, –1.6 kcal/mol)

43–50 Integy moment: proportional to distance between barycenter of Stot and V–(at the above energy levels), calculated from DRY probe

51 Amphiphilic moment52 Critical packing53–54 Hydrophilic-lipophilic balances55 Molecular polarizability56 Molecular weight

exist [6][8][9] which show that relevant information is indeed extracted.Furthermore, the descriptors have a clear chemical meaning and are lattice-independent, and some of them can be projected back into the original 3Dgrid map from which they were obtained.

4. Practical Examples: Structure-Disposition Relationships

4.1. Predicting Drug Absorption

Krarup et al. [10] recently reported a method to generate representativeconformers to assess the conformation-dependent molecular surface area.High-temperature molecular dynamics (MD) was used to obtain 1000 con-formers for each of the 11 investigated compounds (acebutolol, alprenolol,betaxolol, oxprenolol, propranolol, timolol, and the O-cyclopropane-carbox-ylate ester prodrugs of alprenolol, betaxolol, oxprenolol, propranolol, andtimolol). The Boltzmann-averaged polar surface area (PSA) of all conformerswas correlated to the apparent permeability coefficients Papp across Caco-2cells, yielding an excellent linear correlation for all compounds. Although themethod is efficient and yielded good descriptors to predict drug absorption, itis time-consuming and difficult to apply in preclinical research where a verylarge number of compounds must be screened.

Here, we compared the MD averaging technique used by Krarup et al.[10] with the VolSurf descriptors calculated from single conformers. The cor-relation coefficient between PSA [10] and one of the hydrophilic region vol-ume (W7) calculated by VolSurf from a 3D-GRID field obtained with a waterprobe was 0.99. In other words, the two methods of calculating the PSA gaveidentical results, but the second is very much faster and simpler. Moreover, itis completely automatic and avoids the problem of sampling conformers, avery difficult task for highly flexible compounds.

The goodness-of-fit obtained with the VolSurf model is shown in Fig. 1.The two-component model explains 94% of the total variance. The modeldemonstrates that the VolSurf descriptors are barely influenced by conforma-tional sampling and averaging, as was also shown in another work [9] withcompletely different compounds. This is probably due to the peculiarity of theGRID force field which allows some freedom of movement to the externalgroups, hydrogens, and lone pairs.

The protocol of a simple 2D-to-3D conversion, with energy minimizationbut without any MD sampling and Boltzmann-averaging, makes VolSurfdescriptors computationally more efficient and well suited for fast quantita-tive structure-property relationship studies, especially when dealing with alarge number of compounds.


4.2. Predicting Drug Elimination

The kidney and the liver play a major role in drug elimination by excre-tion and metabolism, respectively [11][12]. Surprisingly, little is known aboutthe renal vs. hepatic preference of drug elimination [13], beyond the globaland fuzzy trend that polar xenobiotics tend to be eliminated renally and lipo-philic xenobiotics tend to undergo extensive metabolism. This paucity ofknowledge is due to the fact that investigations in this field have focused onspecific details or on isolated compounds.

In the following example, the VolSurf procedure has been applied to themultivariate modeling of well-known -blockers as a strategy that should alsobe applicable to a much larger dataset. The calculations were performed for aseries of 10 drugs, five of which show preferential hepatic metabolism, threeare mostly excreted renally, and only two have mixed routes of elimination(see Table 2).

It is chemically difficult, just by inspecting their 3D interaction maps(Fig. 2), to discover the chemical features that make the drugs share the same


Fig. 1. Experimental vs. calculated permeation across Caco-2 cells [10] obtained with a two-component VolSurf model

route of elimination. Timolol and sotalol are both freely water-soluble.However, the first shows hepatic elimination, while the second is eliminatedby renal excretion only [14]. Conversely, VolSurf generated 3D-derivedmolecular descriptors which led to a simple but effective mathematical dis-crimination between hepatic and renal elimination. Fig. 3 shows the results ofthe principal component analysis (PCA) on the dataset described by 35 VolSurfdescriptors. The model led to a good separation, in the first principal compo-nent, between the compounds with preferential hepatic or renal elimination.It should be pointed out that no experimental pharmacokinetic informationwas used for deriving the model. Clearly, this information is embedded in themolecular interaction fields and is highlighted by VolSurf descriptors.


Table 2. -Blockers (and two compounds used for external prediction) Classified According to Their Predominant Route of Elimination (hepatic metabolism and/or renal excretion)

No. Name Route of elimination

1 Propranolol Mainly hepatic2 Alprenolol Mainly hepatic3 Oxprenolol Mainly hepatic4 Metoprolol Mainly hepatic5 Timolol Mainly hepatic6 Acebutolol Hepatic and renal7 Pindolol Hepatic and renal8 Nadolol Mainly renal9 Atenolol Mainly renal

10 Sotalol Mainly renal11 Diazepam Compound for ext. prediction12 Sulfacetamide Compound for ext. prediction

Fig. 2. Representation of timolol and sotalol showing their interaction energy with a water probe computed at –3.0 kcal/mol

Since the investigated -blockers are quite similar, the molecular diver-sity of the dataset is low. Accordingly, the model and its interpretation cannotbe of general, but just of local validity. However, the interpretation of the sta-tistical model reveals that hydrophilic, exposed molecular surfaces, togetherwith the ratio between these hydrophilic regions and the total molecular sur-face (capacity factors), are mainly responsible for hepatic vs. renal elimina-tion. Moreover, hydrophilic-lipophilic balance and amphiphilic moments arealso important, whereas molecular size, shape, and polarizability do notappear relevant.

In order to test this simple model, two drugs were used for external vali-dation. Sulfacetamide was taken as a prototype of renally excreted drugs, anddiazepam of hepatically metabolized drugs [13]. The experimental pharma-cokinetic information was not used in the PCA model. VolSurf parameterswere calculated for these external compounds and the PCA model used to


Fig. 3. PCA Score plot for the 10 -blockers reported in Table 2. Filled circles represent thecompounds metabolized in the liver, white circles the compounds mainly excreted by the kid-neys, and the grey circles the compounds eliminated by both routes. The numbering is that inTable 2. The first principal component differentiates the compounds according to their predom-

inant route of elimination. Squares refer to the test compounds.

predict their preferred route of elimination. A projection on the PCA model(see Fig. 3) clearly shows that the route of elimination of diazepam and sul-facetamide has been correctly predicted.

4.3. Protein Binding

Human serum 1-acid glycoprotein (AGP) is a mixture of at least twogenetic variants having specific roles in drug transport [15]. Here, wesearched for correlation between VolSurf descriptors of 26 chemically diversebasic drugs and their binding affinity for the A variant of AGP, expressed asthe ligand-binding association constants K′. Detailed explanations of the pro-cedure can be found in the original paper [6].

The resulting PLS model (r2 = 0.83, q2 = 0.78, fitting error ±0.46 and pre-dicting error ±0.53) showed a calculated error very similar to the estimatedexperimental error of ±0.47 log units. A plot of experimental vs. calculated–log K′ values is presented in Fig. 4. From the corresponding coefficient plots


Fig. 4. Plot of experimental vs. calculated affinity constants for the binding of drugs to the Avariant of AGP [6][15]. The numbers refer to the compounds listed in [6].

at the third PLS dimension (Fig. 5), we can deduce that capacity factors (theratio between polar and apolar regions) are positively correlated with –log K′.In other words, a high total polarity is not a critical factor, whereas an in-creased polarity per surface unit is favorable to affinity. The same correlationwas shown by DRY integy moments and the amphiphilic moment. It isimportant to note that energy-minima distances are highly correlated with –log K′, meaning that high-affinity ligands have energy minima that are bothsignificant and relatively distant from each other in 3D space. This informa-tion could also be useful to rationalize the pharmacophoric pattern. In con-trast, H-bond-donor parameters are negatively correlated with affinity, as aremolecular volume, surface, and rugosity.

4.4. Predicting Blood-Brain-Barrier Permeation

To be effective as therapeutic agents, centrally acting drugs must cross theblood-brain barrier (BBB). Conversely, to be devoid of unwanted CNSeffects, peripherally acting drugs must show limited brain accessibility. Inboth cases, the BBB permeability of drug candidates must be known.However, the experimental determination of blood-brain partitioning is diffi-


Fig. 5. Coefficient plot in the third PLS dimension for the binding of drugs to the A variant ofAGP [6][15]

cult, time-consuming and expensive, and not suitable to screen large collec-tions of chemicals [16]. A broadly applicable method for predicting the BBBpermeation of candidates at an early stage of discovery would have a greatimpact on drug research and development.

The present study [17] was conducted to demonstrate the value ofdescriptors derived from 3D molecular fields in estimating the BBB permea-tion of a large set of compounds and to produce a simple mathematical modelsuitable for external prediction [17]. A dataset containing 229 drug with well-defined BBB profiles was submitted to the following procedure :

a) The three dimensional structure of the compounds was built.b) The compounds were submitted to multivariate characterization based

on their interaction energy with chemical probes. The GRID [2] pro-gram was used to calculate the 3D molecular interaction fields with thewater, hydrophobic, and carbonyl probes.

c) Molecular descriptors were calculated using the VolSurf program [6].d) Chemometric tools (discriminant PLS) were used to correlate the data

and build a BBB permeation model.

It is important to note that steps b), c), and d) were performed automati-cally by the VolSurf program.

Two significant latent variables emerged from the DA-PLS model andcross-validation. The PLS t1-t2 score plot of the resulting model (Fig. 6)shows that the model distinguishes corretly between the BBB+ and BBB–compounds. BBB+ compounds are clustered on the left-hand side of the plot,and BBB– compounds on the right. The model correctly assigns the BBB pro-file to more than 90% of the compounds. Since the prediction error (SDEP)[18] of the discriminant PLS was 0.6 unit, a confidence interval was built inthe t1-t2 space between the BBB+ and BBB– regions (Fig. 6). In this inter-val, BBB prediction was considered borderline and doubtful.

From the coefficient plot of the model so obtained (not reported here),some simple conclusions emerge :

• Descriptors of polarity such as hydrophilic regions, capacity factors,and H-bonding are inversely correlated with BBB permeability. Thismeans that beside H-bonding potential, other factors influence BBBpermeation, e.g., charge distribution and electron lone pairs. Capacityrefers to polar interactions per surface unit. While diffuse polar regionsare tolerable for BBB permeation, dense and localized polar regionsare markedly detrimental.

• An increase in H-bonding capacity is known to be detrimental to per-meation. In addition, the contribution of the integy-moment descrip-tors demonstrates that the 3D distribution as well as the number of H-bonds influence BBB permeation.


• Hydrophobic interactions are directly correlated with BBB permea-tion, but their role appears smaller than that of the polar descriptors.

• Molecular size and shape have no marked impact on BBB permeation.In contrast, critical packing and the hydrophilic-lipophilic balance areseen to play an important role.

Globally, it is the balance of all descriptors, in other words of molecularproperties, which controls BBB permeation. The interpretation of the modelis in good agreement with the known molecular factors influencing BBB per-meation. In addition, and this outlines the originality of the method, VolSurfallows to quantify the relevant 3D molecular properties. Once the model isdeveloped, simply projecting into it the descriptors of new compounds canallow their BBB prediction. As such, VolSurf affords much structural infor-mation of value in the design of BBB+ or BBB– candidates and in definingan ideal property profile in similarity searches.


Fig. 6. The DA-PLS t1-t2 score plot for the global model of BBB permeation. The model offersa good discrimination between the BBB+ and BBB– compounds, since it assigned a correctBBB profile to >90% of the compounds. A confidence interval is built in the t1-t2 space,

where BBB prediction can be borderline and doubtful [17].

5. Conclusion

Molecular properties calculated from 3D molecular fields of interactionenergies are a novel approach to correlate 3D molecular structures with phar-macodynamic, pharmacokinetic, and physicochemical properties. The novelVolSurf descriptors quantitatively characterize size, shape, polarity, hydro-phobicity, and the balance between them.

By quantifying the favorable and unfavorable contributions of physico-chemical and structural properties, VolSurf also offers valuable insights fordrug design, screening, and pharmacological profiling. The computationalprocedure is fully automated and quite fast. The method thus appears as a val-uable new tool in virtual screening where selection or prioritization of candi-dates from large collections of compounds is required.

It is also important to remember that, in contrast to other methods,VolSurf can calculate descriptors for small, medium, and large molecules, aswell as for biopolymers such as DNA fragments, peptides, and proteins.

We are grateful to Dr. Wolfgang Guba (Aventis Pharma, Germany), Dr. Manuel Pastor(Multivariate Infometrics Analysis, Perugia, Italy) and Prof. Sergio Clementi (Laboratory forChemometrics, Perugia, Italy) for their help and valuable discussions. B. T. and P. A. C. areindebted to the Swiss National Science Foundation for a research grant.

REFERENCES

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[2] P. J. Goodford, J. Med. Chem. 1985, 28, 849.[3] D. N. A. Bobbyer, P. J. Goodford, P. M. McWhinnie, J. Med. Chem. 1989, 32, 1083.[4] GRID Version 17, Molecular Discovery Ltd., Oxford, GB, 1999.[5] S. Clementi, G. Cruciani, P. Fifi, D. Riganelli, R. Valigi G. Musumarra, Quant. Struct.-

Act. Relat. 1996, 15, 108.[6] G. Cruciani, P. Crivori, P. A. Carrupt, B. Testa, TheoChem, 2000, 503, 17.[7] M. L. Connolly, http://www.netsci.org/ Science/ Comp-chem/feature14.html[8] R. Mannhold, G. Cruciani, H. Weber, H. Lemoine, A. Derix, C. Weichel, M. Clementi,

J. Med. Chem. 1999, 42, 981.[9] W. Guba, G. Cruciani, in ‘Molecular Modelling and Prediction of Bioreactivity’, K.

Guberrtofte, F. S. Jorgensen (Eds.), Plenum, New York, 2000, p. 89.[10] L. H. Krarup, I. T. Christensen, L. Hovgaard, S. Frokjaer, Pharm. Res. 1998, 15, 972.[11] C. Fleck, H. Braunlich, Pharmacol. Therap. 1984, 25, 1.[12] L. Offerhaus, Pharmac. Ther. 1981, 15, 69.[13] C. Fleck, H. Braunlich, in ‘Progress in Pharmacology and Clinical Pharmacology’,

G.Fischer (Ed.), Springer Verlag, New York, 1991, Vol 8/4.[14] G. Muiesan, E. Montini, Farmaco 1982, 6, 329.[15] F. Hervé, G. Caron, J. C. Duché, P. Gaillard, N. A. Rahman, A. Tsantili- Kakoulidou,

P. A. Carrupt, P. D’Athis, J. P. Tillement, B. Testa, Mol. Pharmacol. 1998, 54, 129.[16] F. Lombardo, J. F. Blacke, W. J. Curatolo, J. Med. Chem. 1996, 39, 4750.[17] P. Crivori, G. Cruciani, P. A. Carrupt, B. Testa, J. Med. Chem., 2000, 43, 2204.[18] G. Cruciani, M. Baroni, S. Clementi, G. Costantino, D. Riganelli, B. Skagerberg,

J. Chemom. 1992, 6, 335.


Molecular-Modeling Approaches to PredictMetabolism and Toxicity

by Antonius M. ter Laak* and Nico P. E. Vermeulen

Dept. of Pharmacochemistry, Free University of Amsterdam, de Boelelaan 1083, NL-1081 HV Amsterdam, Netherlands; fax: +3 12 04 44 76 10; e-mail: [email protected]

1. Introduction

Biotransformation enzymes catalyze various metabolic reactions in xeno-biotic and endogenous compounds, e.g., oxidation, reduction, and conjuga-tion. In order to get more insight into the mechanism of action of an enzymeand its substrate specificity, as well as into the factors determining whether ornot a compound will be metabolized by it, a detailed description of the shapeand physicochemical properties of the active site is a prerequisite [1]. Thecrystal structure of some (iso)enzymes is available, but the structure of theactive sites of most of the important enzymes (both mammalian and non-mammalian) remains to be determined. Basically, there are many differentstrategies to characterize the mechanisms of action and the active site ofenzymes, such as a) chemical modification and affinity labeling, b) site-directed mutagenesis, c) spectroscopic techniques, d) crystallography, e) struc-ture-activity relationships, and f ) small-molecule and homology modeling.All these approaches have been applied to cytosolic glutathione S-trans-ferases, as recently reviewed [1]. In cases of membrane-bound enzymes suchas cytochromes P450 (CYPs), crystallography, one of the most valuable tech-niques to elucidate protein structures, has not yet been successful. In recentyears, this lack of knowledge has resulted in the prediction of various enzymestructures using computer-aided molecular-modeling techniques.

The primary aim of this chapter is to summarize and discuss the require-ments and assumptions of various computer-modeling techniques, the draw-backs and limitations of these modeling techniques, and furthermore to indi-cate some of the possible experimental methods to validate the modeled struc-tures of proteins and their active sites. One important class of enzymes will beused to illustrate these aspects, namely the cytochromes P450 (CYPs). In addi-tion, we discuss the best known computational methods to predict toxicities.


The cytochromes P450 constitute a large superfamily of heme-containingenzymes, capable of oxidizing and reducing substrates of endogenous andexogenous origin. CYPs are grouped in a distinct family when the primarysequence homology with any other family is < 40% [2]. For mammalian CYPamino-acid sequences within the same subfamily, the identity is usually>55% [2].

Cytochromes P450 generally detoxify potentially dangerous compounds,but in a number of cases, non-toxic compounds are bioactivated to toxic reac-tive intermediates, and procarcinogens are activated into their ultimate carci-nogens [3]. CYPs also catalyze key reactions in steroidogenesis in animals,and many account for resistance to exogenous agents in insects and plants,and for flower coloring [4]. The metabolic activities of CYPs can be dividedinto a) monooxygenase activity, usually resulting in incorporation of an oxy-gen atom into the substrate, b) oxidase activity, resulting in the formation ofsuperoxide-anion radicals and hydrogen peroxide (uncoupling of the catalyt-ic cycle [5]), and c) reductase activity, usually producing free-radical interme-diates under anaerobic conditions [5][6][7].

CYPs can also be classified according to the electron-transfer chain deliv-ering the electron required for the one-electron reduction from NAD(P)H.Class-I CYPs are found in mitochondrial membranes of eukaryotes and inbacteria and require an FAD (flavin adenine dinucleotide)-containing reduc-tase and an iron-sulfur protein (putidaredoxin) [4][8], whereas class-II CYPsare bound to the endoplasmic reticulum and interact directly with a cyto-chrome P450 reductase (containing FAD and FMN (flavin mononucleotide))[4][9].

2. Methodological Aspects of Small-Molecule Models

One possibility to derive a model for the active site of an enzyme is thecreation of a small-molecule model or pharmacophoric model. With this tech-nique, information on the active site is derived (indirectly) from the shape,electronic properties and conformations of substrates, inhibitors, or metabol-ic products. Various substrates, inhibitors, or metabolic products are thenaligned by superimposing chemically similar groups. By using this approach,a template is created which describes the size of the active site and the elec-trostatic distribution therein. The general procedure followed to construct asmall molecule model is depicted in Fig. 1. A small-molecule model for sub-strates will be used as an example, although small-molecule models for inhib-itors or metabolites can be obtained in a similar manner.


2.1. Requirements

In order to build a small-molecule model for substrates of a specificenzyme, a first prerequisite is a template molecule from which the model willbe built. The template molecule is usually a substrate which ideally: a) is spe-cifically metabolized by the (iso)enzyme under investigation, b) is large, in


Fig. 1. General procedure for the construction of a small-molecule (pharmacophore) model

order to describe the largest possible portion of the active site, c) is relative-ly rigid, since flexible molecules will have too much conformational freedomwhich complicates the selection of the ‘active’ conformation, d) containsessential functional groups, and e) is regio- and/or stereoselectively metabo-lized. A second prerequisite is the availability of appropriate enzyme kineticsand metabolic data concerning a variety of additional substrates, which arespecifically metabolized by the (iso)enzyme under investigation. Thirdly, acomputer programme is needed which contains the molecular forcefieldparameters required for modeling the substrates under investigation.

After selection of a template molecule, additional substrates are superim-posed onto the template molecule. Some predefined fit criteria have to bemet, otherwise the fit onto the template is rejected. When the fit is accepted,an energy calculation is performed to determine the energy differencebetween the global minimum-energy conformation and the fitted conforma-tion. If this energy difference (E) is within a predefined range, the fit of thecompound on the template molecule is accepted in the model [10][11].

2.2. Assumptions

The construction of a small-molecule model would not be possible with-out taking certain assumptions into account. The first assumption for small-molecule modeling is that all substrates will be oriented in a similar manner(both electronically and sterically) in the active site of the enzyme. The sec-ond assumption concerns the geometry of the substrates. Substrates are usu-ally energy-minimized, using either semi-empirical or ab initio methods, ortheir geometry derived from the Cambridge Structural Database (CSD [12]).In case of energy minimization using semi-empirical or ab initio methods, thein vacuo geometry of the substrate is calculated. This may give a reasonablycorrect geometry for the biologically active geometry only when a) the activesite of the enzyme is mainly hydrophobic in nature, b) charge stabilization bythe apoprotein is not significant during the reaction, and c) the metabolicreaction of the substrate is ‘chemical like’ and does not require specific inter-actions between the substrate and the apoprotein thereby altering the geome-try of the substrate [13][14]. Geometries derived from crystal structures, onthe other hand, are usually influenced by crystal-packing effects. The geom-etries used, either derived from calculations or from crystal structures, do notnecessarily correspond to the biologically active conformation. In some stud-ies, geometries obtained from the CSD [12] and calculated geometries areboth used, despite the fact that this may lead to erroneous conclusions.


2.3. Drawbacks of Small-Molecule Models

In small-molecule models, steric, electronic, and other interactions withthe protein are not explicitly modeled. However, if a substrate can be accom-modated in a small-molecule model but the formation of a certain metaboliteis not confirmed experimentally, this does not necessarily imply a steric (orelectronic) restriction which is neglected by the model. Possibly, other meta-bolic pathways and/or (iso)enzymes may compete with the specific metabol-ic reaction of the (iso)enzyme for the substrate under the applied experimen-tal conditions. The absence of a certain (predicted) metabolite may also bedue to kinetic rather than thermodynamic effects [13], for example when thespecific metabolic conversion is very slow compared to other metabolic reac-tions.

Substrate geometries taken directly from the CSD [12] are usually influ-enced by packing effects which are absent in a biological environment. In asimilar way, the geometries of the substrates/inhibitors obtained by semi-empirical or ab initio optimizations are not necessarily identical with thegeometries in a biological environment, as indicated above.

Small-molecule models for inhibitors are generally more difficult to con-struct compared to substrates or metabolic products. The specific site of reac-tion (e.g., for oxidation or conjugation) is lacking in inhibitor models andhence cannot be used as an easily identifiable site to be superimposed.

2.4. Experimental Validation

After building a small-molecule model, metabolic predictions can in prin-ciple be made using the model. In order to validate the model and its predic-tions, experiments can be designed to test the hypotheses. Generally, the pre-dicted metabolites of a substrate have to be identified in incubations using thepurified or heterologously expressed (iso)enzyme. When using microsomesfor such experiments, other (iso)enzymes can be responsible for the metab-olites as well. The metabolite pattern found in the metabolism experimentscan subsequently be compared with the predicted metabolites. In case the pre-dicted metabolite is not detected experimentally, this does not unequivocallyindicate that the small-molecule model is erroneous (see also Sect. 2.3).

Several easily accessible parameters such as the Michaelis-Menten con-stant (Km), the inhibition constant (Ki), and the binding constant (Ks) do notappear to be very useful for the validation of small-molecule models. Themost useful constant is most likely Ks, as a small-molecule model can onlygive information on the binding of substrates, and not on overall reactionrates. However, for a series of substrates of rat glutathione S-transferase (see


below) [15], recent experiments indicated that although the Km valuesappeared to correlate well with differences observed in the small-moleculemodel, the differences in Ks (and to a lesser extent the differences in Ki) werealmost indistinguishable for the various substrates.

3. Small-Molecule Models for Cytochrome P450 Isoenzymes

Small-molecule models have been derived for only a limited number ofCYP isoenzymes. In recent years, more elaborate computational techniqueshave been used compared to the relatively simple calculations performed inthe 1980s.

3.1. CYP1A1

A very simple small-molecule model for rat CYP1A1 was first derived byJerina and co-workers [16] using benzo[a]pyrene and a variety of other poly-cyclic aromatic hydrocarbons (PAHs) (Fig. 2a). Benzo[a]pyrene is converted


Fig. 2. a) Steric model of the active site of CYP1A1 based on the metabolism of benzo[a]-pyrene; the binding site is asymmetrically positioned toward the activated oxygen speciesbound at the iron atom [16]. b) Expansion of model a) in order to accommodate several otherPAHs [17][18]. c) Proposed model in which some flexibility in the angle of the oxygen

addition to the substrate is allowed [16]. Taken from [19].

stereoselectively via 7,8-epoxidation by CYP1A1, hydration by epoxidehydrolase, and 9,10-epoxidation by CYP1A1 to the ultimate carcinogenbenzo[a]pyrene-7(R),8(S)-diol 9(S),10(R)-epoxide. Based on the PAH-sub-strates used, this model described the active site of CYP1A1 as a hydropho-bic cleft, asymmetrically oriented relative to the heme. This original model ofCYP1A1 substrates was later extended to accommodate several other PAHs[17][18]. The original model had to be extended considerably (Fig. 2b) or acertain degree of flexibility in the position of the substrates had to be incor-porated (Fig. 2c).

Rat CYP1A1 is also known to metabolize, in a regio- and stereoselectivemanner, a variety of small non-PAH substrates such as 7-ethoxycoumarin andzoxazolamine [19]. The binding and orientation of these small substrates inthe active site of CYP1A1 was suggested to be the result of a hydrogen-bond-ing interaction and aromatic interactions with the protein [19]. The sites ofoxidation and the heteroatoms responsible for the hydrogen-bonding interac-tion with the protein were superimposed, as indicated in Fig. 3 [19]. Com-bining the model for PAHs [16][18] with the model for small molecules [19]


Fig. 3. Small-molecule model for CYP1A1 for small non-PAH substrates [19]. 7-Ethoxy-coumarin (solid line) and zoxazolamine (dashed line) are shown when superimposed onto thesteric model for PAHs as shown in Fig. 2a [16] with 1) the site of oxidation, 2) the region of presumed – interactions between substrates and protein, and 3) the location of hetero-atoms in the substrates proposed to form a hydrogen-bonding interaction with the protein.

Taken from [19].

affords a rough small-molecule model which can accommodate many sub-strates of rat CYP1A1.

3.2. CYP1A1/1A2

Computational analysis of compounds oxidized by rat CYP1A1 andCYP1A2 indicated that these isoenzymes preferentially catalyze the hydroxy-lation of essentially flat molecules, further characterized by a small depth anda large area/depth ratio [20][21]. These studies used substrate geometriesfrom crystal structures and from MINDO/3 semi-empirical calculations [20].As crystal structures may be influenced by crystal-packing effects, a directcomparison is, however, not necessarily warranted (see Sect. 2.2). The sub-strates were fitted onto each other, based only on size and shape, and no func-tional groups within the substrates were superimposed [20].

3.3. CYP2B1/2B2

A simple computational analysis of compounds metabolized by ratCYP2B1 and CYP2B2 suggested these substrates to be rather bulky, non-planar molecules characterized by small area/depth ratios and a larger flexi-bility in molecular conformation, when compared to substrates of the ratCYP1A1 and CYP1A2 [20][21]. Again, crystal structures and MINDO/3-optimized geometries were used interchangeably. The substrates were notsuperimposed in this study, and only sizes and shapes were compared [20].

3.4. CYP2C9

Human CYP2C9 is an isoenzyme involved in the metabolism of a largenumber of antiinflammatory drugs, which exist as anions at physiological pH[21]. Based on 12 substrates, a small-molecule model for CYP2C9 has beenderived, using two rigid substrates as templates, namely phenytoin and (S)-warfarin [21]. The geometries of the substrates were partially derived fromcrystal structures and partially from molecular-mechanics calculations usingthe ‘consistent valence forcefield’ [21]. It was possible to superimpose thesubstrates with their sites of hydroxylation and to bring all anionic heteroat-oms in the various substrates at a distance between 3.5 Å and 4.8 Å from acommon (hypothetical) cationic interaction site within the CYP2C9 protein(Fig. 4) [21]. Since the positions of the anionic heteroatoms were rather dif-ferent for the various substrates, a hydrogen bond to the protein was suggest-


ed not to be possible with all the substrates. Instead, a purely cation-anioninteraction was postulated [21]. As indicated by the authors, more calcula-tions are needed to substantiate this preliminary small-molecule model. Fullconformational analysis of the substrates including geometry optimization ofeach conformation might be a useful approach for all substrates.

3.5. CYP2D6

Human CYP2D6 is a polymorphic member of the CYP superfamily andis absent in 5–9% of the Caucasian population as a result of a recessive inher-itance of gene mutations [22][23]. This results in deficiencies in drug oxida-tions known as the debrisoquine/sparteine polymorphism which affects themetabolism of numerous drugs. A decreased metabolism of these drugs isfound in poor metabolizers which have two non-functional CYP2D6 alleles,compared to extensive metabolizers with at least one functional allele. Small-


Fig. 4. Superposition of the hydroxylation sites and hydroxylated aromatic rings of warfarin,phenytoin, and tienilic acid. Possible interaction of their anionic sites with a cationic site of

CYP2C9 (C+) is shown. Taken from [21].

molecule models predicting the involvement of CYP2D6 may identify poten-tial problems for poor metabolizers when either a drug is not metabolized ora prodrug is not activated due to the dependence on the lacking CYP2D6.Relatively few small-molecule models have been derived for this particularhuman CYP isoenzyme, using a variety of substrates or inhibitors [24][25].

The first substrate models were based on substrates containing a basicnitrogen atom at a distance of either 5 Å (Fig. 5a [26]) or 7 Å (Fig. 5b [27])from the site of oxidation, and an aromatic ring system which was coplanarin both models [24][27]. In the 5-Å model, no substrates were actually fittedonto each other [24]. The main problem of these initial models was that nei-ther could explain the other group of substrates.


Fig. 5. a) Initial 5 Å small-molecule model for CYP2D6. Debrisoquine is shown at the activecenter with the basic N-atom attached to a carboxy group and the site of oxidation adjacent to the iron-oxo complex. The heavy line and six circles denote a hydrophobic region. Takenfrom [24]. b) Initial 7 Å small-molecule model for CYP2D6. Juxtaposition of dextromethorphanand bufuralol, with N: basic nitrogen atom, P: lipophilic plane, and O: oxidation site. Takenfrom [28]. c) Combined 5–7 Å small-molecule model for CYP2D6 [11]. Oxidation sites (3) ofall molecules are superimposed. Basic N-atoms are fitted either on the basic N-atom of de-brisoquine (2), or onto that of dextromethorphan (1) and interact with one of the carboxylicoxygen atoms (O1 or O2). Taken from [19]. d) Refined small-molecule model for CYP2D6, con-taining the heme moiety (gray) and aspartic acid residue 301 (2) derived from a protein

model for CYP2D6 [31]. The site of oxidation is indicated (1). Adapted from [10].

An extended model was derived by Islam et al. [28] which indicated a dis-tance in the range of 5 and 7 Å between a basic nitrogen atom and the site ofoxidation. This small-molecule model also contained the heme moiety fromthe crystal structure of CYP101 (CYPcam [29]) above which the templatemolecule of this small-molecule model (debrisoquine) was positioned arbi-trarily [28] in a manner resembling the orientation of camphor in the CYP101crystal structure [30]. The model also included an oxygen atom bound to theiron of the heme moiety, which is involved in the CYP hydroxylation reaction[28]. A set of 15 compounds was fitted onto the template debrisoquine ontowhich some of the known substrates of CYP2D6 (e.g., sparteine and amitrip-tyline) could not be fitted [28]. The prediction, based on this model, that NNK(4-(N-methyl-N-nitrosamino)-1-(3-pyridyl)-1-butanone) is not a substrate forCYP2D6, was experimentally confirmed using human liver microsomes [28].

Another small-molecule model for CYP2D6 was derived by Koymanset al. [11]. This model suggested a hypothetical carboxylate group within theprotein to be responsible for a well-defined distance of either 5 Å or 7 Åbetween the basic nitrogen atom and the site of oxidation in the substrate.This model used debrisoquine and dextromethorphan as templates for the 5-Å and 7-Å compounds, respectively. The oxidation sites of the two tem-plates were superimposed, and the areas next to the sites of oxidation werefitted coplanarily, while the basic nitrogen atoms were placed 2.5 Å apart,interacting with different oxygen atoms of the postulated carboxylate groupin the protein. The final model (Fig. 5c) consisted of 16 substrates, account-ing for 23 metabolic reactions, with their sites of oxidation and basic nitrogenatoms fitted onto the sites of oxidation of the templates and one of the basicnitrogen atoms of the template molecules, respectively. The model was veri-fied by predicting the metabolism of four compounds giving 14 possibleCYP-dependent metabolites. According to this model, four oxidative reac-tions were mediated by CYP2D6, while the ten others were not. In vivo andin vitro metabolism studies with these substrates indicated that 13 out of 14predictions (3 positive and 10 negative predictions) were correct [11], con-firming the relatively high predictive value of the model. More recently, thepredictive value of the model was further confirmed when two metabolites of1-2-[bis(4-fluorophenyl)methoxy]ethyl-4-(3-phenylpropyl)piperazine(GBR 12909) were also correctly predicted and shown to be formed by het-erologously expressed CYP2D6 [14]. The relatively large GBR 12909 ex-tended considerably from the region described by the small-molecule model,indicating the need to expand the model in certain directions [14].

Recently, the actual positions of the heme moiety and the I-helix, contain-ing Asp301 (derived from a protein model of CYP2D6, see below [31]) wereadded to this small-molecule model, thereby incorporating some stericrestrictions and orientational preferences into the small-molecule model [10].


Involvement of Asp301 in substrate binding was initially predicted usinghomology-modeling techniques [32] and recently confirmed, with site-specif-ic mutation and expression experiments, to be important for the activity ofCYP [33]. In this refined small-molecule model, an aspartic-acid residue iscoupled to the basic nitrogen atom of the substrates, thus enhancing the small-molecule model with the direction of the hydrogen bond between the aspar-tic acid in the protein and the (protonated) basic nitrogen [10]. Debrisoquineand dextromethorphan were again used as template molecules. The site ofoxidation above the heme moiety was one of the two possible sites of oxida-tion as suggested by the recently derived protein model for CYP2D6 (seebelow) [31] and is located above pyrrole-ring B of the heme moiety. In thismodel, the sites of hydroxylation or O-demethylation in the substrates werefitted onto the defined oxidation site above pyrrole-ring B of the heme moie-ty, while the C() and C() atoms of the attached aspartic acid moiety werefitted onto the C() and C() atoms of Asp301, respectively [10]. A schemat-ic representation of the refined small-molecule model of CYP2D6 is given inFig. 5d. A variety of substrates fitted in the original substrate model forCYP2D6 [11][14][34] were successfully fitted into the refined substratemodel (for example GBR 12909), indicating that the refined substrate modelfor CYP2D6 (with extra steric and directional restraints) can accommodatethe same variety in molecular structures as the original substrate model. Therefined small-molecule model also gives a more accurate description of theactive site of CYP2D6. This model has later been extended to 40 substrateswith 57 different metabolic pathways and combined with a protein-homolo-gy model of CYP2D6 [35]. This combined model demonstrated a high levelof complementarity of the model with the CYP2D6 substrate-binding site,which justifies the use of small-molecule models for metabolism predictions.A second substrate pharmacophore has been derived specifically for 14 sub-strates which are N-dealkylated by CYP2D6 [36]. This pharmacophore wasalso successfully merged with the CYP2D6 protein model and showed a moreimportant role for the interaction of the aromatic region in the N-dealkylatedsubstrates with Phe481, compared to the hydroxylated O-demethylated sub-strates. It was speculated that this aromatic-aromatic interaction contributesmore significantly to CYP2D6 binding because the N-dealkylated substrateslack an interaction of a basic nitrogen atom with Asp301.

Parallel to the substrate models for CYP2D6, an inhibitor model has beenderived. As no suitable template inhibitor (see Sect. 2.1) was available, thetemplate of this model was derived by superimposing 6 strong reversibleinhibitors of CYP2D6 [25]. The basic nitrogen atoms were superimposed,and the aromatic planes of these inhibitors were fitted in a coplanar manner.All inhibitors used were relatively flexible, resulting in various low-energyconformations. The final template consisted of those conformations of ajmal-


icine, quinidine, chlorpromazine, trifluperidol, prodipine, and lobeline thatcould be aligned relatively well [37]. Consecutively, other inhibitors, such asderivatives of ajmalicine and quinidine, were fitted onto the derived template.The derived preliminary pharmacophore model consisted of a tertiary nitro-gen atom (protonated at physiological pH) and a flat hydrophobic region (Ain Fig. 6) [37]. Furthermore, there appeared to be a region (B in Fig. 6) inwhich functional groups with lone pairs seemed to cause enhanced inhibito-ry potency, while in yet another region (C in Fig. 6), hydrophobic groupsseemed to be allowed but caused no enhanced inhibitory effect [25]. The inhi-bition data were obtained from experiments using human liver microsomesand bufuralol as substrate [25]. The uncertainties in both the template usedand the inhibition experiments used to verify this model were relatively large[11][14]. The features derived for this inhibitor-based small-molecule model[25] were very similar to the features of the proposed substrate models ofCYP [10][11][28]. For this reason, it is not unlikely that the substrate-basedand inhibitor-based small-molecule models can be combined.

3.6. Summary

A wide variety of substrates specifically metabolized by a given CYP iso-enzyme are generally available. This usually enables the selection of a suit-


Fig. 6. View of a CYP2D6 inhibitor model represented by the overall surface of strong inhibi-tors. a) Top-view of the model (right). b) Model rotated 90° around the x-axis (left). The proto-nated nitrogen atom is depicted in dark gray, with the proton in light gray. Three regions areindicated: a flat hydrophobic region (A), a region in which functional groups with lone pairsseemed to enhance the inhibitory effect (B), and a region in which hydrophobic groups were

allowed but did not seem to cause an enhanced inhibitory potency (C). Taken from [25].

able template for the small-molecule model. When a suitable template mole-cule is available, a combination of several structurally different compoundsmay also be successfully used as a template. The earliest reported substratemodels for CYPs are relatively crude small-molecule models, while the morerecently derived models are much more advanced and were constructed usingmore sophisticated computational modeling techniques. The latter models(e.g., for CYP1A1 [19], CYP2C9 [21], and CYP2D6 [10][11][25][28]) dem-onstrate a clear potential to predict the possible involvement of specific CYPisoenzymes in the metabolism of selected substrates and the nature of hypo-thetical interaction sites in the active site of the protein. For the polymorphicisoenzyme CYP2D6, small-molecule models have already been used to pre-dict its involvement in order to identify potentially large interindividual dif-ferences between extensive and poor metabolizers. This may pose risks topoor metabolizers either when a drug is not metabolized or a prodrug is notactivated due to the lack of CYP2D6 activity. Furthermore, these modelsmight be used to rationalize inhibitory properties of various compounds.

4. Protein Models

4.1. Introduction

Another computer-assisted approach to obtain structural information onthe active site of a protein (e.g., an enzyme) is the construction of a protein orhomology model (direct modeling). Homology modeling yields informationon the active site by constructing a three-dimensional model of the proteinbased on the amino-acid sequence and the crystal structure of one or moresimilar proteins. This method affords a three-dimensional representation ofthe protein and, more specifically, of its active site, as well as information onamino acids (potentially) involved in binding and catalysis [13]. Briefly, thereare two methods for constructing protein-homology models as depicted inFig. 7: the ‘cut-and-paste’ method and ‘comparative protein modeling by sat-isfaction of homology restraints’ as included in CONSENSUS [38] and inMODELLER [39].

In both cases, an alignment is made between the amino-acid sequences ofthe unknown structure and one or more crystallized template proteins, ideal-ly supplemented with structural or biochemical data. In the ‘cut-and-paste’protocol, homologous regions present in both the crystal structure(s) and theunknown structure are directly copied from the crystal structure(s) to thehomology model, while the non-identical parts are calculated for the modelor substracted from loop-structure databases (Fig. 7, left-hand side). In caseof comparative homology modeling (Fig. 7, right-hand side), many distance



Fig. 7. General procedures for the construction of homology (protein) models. Left-hand side:‘cut-and-paste’ method. Right-hand side: automated modeling using homology restraints as

implemented in CONSENSUS [38] or in MODELLER [39].

and dihedral restraints on the target sequence are calculated from the align-ment with the template X-ray structures. Several slightly different 3D-struc-tures of the target protein which all satisfy the large set of spatial restraintscan then be obtained using distance geometry [38] or a simulated-annealing

protocol [39]. The variability among these models can be used to estimate theerrors in the corresponding regions of the fold.

In both methods presented in Fig. 7, the homology model is energy-min-imized in the last step using molecular-mechanics methods. Substrates, inhib-itors, and metabolites can then be docked into the minimized homologymodel. The constructed homology model can also be validated and refinedwith experimental data (e.g., from site-directed mutagenesis and/or site-spe-cific modification experiments).

One of the aims of this review is to summarize and discuss various aspectsof homology-modeling techniques. Although a good amino-acid alignmentbetween similar proteins is a prerequisite for the construction of a proteinmodel, it is not our aim to discuss the various methods and software pro-grammes used to obtain (automatic) alignments.

4.2. Requirements

In order to build a homology model of a protein, at least one crystal struc-ture of a similar protein is required, as well as an alignment describing corre-sponding amino acids in the protein under investigation and the crystallizedprotein(s). The crystal structure(s) should preferably have a high resolution(1.5–2.5 Å) and a high (primary-sequence) homology with the protein underinvestigation. Ideally, the crystallized protein(s) belong(s) to the same familyof proteins ((iso)enzymes). The reliability of the alignment depends on thehomology between the crystal structure(s) used and the protein under inves-tigation. When the homology is relatively low, the alignment will containparts of questionable reliability, and consequently, various alignments will bepossible for such regions. In case of low homology, the algorithm used toderive the alignment also has an important influence on the final homologymodel, as different algorithms give rise to different alignments, and hence dif-ferent protein models. Generally, an automatically generated alignment needsto be adjusted manually based on available additional information, such assite-directed mutagenesis [40]. Use of multi-alignment techniques and secon-dary-structure predictions can also help aligning specific regions with a verylow homology [41].

4.3. Assumptions

The most important assumption inherent to homology modeling is that thethree-dimensional structure of the constructed protein is similar to that of thecrystallized protein used as a ‘template’. The validity of this assumption, of


course, depends on the specific protein under investigation and the availabil-ity of homologous crystal structures.

An important factor determining the quality of a homology model is theforcefield used for the molecular-mechanics calculations. Various pro-grammes, employing a variety of forcefields, can be used to build a homolo-gy model, to energy-minimize the model, and to dock the substrates, inhibi-tors, or metabolites. As the energy terms and parameters in the different force-fields are not identical, no direct comparison can generally be made of totalenergies obtained for homology models of the same protein by different pro-grammes. Structural comparisons can be made to a certain extent. However,differences in the forcefields employed will usually have consequences forthe final geometry of the protein model. When selecting a forcefield, oneshould first determine whether that specific forcefield gives an appropriatedescription of all aspects of the protein model under construction, e.g., that itcontains the correct parameters, in case of CYPs for example for the descrip-tion of a heme moiety. A specific set of parameters has been derived todescribe this heme moiety [42][43]. These parameters give an appropriatedescription of heme, but are not available in all homology-modeling pro-grammes. Ab initio calculations would circumvent the dependency of homol-ogy models on forcefields, but protein/enzyme structures are generally far toolarge for ab initio approaches.

4.4. Drawbacks of Homology Models

The drawbacks of homology models are closely related to the assump-tions mentioned above. A homology model will to a certain extent resemblethe crystal structure from which it was derived. This resemblance may be realor merely a consequence of the methodology used. When the homologybetween the available crystal structure(s) and the protein/enzyme for whichthe model is constructed is relatively low, the alignment of the respectivesequences is not straightforward. In the modeling studies mentioned in Sect. 3,several alignment programmes were used. Most of the automated alignmentswere manually adjusted to incorporate additional information (e.g., site-directed mutagenesis data) and to remove errors (e.g., insertions or deletionsin -helices). Although these manual adjustments introduce uncertainties,different authors have nevertheless independently derived almost identicalalignments [9][31][40].

The dependency of the geometry of the final protein model on the force-field used is another drawback. It is therefore advisable to perform the geom-etry-optimization calculations used to construct and optimize the homologymodel also on the crystal structure(s) used as a template, and to determine


first the changes occurring in the template structure(s) caused by this proce-dure. As several homology-modeling programmes and forcefields have beenused to optimize the geometry of resulting protein models, a comparison ofthe various models has to be considered carefully. Even when identical soft-ware packages are used, the forcefield parameters used in the various optimi-zation procedures are not always identical and unfortunately often not men-tioned in the publications.

4.5. Experimental Validation

The validation of protein models has to come from crystallization experi-ments or from other methods such as three-dimensional NMR. Often, howev-er, homology-modeling techniques are used when protein-structure determi-nations using three-dimensional NMR or crystallization have not been suc-cessful. Predictions as to, e.g., the possible role of specific amino acids inbinding of substrates and/or inhibitors and in the mechanism of catalysis canoften be verified experimentally using site-directed mutagenesis experimentsor site-specific modification experiments. Predictions concerning availablespace in the active site above different pyrrole rings can be assessed usingreactions between arylhydrazines or aryldiazenes with heme proteins, leadingto different iron N-arylporphyrins [44]. Such information can be derived fromNMR spin-relaxation studies as well, as performed recently for a number ofCYP2D6 substrates [45].

4.6. Homology Models for Cytochrome CYP Isoenzymes

4.6.1. Overview

Despite extensive efforts, no eukaryotic, membrane-bound CYP has beencrystallized so far. However, crystal structures have been resolved for sever-al soluble bacterial CYPs, for example CYP101 (CYPcam, schematicallyshown in Fig. 8a) without substrate [23][26], with camphor as bound sub-strate [30], with adamantanone, adamantane, camphane, norcamphor or thio-camphor as bound substrate analogs [26][46], with metyrapone or 1-, 2-, or4-phenylimidazole as bound inhibitors [47], with both enantiomers of a chi-ral, multifunctional inhibitor bound [48], and with 5-exo-hydroxycamphoras bound catalytic product [49]. Later on, crystal structures of CYP102(CYPBM3, schematically shown in Fig. 8b) without substrate [8][37][50],CYP107A (CYPeryF) with 6-deoxyerythronolide B as bound substrate [51][52], and CYP108 (CYPterp) without substrate [37][53] have been described


as well. Furthermore, a crystal structure has been published for a solubleeukaryotic Fusarium oxysporum CYP55 (CYPnor) [54] which, in contrast toother CYPs, does not possess monooxygenase activity, but reduces nitricoxide [55]. The core region of all available crystal structures of CYPs (con-taining the D-, E-, I- and L-helices and the heme-coordination region) is verysimilar [4][9][52][56][57], indicating that the three-dimensional structure ofthese regions is well conserved despite a low sequence homology, while otherregions (e.g., the active-site region containing the B′-helix [4][52][57], theloops between the C- and D-helices, the region spanning the F- and G-heli-ces, and some parts of the -sheets) are less similar [4][9][52][56][57]. Forthis reason, the core region of homology models of CYPs based on these crys-tal structures will likely be a reliable representation, while other parts willremain speculative.

Table 1 summarizes homology models built so far based on the availablesoluble bacterial CYP crystal structures. In principle, a crystal structure of amembrane-bound CYP would be the best starting point for homology model-ing of another membrane-bound CYP. In the absence of such a crystal struc-ture, however, CYP102 (a class-II CYP, to which many eukaryotic CYPsbelong) might be a better template CYP for homology-building studies [8][50][58] than CYP101 and CYP108 (class-I CYPs). Due to its larger endog-enous substrates (long-chain fatty acids, alcohols, and amides), CYP102 isexpected to have an active site that more closely resembles the active sites ofother CYPs than CYP101 [59]. This has recently been confirmed by a homol-ogy-modeling study on human thromboxane A2 synthetase (TXAS, CYP5)


Fig. 8. Schematic diagram of a) CYP101 (left) and b) CYP102 (right). Helices are represented as rods and -sheets as flat arrows. Taken from [55].


Table 1. Overview of Homology Models Built for Cytochromes P450, the Crystal Structure(s)Used as a Template, and Some Specifications of the Homology Models

Enzyme model Template Specificationa) and referencesCYP(s)

CYP1 b) 101 Complete CYP model. Little specific informationsupplied [102]

CYP1A1 101 Complete CYP model [103]. Alignment in conflictwith experimental data for CYP2A4/2A5 [30]

CYP1A1 101 Complete CYP model [104]

CYP1A1/1A2 102 Complete CYP model [105]

CYP1A1/1A2/1A6 102 Complete CYP models [106]



CYP1A2 101/102/107A/ Complete CYP model. Comparison with CYP2D6 108 and CYP3A4 [83]

CYP2A1/2A4/2A5 102 Complete CYP models [105]

CYP2A6 102 Complete CYP model. Incorporates data from avariety of site-directed mutagenesis studies toimprove/adjust the alignment. A limited amount ofspecific information about this model is supplied[68]

CYP2A 102 Complete CYP model [109]

CYP2B b) 101 Complete CYP model. Little specific informationabout this model is supplied [102]

CYP2B1 101 Complete CYP models, which do not explain allsite-directed mutagenesis results [67]

CYP2B1 102 Complete CYP model, including a suggestion formembrane attachment [78]

CYP2B1/2B4 102 Complete CYP model. Incorporates data from avariety of site-directed mutagenesis studies toimprove/adjust the alignment [68]

CYP2B4 101/102/107A/ Complete CYP model [110]108

CYP2B1/2B4/2B6 102 Complete CYP models. In agreement with sitedirected mutagenesis antibody recognition site resi-dues associated with binding redox partner residues[77]

CYP2B6 101/102/107A/ Complete CYP model [111]108

CYP2C3/2C9 102 Complete CYP model. Incorporates data from avariety of site-directed mutagenesis studies toimprove/adjust the alignment. A limited amount ofspecific information about this model is supplied[68]

CYP2C9 101 Complete CYP model. Site-directed mutagenesisdata used to improve the multi-alignment of the 2-family [13]


Table 1. (cont.)


CYP2C9 101/102/107A/ Complete CYP models [112]108

CYP2C9/2C18/ 101/102/107A/ Complete CYP models [113]2C19 108

CYP2C9/2C19 102 Complete CYP models [90][91]

CYP2D1/2D6 102 Complete CYP models. Incorporates data from avariety of site-directed mutagenesis studies toimprove/adjust the alignment. A limited amount ofspecific information about this model is supplied[68]

CYP2D6 101 Preliminary CYP model, only containing active siteregions of the protein (11 segments). Asp301 indicat-ed as important for catalytic activity [32]

CYP2D6 101/102/108 A set of 13 complete CYP models. Uses structuralalignment method, multiple alignment (16 CYPsequences) and NMR-derived distance restraints [45]

CYP2D6 101/102/108 Semi-complete CYP model containing active siteregion and well conserved regions (3 segments, onlyhighly variable loops omitted). Uses structural align-ment method and multiple alignment (66 CYPsequences). Incorporates data from site-directedmutagenesis results concerning the 2-family toimprove/adjust the alignment [31]

CYP2D6 102 Complete CYP model. Incorporates data of allelicvariants and site directed mutagenesis studies [114]

CYP2D6 101/102/107A/ Complete CYP models [63][64]108

CYP2D6 101/102/108 Complete CYP model including 51 docked sub-strates [35][36]

CYP2D6 101/102/107A/ Complete CYP model. Comparison with CYP1A2 108 and CYP3A4 [83]

CYP2E1 102 Complete CYP model. Includes data of species dif-ferences between rat, mouse and man [115][116]

CYP3A4 (CYPNF) 101 Complete CYP model. Only partially geometry opti-mized [117]

CYP3A4 101/102/107A/ Complete CYP model [61][62]108

CYP3A4 101/102/107A/ Complete CYP model. Comparison with CYP1A2108 and CYP2D6 [83]

CYP4A1/4A4/ 102 Complete CYP model. Incorporates data from a4A11 variety of site-directed mutagenesis studies to

improve/adjust the alignment [118]


CYP5 (TXAS) 101 or 102 Complete CYP models. Comparisons of modelsderived from CYP101 or CYP102 [60]

using both CYP101 and CYP102 (separately) as templates [60]. The authorsfurther suggested that models based solely on CYP101 should be reexaminedclosely, using the new crystal structures [60] in order to improve these mod-els.

The most reliable homology models so far have been constructed basedon multiple alignments and use site-directed mutagenesis data to enhance thereliability of the alignment [61][62]. Some models have been experimentallyvalidated by site-directed mutagenesis experiments, while in other cases thesite-directed mutagenesis experiments were based on initial predictions fromthe homology models. Recently, a set of protein models for CYP2D6 wasreported which incorporated distance restraints derived from NMR data inorder to enhance the quality of these models [45][63][64]. Although several


Table 1. (cont.)


CYP11A (CYPscc) 101 Complete CYP model [120]

CYP17 (CYP17) 101 Complete CYP model [84]


CYP17 (CYP17) 101 or 102 Complete CYP models. Comparisons of modelsderived from CYP101 or CYP102 [121]


CYP19 (CYParom) 101 Partial CYP model only containing heme region andI-helix [65]

CYP19 (CYParom) 101 Complete CYP model. Little specific information about this model is supplied [102]

CYP19 (CYParom) 101 Complete CYP model [84]

CYP19 (CYParom) 101/102/108 Semi-complete CYP model [56]

CYP19 (CYParom) 101/102/108 Partial CYP model containing heme moiety, I-helix,and C-terminus [123]

CYP19 (CYParom) 101 or 102 Complete CYP models. Comparisons of modelsderived from CYP101 or CYP102 [88]




CYP105/A1/B1 101 Complete CYP models generated with different(CYP SU1/SU2) alignments [126]

a) Complete CYP model = model constructed for complete enzyme, including regions with(very) low homology. Partial CYP model = regions with low homology have been omitted.Semi-complete/preliminary CYP model = regions with low homology, non-essential for cata-lytic activity have been omitted. b) Specific isoenzyme not specified.

homology models are based on multiple alignments methods, the use of site-directed mutagenesis data is less widely spread [13][31][45][65–69].

A selection of three recently constructed homology models using all threeavailable CYP crystal structures (CYP101, CYP102, and CYP108) and avariety of site-directed mutagenesis data (Table 1) will be discussed below:CYP2B1 [40], CYP2D6 [31], and CYP19 [56].

4.6.2. CYP2B1

CYP2B1 is one of the most active and versatile CYPs in the rat, and it cat-alyzes the 16-hydroxylation of androstenedione with a high degree of spec-ificity [70]. A homology model for CYP2B1 was constructed using a consen-sus-modeling method in which the coordinates of the model are weightedaverages of the coordinates of the three crystal structures [40]. The alignmentof the sequences of the three crystal structures was done using a structure-based alignment [9] in which positions of secondary-structure elements werealigned based on a structural superposition, rather than on an alignment basedon primary amino-acid sequences. Molecular-mechanics and molecular-dynamical techniques were used to optimize the protein model [40]. The sub-strates androstenedione and progesterone were docked into the active-sitearea of the protein model, and all site-directed mutagenesis data available forCYP2B1 could be explained by this model, in contrast to previous homologymodels constructed based on CYP101 alone [67]. This indicates the superior-ity of homology models that use all available crystal structures and combinethese with site-directed mutagenesis experiments or other protein-biochemis-try data, relative to models solely constructed from the crystal structure ofCYP101. A stereoview of androstenedione docked into the active site of thehomology model for CYP2B1 [40] is shown in Fig. 9.

The active site could be divided into an upper part containing residuesIle114 and Ile290 (not shown in Fig. 9), and a lower part with residuesGly478 and Ile480, which were shown to be important for activity [70–76].These two groups of residues could not interact with the substrate andro-stenedione simultaneously when it is docked in a 16- or 16-binding orien-tation. The key amino acids indicated by site-directed mutagenesis experi-ments were changed in the model after which androstenedione was dockedinto the mutant protein model in 16-, 16- and 15-binding orientations,thereby confirming the key roles of residues Ile114, Phe206, Ile290, Thr302,Val363, and Gly478, in agreement with site-directed mutagenesis data[70–76] and with the previously derived homology model for CYP2B1 [67].Other complete models of CYP2B1 based on CYP102 have also been de-scribed [77][78]. The latter model gives a suggestion that CYP2B1 could be


attached to the membrane via interactions of an N-terminal -helix, the pre-A helix region and the F-G loop. This model also presents substrate CYP2B1complex models for benzphetamine, testosterone, and benzo[a]pyrene [78].

4.6.3. CYP2D6

A homology model was recently constructed for human CYP2D6, a poly-morphic member of the CYP superfamily which is absent in 5–9% of theCaucasian population [22][23][79]. First, the sequences of the crystal struc-tures of the bacterial CYP101, CYP102, and CYP108 isoenzymes were struc-turally aligned [31] using a method similar to that described by Hasemannet al. [9]. Then, a multi-alignment for 66 members of the CYP2 family wasconstructed [31], which facilitated the alignment of CYP2D6 with the struc-tural alignment of the three crystal structures. This multi-alignment also ena-bled the use of site-directed mutagenesis data of other members of the CYP2family to improve the alignment between CYP2D6 and the structural align-ment of the sequences of the three crystal structures [31]. Molecular-mechan-ics calculations were used to optimize the constructed homology model [31].The active site consisted of the heme moiety, the F-, I- and K-helices, the loopbetween helices B and B′, the loop between the B′ and the C-helix, and -


Fig. 9. Androstenedione docked into the upper part of the binding pocket of the CYP2B1 modelin a 16-binding orientation. The substrate is shown in gray, with all hydrogen atoms displayed.

Taken from [40].

sheets 3 and 5. Three known substrates (debrisoquine, dextromethorphan, andGBR 12909, Fig. 10) and one inhibitor (ajmalicine) were docked into theactive site of the CYP2D6 model [31], indicating the protein model to be ableto accommodate large substrates, which extended considerably the boundar-ies of the previously derived small-molecule model for CYP2D6 [11][14]described in a previous section.

The orientation of the substrates relative to each other when docked intothe active site, the position of the heme moiety, and the position of the I-helixcontaining Asp301 (an amino acid proposed [32] and shown [33] to be cru-cial for the catalytic activity of CYP2D6) were used to improve the previous-ly described small-molecule model for CYP2D6 substrates (see Figs. 5c [11]and 5d [10]). The two amino acids in CYP2D6 for which site-directed muta-genesis data are available, namely Asp301 [33] and Val374 [80][81] wereindeed part of the active site of the derived protein model [31]. EspeciallyAsp301 is an important residue for catalytic activity as it forms a hydrogenbond with the basic nitrogen atom present in the substrates of CYP2D6, asindicated above. As no further site-directed mutagenesis data are yet available


Fig. 10. Orientation of GBR 12909 leading to benzylic hydroxylation docked into the activesite of the homology model for CYP2D6 [31]. The heme moiety is shown in light gray. The pro-tein is depicted in gray with Asp301 highlighted in black. GBR 12909 is shown in black,

with all hydrogen atoms displayed. Adapted from [31].

for CYP2D6, the suggested importance of other amino acids in the active siteof CYP2D6 cannot be validated. The homology model indicated a region ofthe active site to be a hydrophobic envelope in which only planar substratescould be accommodated, in close agreement with previously derived small-molecule models for CYP2D6 [10][11][27][29][82]. Recently, 51 substrateswere docked and evaluated in a refined model of CYP2D6, explaining 72metabolic pathways catalyzed by CYP2D6. It appeared that this model couldpredict correctly 6 out of 8 metabolites observed in a ‘test set’ of 7 com-pounds [30]. A comparable, complete CYP2D6 model was recently publishedtogether with models of CYP1A2 and CYP3A4 based on four bacterial crys-tal structures [83]. In total, 14 CYP2D6 substrates and 4 non-specific sub-strates known to be metabolized by CYP2D6 were successfully docked intothe active site. It was found that almost all substrates have important van derWaals (VDW) interactions with Val370, Phe483, and Leu484 in the activesite, whereas Asp301 is always involved in charge-reinforced hydrogenbonds with the protonated nitrogen atom of the substrates. This paper alsogives a suggestion for membrane attachment of mammalian CYP1A2,CYP2D6, and CYP3A4 [83].

One of several succesful applications of substrate and protein modeling inthe case of CYP2D6 was recently reported, i.e., the design, subsequent syn-thesis, and experimental validation of 7-methoxy-4-(aminomethyl)coumarin(MAMC, Fig. 11) as a novel and very selective substrate for high-througput-screening purposes [127]. In line with computational predictions using thesubstrate and protein models of CYP2D6, the affinity of MAMC was very


Fig. 11. Substrate and protein modelling in the case of CYP2D6 has led to the design of 7-methoxy-4-(aminomethyl)coumarin (MAMC; right). MAMC has been superimposed with

debrisoquine and dextromethorphan (left). Taken from [127].

high for CYP2D6 when compared to nine other human CYPs, and moreover,the metabolic product anticipated from the computer models was fluorescent,thus making metabolic assays and drug-drug interaction assays feasible in amicroplate-reader set-up.

4.6.4. CYP19

CYP19 (aromatase) catalyzes the conversion of C19 steroids to estrogens,one of the most complex and least understood CYP-catalyzed reactions [56].A recently built model for CYP19 [56] was based on the core structure of thethree crystallized CYPs using a structure-based alignment [9] relying on acombination of previously reported alignments from Hasemann et al. [53] andRavichandran et al. [8] Molecular mechanics and molecular dynamics wereused to optimize the homology model [56]. The active site was formed by theheme moiety, the loop between helices B′ and C, the I-helix, and -sheets 1and 4 [56]. The loop between helices B and B′ was not in the active site of thishomology model [56], in contrast with an earlier homology model for CYP19[84] based solely on CYP101 and in contrast to the homology model forCYP2D6 [31] based on the crystal structures of CYP101, CYP102, andCYP108, as described above. Two enantiomers of vorozole, a known inhibitorof CYP19, were docked into the active site of the protein model explainingexperimentally observed results [56], like the necessity for a kink in the I-helixwhich can be accomplished by either a proline residue or two glycine residues.Residues indicated by site-directed mutagenesis experiments to be importantfor catalytic activity, i.e., Glu302 [65], Asp309 [85][86], Thr310 [85][86], andIle474 [87], were indeed part of the active site [56]. Regions important forbinding of CYP19 and its redox partner were also predicted, indicating thatCYP19 cannot be classified as a class-I or a class-II CYP but is of an interme-diate CYP type [56]. Two other complete models of CYP19 based on CYP101and CYP102, respectively, have also been constructed [88]. Three steroidalinhibitors, four non-steroidal inhibitors, and two flavone phytoestrogens weredocked into the active sites. In this case, the authors preferred to evaluate theresults based on the CYP101 template where the F- and G-helix have moreimportant contributions to the structure of the active site [88].

4.7. Summary

All homology models of mammalian CYPs based on four bacterial crys-tal structures presently available indicate certain regions in the CYP isoen-zymes that can be modeled with relative ease and high accuracy (e.g., the


oxygen-binding domain near the heme, the helices D, E, I and L, and some -sheets [31][40][56]), and other regions in which the models are less reli-able due to large differences between the available bacterial CYP crystalstructures in these four regions (e.g., the B′-helix, the loops between the C-and D-helices, the region spanning the F- and G-helices, and some parts ofthe -sheets) [31][40][56].

In the near future, more reliable CYP protein models can be expected.Recently, a first crystal structure has been announced by Johnson and co-workers of the mammalian (rabbit) CYP2C5 enzyme. Generally, very usefulinformation concerning amino acids important for substrate and/or inhibitorbinding can be obtained using homology models, although, due to the relativelow homology in the substrate/inhibitor-binding-site region between the var-ious CYPs, these predictions should always be considered carefully and ver-ified experimentally. Homology models can therefore be very useful to guidesite-directed mutagenesis or site-specific modification experiments, but theycannot completely replace them. Concerning amino acids responsible for thecatalytic activity of a given CYP, homology models can merely be used toverify whether the observed differences can be rationalized using the mod-eled structure, since kinetic information on catalytic activities is not yetobtainable from theoretical interaction studies.

5. Computational Prediction of Toxicity (CPT)

Out of almost 2 million substances registered in the Chemical Abstracts,only 5000 are included in the Royal Society of Chemistry’s Dictionary ofSubstances and their Effects (DOSE) [89]. DOSE contains data on metab-olism and pharmaco-/toxicokinetics, acute and sub-acute and long-term tox-icity, carcinogenicity, teratogenicity, and reproductive effects, etc. Therefore,and because it is generally felt that toxicological animal tests have to bereplaced by fast, reliable and cheaper approaches predicting toxic effects,several toxicological endpoints have been described using so-called compu-tational predictive toxicology (CPT) methods. Chemical carcinogenesis hasbeen the main focus in this regard. The available CPT-techniques range fromstatistical modeling techniques to methods based on mechanistical know-ledge derived from a wide range of sources.

5.1. Selected CPT Methods

A Computer-Optimized Molecular Parametric Analysis of ChemicalToxicity (COMPACT) programme has been developed at the School of


Biological Sciences of the University of Surrey (see Table 2). COMPACTPredictions of toxicity are based on mechanisms of activation and on induc-tion of CYP1A and CYP2E, yielding mutagens and reactive oxygen specieswhich can initiate and promote tumors. The approach is based on relativelysimple computer-calculated molecular descriptors such as area/dept2, colli-sion diameter, E (LUMO–HOMO) and log P. Cluster analysis on thesedescriptors for a 100 compound training set yielded structural requirementsfor active compounds. Criteria for CYP1A, CYP2B, and CYP2E substratesand inducers have been described by COMPACT. Results obtained withCOMPACT have been combined with HazardExpert predictions to improvethe predictability by including metabolism [90–92].

The authors have related CYP selectivity to potential toxicity mecha-nisms: CYP1A, strong evidence of toxicity (reactive intermediates); CYP2E,suspected toxicity (oxygen radicals); CYP3A, possible weak toxicity;CYP4A, likely rodent toxicity (peroxisome proliferation); and CYP2B, lowlevel of toxicity. A rigorous evaluation of the predictive value of COMPACTis still missing, however.

The Computer Automated Structure Evaluation (CASE) programme,developed in the early 1980s by Klopman et al. at Case Western University inCleveland, is based on methods developed by Cramer et al. and Hodes et al.[93–95]. It uses topological descriptors found to be statistically relevant in thecorrelation with toxicological properties. Substructural fragments derivedfrom training sets are used to describe toxicological properties or to predictsuch properties for compounds outside the training sets. A quantitative formof CASE, in which the toxicity of compounds was provided in CASE-unitson a continuous scale, was presented in 1985. Preselected descriptors wereevaluated in a linear-regression analysis to produce QSARs. In later versionsof CASE, calculated log P and (log P)2 as well as imported quantum-mechan-ical molecular parameters were used to derive QSARs. In the early 1990s,CASE was superseded by MultiCASE, which contains CASE as an optionand handles databases in a hierarchical way. MultiCASE uses the concept of

Table 2. COMPACT Flowchart (adapted from [92])

Step Action

1 Construct molecule2 Minimize geometry3 Measure molecular geometry4 Calculate electronic structure5 Compare molecular parameters with training set (2D or 3D)6 Predict CYP isoenzyme selectivity7 Predict potential toxicity


biophores and modulators and breaks up training sets into subsets closer topremises used by a chemist than CASE does [95]. With the introduction ofMultiCASE, the cis/trans geometry of fragments, fragment environments,and expanded and composite fragments were introduced [95]. An exampledescribing the mutagenicity of pyrene is given in Fig. 12. CASE andMultiCASE have been compared as to their ability to predict carcinogenicitywhen trained on the same database.

The Toxicology Prediction by Komputer-Assisted Technology (TOP-KAT) programme was initially developed by Health Design Inc. [96] andlater taken over by Oxford Molecular Group Inc. TOPKAT uses QSTR-meth-odology for assessing specific adverse health effects, e.g., rodent carcinoge-nicity, Ames mutagenicity, developmental toxicity, skin sensitization, daphniamagna EC50values etc. The programme computes probable toxic effects ofchemicals solely from their chemical structure. The data (chemical structure,CAS numbers, experimental toxicity values, reference citations) used todevelop the models have been accumulated, evaluated, and standardized bystatisticians, toxicologists, computational chemists, and computer program-mers specialized in QSTR/QSARs. The descriptors used in TOPKAT modelsquantify the electronic (E-states), shape (14 indices per molecule) and sym-metry (7 indices per molecule) attributes of a molecular structure. TOPKATcomputes probability values for the toxicity of a chemical using a linearQSTR equation. TOPKAT does not consider inorganic compounds, organo-metallic compounds, or mixtures of compounds. TOPKAT has been imple-mented with the TSAR/QSAR package from Oxford Molecular enabling a


Fig. 12. CASE Prediction for mutagenicity of pyrene showing activating (right, top) anddeactivating (right, bottom) fragments. Redrawn from [128].

general toxicity estimate with QSARs. A typical TOPKAT prediction for lido-caine is presented in Fig. 13 [96].

The Deductive Estimation of Risk from Existing Knowledge (DEREK)systems was initially developed by Sanderson et al. [97] based on the LHASAsynthesis-planning programme developed by Corey’s group at HarvardUniversity. DEREK is interactive and rule-based. The rule starts with refer-ences to toxicophores, and the second part of the rule concerns a ‘compu-tational’ description of rule. The DEREK rule base can be separated into threesubsets of rules describing several toxicological endpoints.

HazardExpert (HEX) is another programme, developed by CompuDruginitially in 1987 as ‘a model of chemical toxicity in a compartimentalizedsystem’ and to predict toxicity of chemicals. Originally, a knowledge basecollected by the Environmental Protection Agency (EPA) was used to predictseveral classes of toxicity, including oncogenicity, mutagenicity, and neuro-toxicity in various biosystems, e.g., including mammals, fish, and plants. Theknowledge base has been further developed based on lists of toxic fragmentsreported by more than 20 leading experts. The values used by HEX can be setby the user to create additional biosystems and rules [92]. HEX uses an activefragment approach to predict toxicity. CompuDrug meanwhile developedother expert systems, notably MetabolExpert and Prolog P, to predict metab-olites and log P values, respectively. Initially, these programmes were basedon artificial-intelligence languages, but more recently, HEX was combinedwith neural-network technology to form a ‘hybrid’ system. Rigorous valida-tion tests of HEX have not yet been reported, although recently 456 com-


Fig. 13. TOPKAT Prediction sheet of the rat oral LOAEL for lidocaine. Redrawn from [96].

pounds have undergone HEX evaluation using experimental carcinogenicitydata taken from an IARC data base. Although HEX has been presented as aquantitative form of DEREK, its toxicity predictions are semi-quantitative,predicting one of five concern levels.

Very recently, SciVision has offered a comprehensive toxicological infor-mation system called TOXSYS. TOXSYS contains a wealth of toxicologicalinformation on over 230 000 compounds. It also uses neural-net analysis topredict potential toxicity of compounds. Because of its size and potentialapplicability, TOXSYS is worthwhile mentioning here, although the experi-ence with this programme in toxicological research is minimal as yet (seewww.scivision.com).

5.2. Comparison of Different CPT Methods

The best-known methods presently available make use of some sort of anon-congenericity correction and can be roughly divided into two groups, i.e.,rule-based and correlative methods. In Table 3, we present a comparative de-scription of the CPT methods briefly discussed here. The CASE/MultiCASE


Table 3. Brief Description of Some Computational Predictive Toxicology (CPT) Methods

Name Modeling type Method Ref.

CASE, Derives (Q)SARs via fragment-based correlative [95]MultiCASE automatic data-mining; previously

derived models can be used for predictions

TOPKAT Uses topologically based (Q)SARs derived correlative [96]by expert-guided data-mining

ADAPT Uses fragment-based human-guided correlative [98–100]pattern-recognition modeling tools

DEREK Expert system based on (bio-activating) rule-based [97]toxicophores

Oncologic Expert system utilizing a wide range of rule-based [101]user-provided compound properties

HazardExpert Expert system using positive and negative rule-based [92]conditions/toxicophores supported bymetabolism data and calculated log P, pKaand log D values

COMPACT Expert-derived SARs of toxicities correlative + [90]mediated by cytochrome P450 metabolism rule-based

TOXSYS Toxicological information system with correlativeneural-net analysis to predict toxicity

and TOPKAT methods are both based on correlative models. The CASE andMultiCASE programmes are developed as data-mining systems to find (new)SARs. MultiCASE is particularly useful as a discovery tool, while TOPKATis more aimed at validated assessments of toxicity [96]. The philosophy of theADAPT system is more closely related to that of CASE/MultiCASE than toTOPKAT, and it uses standard techniques with descriptors that can also beapplied to predict other properties than toxicity, e.g., retention times in liquidchromatography [98–100].

DEREK, HazardExpert and OncoLogic [101] are all rule-based systems,but their specific background as well as their applicability differ. DEREK andHazardExpert are semi-quantitative, and no further description of the sup-posed toxicity mechanism is needed. DEREK and HazardExpert include ruleson several toxicological endpoints, while OncoLogic [101] is restricted tocarcinogenicity.

Ideally, the goals for CPT methods should be: a) to generate predictionswith a known reliability or confidence limit, b) to be applicable to all types ofpotentially toxic agents (including organic, inorganic, polymeric compounds,minerals, and mixtures, and c) to accelerate risk assessment and the experi-mental toxicity-assessment programmes. However, no ideal CPT method isavailable as yet. Moreover, a rigorous evaluation and validation of the vari-ous CPT methods is not yet available. Negative predictions are not appropri-ately evaluated and validated at this moment, mainly because of lack ofknowledge in general or on negative indicators of toxicity. As long as this isthe case, CPT methods will not become a major tool in decision-making pro-cesses in drug discovery or environmental risk assessment. The current stat-us of the various CPT methods seems to divide investigators in believers andnon-believers, as apparent from several literature reports.

6. Conclusions

A number of small-molecule models have been derived for CYPs, basedeither on suitable template molecules or on a variety of substrates or inhibi-tors when a single compound was inappropriate as template. Several amongthese small-molecule models have been shown to have a good predictivevalue for metabolism and substrate/inhibitor selectivity, a property especiallyrelevant for isoenzymes which are subject to genetic polymorphisms (e.g.,CYP2D6) [10][11][25]. Despite the potential benefits (especially for thechemical and pharmaceutical industry), the development of small-molecule(pharmacophore) models for biotransformation enzymes has received rela-tively little attention as yet, in contrast to pharmacophore models for receptorligands.


The homology models for CYPs indicate that certain regions in the pro-teins can be modeled with relative ease and high accuracy (e.g., the oxygen-binding domain near the heme, the helices D, E, I, and L, and some -sheets[31, 34, 40, 56], while in other regions the homology models are less reliabledue to large differences between available crystal structures and the modeledCYPs (e.g., the B′-helix, the loops between the C- and D-helices, the regionspanning the F- and G-helices, and some parts of the -sheets) [31][34][40][56]. The topology of homology models are generally prejudiced by the tem-plate crystal structure. However, due to crystal-packing effects, the crystalconformation might differ from the conformation of the protein in a solvent.For this reason, additional information from three-dimensional NMR tech-niques would be useful to supplement the crystal structures.

Generally, useful information on the role of residues in substrate and/orinhibitor binding can be obtained using homology models, although due tothe relatively low homology of CYPs in their substrate-binding sites, thesepredictions have to be considered carefully and should be verified experimen-tally. Homology models can be used to guide site-directed mutagenesis andsite-specific modification experiments, but cannot completely replace them.As for the role of amino acids in the catalytic activity of a given CYP, homol-ogy models can merely be used to verify whether the observed differencescan be rationalized using the modeled structure, since information on catalyt-ic activities cannot be obtained from these theoretical interaction studies.Cautious indications of substrate selectivity can be given in specific cases,although these predictions also have to be considered carefully and verifiedexperimentally.

For the purpose of computational predictions of toxicity, multiple comput-er programmes now exist. However, ideal programmes are not available yet,namely systems able to generate predictions with a high reliability, possessinga broad applicability thus really accelerating risk-assessment programmes. Acomparison of softwares and their validation (including sufficient negativepredictions) are also missing. As long as this is the case, CPT methods willprobably not become a major tool in drug discovery and risk assessment.

Generally speaking, we can conclude that computational approaches(often named ‘in silico’ or ‘in computro’), in parallel with high(er) troughputexperimental technologies, are gradually becoming one of the newer and faster-developing approaches in drug metabolism, drug discovery, and toxi-cology. When new links with other recent developments (neural-network com-puting, genomics, proteomics, and bioinformatics) will be created, in silico orin computro prediction of drug metabolism and toxicity is likely to becomeone of the methodologies with great scientific and practical value.

The authors kindly acknowledge the valuable contributions of Dr. M. J. de Groot and Dr.B. Martens.


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Part VI. Conclusion

Molecular Biology, Drug Design, and Drug Delivery: Bringing It All Together

Vincent H. L. Lee*, Sharon K. Wu, and Chun Chu

Pharmacokinetic Lead Optimization: Fine Art vs. Blind TechnologyBernard Testa


Molecular Biology, Drug Design, and Drug Delivery:

Bringing It All Together

by Vincent H. L. Lee*, Sharon K. Wu, and Chun Chu

University of Southern California, Department of Pharmaceutical Sciences, 1985 Zonal Avenue, Suite 704, Los Angeles, CA 90089-9121 U.S.A.;

Fax: +1 323 442 13 68; e-mail: [email protected]

1. Introduction

The last decade witnessed an increased awareness of a role for membranetransporters in drug absorption that heretofore was assumed to involve onlysimple passive diffusion. Tsuji and Tamai [1] summarized this subject suc-cinctly in a 1996 review. The acceptance of membrane transporters as a con-duit of drug absorption is creating a unique opportunity for integrating drugdesign and delivery with the overall goal of developing drugs with improvedefficacy. At the center stage of this paradigm shift is the emergence of molec-ular biology, along with computational chemistry, cell biology, and materialscience as important resources.

This presentation will focus on the intestinal dipeptide transporter PepT1(Fig. 1) as an integrative example. First, we will review various approachesthat have been applied to determine the substrate-binding domain of thistransporter, including the synergistic approach based on computer modelingand site-directed mutagenesis. Second, we will describe the subcellular distri-bution of PepT1 and its regulation as an opportunity for drug delivery.

hPepT1 is a 708 amino acid, 12-transmembrane domain (TMD), proton-coupled transporter protein that plays an important role in the transport ofnutritional di- and tripeptides as well as peptidomimetics such as penicillins,cephalosporins, and angiotensin-converting-enzyme (ACE) inhibitors [2–9]. A distinguishing structural feature of PepT1 is its unusually large hydrophilicloop with several N-glycosylation sites. By epitope tagging at selected loca-tions, Covitz et al. [10] confirmed that the C-terminal end of hPepT1 is indeedintracellular and that the loops between TMD9 and TMD10 and betweenTMD3 and TMD4 are extracellular, as predicted by the hydropathy model.


The broad substrate specificity of the dipeptide transporter makes it aprime target for the design of peptidomimetic drugs and prodrugs withimproved drug-delivery characteristics [11–15]. As a case in point, L-Dopa,upon derivatization to L-Dopa-L-Phe, a substrate of hPepT1 [16], gained 40-fold in transport across Caco-2 cell monolayers. Dipeptide prodrugs of -methyldopa (Fig. 2), such as Phe--methyldopa, -methyldopa-Phe, and-methyldopa-Pro, achieved 4–20 times higher intestinal permeability than-methyldopa itself in a rat ileum-perfusion model [17]. Similarly, valacyclo-vir (Val-ACV) and val-zidovudine (Val-AZT), L-valyl ester prodrugs of thenucleoside analogs acyclovir and AZT (Fig. 3), were demonstrated to betransported by PepT1 with a 3–10-fold increase in permeability [14]. For rea-sons that are not immediately forthcoming, esterification with L-valine out-


Fig. 1. A schematic model of PepT1 (from [37], with permission)


Fig. 2. Chemical structures of L-dopa and -methyldopa and their prodrugs

performs that with other amino acids in improving drug uptake (Fig. 4).Moreover, the L-valyl ester showed a higher affinity for rPepT2 (see later fordetails) than for rPepT1 (Table 1) [18]. Stereochemically, the L-configurationof amino-acid ester prodrugs afforded a 3–10-fold more favorable membranetransport than the D-configuration in the rat small intestine, CHO/hPepT1cells, and Caco-2 cells [14] [18].

Table 1. Inhibition Constants (Ki) of [14C]Glycylsarcosine Uptake for rPepT1 and rPepT2-Expressing Cells [18]

Transporter Ki (mM)

valacyclovir L-valine methyl ester

rPepT1 2.7 3.6rPepT2 0.22 0.83

2. Substrate Specificity of PepT1: Defining the Parameters of Drug Design

Pinpointing the structural requirements for substrate recognition by pep-tide transporters has been an elusive goal in drug design for several decades.One approach is based on a systematic investigation, in organ perfusions andisolated brush-border membrane vesicles (BBMV), of the structural influenceon the uptake/transport of various di- and tripeptides, -lactam antibiotics,


Fig. 3. Chemical structures of ACV and AZT and their prodrugs

and inhibitors of peptidases [7][19–21]. These early studies led to the conclu-sion that a free carboxylic-acid moiety and an amide bond were necessary forsubstrate binding [6][22–25]. Recent studies by Schoenmakers et al. [26] fur-ther revealed the carbonyl group of the peptide bond as an essential structu-ral feature for transport by the intestinal dipeptide transporter. Thus, reductionof the peptide bond of enalapril resulted in a compound (enamipril (Fig. 5))


Fig. 4. Selectivity of L-amino-acid methyl esters on [14C]glycylsarcosine uptake by LLC-rPepT1 (A) and LLC-rPepT2 (B) (from [18], with permission)

Fig. 5. Chemical structures of enalapril and enamipril

that did not show polarized and saturable transport. Moreover, the intestinalpeptide transporter seems to prefer the trans to the cis backbone conforma-tion. This is the case for an Ala-Pro derivative (Ala- [CS-N]-Pro), where thepeptide carbonyl oxygen is replaced by sulfur (Fig. 6) [27].

Another approach in pinpointing the structural requirements for substraterecognition by peptide transporters is based on molecular modeling [25–30].In its simplest form, molecular modeling is applied to determine the lowest-energy conformation of model substrates. The optimized substrate conforma-tions thus identified are then superimposed starting from the N-terminalregions to map the pharmacophore – a common arrangement of essentialatoms or groups of atoms appearing in each active molecule. Molecularmodeling is expected to improve our understanding of the observed differenc-es in substrate affinity on the basis of three-dimensional structure compari-sons.

By performing computer-aided conformational analysis and MOPAC cal-culations (a semiempirical molecular-orbital program) on penicillins, cepha-losporins, and ACE inhibitors, Swaan and Tukker [24] set out to establishguidelines for predicting, within limits, the transport behavior of untestedmolecules on the basis of affinity for the intestinal peptide transporter. Theiranalysis revealed that affinity for the peptide transporter can be diminished orabolished in three different ways: a) esterification of the carboxylic-acidmoiety, b) introduction of a second negatively charged group, and c) intra-molecular steric hindrance of the free carboxylic acid by either side chainswith a positively charged nitrogen function or groups capable of hydrogen-bond formation [28]. Nevertheless, the predictive capacity of the model ofSwaan and Tukker is limited, because steric and electrostatic effects of non-pharmacophoric groups in the molecule were not considered. To overcomethis shortcoming, Swaan and Tukker [31] combined their pharmacophoremodel with comparative molecular field analysis (CoMFA), a three-dimen-


Fig. 6. Trans and cis conformations of an Ala-Pro derivative Ala-[CS-N]-Pro

sional approach towards building quantitative structure-activity relationships.By incorporating various parameters such as Kt (the ratio of the half-maximalconcentration), Jmax (the maximal transporter flux), and Pc (transporterpermeability, defined as Kt/Jmax) into the model, this refined analysis hasrevealed that substrates with electronegative moieties at one end and stericbulk at the opposite end appear to influence favorably transporter affinity.

In a separate study, Li and Hidalgo [29] and Li et al. [32] sought to deter-mine the distance between the amino and carboxy groups for interaction witholigopeptide transporters. Their analysis revealed 5.5 Å as the distancebetween the -NH2 group and the free –COOH group required for optimalaffinity for the transporter. Distances of 7–9 Å led to lower affinity, and dis-tances greater than 9 Å abolished affinity. In addition, the binding affinity ofsubstrates may be influenced by the position of the second peptide bond rel-ative to the -NH2 group and the molecular dipole moment [30].

There is growing evidence that the presence of a peptide bond is not anabsolute structural requirement for substrate recognition and/or translocation.For example, (4-aminophenyl)acetic acid (4-APAA) (Fig. 7), designed tomimic the spatial configuration of a dipeptide, has been shown to interactwith and translocate through the intestinal peptide transporter in a variety ofmodel systems. These model systems include excised rat intestine, entero-cytes isolated from mouse small intestine, BBMV isolated from rat renal cortex, and Xenopus oocytes expressing PepT1 [33]. As another example, -aminolevulinic acid (-ALA) (Fig. 7) mimics a dipeptide whose peptidebond is replaced by a ketomethylene group, without loss of substrate affinityof the renal peptide transporter [34]. The same is true for arphamenin A, apeptide-hydrolase inhibitor with a ketomethylene group [34][35].

A final example of a substrate without peptide bonds are -amino fattyacids (-AFA) (Fig. 8), which have two ionized head groups and a hydrocar-bon backbone [36]. By transport assays in combination with conformationanalysis based on energy minimization, -AFA with two charged centers(carboxylic carbon and amino nitrogen) separated by at least four methylenegroups (5–6.3 Å) displayed high affinity for binding and transport by themammalian intestinal peptide transporter [36]. Removal of either the amino


Fig. 7. Chemical structures of (4-aminophenyl)acetic acid (4-APAA) and -aminolevulinicacid (-ALA)

group or the carboxy group in -AFA maintained the affinity of the com-pound for interaction with the transporter but abolished the capability fortransport.

3. Structure-Function Relationship of PepT1: Moving Closer to the Substrate-Binding Domain

Cloning of cDNAs encoding PepT1 (a low-affinity type) and PepT2 (ahigh-affinity type) expressed in the intestine and kidney, respectively, is amilestone in transporter physiology and pharmacology [37–43]. Once theindividual transporter cDNA or gene is cloned and sequenced, it is possibleto predict its putative secondary structure and to express the transporter pro-tein for characterizing its transport properties. PepT1 and PepT2 shareapproximately 50% identity in amino-acid sequence, but they share only 21%identity in the large extracellular loop connecting transmembrane domain(TMD) 9 and 10 [39]. The amino-acid sequence in the transmembranedomains, believed to be characteristic of the PepTx family, is more conserved


Fig. 8. Chemical structures of -amino fatty acids

than that in the intra- or extracellular loops [44]. In terms of substrate speci-ficity, PepT1 appears to be relatively cyclacillin-selective, whereas PepT2appears to be relatively cefadroxil-selective (Table 2). Other differencesbetween PepT1 and PepT2 are pH and membrane-potential dependence [45][46]; PepT1 is more dependent on pH (Fig. 9) and membrane potential thanPepT2 (Fig. 10) [45][46].

3.1. Chemical Modification of Amino-Acid Residues

Information about the combination of constituent amino acids and thefunctional moieties that govern transporter function is scarce [47]. In the pasttwo decades, chemical modification of Tyr and His [48], site-directed muta-genesis of His [49][50], and construction of chimeras of PepT1/PepT2 [51]and of PepT2/PepT1 [45][51] have been attempted. Miyamoto et al. [52]were among the first to address the structure-function relationship of the renaldipeptide transporter, which was further extended by Kato et al. [53] andKramer et al. [48][53] for the intestinal isoform. In 1990, Kramer et al. [48]reported that treatment of rabbit brush-border membrane vesicles with N-acet-ylimidazole (a tyrosine modifier) or diethylpyrocarbonate (a histidine modi-fier) abolished dipeptide transport. This finding underscores the importantrole of the tyrosine and histidine residues, respectively, in the function of theintestinal dipeptide transporter. The early finding of a role of histidine inhPepT1-mediated dipeptide uptake was subsequently confirmed by site-directed mutagenesis of His57 (TMD2) and His121 (TMD4), which are con-served across species and isoforms, by Terada et al. [49] in the rat and by Feiet al. [50] in the human PepT1. Although replacement of His57 with aspara-gine in the human homolog resulted in elimination of glycylsarcosine (Gly-Sar) uptake, replacement of His121 with asparagine did not affect transport-er function [50]. Consequently, histidine residues, predicted in transmem-brane domain 2 and 4, are probably involved in substrate binding [49][50].


Table 2. Inhibition Constants (Ki) of [14C]Glycylsarcosine Uptake for Intestinal and Renal Peptide Transporters, PepT1 and PepT2 [7]

Experimental Ki (M)Model

cyclacillin cefadroxil

Caco-2 cells (PepT1) 0.6 ± 0.1 5.4 ± 0.6SKPT cells (PepT2) 41.6 ± 1.5 3.0 ± 0.2hPepT1 in HeLa cells 0.35 ± 0.09 0.87 ± 0.12hPepT2 in HeLa cells 610 ± 100 66 ± 4

3.2. Engineering of Chimeras

The analysis of chimeric proteins engineered from closely related mem-brane transporters is a powerful strategy to determine the role of large struc-tural domains, while limiting disruption in protein folding [54]. Döring et al.[45] were the first to engineer a recombinant chimeric peptide transporter,CH1Pep, for identifying structural components of the transporter proteins thatdetermined their phenotypical characteristics (affinity, substrate specificity,and pH dependence). CH1Pep contains amino-acid residues 1–401 derivedfrom TMD1 to TMD9 of PepT2, and 402–707 derived from PepT1 starting atthe end of TMD9 to TMD12 (Fig. 9). D-Phe-Ala uptake by oocytes express-ing PepT1, PepT2, or CH1Pep was evaluated as a function of pH (Fig. 9),membrane potential (Fig. 10), substrate concentration (Fig. 11), and inhibi-tion by cefadroxil (Fig. 12). The results suggested that CH1Pep converted


Fig. 9. Schematic diagram of two transporters and one chimeric transporter, and pH depen-dence of D-Phe-Ala uptake into Xenopus oocytes expressing the three transporters (from [45],

with permission)

PepT1 into a PepT2-like transporter. It follows that the N-terminal regionprobably determines the phenotypical characteristics of PepT2, and that thelarge extracellular loop between TMD9 and TMD10 and the C-terminalregion may not play a significant role in determining substrate affinity of


Fig. 10. Recordings of inward currents and I–V relationships in Xenopus oocytes expressingthe three transporters (from [45], with permission)

Fig. 11. Concentration dependence of D-Phe-Ala uptake and kinetic constants derived by theleast-squares method in Xenopus oocytes expressing the three transporters (from [45], with

permission)

PepTs [55]. A follow-up study by Fei et al. [51], using PepT1/PepT2 andPepT2/PepT1 chimeras (Fig. 13), confirmed that the TMDs 1–9 rather thanthe large extracellular loop were important for substrate binding and translo-cation. This study further narrowed the possible substrate-binding domain toTMDs 7, 8, and 9.

The chimeric-transporter approach was also utilized by Giacomini’sgroup [56–58] and Young’s group [59][60] to define the potential substrate-binding site for concentrative (CNT) and equilibrative nucleoside transport-ers (ENT), respectively. Examination of the substrate specificity of chimerasconstructed from hCNT1/hCNT2 [60] and rCNT1/rCNT2 [56][58] indicatesthat TMDs 7–8 and TMDs 8–9 are potential determinants of substrate selec-tivity, respectively, and that TMDs 3–6 of hENT1/rENT1 chimeric transport-ers may be responsible for the binding of both substrates and inhibitors [59].By sequence comparisons among homologs or isoforms, molecular determi-nants within the putative substrate-binding site were identified and exchangedby site-directed mutagenesis to assess their roles in transport activity and sub-strate binding [56][57][60].


Fig. 12. Inhibition of D-Phe-Ala uptake into Xenopus oocytes expressing the three transportersby cefadroxil (from [45], with permission)

A limitation of the chimera approach is that one can identify only thoseresidues that confer differences in kinetic behavior; residues that are critical-ly involved in transporter function will be missed if they are conserved in thespecies homologs and isoforms used to create chimeras. Thus, findingsderived from chimeric-transporter studies must be corroborated with furthersubstitution and/or deletion mutagenesis experiments on the individual trans-porter.

3.3. Computer Modeling and Site-Directed Mutagenesis –The Computational Chemistry/Molecular Biology Alliance

Computer modeling offers a systematic approach to the elucidation oftransmembrane-protein structure. Currently, direct structural approaches totransmembrane proteins are very limited. Due to the very nature of mem-brane-bound proteins, they are technically demanding to be crystallized forstructural characterization by spectroscopic methods [61]. That being thecase, a computer-modeling approach offers an attractive alternative. As an


Fig. 13. Secondary structures of chimeric PepTs composed of different domains derived fromthe parental hPepT1 and rPepT2 (from [51], with permission)

example, the three-dimensional structure of the transmembrane glucose chan-nel of Glut1 glucose transporter has been predicted in this way [62].

As pointed out by Bolger et al. [63], elucidating the structure of PepT1 bycomputer modeling is a long term, iterative process, with one iteration involv-ing the theoretical development of the model, the prediction of key aminoacids from the initial model, the experimental construction of mutated formsof the protein based on these predictions, and the measurement of peptideuptake in cell lines transfected with the mutated PepT1 cDNA. The informa-tion obtained from these experiments is then used to refine the model, fol-lowed by a second iterative loop, and so on.

a) Model development. We invoked three assumptions in the developmentof a theoretical model for the transmembrane channel of PepT1 [63]: i) That the -helical transmembrane domains (TMDs) are important forsubstrate binding and translocation, an assumption which has been proven to be correct by Döring et al. [45] and Fei et al. [51], ii) That the -helical transmembrane domains would pack against each other in apairwise manner that could be predicted by calculating the lowest-energyinteraction between two helices, and iii) That the general organization ofthe transmembrane domains could be approximated by considering theamphipathicity and/or hydrophobicity of each transmembrane -helix.

b) A Putative Model of the PepT1 Transmembrane Domain. Based on thepairwise calculations and amphipathicity, a model was constructed thataligned the TMDs with highest amphipathicity next to the centralchannel (Fig. 14). Using the MidasPlus program from the UCSFComputer Graphics Laboratory [64], a preliminary set of amino acidsthat might form a putative channel of the dipeptide transporter wasidentified. They are shown schematically in Fig. 15, which refers tothe central region of the transporter channel as the bubble region. Theentry from the extracellular side to this bubble is restricted by W294,Y588, and E26. In the center of the bubble are Y12, E595, D341, andY91. Flanking the exit are Y167 and R282.

c) Mutations Based on Predictions from the Model. As an illustration ofour experimental approach to test this model prediction, we focused onY167, which was mutated to alanine. Alanine was chosen because ithas a short, nonpolar side chain, does not cause major conformationaldisturbances to the protein structure, and is also routinely found in bothburied and solvent-exposed positions in proteins. Mutation of Y167 toalanine was achieved and the mutated protein was successfully ex-pressed in human embryonic kidney HEK293 cells [65].

As shown in Fig. 16, the Y167A mutation essentially abolished the uptakeof [3H]Gly-Sar by HEK293 cells [65]. The abolished function of Y167A-



Fig. 14. A schematic diagram of the twelve transmembrane arrangement of human PepT1(looking from the top into the cell). The shaded area in the center represents the putative channel

(from [70], with permission).

hPepT1 was not due to altered biosynthesis or processing which could gener-ate immature transporter proteins. Moreover, immunofluorescence microsco-py revealed that both wild-type and Y167A-hPepT1 were expressed at com-parable levels at the plasma membrane, suggesting that the diminished uptakeof [3H]Gly-Sar by Y167A-hPepT1 is a result of an effect of the mutation onthe transport function rather than an effect on protein folding and delivery tothe plasma membrane.

To further investigate the obligatory role of Y167, site-directed mutagen-esis was used to generate the Y167F-, Y167H- and Y167S-hPepT1 mutations.As can be seen in Fig. 16, none of these mutations restored the transport func-tion of hPepT1. This finding indicates that the strategic role of Tyr167 inPepT1 function resides in both the chemical and spatial property of its phe-nolic group.

The work just described is representative of one circuit around the ‘pre-diction-mutagenesis-uptake’ loop, and additional mutations are required tomap the substrate-binding domain. Moreover, substrate-specificity studies onthe library of mutated PepT1 forms generated may provide important base-line information for understanding possible genetic polymorphism of thisimportant transporter.


Fig. 15. A schematic diagram of the putative transmembrane channel for human PepT1 withthe key amino-acid residues (side view) (from [70], with permission)

Fig. 16. Uptake of [3H]Gly-Sar (20 nM) in HEK293 cells transfected with wild-type hPepT1(WT), plasmid vector (MC) alone or the Y167 mutated hPepT1. Values represent mean ±s.e.m. (n = 3). Asterisks indicate statistical difference at p < 0.05 (from [65], with permission).

4. Subcellular Distribution of PepT1: An Opportunity for Drug Delivery

PepT1 may function at the apical plasma membrane and other subcellularlocations. As with other membrane transporters [66], the localization ofPepT1 may depend upon the regulation of its intracellular trafficking, asshown in Fig. 17. The most well-characterized transporters in this regard arethe gastric H+,K+-ATPase, collecting-duct aquaporin, Glut4 glucose trans-porter of fat and muscle cells, the ubiquitous Na+,K+-ATPase, and cysticfibrosis transmembrane-conductance regulator (CFTR). Of these transport-ers, only CFTR has been reported to function at a distinct intracellular site[67]; for the remainder, the mode of regulation of these transporters by vesic-ular trafficking appears to be to sequester the transporters in an ‘inactive’form. Recent reports [68–70] suggest that a significant fraction of PepT1resides intracellularly at steady-state. In particular, PepT1 is localized to lyso-somes.

The distribution of PepT1 in subcellular locations other than the apicalmembrane represents an opportunity for manipulating the population densityof PepT1 at the apical membrane for the optimization of drug absorption. Thismay be achieved in the following ways: a) hormonal regulation, b) pharmaco-logical regulation, c) dietary control, and d) biopolymer-mediated regulation.


Fig. 17. Putative trafficking pathways for PepT1 (from [70], with permission)

4.1. Hormonal Regulation

Protein-kinase cascades have been demonstrated to regulate the activityof membrane-transport proteins either directly or indirectly in many modelsystems. Direct effects occur through phosphorylation of the transporter. Thismay change its kinetics, such as affinity, maximum velocity, or turnover num-ber, as is the case for the taurine transporter [71]. Indirect regulation alters the rate of insertion into or retrieval from the plasma membrane, as is the casefor the Na+-glucose cotransporters Glut1 [72][73] and Glut4 [74], and the Na+-/Cl–-coupled neurotransmitter transporters such as -aminobutyric-acid(GABA) transporter and norepinephrine (NE) transporter [75].

Activation of protein-kinase C (PKC) by phorbol esters has been shownto lead to both stimulation [76] and inhibition of transporters [77][78]. Theactivity of PepT1 in the human colon-carcinoma cell line Caco-2 [79] and inthe Madin-Darby canine kidney (MDCK) cells [80] appears to be regulatedby PKC. Treatment of Caco-2 cells with phorbol 12-myristate 13-acetate(PMA) and mezerein, stimulators of PKC, resulted in a 34% and 29% reduc-tion in glycylsarcosine (Gly-Sar) uptake, respectively. This inhibition, whichwas blocked by staurosporine (a PKC inhibitor), was associated with adecrease in Vmax and no change in Kt [79]. There was no change in the pro-ton gradient that drives the dipeptide transporter. In MDCK cells, treatmentwith PMA decreased Gly-Sar uptake by 21%, whereas treatment with stau-rosporine increased Gly-Sar uptake by 34%. Inhibition was associated with adecrease in Vmax, with no change in Kt. In these studies, no attempt was madeto determine whether the PKC effects on PepT1 function occurred throughphosphorylation of the transporter or through indirect effects on the traffick-ing machinery.

The acute translocation of PepT1 from the intracellular PepT1 pool to theapical surface was reported by Thamotharan et al. [81] in 1999. They foundthat preincubation of Caco-2 cells with 5 nM insulin for 1 h stimulated Gly-Gln uptake by 80%, consistent with an elevation of the apical expression ofPepT1 by the same magnitude. This effect manifested itself within 60 min.There was no change in the mRNA level of PepT1. Moreover, disruption ofthe trans-Golgi network (TGN) with 5 M brefeldin A, thereby halting themigration of newly synthesized PepT1 to the apical membrane, did not affecteither the basal or insulin-stimulated dipeptide uptake. By contrast, 10 M

colchicine, which depolarizes microtubules (MTs), abolished insulin-stimu-lated dipeptide uptake, even though it itself did not have any effect on basaldipeptide uptake. This finding suggests that insulin may stimulate the trans-location of PepT1 to cell surface in a MT-dependent manner.


4.2. Pharmacological Regulation

Fujita et al. [82] recently reported that a selective 1 ligand, (+)-pentazocine,increased the uptake of Gly-Sar in Caco-2 cells in a concentration- (0.001–10 M) and time-dependent manner (1–24 h). A minimum of 2 h of incuba-tion was required, and the maximal increase in dipeptide uptake was 200%.Kinetically, this can be attributed entirely to an increase in the maximumvelocity of dipeptide uptake. Semi-quantitative RT-PCR suggests that (+)-pentazocine up-regulates PepT1 in Caco-2 cells at the level of increasedmRNA. This study sets the stage for further studies on drug-drug interactionsat the level of PepT1.

4.3. Dietary Regulation

Walker et al. [83] reported that adding 4–10 mM Gly-Gln to the culturemedia of Caco-2 cells for 3 days resulted in a 1.92-fold increase in the amountand a 1.4-fold increase in the half-life of PepT1 mRNA, a 1.72-fold increasein PepT1 expression at the apical membrane, and a 1.64-fold increase in theVmax of Gly-Sar uptake. These findings were corroborated by Shiraga et al.[84], who observed an up-regulation of dipeptide-uptake activity in the smallintestine of rats fed a diet containing 20% or 50% casein or 20% of a dipep-tide (Gly-Phe) for three days. These investigators further demonstrated thatthe up-regulation of dipeptide transport activity by dietary protein was causedby transcriptional activation of the PepT1 gene by selective amino acids anddipeptides in the diet. Paradoxically, starvation also increased the expressionof PepT1 [85][86]. Ogihara et al. [86] observed a 2-fold increase in theexpression of PepT1 at the villus tips of the jejunum in rats starved for 4 days.Thamotharan et al. [85] observed a similar increase in rats starved for onlyone day, roughly corresponding to a 3-fold increase in PepT1 protein expres-sion in the brush-border membrane, and a 3-fold increase in PepT1 mRNAexpression in the intestinal mucosa. On this basis, these investigators suggest-ed an increase in PepT1 gene expression as a possible underlying mechanism.

4.4. Biopolymer-Mediated Regulation

An intriguing but untested way to manipulate the ratio of PepT1 betweenthe apical plasma membrane and the intracellular pool is that based on bio-polymers that show an affinity for cell surfaces and which further trigger bio-chemical changes in the underlying cells. An example of such a polymer ischitosan, a cationic polymer that is available in varying degrees of acetylation


and molecular weight [87–93]. The efficacy and toxicity of these polycation-ic polymers as absorption promoters are a function of their degree of acetyla-tion and molecular weight [93][94].

Takeuchi et al. [95] demonstrated that chitosan (15% acetylation, Mr

150000), when coated on multilamellar liposomes (dipalmitoylphosphatidyl-choline/dicetyl phosphate 8 : 2) that were loaded with insulin, enhanced theenteral absorption of insulin in the rat. Based on the hypoglycemic response,the oral bioavailability was 5–10%, when compared with <1% from uncoat-ed liposomes. Although Takeuchi et al. [95] attributed such an enhancementin oral insulin bioavailability to mucoadhesion, it is conceivable thatenhanced epithelial permeability was an additional factor. Indeed, Arturssonet al. [88] and Kotzé et al. [96] have provided evidence for expansion of theparacellular pathway by chitosan. Moreover, Witschi and Mrsny [97] pro-posed that chitosan might also enhance protein transcytosis. Using a humanairway-epithelial cell line (Calu-3), these investigators found that spray-driedchitosan microspheres (14–17% acetylation, Mr 300000, 2–4 m in diame-ter) enhanced the transport of bovine serum albumin by 20 times over a 6 hperiod. Induction of cytokine release, such as IL-6 and IL-8, was suggestedas the triggering factor. It would be interesting to evaluate whether chitosanalso affects the trafficking of membrane-bound transporter proteins, such asPepT1.

5. Conclusion

We are entering a new era of drug design in which integration with drug-delivery has the potential of becoming the norm. A significant chapter of thisexciting development is the recognition of membrane drug transporters as aviable conduit of drug absorption. To derive maximal benefit from these drugtransporters, it would be desirable to have knowledge about the substrate-binding domain for designing an optimal drug substrate and to have knowl-edge about the regulation of transporter distribution between the apical mem-brane and the intracellular pool for designing drug-delivery approaches thatmaximize drug uptake. We feel that molecular biology, together with compu-tational chemistry, cell biology, and material science, is the foundation sci-ence best suited for bringing drug design and drug delivery together.

The modeling and molecular biology work cited in this chapter was supported in part by agrant (GM59297) from the National Institutes of Health, Bethesda, Maryland.


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Pharmacokinetic Lead Optimization:Fine Art vs. Blind Technology

by Bernard Testa

Institut de Chimie thérapeutique, BEP, Université de Lausanne, CH-1015 Lausanne, Switzerland;

Tel: +41 21 692 45 21; Fax: +41 21 692 4525;e-mail: [email protected]

The present chapter brings this book to a conclusion by discussing thehigh-throughput (HT) revolution in drug research, the problem of pharma-codynamic (PD) vs. pharmacokinetic (PK) optimization, and the way the HTrevolution has made PK optimization a challenge and an integral componentof ‘drug’ discovery. The chapter ends by debating the scientific vs. technolog-ical character of HT drug research.

1. Pharmacodynamics vs. Pharmacokinetics

The last years have witnessed not one but two metamorphoses in the phar-maceutical industry, the one an accelerating structural evolution that contin-ues to make the headlines, and the second a massive scientific revolution ofwhich the public is not aware. The first of these metamorphoses, of course, isthe spiral of mergers and spin-offs that so hypnotizes finance soothsayers andsurfing shareholders.

The second metamorphosis is the HT revolution that has profoundlytransformed drug research [1] and forms the broad context of this book. Onemajor aspect of this revolution is the new paradigm of drug research where-by pharmacokinetic optimization no longer hobbles behind pharmacodynam-ic screening and optimization, but has become an essential component of anintegrated discover-and-optimize strategy.

A first and plausible explanation to this new paradigm may be a belatedrecognition that the demarcation between PD events (‘what the drug does tothe body’) and PK events (‘what the body does to the drug’) is far less clear-cut than previously assumed, and that no reliable PD assessment can neglect


PK factors [2] [3]. Indeed, drugs often modify physiological conditions (e.g.,regulation of enzymes, permeability of cells, blood flow) and so affect themanner in which they are handled in the body. Symmetrically, where drugsare distributed in the body and which active metabolites they generate maymarkedly influence their PD effects (Fig. 1).

2. The ‘High-Throughput’ Revolution in Drug Research

As satisfying and rational as the above argument may appear, it cannotaccount for the forefront position taken by PK optimization in drug research.The real cause of this state of affairs is less scientific and mostly strategic,being a direct consequence of the explosive use of HT techniques in ‘drug’discovery (in fact, lead and candidate discovery).

A highly schematic view of the various phases in drug research and devel-opment (R&D) is shown in Fig. 2 [4]. Until the 1970s, traditional drugresearch began with mass synthesis and screening to identify lead candidates,from which preclinical and clinical testing led to preclinical candidates, clin-


Fig. 1. What the drug does to the body (PD events) influences what the body does to the drug(PK events) and vice versa (modified from [2])

ical candidates, and ultimately drugs. Rational design based on quantitativestructure-activity relationships (QSARs) became widestread in the 1970s andhas helped to guide the synthesis of more promising compounds. Rather thanshortening the road that goes to candidates and drugs, the HT revolution inthe 1990s has lengthened it by one or more additional phases (not necessari-ly years), and has also congested it. Indeed, the ‘new drug research’ nowrelies extensively on such techniques as combinatorial chemistry, libraryscreening, and de novo design to generate massive numbers of ‘hits’ whichflood the road to candidates [1] (Fig. 2).

To reduce the time-consuming development and high rate of attrition ofactive compounds ultimately doomed by hidden pharmacokinetic defects,medicinal chemists had to integrate additional criteria into drug design and toset up more stringent screens into the early phases of lead discovery and opti-mization.

3. Can PD and PK Properties Be Optimized Simultaneously ?

Successful drugs in uncounted number testify to a positive answer to thisquestion, whereas innumerable (and mostly unpublished) failures bring anegative answer. The only valid answer, of course, is that no general answerexists, each research project being a particular case.


Fig. 2. Schematic representation of the long road from hits and active compounds to (thera-peutically used) drugs (modified from [4])

Fig. 3 is proposed to illustrate this fact. The left panel pictures PD opti-mization in a given research project. Here, the outer oval symbolizes therange of structural diversity (SD) accessible to the project. Such a range isobviously impossible to define with any rigor, and its limits are drawn asfuzzy. Within this SD space, a sub-ensemble of pharmacodynamically activestructures is assumed to exist and to form the PD space represented by thesmaller PD oval. In analogy with conformational spaces, regions of higheractivities in the PD space correspond to ‘local maxima’, and the region ofhighest activity is the ‘global maximum’. What medicinal chemists do whenthey try to discover the most active compounds is to explore the SD space,beginning either at random (points A and B), as is the case with libraryscreening, or with virtual or proven hits (point C). From these starting points,the objective of medicinal chemists is to remain in the PD range or reach it,and to converge toward the ‘global maximum’.

The right panel of Fig. 3 is identical to the left panel, except that PK opti-mization has been added. Within the accessible structural diversity, there arepostulated to exist pharmacokinetically well-behaved compounds, e.g., hav-ing a high bioavailability, a desired half-live, a good distribution, and no sus-pect metabolite. The ensemble of these well-behaved compounds forms the


Fig. 3. The left panel pictures PD optimization in a given research project. The SD oval repre-sents the fuzzy range of structural diversity accessible to the project. The PD oval representsthe space of pharmacodynamically active structures, with regions of higher and highest activ-ities. A, B and C are starting points in the project. The right panel is identical to the left panel,except that PK optimization has been added. The ensemble of pharmacokinetically

well-behaved compounds forms the PK space, represented here by the indented PK oval.

PK space, represented here by the indented PK oval. The situation portrayedin the right panel of Fig. 3 is an ideal case, since there is overlap between thePD and PK spaces. In other words, this example assumes the existence ofcompounds which combine high activity and good PK profile. Thus, the hitC combines a promising activity and a good PK profile, whereas the startingpoints A and B are inactive and have poor PK properties. The starting pointB is seen to lead first to active but pharmacokinetically poor compounds, untilfurther design ultimately leads to maximal activity and good PK propertiesco-existing in the same compounds.

The schematic representation of Fig. 3 makes it explicit that the PD andPK spaces may or may not overlap, simply because the structural require-ments for PD activity may or may not be compatible with the structuralrequirements for good PK properties. Two possible situations, partial overlapand complete absence of overlap, are shown in highly simplified form in theleft and right panels of Fig. 4, respectively. In the left panel, case A is seen to evolve toward candidates with good PD and PK properties. In contrast, this objective is out of reach in case B, which may perhaps be saved by a prodrug [5] or formulation strategy (see also chapter by Testa and Mayer,p. 85).


Fig. 4. Two possible situations in a research project, namely partial overlap of PD and PKspaces (case A), and complete absence of overlap (case B). Case A is seen to evolve towardcandidates which combine good PD and PK properties, whereas this objective is out of reach

in case B

4. Impact of the HT Revolution on PK Optimization

Knowing that they could face either situation of Fig. 4 when embarkingon a new project, research directors will want to know as soon as possiblewhether case A or case B applies. Two types of applications of HT techniquesmay answer this question, namely computational approaches (i.e., virtualexperiments) and physical approaches (i.e., material experiments). Of course,both approaches are not mutually incompatible and may be applied in parallelor sequentially.

When QSAR and/or 3D models of the target exist, the first strategy toaddress this problem is virtual screening, a computational HT technology ofpractically unlimited potential [6]. Utilizing, for example, pre-existing QSARand 3D-QSAR models, virtual screening can be used to predict the targetactivity of virtual molecules. Alternatively, docking simulations can suggestaffinity or lack thereof. In parallel, virtual screening can calculate variousphysicochemical and structural properties of virtual molecules (see chapterby Martin et al., p. 485) and compare them with ranges of values known tobe correlated with fair PK behavior [7]. This is exemplified by the ‘rule-of-5’,which is based on a distribution of calculated properties among several thou-sands of drugs belonging to different therapeutic classes [8]. It predicts that acandidate is more likely to show poor absorption or permeation when it has>5 H-bond-donor groups, >10 H-bond-acceptor groups, a molecular weight>500, and a calculated log P (CLOGP) >5. Substrates of biological transport-ers are exceptions to the rule. When two of these criteria are met, poor absorp-tion or permeability may be expected. In other words, the rule-of-5 is a ‘wide-spectrum’ qualitative screen to identify potentially poor PK performers.

This strategy is equivalent to carrying out a research project in silico, withthree objectives:

• A partial knowledge of the boundaries of the PD and PK spaces (Fig. 4).

• Indications about the overlap or non-overlap of the PD and PK spaces(i.e., case A or B in Fig. 4).

• Based on the above, chemical proposals for synthesis and physical HTscreening.

These computational HT techniques can thus pave the way for the physicalHT techniques or may make them more efficient. However, they cannotreplace them entirely and never will.

The physical HT techniques (experimental is an inadequate word since insilico simulations are also experiments) allow to generate huge numbers ofcompounds and to screen them. Schematically, HT pharmacodynamic screenswere the first to be developed and introduced, generating very large numbers


of active but pharmacokinetically poor compounds, and resulting in highrates of attrition in later stages of the R&D process. Hence, the developmentand introduction of HT pharmacokinetic screens [9], as presented in a num-ber of chapters grouped in the Part ‘Biological Strategies’. A most recent andsignificant progress is the development of HT physicochemical techniques tomeasure molecular properties of great pharmacokinetic relevance such aspKa, solubility, and lipophilicity. Various chapters in the Part ‘Physico-chemical Strategies’ bear witness to these newest techniques and to theirvalue.

5. The Fine Art, Rational Methods, and Blind Technologyof the ‘New Drug Research’

This text makes ample use of the words ‘technology’ and ‘technique’. Butdoes this imply that drug research is becoming less and less scientific, andmore and more technological? The answer to this question depends on howone views science and technology. Certainly the two have different outcomes,since science by definition creates knowledge and understanding, whereastechnology creates products and empirical rules.

To those whose understanding of science and technology is restricted tothis opposition, current drug research indeed appears as markedly more tech-nological than it was a decade ago. However, opposites often hide a unifyingtruth, making them apparent rather than genuine. In the case of science vs.technology, their unifying banner is the plain fact that one cannot progresswithout the other. Technological advances are based on scientific discoveriesand have been so since a few centuries, while science cannot dispense of thefruits of technology to obtain new data, to discover new facts, and to verifynew theories. In this unified view, science and technology support each otherforward in a spiral of progress and discovery (Fig. 5).

The HT revolution in drug research is no exception to this rule, withcaveats to be discussed later. Discoveries in synthetic chemistry, biochemis-try, biology, molecular pharmacology, informatics, and robotics, to namesome basic sciences in drug research, have led to the creation of new andpowerful instruments. These, in turn, are having a remarkable impact on theeffectiveness of drug research as well as on advances in the basic sciences. Totake an example, just imagine what our knowledge of the brain’s molecularmechanisms would be, were it not for the use of drugs as research tools. Andfrom these neurochemical and neurological advances, new assays and newinstruments are invented, which lead to the discovery of new bioactive agentsand the uncovering of new knowledge, in a seemingly never-ending spiral(Fig. 6) [10].



Fig. 5. Rather than being opposites, science and technology support each other forward in aspiral of progress and discoveries

Fig. 6. The spiral of creativity in drug research [10]

The title of this essay and the heading of this section quite provocativelyqualify technology as ‘blind’ and oppose it to ‘fine art’. Yet our discussion onthe roles of science and technology views them as equal and mutually sup-porting partners. Again we face a contradiction, and again it is an apparentone. What is implied in title and heading, and is now made explicit, is thatthere are restrictive conditions on the spirals of progress shown in Figs. 5 and6. These conditions are a) that technology should not be used blindly, and b)that science should not be myopic.

Blind technology means ‘technology-become-routine’, and all executivesknow that routine is a major source of errors and failures. Blind technologyis also evident in the automatic processing of the huge amounts of data gen-erated by the HT techniques, when algorithmic screens become black boxeswhose output (decisions) cannot be verified by human intelligence.

To avoid becoming blind, technology cannot be used without understand-ing and knowledge. And here lies a danger-in-waiting, that the HT techniquesused in drug research are becoming so complex that their assumptions andboundary conditions escape the understanding of the researchers who usethem. These researchers will then fail to see the limits and limitations of theHT techniques and will accept as amenable to legitimate extrapolation anyfigure turned out and any choice made by an assemblage of robots and com-puters.

Myopic science is a ‘science-become-technology’ assigned short-termgoals rather than broad objectives, or worse a captive science discouragedfrom exploration and forced to utility. The danger is all too clear for currentdrug research, whose objectives are financial as much as medical [10] [11].

The above discussion features some of the characteristics of modern drugresearch, and some of the dangers of unbalanced research. But what are theconditions for a balanced, recurrently successful, simultaneously knowledge-and product-oriented research? In this writer’s view, for research groups andinstitutions to remain vigorously successful in the long term, they must oper-ate on three levels. As schematized in Fig. 7, these three levels are a qualita-tive one (experience, intuition), a rational one (logic, rules, and reasoning),and a mechanical one (techniques, automation). In other words, long-termsuccess implies a balanced convergence of art, science, and technology.Should techniques and especially automation cease to be means to becomegoals, or should they receive ruling priority over the human and scientificcomponents (Fig. 8), progress could not be sustained and research institutionswould become gadget factories. The tragic medical needs of humankind makesuch a prospect grotesque, and the intelligence of scientists should make itunlikely.



Fig. 7. Interplay and equilibrium between the qualitative, rational, and mechanical levels atwhich drug research (and comparable human endeavors) operate

Fig. 8. Unbalanced, non-viable predominance of technology and especially automation, firstover the qualitative component of drug research, and ultimately over the rational component.

Should techniques cease to be means to become goals, progress could not be sustained.

REFERENCES

[1] T. Mander, Drug Discov. Today 2000, 5, 223.[2] B. Testa, Trends Pharmacol. Sci. 1987, 8, 381.[3] B. Testa, Med. Chem. Res. 1997, 7, 340.[4] B. Testa, Pharmaceutical News 2000, 7, 13.[5] B. Testa, J. Caldwell, Med. Res. Rev. 1996, 16, 233.[6] W. P. Walters, M. T. Stahl, M. A. Murcko, Drug Discov. Today 1998, 3, 160.[7] B. Testa, P. Crivori, M. Reist, P. A. Carrupt, Perspect. Drug Discov. Design, 2000, 19,

179.[8] C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Adv. Drug Deliv. Rev. 1997,

23, 3.[9] A. Pagliara, M. Reist, S. Geinoz, P. A. Carrupt, B. Testa, J. Pharm. Pharmacol. 1999, 51,

1339.[10] B. Testa, Pharmaceutical News 1996, 3, 10.[11] J. A. Heinemann, Drug Discov. Today 2000, 5, 222.


A

Absorbability 447Absorption 93, 200–202, 208, 209,

213, 214, 258, 262, 275, 447, 539enhancers 184gastrointestinal 17, 51,

266–272, 447, 452human 448human intestinal 500–502, 504,

505, 509, 535in vivo 424intestinal 11, 12, 57, 85, 211, 401oral 8, 52, 53–59, 63, 95, 203,

263, 499, 534–536passive oral 315, 318, 322percutaneous 156, 158potential (APsuv) 501, 502surface available for 270transcellular 205windows of 267

Absorption, distribution and excre-tion (ADE) 199–214

Absorption-Distribution-Metabolism-Excretion (ADME)3, 17, 117, 199screens 5, 11, 13

Absorption models, computational503–509

ABT-418 223Acceptors (storage sites) 191ACE See ‘Angiotensin-converting

enzyme’Acebutolol 542, 544Acetaminophen 221, 230, 455Acetonitrile (ACN) 431, 432,

434–444

N-Acetyl-1,4-O-benzyl-Hyp-Arg-CMK 520

Acetylcysteine 160Acetylsalicylic acid 158, 193,

455N-Acetyltransferase (NAT) 231,

235, 236a1-Acid glycoprotein (AGP) 189,

192–194, 210, 213, 546Acids 65, 192, 276, 308, 322, 358,

366, 448diprotic 284, 309distribution of 334monoprotic 278, 279, 283, 307,

308multiprotic 278

Acrivastine 369–371Active-species rule 355Acyclovir (ACV) 263, 266, 455,

592, 594ADAPT 582, 583

descriptors 503Additivity rule 385, 388ADE

in vivo behavior 211profile 213

Adhesive-tape-stripping 157Adipose tissues 72, 191ADME See ‘Absorption-Distribu-

tion-Metabolism-Excretion’ADP/ATP Ratios 249Aerosols 177, 178w-AFA See ‘w-Amino fatty acids’Aggregates 305Aggregation 177Agonists 513, 515, 516b-agonists 246

Subject Index

(Compiled by Bernard Testa and Xiangli Liu)


628 SUBJECT INDEX

AGP See ‘a1-Acid glycoprotein’Agrochemicals 250Ajmalicine 563, 575Ala-Leu-Gly 440, 442, 443Aldehyde oxidase 223, 231, 232Alfentanil 57Alkaline phosphatase (ALP) 130,

137, 138, 141, 142Alleles 559Allelic variant forms 237Allometry 20Allosteric effectors 515ALP See ‘Alkaline phosphatase’a-Helices 513Alprazolam 455Alprenolol 263, 455, 534, 536,

542, 544Alzheimer’s disease 134American Type Culture Collection

(ATCC) 101Ames tests 247Amiloride 263, 306, 319–323Amine oxidase 72Amino-acid carriers 132w-Amino fatty acids (w-AFA)

597, 598d-Aminolevulinic acid (d-ALA)

597m-Aminophenol 286, 287, 368(4-Aminophenyl)acetic acid

(4-APAA) 597Amiodarone 259, 410Amitriptyline 306, 319–321, 455,

561Amoxicillin 506Amphipathicity 604Amphiphiles 403Amphiphilic moment 541, 545,

547Ampholytes 276, 284, 286–302,

308, 309, 322, 366–377AB-AB 374

AB-ABic 372, 375, 376, 378AB-ABnc 370AB-BA 368BA-BA 368diprotic 279internally-compensated 368ordinary 286zwitterionic 286

Androstenedione 573, 574Angiotensin II 134Angiotensin-converting enzyme

(ACE) 130, 141inhibitors 591, 596

Aniline, N,N-dimethyl- 75Animal-plant ‘warfare’ 71Animal variability 210Antagonists 513, 515Antiarrhythmics 192, 495Antiasthmatics 182Anti-atheromatous agents 194Antibodies 236

steroid-binding 517Antidepressants 192Antihistamines 191, 196, 466Anti-HIV agents 55Antihypertensives 191Antiinflammatory agents 558Antimalarials 466, 470Antimicrobial agents 191Antipyrine 453, 455Apical (AP) chamber 201Apoptosis 248, 249Aqueous-pore pathway 501Arachidonic acid 134Aromatase See ‘CYP19’Aromatic density 500Aromatic ethers 515Aromatic-hydrocarbon (Ah) recep-

tor 513Arphamenin A 597Artificial model-membrane technol-

ogy 264, 265, 267, 270–272

SUBJECT INDEX 629

Artursson’s equation 202Arylacetylenes 92[(Arylcarbonyl)oxy]propanolamines

87Aryldiazenes 568Arylhydrazines 568Aspartic proteases 377Astrocytes 137, 139, 141, 142, 257Atenolol 61, 62, 71, 263, 453,

455, 532–534, 536, 544Atropine 61, 455Attributes, eometric and steric 68Attrition rate See ‘Rate of attrition’Automation 623, 624Available Chemicals Directory

(ACD) 500Axial dispersion number (DN) 222Azapropazone 192, 375, 376AZT 592

B

Bacterialcontamination 103meningitis 134mutagenicity 246

Bacteriorhodopsin 449Barriers

blood-brain See ‘Blood-brain barrier’

blood-cerebrospinal-fluid (CSF)60

endothelial 99epithelial 99gastrointestinal 63pharmaceutical 88pharmacodynamic 88pharmacokinetic 88

Bases 65, 276, 308, 309, 322, 358,364, 366, 448diprotic 284

distribution of 334monoprotic 278, 279, 283multiprotic 278

Basic fibroblast growth factor(bFGF) 142

Basolateral (BL) chamber 201Basolateral (abluminal) side 129BBB See ‘Blood-brain barrier’BBMV See ‘Brush-border-mem-

brane vesicles’Beer’s law 289Benzenes 385Benzodiazepines 192Benzoic acid 158, 159, 169, 284,

285, 358, 359, 362, 363Benzo[a]pyrene 230, 556, 574(7R,8S,9S,10R)-Benzo[a]pyrene-diol

9,10-epoxide 5571,2,4-Benzotriazine 1,4-dioxide,

3-amino- 89, 91Benzphetamine 574Benzylalkylamines 357, 472, 476–

478Betamethasone 455Betaxolol 542Bilayers 403, 449, 465, 469–471,Binding 67, 71, 564

affinity 77, 257constant (Ks) 555profile 192spectra 220

Bioadhesion 183Bioavailability (BA) 21, 86, 92,

117–124, 173, 174, 208, 209, 217,447, 453absolute oral 118definition of 117influencing factors of 117nasal 184oral 5, 174topical 157

Biodegradation 447

630 SUBJECT INDEX

Bioequivalence (BE) 174, 177Bioinformatics 584Bioisosteres 123Biological

factors (BF) 81surrogates 120systems 67, 71, 81

Biomakers 23of toxicity 251

Biopharmaceutical drug classifica-tion 173

Biophores 580Biopolymers 541Bioprecursors 89, 90Biostability 173Biotransformation See ‘Drug

metabolism’Bjerrum difference curves 282, 283b-Blockers 71, 87, 495, 543, 545Blood-brain barrier (BBB) 10, 11,

53, 55, 58–63, 127–149, 206,257, 370, 401, 504, 534, 547characteristics 128–134functions 128–134transport studies 143–148

Blood circulation 189Blood-to-tissue transfer 194Boc-D-Phe-Pro-Arg-H 520Boltzmann relation 413Born equation 439Bradykinin 134Brain

capillaries 206distribution 60penetration 7, 51, 59–63, 90,

508, 533, 534 (see also ‘BBB’)Brain-selective dihydropyridine car-

riers 92Brefeldin A 608Bromocriptine 259Brush-border-membrane vesicles

(BBMV) 206, 594, 597

Butan-1-one, 4-(N-methyl-N-nitro-soamino)-1-(3-pyridyl)- 561

Butylamine,N,N-dimethyl- 5294-phenyl- 358

Butyric acid 1432-hydroxy- 3583-hydroxy- 358, 364

C

Cadherin 131Caffeine 158, 160, 193, 455Calcein release 46Calcium-channel antagonists 470Calcium homeostasis 249Cambridge Structural Database

(CSD) 514, 554Capacity factors (log k¢) 441–443,

545, 547, 548Capillaries 128, 135, 137Carbamazepine 61, 263, 453, 455Carbamoylsarcosin 515Carbonyl reductases 231Carcinogenicity 245, 247, 248,

578, 580, 582Carcinogens 552

non-genotoxic 248Carrier mechanisms 131CASE See ‘Computer Automated

Structure Evalution’Cassette analysis 209Cassette dosing (N-in-one dosing)

6, 208, 252Catalysis 67, 71, 77, 236, 564

intramolecular 89Cefadroxil 602Cefixime 455Cefoxitin 455Ceftriaxone 455Cell adhesion 249

SUBJECT INDEX 631

Cell-banking practices 102Cell-cell communication 248Cell cross-contamination 103Cell cultures 99–115, 168–170,

184, 185characterization 103–115methods 102model 99, 155monolayers 52techniques 100–103

Cell-cycle perturbation 249Cell division 249Cells

banks of 229Caco-2 8, 17, 52, 55, 57, 58,

101, 105, 106, 121–124, 139,141, 201–204, 264, 447, 450,499, 500, 503, 504, 542, 593,609

CHO/hPepT1 593ECV304 101, 105, 106, 138,

141, 143endothelial 99, 129, 130, 135,

137, 138, 141, 148, 206, 257,424

epithelial 99, 130, 424glial 139, 141HEK293 604HeLa 103human airway-epithelial (Calu-3)

610human alveolar epithelial 185human embryonic kidneyHEK293 604human umbilical cord vein-endo-

thelial 138isolated 218, 219MDCK (Madin-Darby canine kid-

ney) 8, 52, 55, 56, 101, 105,106, 133, 139, 447, 499, 608

MDR1-MDCK 105, 106multidrug-resistant (MDR) 55

PBMEC/C1-2 105, 106RBE4 138, 141, 142RPMI 2650 185T24 105, 106transgenic 218

Central nervous system (CNS) 127Cephalexin 124Cephalosporins 591, 596Cetirizine 193, 196, 369, 370,

372–375, 472, 479, 480Charge delocalization 376Chemical carcinogenesis 578Chemical diversity 82, 264Chemical potential, 328

standard 328Chemical shift (d) 468, 475

anisotropy (CSA, Ds) 469, 470Chemoselectivity 69, 70Chemotaxis 249Chemotherapeutic agents 89Chimeras, of PepT 600–603Chitosans 183, 610Chlorambucil 455Chloramphenicol 455Chloroalkanes, dechlorination of

73, 74Chlorophenols 360Chloroquine 455, 470, 472Chlorpheniramine 196Chlorpromazine 61, 263, 306,

319–321, 563Chlorpropamide 455Chlorprothixene 455Chlorthalidone 455Chlorzoxazone 234Cholesterol (CL) 38, 44, 404, 423,

429, 450, 465Cholesteryl esters (CE) 404Chromone-2-carboxylic acids 71Cimetidine 57, 61, 263, 455Cingulin 131Ciprofloxacin 530, 531, 534, 536

632 SUBJECT INDEX

Circular Dichroism (CD) 467Circulating proteins 189Clastogenicity 247Claudin 131Clearance 6, 156, 208, 210, 219,

220, 272hepatic 219, 221, 222, 226in vivo 10intrinsic 219, 224, 232metabolic 10, 11, 71mucociliary 182, 183organ 222plasma 219prediction of 232quantitative prediction of 225renal 71, 219

Cleared volume (Vcl) 147Clinical trials 26Clofibric acid 76, 231CLOGP 259, 262, 265, 269Clonazepam 455Clonidine 533Clotrimazole 233Clozapine 259, 263Cluster analysis (CA) 503Coagulation factors 191Cocaine 455Co-cultures 257Codeine 455Colchicine 191, 608Collecting-duct aquaporin 607Combinatorial chemistry 4, 13,

199, 485, 617Combinatorial libraries 487, 499COMPACT See ‘Computer-

Optimized Molecular ParametricAnalysis of Chemical Toxicity’

Comparative molecular field analy-sis (CoMFA) 596

Complexity 52Compliance 26, 268Compound libraries 252

Compoundsdiprotic 426monoprotic 426

Comprehensive Medicinal Chemis-try (CMC) database 500

Computer Automated StructureEvalution (CASE) 579, 582,583

Computer-Optimized MolecularParametric Analysis of ChemicalToxicity (COMPACT) 578, 579,582

Concentration-time curves (AUC)209

Conductor-like screening model(COSMO) 384

Confocal Laser ScanningMicroscopy (CLSM) 107

Conformation 86, 504, 509,528–531, 535, 542, 552, 554,584, 596, 618

Corticosteroids, 21-hydroxy-3,20-dioxo- 517

Corticosterone 455Cortisone 160

derivatives 448Cosmetic products 157Coumarin 234, 455

4-(aminomethyl)-7-methoxy-576

7-ethoxy- 227, 557trifluoro(benzyloxy)- 238

Covalentadducts 67binding 66binding to proteins 246bond 73

Creatinase 515Creatine 515Critical micelle concentration

(CMC) 406, 453Critical packing 541

SUBJECT INDEX 633

Cromoglycate 70Cryopreservation, 229

methods 102CSF-to-plasma partition coefficient

60Cyclic-AMP 3804-Cyclohexylbutyl-Pro-Arg-H 5203-Cyclohexylpropionyl-Pro-Arg-H

520Cyclophylin 521Cyclosporin A 63, 193, 521CYP 10, 12, 16, 53, 72, 77, 134,

209, 223, 227, 231, 233, 235,551, 552, 556–564, 567–579,583, 584

CYP1A 231CYP1A1 556–558, 564, 570, 579CYP1A1/2 224CYP1A2 77, 228, 234, 235, 558,

570, 576, 579CYP1A6 570CYP2A1 570CYP2A4 570CYP2A5 570CYP2A6 234, 570CYP2B 227, 231CYP2B1 558, 570, 573, 574CYP2B1/2 228CYP2B2 558CYP2B4 570CYP2B6 570CYP2C 227CYP2C3 570CYP2C5 578CYP2C8 234, 237CYP2C9 233, 234, 236, 237, 558,

559, 564, 570, 571CYP2C18 571CYP2C19 233, 234, 236, 237, 571CYP2D1 571CYP2D6 233–235, 559–564,

571–577, 583

CYP2D15 235CYP2D17 235CYP2E1 227, 234, 571, 579CYP3A 231CYP3A1/2 228CYP3A4 53, 55, 56, 59, 76, 119,

122, 224, 228, 233, 236–238,502, 510, 571, 576

CYP3A4/5 234CYP3A5 237CYP3A12 235CYP4A 231CYP4A1 571CYP4A4 571CYP4A11 571CYP5 (TXAS) 571CYP11A 572CYP17 572CYP19 (aromatase) 231, 572, 573,

577CYP51 572CYP55 569CYP101 236, 568, 569, 572–574,

577CYP102 236, 568, 569, 572–574,

577CYP105 572CYP107A 568CYP108 569, 573, 574, 577Cysteine conjugate b-lyase 92Cystic fibrosis transmembrane-con-

ductance regulator (CFTR) 607Cytidine-deaminase 522Cytoarchitecture 108Cytochrome b5 235, 236Cytochromes P450 See ‘CYP’Cytochromes P450 reductase 91,

133, 235, 552Cytogenetics 247Cytokine release 249Cytokines 134Cytoplasmic 7H6 antigen 131

634 SUBJECT INDEX

Cytoskeletal changes 249Cytosol 217, 231Cytotoxicity 249

D

DADLE 122Dapsone 455DARC/PELCO Methodology 508Databases 239, 247DDT 72De novo design 617Debrisoquine 561, 575, 576Deductive Estimation of Risk from

Existing Knowledge (DEREK)251, 581–583

Deliverydermal 90inhalation 174iontophoretic 168oral 55, 257

Delivery systems 44liposome-based 44

Dlog D 501Dlog P 60, 526, 527, 533Density 180Deposition 178

lung 177nasal 182

DEREK See ‘Deductive Esti-mation of Risk from ExistingKnowledge’

Dermal exposure 155Dermal irritation 250‘Dermatopharmacokinetic’ experi-

ment 157L-Descriptors 526, 527Desipramine 60, 225, 263Desolvation 504, 514, 523, 540Detergent dialysis 406Detoxification 65, 66, 228

Developability 117Dexamethasone 143, 228, 231Dextromethorphan 234, 575, 576DHF See ‘Dihydrofolate’Diameter

aerodynamic 179equivalent 179mass median aerodynamic 180

Diarylphosphates 356Diarylpyrazines 383, 387, 389, 395Diastereomers 69Diazepam 61, 193, 221, 226, 455,

534, 536, 544, 545Diazines 385–387, 390–394Diclofenac 259, 262, 306, 315,

319, 320, 322, 323, 407Dielectric constant (e) 439diff (log PN-I) 352, 354, 360,

362–365, 368Differential-scanning calorimetry

(DSC) 35, 467Diffusion, 190, 340

chambers 144coefficient 449paracellular 201passive 58, 119, 122, 123, 190,

257, 262, 305, 429, 447, 510,525

passive transcellular 212transcellular 201, 204, 264

Diffusivity 305, 313Diflunisal 455Digitonin 61Digoxin 56, 61Dihydrocodeine 455Dihydrofolate (DHF) 515, 516Dihydrofolate reductase (DHFR)

515b-Diketones 356Diltiazem 259, 455Dioleoylphosphatidylcholine

(DOPC) 451

SUBJECT INDEX 635

Dioxins 72, 224Dipalmitoylphosphatidylcholine

(DPPC) 470, 473Dipeptides 374Diphenylphosphate 3582,3-Diphosphoglycerate 518Diphytamoylphosphatidylcholine

(DPhPC) 451Dipolarity/polarizability 410Discrete dosing 209Dissociation constants (pKa) 211,

212, 259, 265, 276–302, 307–312, 316, 319, 321, 335, 336,344–347, 351–380, 415, 417,418, 420, 422, 426, 438–444,459, 486, 487, 493, 496apparent 336in lipid phases 351–380in membranes 357–359, 362,

377limiting 278

Dissolution 173, 177, 258, 453Dissolution template titration (DTT)

316Distance function 528Distribution coefficients (log D)

54, 57, 59, 61, 62, 71, 121, 258,262, 264–267, 271, 275–302,314, 354, 365–367, 369, 402,413, 417–419, 454–457, 459–461,494, 501–503, 506–509, 526

Distribution profiles 480Diuretics 191Diversity selection 486DNA 249, 513, 541Domains

fluid 37gel 37lipid 37

L-Dopa 453, 506, 592, 593L-Dopa-L-Phe 592Doxorubicin 455

Drug-antibody conjugates 90Drug delivery 3, 34, 44, 132

elimination See ‘Elimination’excretion See ‘Excretion’nasal 173–187pulmonary 173–187topical 155transdermal 155, 156

Drug-discovery pipeline 199Drug metabolism 3, 7–12, 18, 51,

63, 65–83, 85, 118, 133, 200,207, 211, 214, 217–239, 246, 258,272, 539, 543, 551–584gut-wall 505hepatic first-pass 217intestinal 56intestinal-wall 213modulation of 86pharmacodynamic consequences

66pharmacokinetic consequences

66polymorphic 218predictions 65, 77–82

Drug-metabolism models, in vitro220

Drug-metabolizing enzymes 55,94, 225cDNA-expressed 235–238

Drug-protein complex 190Drugs

absorption 8, 217, 542, 607candidates 4, 10, 13, 17, 19, 20,

65, 117, 187, 199, 202, 204, 207,208, 214, 217, 223, 249, 447

cationic amphiphilic 466, 470,472, 476

chiral 75cholesterol-lowering 353delivery See ‘Drug delivery’design 7discovery 214

636 SUBJECT INDEX

disposition 11, 51–63, 199distribution 58, 85, 93, 189,

214, 258, 539elimination 217, 543–546low-clearance 221metabolism See ‘Drug

metabolism’permeation 525–528, 533prochiral 75targeting 617toxicity 551–584transport 190unmetabolized 86

Drugs vs. non-drugs 499, 500Dry-powder inhalers (DPI) 177

E

E. Coli D-Alanine: D-Alanine ligase378

ECV304 140Efficiency number (RN) 222Efflux, 123, 148, 201, 424

mechanisms 149P-gp-mediated 146pumps 12

Eicosanoids 134Electrochemical double-layer theory

420Electrochemical gradient 131Electrochemistry 327–348Electron Spin Resonance (ESR)

467Electronic distribution 86Electrophilicity 74Electrostatic effects 439Electrostatic forces 540Electrotransport 168Elimination 258

hepatic 543–546Enalapril 595

Enamipril 595Enantioselectivity, substrate 75Endocytosis 132, 133, 183

receptor-mediated 149Endothelin antagonists 507Endothelium 128, 131, 201Endpoints 23, 248

pharmacological 245toxicity 249

Enkephalin 122Enthalpy 513, 523Entropy 513, 514, 522, 523Environmental Protection Agency

(EPA) 581Enzymatic constraints 77Enzymes 218, 551

conjugating 228drug-metabolizing See ‘Drug-

metabolizing enzymes’induction 217, 218, 220, 224,

228, 231, 249inhibition 246leakage 249purified 218, 219recombinant 219, 220zonation 221

Epidermal equivalents 171Epidermis 165, 1697,8-Epoxidation 557Epoxide hydrolase 133, 231, 557Equilibrium dialysis 415–417Erythromycin 192Esmolol 87Estradiol 17b-O-glucuronide 58Estrogens 577Ethinylestradiol 455Ethoxybenzamide 225N-Ethyl-1,4-O-benzyl-Hyp-Arg-

CMK 520Etilefrine 455Etofylline 455Etretinate 193

SUBJECT INDEX 637

European Collection of Animal CellCultures (ECACC) 101

European Concerted Action COSTB1 25

Excess molar refraction (R2) 527Excretion 86, 212, 214, 258, 539,

543renal 543–546

Extrusion method 405

F

Factor-VIII-related antigen 130,136, 141

Famotidine 263, 455Faraday constant 413Felbamate 455Fexofenadine 63Fick’s law 146, 184, 190, 305,

313, 449, 459First-into-man administration 18First-pass effect 9, 173, 174Flavin-containing monooxygenase

(FMO) 223, 231–233, 235, 236

Flexibility 479, 486, 503, 528 (seealso ‘Conformation’)

Flip-flopeffect 56mechanism 53model 54

Fluocortolone 455Fluorescence 467Flurbiprofen 259Fluvoxamine 234Flux 313, 314, 319, 322, 323, 342,

452, 458–460, 500Flux-factor profile 318Force fields 509, 526, 527, 532,

540, 542, 567Formulation strategy 619

Forscarnet 534, 536Fourier-transform infrared spec-

trometry (FT-IR) 467Frequency-domain spectrum 468Furans 385–387, 390–394, 515Furosemide 259, 263, 267, 306,

319, 320, 322, 323, 453, 455

G

Galvani potential difference 330–348

g-Loops 513Gastric H+,K+-ATPase 607GBR 12909 561, 575Gel state 404Gene-array assays 251General anaesthetics 466Genes

heat-shock 249reporter 249, 250stress 249

Genetic polymorphism 605Genomics 23, 239, 584Genotoxicity 245–247Gibbs energy of solvation 333Gibbs energy of transfer 329, 330,

334, 336Gibbs’ phase rule constraint 312Gibbs’ pKa 309, 311, 312Globularity 541Glucosamine 380Glucose 506Glucuronidation 221, 224Glucuronides 379Glucuronyltransferase 76Glut1 132, 137, 138, 141, 146,

608Glut4 608g-Glutamyltranspeptidase (g-GTP)

130, 137, 138, 141, 142

638 SUBJECT INDEX

Glutathione conjugation 74Glutathione S-transferases 231,

235, 551, 555Glycocholic acid (GC) 453, 454Glycopeptides 132Gly-Gly 440, 442, 443Gly-Gly-Gly 440, 442, 443Gly-Gly-Ile 440, 442, 443Gly-Gly-Phe 440, 442, 443Gly-Gly-Val 440, 442, 443Gly-Sar 609Good-laboratory-pratice (GLP) con-

ditions 101Gouy-Chapman diffuse double-layer

theory 413GRID See ‘Force fields’Griseofulvin 306, 319–323, 455Growth factors 142g-GTP See ‘g-Glutamyltrans-

peptidase’Guanabenz 263, 267

H

Haloprogin 160Hammett constant s 75Haptophore 76HazardExpert (HEX) 579, 581–

583H-Bonding See ‘Hydrogen

bonding’H-Bonds See ‘Hydrogen-bonds’Headgroups 403, 410, 414, 422,

465, 466, 470, 473, 478–480, 508

Heat capacity 36Hemoglobin 518Henderson-Hasselbalch functions

419Heparins 191Hepatocytes 218, 220

cryopreserved 229–231cultured 227–229freshly isolated 225–227

Heptastigmine 455Heterogeneity, dynamic 37Hexadecane 264, 265, 267, 270,

271, 449Hexanoic acid 358Hexose phosphates 380Hexylamine 358, 364High-performance liquid chroma-

tography (HPLC) 431, 467High-resolution NMR experiments

475High-throughput (HT)

assays 268, 286enzyme-induction screens 237enzyme-inhibition screens 237microtiter-plate methods 317,

318revolution 615, 617, 620, 621screening (HTS) See ‘High-throughput screening’solubility pH measurements

319–322technique 94, 285, 623

High-throughput screening (HTS)4, 8–10, 13, 17, 77, 118, 199–214, 218, 219, 247, 248, 257,447, 450, 453, 487, 576automated 239in vivo 208pharmacodynamic 620pharmacokinetic 621physical 620physicochemical 621

Histamine 134HIV Protease inhibitors 120HIV-1 Proteases 377HMG-CoA Reductase 353HPLC Column-switching techniques

383

SUBJECT INDEX 639

HTS See ‘High-throughputscreening’

Human skinviability 166

HYBOT Approach 504, 505Hydrochlorothiazide 259, 263,

266, 453, 455Hydrocortisone 159, 455Hydroflumethiazide 455Hydrogen bond (H-bond) 258,

376, 432, 576geometry 515, 517, 528, 529intramolecular 530, 531ionic 518roles of 514–522strength 258, 260, 514, 518

Hydrogen-bond acceptors (H-bondacceptors) 213, 257, 260, 384,390, 391, 486, 500, 504, 515,532, 535, 620basicity (b) 526, 527, 531capability 432parameter 384, 385, 389, 390,

392, 393, 398, 433Hydrogen-bond donors (H-bond

donors) 213, 257, 260, 384,486, 500, 504, 532, 534, 620acidity (a) 526, 527, 531capability 432parameter (a) 434, 435

Hydrogen bonding (H-bonding),16, 52, 55, 58, 60, 262, 383, 410,448, 461, 513–523, 526, 540,541, 548capacity 51, 56, 504, 507, 525,

527, 540descriptors 507

Hydrolases 72, 207Hydrophilic-lipophilic balance

541, 544Hydrophilicity 333Hydrophobic filters 264

Hydrophobic substituent constant(p) 385–389

Hydrophobicity 55, 492, 507, 509,526, 540, 604

2-(5-Hydroxynorvaline)-cyclosporinA 521

Hydroxyzine 196, 372, 374, 472,479, 480

Hygroscopicity 178Hyperbilirubinemia 191Hyphenated techniques 77

I

IARC Data base 582Ibuprofen 259Image-analysis software 540Image-compression process

540Imaging techniques 23Imidazoles, 4-phenyl- 568Imipramine 61, 192, 193, 225,

263, 455, 533Immobilized artificial membrane

(IAM) 52, 206, 429–444, 501,526

Immobilized liposome chromatogra-phy (ILC) 501

Immunosuppressants 191In silico screening See ‘Virtual

screening’Inclusion bodies 249Indinavir 120, 223Indomethacin 61, 306, 322, 323,

455Inductive electronic substituent con-

stant (sI) 391Inhalation 178

devices 181route 173

Inhalers 182

640 SUBJECT INDEX

Inhibition 217, 220, 224, 226, 233,236, 238constant (Ki) 555

Inhibitors 513, 515, 519, 552, 562,563, 567, 583

Insulin 133, 610Insulin-like growth factors (IGF-1

and IGF-2) 133Integy moment 509, 541, 547, 548Interaction fields, molecular (MIFs)

539, 540Interactions

dipole-dipole 432dispersion 540drug-drug 6, 9, 21, 26, 85, 202,

209, 217, 218, 233, 234, 239drug-food 202drug-membrane 465–481drug-phospholipid 465–481drug-protein 189electrostatic 192hydrophobic 55, 192, 513, 523,

540, 549induction 540ionic 55, 466solute-solvent 431, 438–443solvent-solvent 431–438van der Waals 576

Interface between two immiscibleelectrolyte solutions (ITIES)338–345

Interfaces, liquid/liquid 338–345Interfacial

deprotonation 337, 344protonation 344

Inter-individual factors 81Interleukins 134Internal compensation 374–376Interstitial fluids 429Intestinal mucosa 118–124, 201, 429Intestinal rings, everted 206Intracellular fluids 429

Intra-individual factors 81Intuition 623, 624Ion pairs 351–380, 414, 423

hydrophobic 378internally compensated 380 (see also ‘Zwitterions’)stabilization 356

Ion partitioning 327–348, 352–358, 363

Ionic partition coefficients, standard333

Ionic partition diagrams 345–347Ionization 51, 57, 178, 193, 275,

277, 352, 408, 415, 439, 448,479, 509 (see also ’Dissociationconstants’)ionic strength 282, 423profiles 292–298, 370, 371, 373

Isoelectric point 368Isolated perfused liver 220–222Isolated perfused porcine skin flap

(IPPSF) 165Isoproterenol 455Ivermectin 63

K

KA-672 60Kamlet-Taft equation 432, 441,

443Keratinocytes 168, 170, 171Ketamine 193Ketoconazole 224, 233, 234Ketoprofen 61, 259, 453, 455Kinetic parameters 225, 232, 236

L

Labetalol 259, 263, 290–292,295–299, 301, 368–370

SUBJECT INDEX 641

b-Lactam antibiotics 594Lactate dehydrogenase 518Lactoferrin 133Lactulose 534, 536Lamellarity 406Langmuir adsorption isotherm

410, 411Lasinavir 259LDL 141

uptake 111Lead candidates 249Lean tissues 191Lecithin 264, 449Leptin 133Leukocytes 191Lidocaine 192, 193, 227, 455, 581Ligand-protein complexes 513, 514Ligands 583

affinity 515, 518–521binding 513, 514–522orientation 513, 515orientation and conformation

514–516recognition 513, 516–518

Linear free-energy relationships(LFERs),biological application 72, 75,

167, 260, 365, 366, 501, 503,506, 527

Lipid bilayers 33, 34, 38components 33lateral heterogeneity 38lateral organization 35–44organization 33permeability 38phase diagrams 42, 43phase structure 35single component 36two-component 40vesicles 403

Lipids 134, 403, 423, 465arrangement of 404

domains 37molecules 35peroxides 408stability 408

Lipophilic residues 66Lipophilicity 16, 51, 52, 58, 60–

62, 65, 66, 70, 75, 86, 89, 121,167, 178, 193, 207, 211, 212,224, 262, 305, 348, 351, 361,383, 410, 417, 444, 450, 454,458, 459, 461, 475, 478, 479,501–503, 515, 525, 526 (see also‘log P’)profiles See ‘Lipophilicityprofiles’

Lipophilicity profiles 275–302,367, 369, 371, 372, 375–377, 419high-throughput 284partial 279–283

Lipopolymers 44, 47Lipoproteins 189, 192, 193Liposomes 34, 36, 38, 40, 44, 46,

47, 184, 362, 401, 403–410, 501,610charged 420stability 408stealth 34, 45zetapotential 408–410

Liquid-crystal state 404Liquid-crystalline phase 465Liver 217Liver slices, precision-cut 220Lobeline 563Local anaesthetics 466, 470Log D See ‘Distribution coeffi-

cients’Log k¢ 384, 390–395, 397–399,

526Log kw 390, 391, 393, 394, 398Log P 51, 52, 258, 259, 262, 276,

277, 283, 297, 353, 354, 358,359, 361, 362, 364, 365, 372,

642 SUBJECT INDEX

374, 379, 383–399, 402, 412,415, 417–419, 422, 485–496,500, 503, 515, 526, 531, 579,581, 620 (see also ‘Lipophilicity’)calculators See ‘Log P pro-grams’

Log P programs,comparison of 488–496

Logic 623, 624b-Loops 513Lorazepam 455Lovastatin 354T-Lymphocytes 191

M

Macrolide antibiotics 192Macrophage inflammatory proteins

134Malate dehydrogenase 518Mammalian-cell chromosomal

effects 247Mannich bases 92Mannitol 202, 203, 534, 536Many-particle systems 35MAO See ‘Monoamine oxidase’Maximal velocity (vmax) 67, 75McGowan molecular volume (vx)

527MDR1 120, 121, 123Mdr1 Gene 114Measurement compartment 156Mebendazole 455MedChem database 51Mefloquine 470, 472Membrane-associated guanylate

kinases (MAGUK) 131Membrane-binding constants 460Membranes 305, 362, 365, 401,

404artificial 264, 449–462

bilayer 34binding 459biological 33, 47, 190, 201black lipid 449cellular 52, 429constituents 53crossing 53–55dynamics 467functions 465lateral organization 465liposomal 33–47morphology 53organization 466, 467partitioning 353permeability 183, 204, 206,

207, 305, 458permeation 34, 173, 539perturbation 246, 249properties 465–467stability 449transport 52, 56, 57, 504, 510,

513S-Mephenytoin 234Mepyramine 533Merck Molecular Force Field

(MMFF94s) 529MetabolExpert 581Metabolic

control 86intermediates 89, 90, 92products 552profiles 232, 233, 239profiling 220promotion 86, 87reactions 79, 80stability 118, 232, 234stabilization 86, 87switching 86, 87

Metabolic predictions, goals in 78Metabolism See ‘Drug metabolism’Metabolite profile 223Metabolites 555, 561, 567, 576

SUBJECT INDEX 643

Metabolizersextensive 564poor 559, 564

MetaFore project 78–82Metergoline 456Methadone 193Methimazole 233Methods

in vitro 18NMR 469–480non-recirculating single-pass 221pH-metric 321potentiometric 316, 468recirculating 221shake-flask 285, 299, 318, 383,

384sonication 406turbidimetric 269

Methotrexate (MTX) 263, 515, 516a-Methyldopa 592, 593a-Methyldopa-Phe 592, 593a-Methyldopa-Pro 592, 593N-Methylephedrine 342, 380N-Methyl-D-Phenylglycyl-Pro-

Arg-H 519, 520Methylprednisolone 456Methysergide 456Metoclopramide 456Metolazone 259, 263, 266, 456,

534, 536Metoprolol 61, 62, 263, 453, 456,

534, 536, 544Metronidazole 456Metyrapone 568Mevastatin 354Mezerein 608Micelles 305Michaelis constant Km 67, 555Michaelis-Menten kinetics 67, 72,

232Miconazole 306, 315, 319, 321–323Microconstants 287, 288, 293, 298

Microheterogeneity 432Micromeritics 179Micronucleus assay 247, 250Microsomes 218, 230–234Microspecies 298Microtubules (MTs) 608Microvessels 136, 137

cerebral 135pial 135

Midazolam 456Mifepristone 192Mitochondria 129, 231Mitochondrial oxidation 249Mixtures, acetonitrile-water 431–

438MLP See ‘Molecular Lipophilicity

Potential’Mobile phases 431Modeling

homology 236, 237, 564–578in silico 11molecular 75, 467, 551–584,

596, 603–606pharmacodynamic 22pharmacodynamic variability 23pharmacokinetic 21pharmacokinetic/pharmaco-

dynamic 19, 20, 23, 27population 21

Modelsaqueous pore 54BBB 134–143cell-based 219cell-culture 99, 155clearance-based 21compartmental 21empirical 15, 21ex vivo 155flip-flop 54full physiological 21homology See ‘Modeling,homology’

644 SUBJECT INDEX

‘hybrid’ 161in vitro 120–124, 127, 162–168,

199, 217–239, 217in vivo 155, 156–162isolated perfused-lung 185mechanistic 15membrane 54pharmacophore 237, 552–556,

562pH-partition 285protein 564–578solubility/diffusion 53solution/partitioning membrane

53subcellular-based 219transgenic animal 210tube 55wholeanimal 210whole organ 219

Moleculardescriptors 486, 540diversity 485, 488dynamics (MD) 542factors See ‘molecular factors’fields 539, 548, 550hashkey 502‘machines’ 67modeling See ‘Modeling,molecular’parameters 579polarizability 541, 545properties See ‘Molecularproperties’shape 58, 503, 540, 541, 545,

549, 580size 51, 58, 258, 448, 502, 541,

545, 549structures 550surface 58, 532, 536surface area 542surface properties 16volume 262, 410

weight 54, 160, 212, 486, 500,541, 620

Molecular Electrostatic Potentials(MEPs) 531

Molecular factorsglobal 81proximal 80

Molecular Lipophilicity Potential(MLP) 526, 528, 531, 536

Molecular properties 51, 81,485–496, 499, 539computed 502, 503

Molsidomine 456MolSurf program 504, 526, 527Monensin 356, 358, 364Monoamine oxidase (MAO) 72,

130, 133, 141, 207, 231Monoclonal antibodies 892/4/A1 Monolayers 257Monooxygenase activity,

uncoupling of 236Monosaccharides 448Morphine 61, 63, 212, 456MTX See ‘Methotrexate’Mucoadhesive compounds 183Mucociliary function 183Multicase 579, 582, 583Multidrug resistance (MDR) 114

(See also ‘MDR1’)Multi-drug resistance protein (MRP)

60, 133, 139, 143Multiple linear regression (MLR)

502, 503Mutagenicity 580, 581Mutations 245Mycoplasms 102, 103

N

Na+/K+-ATPase 131, 607Nabumetone 94

SUBJECT INDEX 645

Nadolol 544Naloxone 456b-Naphthoflavone (bNF) 224, 2312-Naphthoic acid 306, 3191-Naphthol 221, 224Naproxen 259, 453, 456Nasal products 175NAT See ‘N-Acetyltransferase’National Institute of Standards and

Technology (NIST) 436Nebulizers 176, 182NEP 24.11 519Nephelometric titration 258, 260,

269Nernst equation 330, 344, 345Neu5Ac2en 520Neuraminidase 520

inhibitors 85, 93Neuroleptics 192, 495Neurotoxicity 581Neurotransmitters 131New chemical entities 257, 268New drug entities 15‘New drug research’ 621–624Nicardipine 193Nicotinate esters 72(S)-Nicotine 223Niflumic acid 290, 295, 297, 300Nitrazepam 456Nitrendipine 456Nitric-oxide synthase 133Nitrofurantoin 456Nitrophenols 358, 360, 361NMR

parameters 468–481spectroscopy 465–481

NOE effect (Nuclear Overhauserenhancement) 469

Non-steroidal antiinflammatorydrugs (NSAIDs) 192, 195

Nordiazepam 456, 534, 536Norepinephrine 380

Norfloxacin 456Nortriptyline 259, 306, 319, 321NSAIDs See ‘Non-steroidal anti-

inflammatory drugs’Nuclear spin (I) 468Nucleotides 131

O

Occludin 131Oestrogen receptor 516, 517Oleic acid (OA) 409, 421, 422Oligopeptides 55Olsalazine 534, 536Omeprazole 226, 228, 456Oncogenicity 581–583Ondansetron 61Opioid peptides 123Optimization

pharmacodynamic 85, 95, 615,617, 619

pharmacokinetic 85, 95, 615,617, 619

Oral inhalation products 176Organelles 112, 249, 429Organs, perfused 219Oseltamivir 92–94, 521Oxaceprol 70Oxazepam 456, 534, 536Oxicams 192Oxidative stress 66, 246, 249Oxprenolol 456, 534, 536, 542,

544Oxyphenbutazone 61

P

PAHs See ‘Polycyclic aromatichydrocarbons’

Papaverine 456

646 SUBJECT INDEX

Paracellular route 54, 63, 119,144, 184

Parallel artificial membrane-permea-tion assay (PAMPA) 204, 313,451–462

Partial least squares (PLS) 502–504

Partition chromatography 285Partition coefficients 17, 160, 258,

260, 264, 275, 280, 305, 335,348, 351, 354, 383, 401–403,412, 415, 431, 448, 449, 466 (seealso ‘log P’)ionic 348standard 329, 331, 333, 346standard ionic 348

Partition data, quantitative analysis419–421

Partition model, four-equation275, 277, 278

Partitioning 447, 539liposome/water 401–426

Pathways, follicular 159Penetration 67, 71Penetration enhancers 161Penicillin 591, 596Pentamidine 456(+)-Pentazocine 609Pentoxifylline 456PepT1

hormonal regulation 608subcellular distribution 607–610trafficking pathways 607

PepT2 599–602Peptidases 118, 119Peptides 35, 118, 122, 173, 439,

440, 442, 448, 507, 508, 541Peptidomimetics 118, 119, 591PepTx family 598Pericytes 137Permeability 7, 43, 44, 54, 56, 63,

177, 203, 213, 257, 268, 272,

313, 315, 450, 453, 459, 460, 501(see also ‘Permeability coeffi-cient’ and ‘Permeation’)apical-to-basolateral 202apparent 202, 203basolateral-to-apical 202BBB 142, 147bilayer 38, 47Caco-2 501, 503, 507, 508calculation of 146capillary 195effective (Peff) 314, 506gastrointestinal 257in vivo 506inhalation mucosa 174intestinal 202intrinsic 203, 314, 315, 318,

319, 461membrane 183, 204, 206, 207,

305, 458paracellular 148passive 37physicochemical methods 262–

268transcellular 271

Permeability-absorption relation-ships 203

Permeability coefficient, 167, 184,506apparent (Papp) 146, 202, 203,

449, 508, 542, 543endothelial (Pe) 147

Permeability studies,high-throughput artificial mem-

brane 447–462Permeation 36, 44, 201, 424, 475

(see also ‘Permeability’)barrier 99BBB 127–149, 547–549coefficient (Papp) 449intestinal 118–124in vivo 424

SUBJECT INDEX 647

membrane 34, 173, 539paracellular 211passive 99, 129, 424, 448, 449passive transcellular membrane

211skin 155–171

Personal-care products 157P-glycoprotein (P-gp) 12, 53, 55,

56, 58, 60, 63, 104, 107, 110, 111,114, 120, 132, 133, 138, 139,141–143, 149, 202, 204, 206,207, 424, 501, 502, 505, 510efflux 56, 59,

P-gp See ‘P-glycoprotein’Pharmaceutical industry 3Pharmacodynamic events 615, 616Pharmacodynamics (PD) 16, 19,

66, 85, 615, 616Pharmacokinetic

behavior 63, 99defects 85, 617events 615, 616parameters 210prediction 232problems 95properties 208screening 232

Pharmacokinetics (PK) 3, 5, 10,11, 16, 19, 51, 85, 191, 208, 217,234, 239, 257, 466, 539, 578,615, 616population 24

Pharmacophores 76, 81, 486, 547,583, 596

Phase-coexistence region 41Phase diagrams 42, 43D-Phe-Ala 601, 602Phe-a-methyldopa 592, 593Phenacetin 230, 234Phenazone 534, 536Phenazopyridine 306, 319Phenobarbital 193, 228, 231, 456

Phenols 221, 359, 4242,6-dichloro- 359, 3612,6-dinitro- 358

Phenotyping 218, 220, 233, 237Phenprocoumon 763-Phenyl-1,2-(Boc-NH)-propyl-Pro-

Arg-H 520Phenylbutazone 192, 4562-Phenylbutyryl-Pro-Arg-H 520Phenylpropionyl-Pro-Arg-H 520Phenytoin 259, 263, 456, 558, 559Phe-Phe-Phe 375D-Phe-Pro-Arg-H 519, 520pH-metric technique 280, 300pH-metric theory 275Phorbol esters 608Phosphatidylcholine (PC) 206,

404, 408, 409, 416, 421, 422,429, 450, 451, 474, 477

Phosphatidylethanolamine (PE)404, 409, 421, 429

Phosphatidylinositol (PI) 404, 409,421

Phosphatidylserine (PS) 404, 429Phosphoinositol 380Phospholipase A2 (PLA2) 39, 40,

45Phospholipids 36, 39, 44, 45, 52,

265, 402–404, 408, 429, 431,444, 449, 465, 470, 471, 475,476, 478, 479, 481

Photon-correlation spectroscopy(laser-light scattering) 409

pH-Partition theory 57Physicochemical parameters 127,

212, 257–272, 285Phytoestrogens 577Pindolol 61, 62, 259, 284, 285,

456, 534, 536, 544Piroxicam 193, 263, 266, 306,

315, 319, 322, 323, 376, 377Pivampicilline 94

648 SUBJECT INDEX

pKa See ‘Dissociation constants’Plasma 429Plasma binding (see also ‘Protein

binding’)non-restrictive 191restrictive 191, 196

Plasmalemma 129Plasma-to-tissue gradient 190Plasmepsin proteases 377Platelet-activating factor (PAF)

134Polar surface area (PSA) 52, 258,

264, 268, 269, 271, 454–457,504–508, 526, 527, 542dynamic 504, 507, 508, 527percentage 508

Polarity 262, 432, 509, 526Polarity/polarizability parameter

(p*) 433Polarizability 258, 260, 269, 432Pollutants 424Polycarbonate filters 264Poly(ethylene glycol) 44Polycyclic aromatic hydrocarbons

(PAHs) 556, 557Polyhalogenated biphenyls 72Polyhalogenated insecticides 72Polymorphisms 236

genetic 583Population analysis by topology-

based QSAR (PATQSAR) 508Positron-emission tomography

(PET) 61Post-dose pooling 209Potential

distribution 331–333electrochemical 329–331, 334genotoxic 250half-neutralization (pKa≤) 359,

363–365half-wave 344hydrogen-bonding 52, 119, 122

irritation 250molecular hydrogen-bonding

(MHBPs) 525–536surface (y) 409, 413, 414, 420,

421, 423Potentiometric titration 258,

417–418Pourbaix diagrams 345Powders, 179

respirable 179Powder-type spectrum 470Practolol 456, 534, 536Pravastatin 353, 354Precision-cut liver slices 222–225Predictions,

global 82local 82

Prednisolone 456Prednisone 456Pressurized-metered dose inhalers

(pMDI) 177Primidone 456Principal component analysis (PCA)

503, 544Probenecid 306, 319, 322, 323,

456Probucol 193Procainamide 230, 456Procarcinogens 552Prodipine 563Prodrugs 65, 66, 85, 86, 88, 119,

123, 542, 592–594, 619candidates 94carrier-linked 89, 90cyclic 122design 85–95formaldehyde-releasing 92intramolecular activation of 91macromolecular 89, 90organ-selective activation of 90R&D 93, 94

Profens 192

SUBJECT INDEX 649

Progesterone 263, 573Progesterone receptor 517Prolog P 581Promethazine 456Properties

ADE 214ADME 502core molecular 68electronic 73, 552hydrogen-bonding 503molecular See ‘Molecularproperties’molecular-orbital 73pharmacokinetic 208, 514physicochemical 16, 52, 93, 94,

160, 207, 211, 431, 501, 539,551

stereoelectronic 76steric 68surface 68, 540

Propoxyphene 456Propranolol 58, 61, 62, 71, 193,

194, 202, 203, 259, 263, 306,319, 321–323, 358, 364, 407,415, 416, 420–422, 453, 456,542, 544

Propyleneglycol dipelargonate(PGDP) 501

Propylthiouracil 456Proquazone 456Protease inhibitors 63Protein binding 10, 17, 189–196,

207, 209, 212, 218, 219, 221,222, 546, 547profiles 192–194

Protein expression 112Protein folding 236Protein-kinase cascades 608Proteins 35, 173, 465, 513, 541Proteoliposomes 143Proteomics 23, 251, 584Proton uncoupling 424

Proton-transfer process 378Proxyphylline 456PSA See ‘Polar surface area’Psychotropic agents 191Pulmonary route 176, 185Putidaredoxin 552Pyrazines 387, 388, 395–397Pyrene 580Pyridines 385–387, 390–394Pyridoxine 290, 295, 297, 300Pyrimethamine 456

Q

QSAR See ‘Quantitative structure-activity relationships’

Quadrupolar splitting 469, 472Quantitative Structure-Absorption

Relationships (QSAbR) 502Quantitative structure-activity rela-

tionships (QSARs), 354, 579,580, 597, 617, 6203D-QSAR 620

Quantitative Structure-MetabolismRelationships (QSMR) 72, 77

Quantitative Structure-PermeationRelationships (QSPeR) 502

Quantitative Structure-ToxicityRelationships (QSTR) 580

Quantitative three-dimensional SMR(3D-QSMR) 76

Quenched Molecular Dynamics(QMD) 529, 535

Quercetin 58Quinacrine 470, 472Quinazoline 517, 518Quinidine 234, 347, 348, 456,

563Quinine 259, 470, 472

650 SUBJECT INDEX

R

Raffinose 534, 536Randles-Sevcik equation 343Ranitidine 263, 266, 453, 456Rate of attrition 4, 117, 617, 621Rational design 617Reactions

immuno-allergic 189interfacial acid-base 343, 344,

348metabolic 79, 80methylation 79non-enzymatic 89of conjugation 66of cyclization-elimination 89of functionalization 66post-metabolic 66proton-transfer 344redox 79

Reactive metabolites 67Reasoning 623Receptor polymorphism 16Recognition 67Reductases 207Regioselectivity 70, 554, 557, 573,

575product 69, 73, 74substrate 69

Regulatory authorities 25Relaxation rates 475–481Residual deuterium quadrupole

splitting 471Respiratory tract 175

deposition 177all-trans-Retinoic acid 142Reversed-phase HPLC 383–399Rifabutin 228Rifampin 454, 456Rifapentine 228Risk assessment 223, 246Ritropirronium 61

Royal Society of Chemistry’sDictionary of Substances andtheir Effects (DOSE) 578

rPepT1 593rPepT2 593RP-HPLC See ‘Reversed-phase

HPLC’Rufinamide 259Rugosity 541Rule-of-five 58, 93, 213, 500, 525,

620Rules 623, 624

S

Saccharin 70, 456Salicylamide 225, 517, 518Salicylic acid 61, 362, 363, 456Science-become-technology 623Scintigraphy

nasal 181pulmonary 181

Scopolamine 163, 456Screening 17 (see also ‘HTS’)

in silico metabolic 77library 617, 618mechanism-based 246

Screens (see also ‘HTS’)algorithmic 623medium throughput 250pharmacokinetic 24

Scytalone dehydratase 517, 518,521

SDS Polyacrylamide-gel electropho-resis (SDS-PAGE) 112

Selectivityb1/b2 62product 69substrate 69substrate-product 69types of 69

SUBJECT INDEX 651

Self-binding 305Serotonin 134Serum albumin 189, 192, 193, 610

human 212Seven-transmembrane-domain

(7-TM) receptors 59, 60Shake-flask method 258, 280, 316,

321, 396SHB See ‘Surface of H-bonds’Shed snake skin 167b-Sheets 513Sigmoidal permeability-lipophilicity

relationships 54Signal transduction 249Simvastatin 354Site-directed mutagenesis 566,

568, 572, 573, 575, 577, 584, 603

Skin,in vitro permeation 164sandwich flap 161sensitization 580uptake 159

Small-molecular models See‘Models, pharmacophores’

Small unilamellar vesicles (SUV)502

Soft drugs 65, 86, 87Solubility, 7, 10, 16, 51, 93, 118,

173, 178, 190, 211–213, 257, 258,268, 305, 313, 315, 447, 448, 453calculated 260effective 311intrinsic 261, 307, 309, 314,

318, 319kinetic 258, 260, 269profiles 305–324thermodynamic 261true 269water 258–262, 496, 503

Solubility parameters,three-dimensional 503

Solubility pH profiles 307–316,318, 321

Solvation 514, 540Solvatochromic approach 527Solvatochromic parameters 432,

436, 438, 443, 529Sotalol 544Sparteine 561Specific heat 35, 36, 43Sphingomyelin (Sph, SM) 404,

429Spin coupling (J) 468, 475Spin-lattice relaxation rates 469,

475Spin-spin relaxation rates 469, 475Spiral of creativity 622Spiral of progress and discovery

621, 622Stability 7, 258Statins 353–355Staurosporine 608Stereochemical factors 75, 76Stereoselectivity, 554, 557

product 69, 75substrate 69

Steric hindrance 68, 356–359, 362Steroid hormones 513Steroid receptors 516Steroidogenesis 552Steroids 190, 577

4,5-didehydro- 51717-hydroxy-3-oxo- 5173-keto- 516

Sterols 465Stratum corneum 157, 158, 160,

164–168, 170Structure-absorption relationships

499–510, 526Structure-activity relationships

383, 485, 519Structure-binding domains,

of PepT1 598–606

652 SUBJECT INDEX

Structure-disposition relationships542–550

Structure-function relationships598–606

Structure-metabolism relationships(SMR) 65–83, 86

Structure-permeation relations525–536, 533–536

Structure-toxicity relationships245, 248

Studiesclinical 21–26deposition 175–183dissolution 183, 184in vitro 7–11, 17, 18, 175in vivo 5–7, 18–20NMR spin-relaxation 568permeation 184–186phase-1 clinical 21, 22phase-2 clinical 22, 23, 25phase-3 clinical 24–26population-kinetic 24postmarketing 26, 27toxicology 17

Subcellular fractions 218–220,231–234

Substrate specificity 237, 551, 592of PepT1 594–598

Substrates 513, 515, 516, 552,561–563, 567, 575, 576, 583binding 236recognition 596, 597selectivity 584specificity See ‘substratespecificity’translocation 597

Substructures 485, 486Sulfacetamide 544, 545Sulfadiazine 456Sulfaphenazole 234, 237Sulfasalazine 263Sulfate-binding protein 518

Sulfation 221, 224Sulfinpyrazone 456Sulfisoxazole 456Sulfonamides 356Sulfonanilides 356Sulfonylureas 356Sulfotransferases 221, 231, 235Sulindac 456Sulphasalazine 534, 536Sulpiride 61, 62, 263, 456, 534,

536Suprofen 456Surface of H-bonds (SHB) 529,

531Symmetry 580Symplekin 131Systems

biological 67, 71, 81drug-delivery 44efflux 258, 272fragmental 528knowledge-based 77lymphatic 189many-particle 35predictive expert 77site-specific chemical delivery

89, 90two-chamber 143

T

Tacrine 61, 457Talinolol 56Target factor analysis (TFA) 290,

291, 293Tautomeric ratio 287, 289,

293–295Tautomers 494Taxol 58, 234Technology 621–624Technology-become-routine 623

SUBJECT INDEX 653

TEER See ‘Transepithelial andtransendothelial electical resis-tance’

Tenoxicam 192Teratogenicity 245, 578Terbutaline 263, 266, 457Terfenadine 196, 224, 259, 306,

319, 322–323Testing sequence 214Testosterone 159, 227, 229, 234,

263, 457, 574Tests

in vitro 200–207in vivo 207–210

Tetrabutylammonium (TBA+) 339Tetrabutylammonium tetraphenyl-

borate (TATB) 333Tetracaine 358, 472–474Tetracycline 457Tetramethylammonium (TMA+)

340Tetraphenylarsonium (TPA+) 334Tetraphenylborate (TPB–) 332,

334, 339Tetrazole, 5-phenyl- 358Tetrazoles 356Theophylline 224, 457Therapeutic

drug monitoring 26margins 246

Thermolysin 519Thiocolchicoside 186Thiophenes 385–387, 390–394Three-dimensional NMR 568Thromboxane A2 synthase (TXAS,

CYP5) 569Tienilic acid 559Tight junctions (TJ) 54, 110, 119,

128, 132, 184, 206proteins 99, 130, 131

Time-point protocols 6Timolol 263, 542, 544

Tissue distribution 189, 194–196,219

Tissue samples 185Tissue slices 218, 219Tolbutamide 222, 234Toluene, 2,4-difluoro- 520TOPKAT See ‘Toxicology

Prediction by Komputer-AssistedTechnology’

Topological kappa index 500Toxic effects,

classification of 245Toxicity 214

computational prediction of578–583

developmental 580local 245, 250, 251reproductive 245, 248target organ 245, 248–250

Toxicokinetics 18, 19, 578Toxicological issues 245–252Toxicology Prediction by

Computer-Assisted Technology(TOPKAT) 251, 580–583

Toxicophores 581Toxification 65–67, 228TOXSYS 251, 582Tranexemic acid 534, 536Transcellular electrical resistance

104Transcellular route 63, 119, 120Transcytosis 133

receptor-mediated 149Transepithelial and transendothelial

electrical resistance (TEER)104, 129, 138–142, 144, 145,149, 202, 453

Transferinterfacial 340mucosal-to-serosal 206

Transfer potentialformal 343

654 SUBJECT INDEX

half-wave 342, 343standard 330, 332, 343

Transferrin 129, 133, 141Transgenic animals 210, 252Trans-Golgi Network (TGN) 608Transition temperature, main 404Transmembrane movement 53Transport

active 201, 205, 211, 213, 258,272, 447

carrier-mediated 99, 104, 114,146, 202, 525

equilibrative 131mechanisms 132membrane 52, 56, 57, 504, 510,

513paracellular 104, 114, 525P-gp-mediated 115processes 113receptor-mediated 133transcellular 184, 525transendothelial 207trans-membrane 475transporter-facilitated 119–123transporter-restricted 119–123vesicular 201, 525

Transporter PepT1 (see also‘PepT1’)intestinal dipeptide 591–610

Transporters 56, 99, 119–124, 131–133, 190, 213, 424, 505, 510, 620g-aminobutyric-acid (GABA)

608anion-exchange 510glucose (GLU) 132Glut1 604Glut4 607membrane 591–610monocarboxylic-acid 510norepinephrine (NE) 608oligopeptide (PET) 119, 123,

124, 510, 597

organic anion 212organic cation 212taurine 608

Tricyclic antidepressants 466Trifluperidol 563Triglycerides 404Trimetazidine 342Trimethoprim 457Tripeptides 374Trp-Phe 374, 375Tubular uptake/secretion,

active 212Tumor-necrosis factor (TNF) 134,

142Tumors 134, 245Turbidimetric assays 317Turnover number (kcat) 67Two-phase titration 283, 336, 337Tyr-Gly-Gly 440–443Tyrosyl-tRNA-synthase 518

U

UDP-glucuronosyl transferase(UDPGT) 221, 231, 232, 235,236

Ultracentrifugation 419Ultrafiltration 419Ultra-HTS 200Umbelliferol, 4-methyl- 379Uncoupling oxidative phosphoryla-

tion 365University of Wisconsin organ solu-

tion 227Uridine, 5-bromo-2¢-deoxy- 31Using chamber 144, 499

V

Valacyclovir (Val-ACV) 120, 592,593

SUBJECT INDEX 655

Valproic acid 193Valsartan 259, 263, 267Val-zidovudine (Val-AZT) 592,

593Verapamil 56, 133, 204, 453, 457Vesicles

large unilamellar (LUVs) 403,405

multiamellar (MLVs) 405small unilamellar (SUVs) 403,

406unilamellar 403

Vinblastine 56, 204Virtual screening 11–13, 77, 200,

210–213, 247, 251, 258, 262,264–266, 485–496, 584, 617, 620

Vitamins 448VolSurf 536, 539–550

descriptors 541, 542, 544, 546parameters 509

Voltammetry 340, 341cyclic 327–348

Volume of distribution 191, 210,230, 373

von Willebrand Factor (vWF) 130Vorozole 577

W

Warfarin 76, 192, 193, 263, 457,558, 559

Waste products 130

Water, role in ligand binding 514,521–523

Weak acids, distribution of 334–338

384-Well plates 218, 232, 314,324, 450

Western-Blot Analysis 112, 130Whole-brain/blood partitioning 60Wide-line experiments 469–474World Drug Index (WDI) 500

X

Xanthine oxidase 231Xenobiotics 18, 222, 543, 551

polyhalogenated 71X-Ray 467

Z

Zanamivir 92, 93, 520, 521Zebularine 522

3,4-dihydro- 522Zetapotential 46, 47, 262,

408–410, 420–422Zidovudine (AZT) 594Zileuton 224Zolpidem 457Zoxazolamine 557Zwitterions 279, 286–302, 366–

377, 495 (see also ‘Ion pairs’)

Documents

Pharmokinetic Optimization in Drug Research