Publications of the University of Eastern Finland

Dissertations in Health Sciences

isbn 978-952-61-0754-7

Publications of the University of Eastern FinlandDissertations in Health Sciencesd

issertation

s | 108 | L

aur

a Ma

rtik

ain

en | In V

itro and in S

ilico Meth

ods to Predict C

ytochrome P

450 En

zyme Inhibition

Laura MartikainenIn Vitro and in Silico Methods

to Predict Cytochrome P450 Enzyme Inhibition Laura Martikainen

In Vitro and in Silico Methodsto Predict Cytochrome P450 Enzyme Inhibition

Cytochrome P450 (CYP) enzymes are

important enzymes involved in drug

metabolism. In the present study, the

overall goal was to develop novel in

vitro and in silico methods to assess

the metabolic features of xenobiotics.

In vitro high throughput screening

and in silico studies can provide early

prediction of the possible involve-

ment of CYP enzymes in the metabo-

lism of drugs or drug candidates.

Therefore these studies could poten-

tially make drug design more effec-

tive, less costly and improve drug

safety and replace some of the in vivo

experiments.

LAURA MARTIKAINEN

In Vitro and in Silico Methods to PredictCytochrome P450 Enzyme Inhibition

To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland forpublic examination in Mediteknia Auditorium, Kuopio Campus,

on Friday, June 1st 2012, at 12 noon

Publications of the University of Eastern Finland Dissertations in Health Sciences

108

School of Pharmacy, Faculty of Health Sciences, University of Eastern FinlandKuopio

2012

KopijyväKuopio, 2012

Series Editors:Professor Veli-Matti Kosma, M.D., Ph.D.Institute of Clinical Medicine, Pathology

Faculty of Health Sciences

Professor Hannele Turunen, Ph.D.Department of Nursing Science

Faculty of Health Sciences

Professor Olli Gröhn, Ph.D.A.I. Virtanen Institute for Molecular Sciences

Faculty of Health Sciences

Distributor:University of Eastern Finland

Kuopio Campus LibraryP.O.Box 1627

FI-70211 Kuopio, Finlandhttp://www.uef.fi/kirjasto

ISBN (print): 978-952-61-0754-7ISBN (pdf): 978-952-61-0755-4

ISSN (print): 1798-5706ISSN (pdf): 1798-5714

ISSN-L: 1798-5706

III

Author’s address: School of PharmacyUniversity of Eastern FinlandKUOPIOFINLAND

Supervisors: Professor Hannu Raunio, M.D., Ph.D.School of PharmacyUniversity of Eastern FinlandKUOPIOFINLAND

Adjunct Professor Risto Juvonen, Ph.D.School of PharmacyUniversity of Eastern FinlandKUOPIOFINLAND

Reviewers: Adjunct Professor Olli Pentikäinen, Ph.D.Department of Biological and Environmental ScienceUniversity of JyväskyläJYVÄSKYLÄFINLAND

Laboratory Manager Päivi Taavitsainen, Ph.D.Orion PharmaTURKUFINLAND

Opponent: Professor Jukka Hakkola, M.D., Ph.D.Institute of BiomedicineDepartment of Pharmacology and ToxicologyUniversity of OuluOULUFINLAND

IV

V

Martikainen, LauraIn Vitro and in Silico Methods to Predict Cytochrome P450 Enzyme InhibitionUniversity of Eastern Finland, Faculty of Health Sciences, 2012Publications of the University of Eastern Finland. Dissertations in Health Sciences 108. 2012. 99 p.

ISBN (print): 978-952-61-0754-7ISBN (pdf): 978-952-61-0755-4ISSN (print): 1798-5706ISSN (pdf): 1798-5714ISSN-L: 1798-5706

ABSTRACT

Cytochrome P450 (CYP) enzymes are important enzymes involved in drug metabolism.The CYP enzymes are a family of heme proteins involved in the metabolism of numeroustoxic and pharmacologically active compounds and can cause drug-drug-interactions withco-administered drugs as well as unwanted adverse side effects. Several in vitro methodshave been developed and are in widespread use to evaluate main kinetic properties(absorption, distribution, metabolism, excretion) in drug development projects. Thesestudies help in identifying molecules that have favourable kinetic properties well beforeclinical studies in humans. The most modern technological breakthrough is the use ofcomputational (in silico) methods to predict kinetic properties of lead molecules.

In the present study, the overall goal was to develop novel in vitro and in silico methodsto assess the metabolic features of xenobiotics. The metabolic focus of this thesis work isinhibition of CYP enzymes. The four aims of the present study were (1) to compare three invitro inhibition screening tests for CYP enzymes (2) to create new inhibition structure-activity databases for human CYP enzymes (CYP1A2, CYP2B6, and CYP2E1) (3) toconstruct quantitative structure-activity relationship (QSAR) models of inhibitors of theseenzymes and (4) to identify more potent and selective inhibitors for these enzymes.

Three assay types, the traditional single substrate assays, the fluorescent probe methodwith recombinant human CYPs, and a novel n-in-one technique, yielded similar resultswith the majority of the CYP forms tested. For CYP1A2, CYP2B6 and CYP2E1, newinhibition structure-activity databases were created, which were large and composed ofstructurally diverse compounds. The created quantitative structure-activity relationship(QSAR) models revealed the key molecular characteristics of inhibitors of the CYP1A2,CYP2B6 and CYP2E1 and were also able to accurately predict inhibitory potencies of thetest set of compounds. For CYP2B6, three novel potent inhibitors were discovered, 4-(4-chlorobenzylpyridine) (CBP), 4-benzylpyridine (BP) and 4-(4-nitrobenzylpyridine) (NBP).In particular CBP is a suitable inhibitor for in vitro screening studies. In vitro high-throughput screening and in silico studies can provide early prediction of the possibleinvolvement of CYP enzymes in the metabolism of drugs or drug candidates. Thereforethese studies could potentially make drug design more effective, less costly and improvedrug safety and replace some of the in vivo experiments.

National Library of Medical Classification: WH 190, QU 143, QU 120, QV 38Medical Subject Headings: Cytochrome P450 Enzyme System; Drug Interactions; Enzyme Inhibitors;Xenobiotics/metabolism; Quantitative Structure-Activity Relationship; High-Throughput Screening Assays

VI

VII

Martikainen, LauraIn vitro ja in silico menetelmät sytokromi P450 entsyymien inhibition ennustamiseen.Itä-Suomen yliopisto, terveystieteiden tiedekunta, 2012Publications of the University of Eastern Finland. Dissertations in Health Sciences 108. 2012. 99 s.

ISBN (print): 978-952-61-0754-7ISBN (pdf): 978-952-61-0755-4ISSN (print): 1798-5706ISSN (pdf): 1798-5714ISSN-L: 1798-5706

TIIVISTELMÄ

Sytokromi P450-entsyymit (CYP) ovat ihmisen tärkeimpiä lääkeaineita metaboloiviaentsyymejä. Ne ovat ryhmä hemiä sisältäviä proteiineja ja osallistuvat monien toksisten jafarmakologisesti aktiivisten aineiden metaboliaan, mikä voi aiheuttaa yhteisvaikutuksia jaei-toivottuja haittavaikutuksia. Lukuisia in vitro menetelmiä on kehitetty ajan kuluessa ja neovat laajasti käytössä farmakokineettisten ominaisuuksien tutkimuksessa. Näidenmenetelmien avulla saadaan selville ne molekyylit, joilla on sopivat farmakokineettisetominaisuudet ennen kuin kliiniset kokeet ihmisellä aloitetaan. Nykyinen teknologinenedistysaskel on käyttää tietokonemalleja (in silico) näiden ominaisuuksien ennustamiseen.

Tämän väitöstutkimuksen kokonaistavoitteena oli kehittää ja soveltaa uusia in vitro ja insilico menetelmiä vierasaineiden metabolisten ominaisuuksien tutkimiseen. Tutkimuksessakeskityttiin mittaamaan, mallintamaan ja ennustamaan vierasainemolekyylien CYP-välitteisen metabolian inhibitiota. Väitöskirjatutkimuksen tavoitteena oli: (1) verratakolmea CYP-entsyymien inhibiittoreiden in vitro seulontamenetelmää (2) tuottaa rakenne-aktiivisuus-tietokannat ihmisen CYP-entsyymien (CYP1A2, CYP2B6, CYP2E1)inhibiittoreista (3) tehdä rakenne-aktiivisuus tietokonemallit näille entsyymeille (4) löytääin vitro käyttöön uusia CYP-entsyymien inhibiittoreita, jotka ovat voimakkaampia jaselektiivisempiä kuin tällä hetkellä käytetyt.

Kaikki tutkimuksessa käytetyt seulontamenetelmät: multisubstraattianalyysi,perinteinen yhden substraatin menetelmä ja 96-kuoppalevymenetelmä fluoresoivillasubstraateilla, antoivat samanlaisia tuloksia muutamia poikkeuksia lukuun ottamatta.CYP1A2-, CYP2B6- ja CYP2E1-entsyymien inhibiittoreista tuotettiin tietokannat, jotkasisälsivät suuren määrän rakenteellisesti erilaisia yhdisteitä. Rakenne-aktiivisuus-mallienavulla saatiin selville inhibiittoreiden tärkeimmät ominaisuudet ja näiden mallienluetettavuutta testattiin ennustamalla inhibitioarvot molekyyleille, joita ei otettu mukaanmalleihin. CYP2B6-entsyymille löydettiin kolme uutta potenttia inhibiittoria: 4-(4-klooribentsyylipyridiini) (CBP), 4-bentsyylipyridiini (BP) ja 4-(4-nitrobentsyylipyridiini)(NBP). Erityisesti CBP on soveltuva inhibiittori in vitro seulontatutkimuksiin. In vitro ja insilico tutkimukset voivat alustavasti ennustaa CYP-entsyymien osallistumisen uusienlääkeaineiden metaboliaan. Siksi nämä tutkimukset voivat tehdä lääkekehityksestätehokkaampaa, laskea kustannuksia, parantaa lääketurvallisuutta ja korvata joitain in vivotutkimuksia.

Luokitus: WH 190, QU 143, QU 120, QV 38Yleinen suomalainen asiasanasto: sytokromit; entsyymit; inhibiittorit; lääkeaineet; lääkkeet; yhteisvaikutukset;rakenne; aktiivisuus; vierasaineet; aineenvaihdunta

VIII

IX

To my family

X

XI

Acknowledgements

The present study was carried out in the Department of Pharmacology and Toxicology,University of Kuopio during the years 2003-2009 and finalized in the School of Pharmacy,Faculty of Health Sciences, University of Eastern Finland in 2010-2012.

First, I want to express my deepest gratitude to my principal supervisor ProfessorHannu Raunio, for giving me the opportunity to start my doctoral studies and for hisprofessional guidance, encouragement, and invaluable advice during these years. His doorhas always been open and he took time to answer my questions. I would also like towarmly thank my second supervisor, Adjunct Professor Risto Juvonen, for his enthusiasmin research work and expertise in enzymes and biochemical assays. I owe my deepestgratitude to Minna Rahnasto-Rilla, Ph.D., who is not my official supervisor, but without adoubt, is the person without whom this thesis would not have been completed. Her in silicoexpertise has been invaluable.

I am very grateful to the official reviewers of this thesis, Päivi Taavitsainen, Ph.D. jaAdjunct Professor Olli Pentikäinen, for their valuable comments and constructive criticism.I am deeply honoured to have Professor Jukka Hakkola as my official opponent. I also wishto thank Ewen MacDonald, Ph.D., for his skilful work in revising the English language ofthis thesis and my manuscripts. Additionally, I am very thankful to Hannele Jaatinen forher excellent technical assistance in the laboratory.

I would also like to thank my all co-authors of the original publications for theirsignificant contributions. Especially, I am deeply indebted to Adjunct Professor MiiaTurpeinen from University of Oulu for her important inputs into my research projects.

I want to thank the rest of the “CYP-people”, Niina Tani and Kaisa Salminen for theirhelp throughout these studies as well as for their friendship. I wish to thank all mycolleagues and friends in the university, and all of the personnel of the School of Pharmacyfor creating such an excellent working environment. Especially I want to thank everybodyin the Mediteknia coffee room during the long years for lightening my days, especiallyJaana Leskinen and Jenni Hakkarainen for their friendship and profound conversations.Furthermore I wish to thank all the present and former young (and not so young) scientistsin the pharmacology and toxicology unit for all the memorable events.

I warmly thank all of my dear friends outside the university. Especially I heartily thank“the girls”: Anna, Sanna, Mari K, Mari R-F and Päivi for their friendship during manyyears. We see each other far too rarely nowadays, but the get-togethers are alwaysmemorable.

My warm thanks belong to my parents-in-law Leena and Kyösti, sister-in-law Riika-Mariand brother-in-law Antti for supporting me and our family in our daily lives.

XII

I am very grateful to my mother Arja and her spouse Veijo and to my father Esko and hisspouse Rauni for their love and support during the years. I also express my loving thanksto my sister Taina and her family; Janne, my goddaughter Aino and Eino.

Nelli and Leo, my “theses in flesh” and the ones who delayed my dissertation (vitsi,vitsi); you are more important than anything. You are the ones who bring the sunshine intomy life.

Foremost, I owe my deepest and most loving thanks to my darling husband, Janne. Wemet during this project and share the same enthusiasm for science and research. It hashelped a lot to understand each other. You have encouraged me even when I have lost hopeof ever finalizing this work.

This work has been funded by the Finnish Funding Agency for Technology andInnovation (TEKES), Graduate school of Toxicology, the Graduate School forPharmaceutical Research, the Finnish Cultural Foundation (Fund of North Savo), Faculty ofHealth Sciences and the School of Pharmacy in University of Eastern Finland, KuopioUniversity Foundation, Finnish Pharmacists´ association, Finnish Pharmacological Society,Suomen Proviisoriyhdistys ry, and Lääketutkimussäätiö (Society for Drug Research). Iwarmly thank all the people who have made this work possible.

Kuopio, April 2012

Laura Martikainen

XIII

List of the original publications

This dissertation is based on the following original publications:

I Turpeinen M, Korhonen LE, Tolonen A, Uusitalo J, Juvonen R, Raunio H andPelkonen O. Cytochrome P450 (CYP) inhibition screening: comparison of tests.Eur J Pharm Scien. 29: 130–138, 2006

II Korhonen LE, Rahnasto M, Mähönen NJ, Wittekindt C, Poso A, Juvonen RO andRaunio H. Predictive three-dimensional quantitative structure-activityrelationship of cytochrome P450 1A2 inhibitors. J Med Chem. 48: 3808–15, 2005

III Korhonen LE, Turpeinen M, Rahnasto M, Wittekindt C, Poso A, Pelkonen O,Raunio H and Juvonen RO. New potent and selective cytochrome P450 2B6(CYP2B6) inhibitors based on three-dimensional quantitative structure-activityrelationship (3D-QSAR) analysis. Br J Pharmacol. 150: 932–42, 2007

IV Martikainen LE, Rahnasto-Rilla M, Neshybova S, Lahtela-Kakkonen M, Raunio Hand JuvonenRO. Interactions of inhibitor molecules with the human CYP2E1enzyme active site. Submitted

The publications were adapted with the permission of the copyright owners.

XIV

XV

Contents

1 INTRODUCTION ............................................................................................................. 12 REVIEW OF THE LITERATURE ..................................................................................... 32.1 Drug metabolism ............................................................................................................. 32.2 Human cytochrome P450 (CYP) enzyme system .......................................................... 32.3 CYP1 –family .................................................................................................................... 5

2.3.1 CYP1A2 .................................................................................................................... 62.4 CYP2 –family .................................................................................................................... 6

2.4.1 CYP2A6 .................................................................................................................... 62.4.2 CYP2B6 ..................................................................................................................... 72.4.3 CYP2C8 .................................................................................................................... 72.4.4 CYP2C9 .................................................................................................................... 72.4.5 CYP2C19................................................................................................................... 82.4.6 CYP2D6 .................................................................................................................... 82.4.7 CYP2E1 ..................................................................................................................... 8

2.5 CYP3 –family .................................................................................................................... 92.5.1 CYP3A4 .................................................................................................................... 9

2.6 Genetic polymorphism of CYPs ..................................................................................... 92.7 CYP inhibition ................................................................................................................ 10

2.7.1 Reversible inhibition ............................................................................................. 102.7.2 Mechanism-based inhibition ................................................................................ 11

2.8 Drug-drug interactions.................................................................................................. 122.8.1 Guidelines on the investigation of drug interactions ......................................... 132.8.2 Predicting DDIs ..................................................................................................... 152.8.3 Methods in CYP-mediated DDI research ............................................................ 162.8.3.1 In vitro techniques ............................................................................................... 172.8.3.2 In silico techniques .............................................................................................. 192.8.3.3 Quantitative structure-activity relationship ..................................................... 20

3 AIMS OF THE STUDY ................................................................................................... 224 GENERAL EXPERIMENTAL PROCEDURES ............................................................. 234.1 Materials ......................................................................................................................... 23

4.1.1 Chemicals ............................................................................................................... 234.1.2 Biological material ................................................................................................. 244.1.2.1 Human liver microsomes................................................................................... 244.1.2.2 cDNA expressed human P450s ......................................................................... 24

4.2 Methods .......................................................................................................................... 244.2.1 Fluorescent probe assays ...................................................................................... 244.2.2 Comparative Molecular Field Analysis (CoMFA) .............................................. 25

5 COMPARISON OF CYTOCHROME P450 INHIBITION SCREENING TESTS .... 275.1 Introduction.................................................................................................................... 285.2 Materials and methods .................................................................................................. 28

5.2.1 Single substrate assays .......................................................................................... 285.2.2 N-in-one assay ....................................................................................................... 29

XVI

5.2.3 HPLC conditions ................................................................................................... 295.2.4 LC/ESI/MS conditions ........................................................................................... 295.2.5 In vitro inhibition and reference inhibitors ......................................................... 30

5.3 Results ............................................................................................................................ 305.3.1 Sotalol and propranolol ........................................................................................ 305.3.2 Citalopram and fluoxetine ................................................................................... 305.3.3 Oxazepam and diazepam ..................................................................................... 325.3.4 Effect of CYP-specific reference inhibitors in the fluorescent probe assay ...... 32

5.4. Discussion ..................................................................................................................... 336 INHIBITION STUDIES AND 3D-QSAR OF CYP1A2 INHIBITORS ..................... 376.1 Introduction ................................................................................................................... 386.2 Materials and methods ................................................................................................. 38

6.2.1 Determination of inhibition potency ................................................................... 386.2.2 GRID/GOLPE model............................................................................................. 38

6.3 Results ............................................................................................................................ 396.3.1 Inhibition of CYP1A2 activity .............................................................................. 396.3.2 CoMFA and GRID/GOLPE analysis .................................................................... 45

6.4 Discussion ...................................................................................................................... 497 INHIBITION STUDIES AND 3D-QSAR OF CYP2B6 INHIBITORS ...................... 537.1 Introduction ................................................................................................................... 547.2 Materials and methods ................................................................................................. 54

7.2.1 Determination of inhibition potency ................................................................... 547.2.2 Enzyme assays using human liver microsomes ................................................. 54

7.3 Results ............................................................................................................................ 557.3.1 Inhibition of CYP2B6 activity in vitro and initial CoMFA model ..................... 557.3.2 The final CoMFA model ....................................................................................... 597.3.3 CYP2B6 selectivity ................................................................................................ 61

7.4 Discussion ...................................................................................................................... 648 INHIBITION STUDIES AND 3D-QSAR OF CYP2E1 INHIBITORS ...................... 678.1 Introduction ................................................................................................................... 688.2 Materials and methods ................................................................................................. 68

8.2.1 Determination of inhibition potency ................................................................... 688.2.2 Protein structure of CYP2E1 ................................................................................ 698.2.3 Molecular docking and CoMFA model .............................................................. 69

8.3 Results ............................................................................................................................ 698.3.1 Inhibition of CYP2E1 ............................................................................................ 698.3.2 Docking of the training set molecules ................................................................. 758.3.3 CoMFA model ....................................................................................................... 76

8.4 Discussion ...................................................................................................................... 789 GENERAL DISCUSSION .............................................................................................. 8010 CONCLUSIONS ............................................................................................................ 84REFERENCES ..................................................................................................................... 85

XVII

Abbreviations

ADMET Absorption, distribution, metabolism, excretion and toxicity

AHR Aromatic hydrocarbon receptor

ADR Adverse drug reaction

AUC Area under the plasma concentration-time curve

BP 4-Benzylpyridine

Cmax Maximum plasma concentration

CBP 4-(4-Chlorobenzylpyridine)

CLint Intrinsic clearance

CoMFA Comparative molecular field analysis

CYP Cytochrome P450

3D-QSAR Three-dimensional quantitative structure activity relationship

DDI Drug-drug interaction

DMSO Dimethyl sulphoxide

EM Extensive metabolizers

EMA European Medicines Agency

F Bioavailability

FDA The U.S. Food and Drug Administration

GOLD Genetic optimization for ligand docking

HPLC High-performance liquid chromatography

HTS High-throughput screening

[I] Inhibitor concentration (unbound)

IC50 Concentration of a drug that is required for 50% inhibition

IM Intermediate metabolizers

XVIII

Ki Inhibition constant

Km Michaelis-Menten constant for a substrate (concentration of substrate at whichhalf-maximal velocity is achieved)

LC Liquid chromatography

MS Mass spectrometry

NADPH Nicotinamide adenine dinucleotide phosphate

NBP 4-(4-Nitrobenzylpyridine)

NCE New chemical entity

PAH Polycyclic aromatic hydrocarbon

PDB Protein Data Bank

pIC50 Log P of the concentration of a drug that is required for 50% inhibition

PLS Partial least squares

PM Poor metabolizers

q2 Square of the cross-validated correlation coefficient

r2 Square of the correlation coefficient

[S] Substrate concentration

SNP Single-nucleotide polymorphism

SPRESS Standard deviation of the error of predictions

UM Ultrarapid metabolizers

Vmax Maximal reaction velocity

1 Introduction

The discovery and development of therapeutically useful drugs requires first an innovativeidea and then expenditure of an enormous amount of labour, money and time. A newchemical entity (NCE) proceeds from discovery through development to registration andalong the way, costs can exceed one billion euro (DiMasi and Grabowski, 2007). One of thereasons why many clinical drug candidates fail is inappropriate pharmacokinetics. Thestudies of absorption, distribution, metabolism, excretion and toxicity (ADMET) of newdrug molecules are carried out today already in the early phase of drug development. Themetabolic characteristics of lead compounds need to be scrutinized to avoid drugs beingwithdrawn from clinical studies or the market due to major kinetic problems, such assevere interactions related to metabolism. Metabolism of compounds occurs in manytissues, with the liver being the most important organ, but also the kidneys, skin, lungs,and intestine can metabolize some drugs. Drug metabolism is divided into two types ofreactions, namely phase I (hydrolysis, oxidation, and reduction) and phase II reactions(conjugation). The metabolism pathway of a drug is mediated by phase I, phase II, or acombination of both phases.

Cytochrome P450 (CYP) enzymes are a major class of enzymes involved in drugmetabolism. The CYP enzymes are a family of heme proteins participating in themetabolism of numerous toxic and pharmacologically active compounds. The function ofmost CYP enzymes is to catalyze the oxidation of xenobiotic substances such as drugs andother toxic chemicals as well as endogenous compounds e.g. steroid hormones and fattyacids. The CYP enzymes biotransform lipophilic substances into more polar forms whichcan then be excreted by the kidneys. About ten CYP forms participate in the metabolism ofxenobiotics in humans with three families being involved in the majority of the drugbiotransformations; CYP1, CYP2 and CYP3.

Several in vitro methods have been developed and are widely used to detect mainpharmacokinetic properties (ADME: absorption, distribution, metabolism, excretion).These can help in identifying molecules with favourable pharmacokinetic properties wellbefore the start of any clinical studies in humans. Nowadays more and morecomputational (in silico) methods are being used to predict the kinetic properties of leadmolecules. Rapid advances in several different technologies have yielded increasinglyprecise information about the interactions between CYP enzymes and their ligands. Inparticular quantitative structure-activity relationship (QSAR), crystal structures of CYPenzymes and pharmacophore approaches have been used to explore the active sites of CYPenzymes. These new computational models are promising tools for screening more rapidlythe quantitative interactions between chemicals and CYP enzymes. The methods used instudying kinetic properties can be applied directly to other xenobiotics, including thethousands of chemical compounds to which we are exposed. Thus, these in vitro and insilico methods are applicable to both drug development and the risk assessment ofchemicals. Studies on the metabolic properties of drugs and chemicals often include in vitrotests with inhibitors of individual CYP forms. The use of chemical inhibitors is a routine

2

approach when the relative contribution of CYPs to the metabolism of a particularcompound, e.g. a drug, needs to be evaluated.

Thus the aims of present study were 1) to compare three in vitro inhibition screeningtests for CYP enzymes, 2) to create new inhibition structure-activity databases for humanCYP enzymes (CYP1A2, CYP2B6, and CYP2E1) 3) to construct QSAR models of inhibitorsof these enzymes and 4) to find more potent and selective inhibitors for these enzymes.

3

2 Review of the literature

2.1 Drug metabolism

Nowadays evaluation of ADMET (absorption, distribution, metabolism, excretion andtoxicity) properties has become one of the most important issues in drug discovery. Inother words, it is not sufficient that a drug possesses biological activity and therapeuticefficacy. If it has undesired ADMET properties, then this can lead to failure during theclinical phases. Multiple in silico, in vitro and in vivo ADMET filters are being developedand implemented at various stages of the drug discovery and development process.

Drug metabolism is divided into two phases: phase I and phase II. In phase I, enzymesintroduce reactive or polar groups into xenobiotics. The common chemical reactions duringthis phase are oxidation, reduction, and hydrolysis. The modified compounds are thenconjugated to polar compounds in phase II reaction. Phase II metabolism involves theintroduction of a hydrophilic endogenous species, such as glucuronic acid or sulphate,onto the drug molecule. Drugs usually first undergo phase I reactions followed by phase IIreactions, but sometimes they can be conjugated without any prior phase I reaction. Themetabolism of drugs into less lipophilic metabolites is essential for their elimination fromthe body. Metabolism usually leads to the inactivation of drug, but sometimes themetabolites are pharmacologically active or even toxic.

2.2 Human cytochrome P450 (CYP) enzyme system

CYP enzymes are the principal drug metabolizing system utilized in phase I metabolism.CYP enzymes are a multigene family of heme-containing enzyme forms that are involvedin oxidative metabolism of drugs, steroids and carcinogens. They are called P450 becausein the presence of carbon monoxide, the enzymes have an absorption maximum at thewavelength of 450 nm and P stands for pigment (reviewed by Nebert and Russell, 2002).This protein family is commonly referred to as CYPs, as an abbreviation for cytochromeP450s. In mammalian tissues, CYP enzymes are mainly expressed in the liver, but they arealso located in the intestine, skin, lungs and kidneys. There are two main functional rolesfor CYP enzymes: metabolism of xenobiotics and biosynthesis of critical signallingmolecules. There are many members of the CYP superfamily currently known, and thenumbers continue to grow. At present almost 12 000 CYP genes have been identified andabout sixty CYP genes are reported to exist in the human genome; humans have 18 CYPgene families, and 44 CYP gene subfamilies (Nebert and Russell, 2002; Nelson, 2009). Ofthese sixty, it is estimated that more than 90% of human drug oxidation is attributable tosix CYP is enzymes, CYP1A2, 2C9, 2C19, 2D6, 2E1 and 3A4 (Shimada et al. 1994). SomeCYPs are highly regio- and stereospecific in the oxygenation of substrate, whereas otherssuch as the human liver CYP3A4 metabolize over 50% of the current marketedpharmaceuticals (Denisov et al. 2005). In other words, a drug can either be a substrate for

4

only one CYP enzyme or it can be metabolized by several CYP enzymes. In general, thesubstrates for CYP metabolism are hydrophobic and poorly soluble in water. Extensivelists of the various substrates and corresponding CYP systems involved have beenpublished. Figure 1 shows the most important is forms participating in thebiotransformation of pharmaceutical agents.

Figure 1. Proportion of drugs metabolized by CYP enzymes (modified from Wrighton andStevens, 1992)

The primary catalytic function of CYP enzymes is the addition of one oxygen atom frommolecular oxygen (O2) into various substrates and another oxygen atom is further reducedto water (RH + O2 + NADPH + H+ � ROH + NADP+ + H2O, where RH is the substrate). Anexternal reducing equivalent from NADPH (nicotinamide adenine dinucleotide phosphate)is required (Lewis, 1998; Guengerich, 2001). The general cycle is presented in Figure 2.

Fe = iron atom in P450 heme, RH = substrate, ROH = product

Figure 2. Generalized CYP catalytic cycle (modified from Guengerich and Johnson, 1997)

5

CYP enzymes are grouped into families and subfamilies. All isoenzymes in the samefamily have at least 40% and those in the same subfamily at least 55% amino acidsimilarities. First the name CYP (indicating P450 enzymes) is followed by an Arabicnumber denoting the family, a letter designating the subfamily (when two or more exist),and an Arabic numeral representing the individual gene within the subfamily (Figure 3)(Nelson et al. 1996).

Figure 3. Nomenclature for the CYP enzymes and allelic forms

In recent years, X-ray crystallography techniques have become a major tool for clarifyingthe CYP structure (Wang JF et al. 2009) and an increasing number of crystal structures havebeen published in Protein Data Bank (http://www.pdb.org/) (Berman et al. 2000). All of theCYP crystal structures determined show approximately the same overall mixed alpha–betafolding. The heme is coordinated by a proximal cysteine and substrates bind in the activesite on the distal side of the heme in the center of the protein. The F and G helices and F/G-loop with the B/C-loop provide access to the active site of CYPs (Otyepka et al. 2007). Thehighly conserved parts of CYP structures are the proline-rich cluster close to the N-terminus, the loop following the A helix, the C and I helices, the proton transfer groove,parts of the K helix and the cysteine pocket (Mestres, 2005; Otyepka et al. 2007; Wang JF etal. 2009). Many conserved and essential amino acids in the active site of CYP enzymes havebeen identified, even though the mechanism of ligand binding is different and theirsubstrates are diverse (Wade et al. 2005; Mestres, 2005). Lipophilicity relationships,hydrogen bonding and ��� stacking interactions are of great concern in substrateselectivity and binding affinity along with the volume, shape and the flexibility beingdeterminants of the diversity in recognizing substrates with the stereoselectivity of theCYPs being controlled by the rigid and funnel-shaped access channel (Lewis et al. 2004;Kirton et al. 2005; Wang JF et al. 2009).

2.3 CYP1 –family

The CYP1-family consists of three members: CYP1A1, CYP1A2 and CYP1B1. Theirexpressions are all regulated by the aromatic hydrocarbon receptor (AHR) (Nebert et al.2004). CYP1A1 is expressed in virtually every tissue of the body such as the lung, skin,larynx and placenta, and it is known for its capacity to activate compounds withcarcinogenic properties (Nebert et al. 2000; Androutsopoulos et al. 2009). CYP1A1 isdetected after induction of polycyclic aromatic hydrocarbons (PAHs). There is also apositive association with increased risk of cancer and single nucleotide polymorphisms ofthe CYP1A1 (Shi et al. 2008; Varela-Lema et al. 2008; Zhuo et al. 2009; Sergentanis and

6

Economopoulos, 2010). CYP1B is the next subfamily in the CYP1-family and it has only onemember, CYP1B1 (Murray et al. 2001). The CYP1B1 gene is also transcriptionally activatedby PAHs, with protein being expressed in variety of human cancers and it has beensuggested that CYP1B1 may be a marker of tumorigenesis (Murray et al. 2001). CYP1B1metabolizes numerous PAHs as well as many N-heterocyclic amines, arylamines, aminoazo dyes, and several other carcinogens (Guengerich, 2000).

2.3.1 CYP1A2The CYP1A2 enzyme is expressed in the liver and accounts for about 12% of the total CYPcontent. The human CYP1A2 gene is PAH-inducible in liver, gastrointestinal tract, nasalepithelium, and brain (Nebert et al. 2004). Several important drugs e.g. theophylline,tacrine, clozapine, olanzapine, phenacetin are predominantly metabolized by CYP1A2(Faber et al. 2005; Pelkonen et al. 2008). CYP1A2 is also important for the metabolism ofendogenous substrates such as melatonin, estrone and estradiol, bilirubin anduroporphyrinogen (Gunes and Dahl, 2008), and environmental toxins as well as for theactivation of many environmental carcinogens including dietary heterocyclic amines,certain mycotoxins, tobacco-specific nitrosamines, and aryl amines (Guengerich, 1993;Eaton et al. 1995). Potent inhibitors of CYP1A2 include furafylline, fluvoxamine,ciprofloxacin, �-naphthoflavone and rofecoxib (Pelkonen et al. 2008; Khojasteh et al. 2011).Caffeine, phenacetin, melatonin and 7-ethoxyresorufin are the compounds most widelyproposed as good specific probe substrates for use in in vitro testing.

2.4 CYP2 –family

The CYP2 -family is one of the largest and most diverse families which play an importantrole in mammalian drug metabolism. The human CYP2A subfamily consists of three genesand two pseudogenes: CYP2A6, CYP2A7, CYP2A13, CYP2A7P(T) and CYP2A7P(C)(Fernandez-Salguero and Gonzales, 1995; Honkakoski and Negishi, 1997). CYPs 2C8, 2C9,2C18 and 2C19 share > 82% amino acid identity, but they only exhibit relatively littleoverlap of substrate specificity (Goldstein and de Morais, 1994). CYP2C8, 2C9, and 2C19proteins are primarily located in the liver, accounting for ~20% of total CYP contents,whereas CYP2C18 protein seems to be primarily expressed in the skin (Shimada et al. 1994;Zaphiropoulos, 1997). Members of the CYP2D family constitute only about 2-4% of totalhepatic CYP content, however, they are responsible for the metabolism of 30% ofcommonly prescribed therapeutic compounds.

2.4.1 CYP2A6In humans, there are three functional genes in the CYP2A subfamily: CYP2A6, CYP2A7 andCYP2A13. CYP2A6 is the most important member: it metabolizes over 30 drugs fromvarious therapeutic categories (Rendic, 2002; Rendic and Guengerich, 2010). CYP2A6 ishighly polymorphic and it is expressed in the human liver accounting for about 1-10% oftotal CYPs (Koskela et al. 1999; Raunio et al. 2001). CYP2A6 is also recognized for its abilityto metabolize nicotine (Hukkanen et al. 2005) and it is involved in the metabolism of anumber of endogenous substances e.g. contributing to the metabolism of steroids with

7

other CYP enzymes and in the metabolic activation of a number of procarcinogens andenvironmental compounds (Rendic, 2002; Rendic and Guengerich, 2010). Severalcompounds inhibit CYP2A6 and several of them are mechanism-based (suicide) inhibitorse.g. selegiline (Siu and Tyndale, 2008), methoxsalen (Yoo et al. 2007), and isoniazide (Wenet al. 2002). The reversible inhibitors include pilocarpine (Kinonen et al. 1995) andtranylcypromine (Taavitsainen et al. 2001).

2.4.2 CYP2B6CYP2B6 was thought to be a minor member of the CYP family but recently it has attractedmore interest as its significance in the metabolism of xenobiotics has been appreciated.CYP2B6 is mainly expressed in the liver and it has been estimated to representapproximately 1-10% of the total hepatic CYP content (Lang et al. 2001). It can be foundalso in various extrahepatic tissues including the kidney, skin, brain, intestine and lung,and it metabolises over sixty clinically used drugs and a number of procarcinogens andenvironmental compounds (Mo et al. 2009b). Some clinically used drugs that aremetabolized by CYP2B6 include cyclophosphamide, tamoxifen, S-mephenytoin, bupropionand diazepam (Ekins and Wrighton, 1999a; Turpeinen et al. 2006). Several inhibitors ofCYP2B6 have been described including ticlopidine, clopidogrel, thioTEPA, memantine,and 2-phenyl-2-(1-piperidinyl)propane (Turpeinen et al. 2006). Clopidogrel, ticlopidine(Richter et al. 2004), mifepristone (Lin et al. 2009), duloxetine (Paris et al. 2009),phencyclidine (Shebley et al. 2009) and 17�-ethynylestradiol (Kent et al. 2002) aremechanism-based inhibitors of CYP2B6.

2.4.3 CYP2C8CYP2C8 accounts for about 7% of total hepatic CYP contents and extrahepatic CYP2C8mRNA has been detected in numerous tissues including the kidney, intestine, adrenalgland, brain, mammary gland, ovary, and heart, as well as in breast cancer tumours. Itmetabolizes ~5-8% of drugs (Shimada et al. 1994). Its known substrates include paclitaxel(Rahman et al. 1994), amodiaquine (Li et al. 2002), rosiglitazone (Baldwin et al. 1999) andits inhibitors are montelukast (Walsky et al. 2005), quercetin (Rahman et al. 1994; Li et al.2002), gemfibrozil (Wang et al. 2002). CYP2C8 is significantly induced by the prototypicalinducers including rifampicin, phenobarbital, and dexamethasone (Raucy et al. 2002).

2.4.4 CYP2C9CYP2C9 metabolizes approximately 20% clinical drugs. The majority of CYP2C9 substratesare acidic compounds such as several nonsteroidal anti-inflammatory drugs e.g. diclofenacand hypoglycaemic agents (Miners and Birkett, 1998), as well as S-warfarin and phenytoin(Mo et al. 2009a). This isoenzyme also participates in the oxidation of several importantendogenous compounds such as progesterone, testosterone, 7�-ethinylestradiol, all-trans-retinoic acid and arachidonic acid (Zhou SF, 2009a). Inhibitors of CYP2C9 includecompounds such as sulfaphenazole and benzromarone (Mancy et al. 1996; Takahashi et al.1999; Locuson et al. 2003) and it is induced by rifampicin and barbiturates (Pelkonen et al.2008). CYP2C9 is highly polymorphic with more than 33 variants (*1B through to *34) anda series of subvariants of CYP2C9 have been reported (Wang B et al. 2009). A largeinterindividual variation has been noted. The most common allele is named CYP2C9*1,

8

and it is considered as the wild-type allele. The variant *2 and *3 alleles are present inapproximately 35% of Caucasian individuals (Lee et al. 2002). CYP2C9*2 was the firstidentified and is the most common allelic variant of CYP2C9, but it has a lesser impact onenzyme activity than CYP2C9*3

2.4.5 CYP2C19CYP2C19 protein is mainly present in the liver, but significant activity has also beenidentified in the gut wall (Obach et al. 2001). CYP2C19 participates in the metabolism ofmany commonly used drugs, e.g. citalopram, diazepam, omeprazole, phenobarbital,proguanil and propranolol (Rendic, 2002). Some proton pump inhibitors like omeprazoleand lansoprazole are inhibitors of CYP2C19 (Ogawa and Echizen, 2010) as are theantifungal drug fluconazole and antiplatelet drug ticlopidine (Wienkers et al, 1996; Ko etal, 2000). The most selective inhibitor of CYP2C19 is (�)-N-3-benzyl-phenobarbital (Suzukiet al. 2002; Cai et al. 2004; Khojasteh et al. 2011). 2-4% of Caucasian and 10-25% of Asianpopulations are totally deficient in with CYP2C19, CYP2C19*2 and CYP2C19*3 being themost prevalent (Brockmoller et al. 2000).

2.4.6 CYP2D6CYP2D6 represent only a few percent of the total human CYP content, but it is estimated tobe involved in the metabolism of approximately 30% of the drugs from a wide variety oftherapeutic indications. CYP2D6 has been identified also in human kidney, intestine,breast, lung, placenta and brain at low to moderate levels (Zhou SF et al. 2009b). CYP2D6 isperhaps the most widely recognized polymorphic enzyme. Over 100 allelic variants ofCYP2D6 have been reported, some of which can lead to altered enzyme activity(http://www.imm.ki.se/CYPalleles). In Caucasians, the phenotypes can be divided intoultrarapid metabolizers (UMs, 3-5%), extensive metabolizers (EMs, 70-80%), intermediatemetabolizers (IMs, 10-17%) and poor metabolizers (PMs, 5-10%) (Sachse et al. 1997). PMsmay experience increased adverse effects of drugs due to reduced drug clearance rates andassociated elevated drug plasma levels; others may experience reduced effect due to theinability to activate prodrugs such as codeine (Ingelman-Sundberg, 2005). The typicalsubstrates for CYP2D6 are usually lipophilic base e.g. some antidepressants,antipsychotics, antiarrhythmics, antiemetics, ß-adrenoceptor antagonists (ß-blockers) andopioids (Zhou SF et al. 2009b). Quinidine is most commonly used reference inhibitor(Khojasteh et al. 2011). CYP2D6 is generally not regulated by many known environmentalagents and is not inducible by the common known enzyme inducers such as steroids (ZhouSF et al. 2009b).

2.4.7 CYP2E1The highest levels of CYP2E1 enzyme are present in liver, where its expressionpredominates in the zone around the centrilobular vein (Haufroid et al. 2003). To lesserextent CYP2E1 is expressed also in extrahepatic tissues, such as lung, kidney, nasalmucosa, bone marrow (Lucas et al. 2001), �-cells in pancreas (Murdock et al. 2004), brain(Lieber, 1999), breast (Kapucuoglu et al. 2003) and heart (Anzenbacher et al. 2001). CYP2E1is also constitutively expressed in proliferating keratinocytes and epidermal cells (Du et al.2004) and it can be detected also in the white cell fraction of peripheral blood from

9

humans, rabbits and rats (Raucy et al. 1997). CYP2E1 levels vary extensively due, in part, topathophysiological conditions (including obesity and diabetes) and the enzyme isinducible by xenobiotics such as ethanol or volatile organic compounds (Lucas et al. 2001).CYP2E1 catalyzes the metabolism of wide variety of endogenous substances andxenobiotics including therapeutic agents, procarcinogens, and low molecular weightsolvents (Haufroid et al. 2003). In general, the majority of compounds are neutral with lowmolecular weights and relatively low log P values (Anzenbacher et al. 2001; Lewis et al.2000; Lewis et al. 2003a; Park and Harris, 2003). CYP2E1 is mainly responsible formicrosomal ethanol oxidation. Fatty acids are very important endogenous substrates ofCYP2E1 and they are converted preferentially to their (�-1)-hydroxylated metabolites(Adas et al. 1998; Poloyac et al. 2004). The best known CYP2E1 inhibitors include clinicallyused drugs, e.g. disulfiram, as well as a herbicide 3-amino-1,2,4-triazole, which is a specificCYP2E1 inhibitor. Other CYP2E1 inhibitors are dilinoleylphosphatidylcholine (the majorcomponent of polyunsaturated phosphatydilcholines from soy beans) (Aleynik et al. 2001)and diallylsulphide (which is a mechanism-based inhibitor found in Allium vegetables)(Loizou and Cocker, 2001).

2.5 CYP3 –family

The CYP3A subfamily of enzymes consists of four isoforms; CYP3A4, CYP3A5, CYP3A7and CYP3A43. CYP3A4 is present in the largest quantity of all the CYPs in the liver(Shimada et al. 1994). CYP3A5 is a minor enzyme, which is expressed in the lungs andCYP3A7 is the major form of CYP in human fetal liver (Kitada and Kamataki, 1994; Daly,2006).

2.5.1 CYP3A4CYP3A4 is expressed predominantly in the liver where it accounts for about 33% of thehepatic CYP enzyme content. CYP3A4 is the most abundant human hepatic CYP isoformresponsible for the metabolism of almost 50% of known drugs (Wrighton et al. 2000).Therefore most drug interactions are a result of CYP3A4 inhibition. Among the substratesof CYP3A4, there are members of several important drug classes: antiarrhythmic agents,anxiolytics, HIV protease inhibitors, lipid-lowering agents, and strong opioids (Zhou,2008). CYP3A4 has large active site (~1368 Å3) which enables the binding of multiplesubstrates and this might be the cause of atypical kinetics (Korzekwa et al. 1998; Shou et al.2001; Galetin et al. 2003; Yano et al. 2004).

2.6 Genetic polymorphism of CYPs

The activity of CYP oxidases differs in different people and different populations. Thegenetic variation in a population is termed “polymorphism” when two or more clearlydifferent phenotypes exist in the same population of a species. Polymorphic forms of CYPsare responsible for the development of a significant number of ADRs (adverse drugreactions) and 10-20% of all drug therapies are affected by these polymorphisms

10

(Pirmohamed and Park, 2003; Ingelman-Sundberg, 2004a). Polymorphisms consist of genedeletions, gene duplications, single-nucleotide polymorphism (SNPs), and the frame shiftmutations. Four major phenotypes are found:

� The ultrarapid metabolizers (UM), carrying multiple functional genes (greater-than-normal CYP function)

� The extensive metabolizers (EM), carrying two functional genes (normal CYPfunction)

� The intermediate metabolizers (IM), carrying one functional and one defective gene(intermediate between the poor and extensive metabolizers)

� The poor metabolizers (PM), lacking of functional CYP enzymes which may resultfrom gene deletions or defective genes (little or no CYP function)

All genes encoding CYP enzymes in families 1-3 are polymorphic and these enzymes areresponsible for metabolizing about 40% of all CYP-mediated drugs (Ingelman-Sundberg,2004a).

2.7 CYP inhibition

Inhibition denotes a decrease in enzyme activity. Determination of CYP inhibition is animportant predictor of potential drug-drug interactions (DDIs). CYP enzyme inhibition canbe either reversible or irreversible, the former being the more common type. Reversibleinhibition is the most common mechanism responsible for documented drug interactions,competitive inhibition being the most important type.

2.7.1 Reversible inhibitionReversible inhibition can be divided into competitive, uncompetitive and mixed-typeinhibition (Pelkonen et al. 2008). In competitive inhibition, the binding of the inhibitorprevents the binding of the substrate to the active site of the enzyme. An inhibitor maybind to the same binding region or it may partly cover it (Kramer and Tracy, 2008). Inparticular when the substrate concentration is low, the inhibition is conspicuous. Theequation for competitive inhibition is shown below.

Eq. 1

v = ���� � [�]�� 1 + []��� + [�]

Where v is the observed velocity of the reaction, Vmax the maximum velocity, [S] thesubstrate concentration, Km the Michealis-Menten constant, [I] the free inhibitorconcentration and Ki the inhibition constant. When inhibition is competitive, the apparentKm is increased but the Vmax is not affected by increased inhibitor concentrations.

11

In uncompetitive inhibition, the inhibitor does not bind to the free enzyme but instead tothe enzyme-substrate complex and therefore the inhibition increases as the substrateconcentration increases. The equation for uncompetitive inhibition is shown below.

Eq. 2

v = ���� � [�]�� + 1 + []��� � [�]

When inhibition is uncompetitive, both the Km and Vmax decrease. Non-competitive inhibition is a special case of mixed type inhibition. In non-

competitive inhibition the inhibitor binds not to the active but to another site on theenzyme and which evokes conformational changes resulting in a reduced metabolic rateregardless of substrate concentration. The equation for non-competitive inhibition is shownbelow.

Eq. 3

v = ���� � [�]�� 1 + []��� + 1 + []��� � [�]

When inhibition is non-competitive, the Vmax is decreased but the Km remains unchanged.

2.7.2 Mechanism-based inhibitionIrreversible inhibition is also called mechanism-based inhibition or suicide inhibition. Thisinvolves the permanent inactivation of enzymes. Irreversible inhibitors are covalently ornoncovalently bound to the target enzyme and dissociate so slowly from the enzyme, thatin effect, the recovery requires resynthesis of the new enzyme molecules. Mechanism-based inhibitors of CYP enzymes can be classified into two groups, namely irreversible andquasi-irreversible inhibitors (Kalgutkar et al. 2007; VandenBrink and Isoherranen, 2010). Inthe case of quasi-irreversible inhibitors, reactive intermediates coordinate with the hemeprosthetic group leading to the formation of a catalytically inactive metabolite-inhibitorcomplex with the CYP enzyme. In irreversible inhibition, reactive intermediatesmetabolically generated from the inhibitors covalently react with an active site amino acidresidue within the apoprotein and/or cause direct alkylation/arylation of the hemedestruction (Kalgutkar et al. 2007). There are many experimental compounds that havebeen shown to be mechanism-based inhibitors in vitro and several commonly used drugshave been described as being mechanism-based inhibitors in vivo (Kalgutkar et al. 2007;Zhou ZW et al. 2009). Some of the most important mechanism-based inhibitor drugs arelisted in Table 1. Experimentally, irreversible and reversible CYP inhibition can bedifferentiated through the examination of the inhibitory concentration (IC50) at differenttime intervals; for irreversible inhibitors, the IC50 value changes over time, but forreversible inhibitors, the IC50 value remains independent of time.

12

Table 1. Drugs that cause drug-drug interactions in humans which are proposed to occur viamechanism-based inactivation of CYP enzymes (modified from Kalgutkar et al. 2007)

CYP1A2 CYP2D6 CYP2B6 CYP2E1

Enoxacin Paroxetine Clopidogrel Disulfiram

Zileuton Cimetidine ThioTEPA

Furafylline Ticlopidine

CYP2C8 CYP2C9 CYP2C19 CYP3A4

Gemfibrozil (viaglucuronide metabolite)

Tienilic acid Ticlopidine Clarithromycin

Suprofen Diltiazem

Erythromycin

Fluoxetine

Mibefradil

Nefazodone

Nelfinavir

Ritonavir

Saquinavir

Troleandomycin

2.8 Drug-drug interactions

It is very common that the patient needs to be medicated with two or more drugs. Cases offatal DDIs have been reported and these have led to the withdrawal of several marketeddrugs or restrictions to their use during the past decades (e.g. cerivastatin, mibefradil,soruvidine, terfenadine) (Li, 2001). DDIs are usually divided into pharmacokinetic orpharmacodynamic interactions, or combinations of these two interaction types and all ofthese may be involved in these toxic drug interactions. Possible causes of DDIs aredescribed in Table 2.

Metabolic interactions can be traced primarily to CYP enzymes and they are mainlybased on inhibition or induction of these enzymes. DDI may cause overtreatment andserious adverse effects by elevating the blood concentration of a drug whose metabolism isinhibited by the coadministered drug(s). There are many significant DDIs, which havebeen known for years. It is an interesting prospect that we can truly predict druginteractions. In other words, it is crucial to be able to identify those drugs which mayinteract with other compounds and to find proper methods to make this identificationbefore patients suffer the consequences (Zhou ZF et al. 2009). Many of these methods can

13

easily predict the type, mechanism and magnitude of interactions, but the clinicalconsequences are more complicated to anticipate (Kremers, 2002; Pelkonen et al. 2011).

Table 2. Possible causes of DDIs (modified from Kremers, 2002).

Competition for gastrointestinal absorption

Interaction during membrane crossing (blood vessels, hepatic, renal)

Binding to plasma proteins

Binding to transport proteins and p-glycoproteins

Pharmacodynamic interactions at the receptor level

Inhibition of metabolism

Induction of metabolism

Competition for active renal excretion

2.8.1 Guidelines on the investigation of drug interactionsDDIs are an important issue in clinical practice and drug development. The U.S. Food andDrug Administration (FDA) first published its guidance for drug interactions in 1997,supplemented this information in 1999 and has published additional guidance in 2006,with a new draft has issued in February 2011. The European Medicines Agency (EMA) alsopublished guidance in 1995 and recently a new draft has been published (Boobis et al. 2009;EMA, 2010). According to these guidelines, pharmacokinetic interaction studies shouldgenerally be performed in humans. An in vivo interaction study usually has a cross-overdesign and should be performed in healthy adults. A parallel group design is also possibleif the potential inhibitor or inducer has a very long elimination half-life. The in vitro studiesshould be conducted before phase I clinical studies and those enzymes involved inmetabolic pathways contributing to � 25% of the oral clearance should be verified ifpossible in vivo (EMA, 2010).

In the in vitro inhibition studies, the inhibition mechanisms (e.g. reversible or timedependent inhibition) and inhibition potency (e.g. Ki), are usually investigated usinghuman liver tissues, e.g. human liver microsomes or cDNA-expressed enzymes underlinear substrate metabolism and standardized assay conditions. A marker substrate is usedto monitor the enzyme activity and known potent inhibitors should be included as positivecontrols in the study. A wide range of concentrations of the investigational drug should beincluded in these studies and Ki values should be determined. FDA and EMA haverecommendations/guidelines about which enzymes should be tested in inhibition studies.According to FDA it is most important to identify potent inhibitors of CYP3A4 and afterthat, inhibitors of CYP2C9, CYP2D6, CYP2C19 and CYP1A2. Furthermore CYP2C8 andCYP2B6 should also be studied (FDA, 2006; Fowler and Zhang, 2008). EMA hasrecommended that in vitro studies should be performed to investigate whether theinvestigational drug inhibits the CYP enzymes most commonly involved in drugmetabolism. These presently include CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19,

14

CYP2D6, and CYP3A. It should also be evaluated if time-dependent inhibition is present(Zhang et al. 2010). In addition, if a metabolite is present in the circulation at molar total orunbound concentrations (AUC) of at least 1/5 of the concentration of the parent compound,the metabolites found in vitro should be investigated for their pharmacological activity andpharmacokinetics. Examples of probe substrates for studying CYP inhibition or inductioneffects of the new drug are listed in Table 3 (FDA, 2006; EMA, 2010).

Table 3. Examples of well validated inhibitors and substrates of specific enzyme activities invitro (FDA, 2006; EMA, 2010)

Enzyme Inhibitor Substrate

CYP1A2 furafylline (1)(2) phenacetin O-deethylation (1)(2)

CYP2A6 tranylcypromine (2)

methoxsalen (2)coumarin-7-hydroxylation (2),nicotine C-oxidation (2)

CYP2B6* ticlopidine (1)(2), thiotepa (1),clopidogrel (2)

efavirenz hydroxylation (1)(2), bupropionhydroxylation (1)(2)

CYP2C8 montelukast (1), quercetin paclitaxel 6-hydroxylation (1), amodiaquine N-deethylation (1), taxol 6-hydroxylation (2)

CYP2C9 sulfaphenazole (1)(2), S-warfarin 7-hydroxylation (1)(2), diclofenac 4´-hydroxylation (1)(2), tolbutamide methyl-hydroxylation (2)

CYP2C19* ticlopidine (1)(2),nootkatone (1)(2), loratadine(1)

S-mephenytoin 4´-hydroxylation (1)(2)

CYP2D6 quinidine (1)(2) bufuralol 1´-hydroxylation (1)(2), dextromethorphanO-demethylation (2)

CYP2E1 diethyldithiocarbamate (2)

clomethiazole (2)

diallyldisulfide (2)

chlorzoxazone 6-hydroxylation (2)

CYP3A4 ketoconazole (1)(2),itraconazole (1)(2)

midazolam 1-hydroxylation itraconazole (1)(2)

testosterone 6�-hydroxylation (1)(2) , plus onestructurally unrelated substance such as nifedipine,triazolam or dexamethasone (1)(2)

*presently no specific inhibitor known for in vitro use. Listed inhibitor(s) are not specific but can be usedtogether with other information or in a single enzyme system.

1 EMA 2 FDA

15

2.8.2 Predicting DDIsIntrinsic clearance of a drug forms the foundation for in vitro– in vivo correlations. It is ameasure of enzyme activity toward a drug and is not influenced by protein binding orhepatic blood flow. The rate of metabolism of a drug is related to the substrateconcentration by the parameter of intrinsic clearance.

Eq. 4Rate of metabolism = ���� � [�]Where [S] is the substrate concentration and CLint is the intrinsic clearance.

Traditionally, the method used for estimating the potential of an NCE to inhibit theactivity of a particular CYP enzyme involved the generation of in vitro data (e.g. IC50

values) using classical Michaelis-Menten kinetics (Hutzler and Tracy, 2002). The rate ofmetabolism can be described by the Michaelis-Menten equation:

Eq. 5

Rate of metabolism = ���� � [�]�� + [�]

Where Vmax=maximal velocity, [S]=substrate concentration and Km=concentration ofsubstrate at which half-maximal velocity is achieved.

A number of factors have to be taken into account in in vitro-in vivo predictions. Only theunbound drug interacts with enzymes, thus drug binding to protein, both in vitro and invivo, must be taken into consideration (Tracy, 2003; Wienkers and Heath, 2005). There is apossibility of erroneous results e.g. due to substrate-dependent values. Several types ofatypical kinetic profiles have been observed in drug metabolism reactions. CYP2C9 andCYP3A4 are the best known for this type of problem. Atypical kinetics include biphasickinetics, autoactivation (compound activates its own metabolism), substrate inhibition (thereaction velocity of product formation decreases once a particular substrate concentrationis surpassed), heteroactivation (enzyme activity for a substrate is increased in the presenceof another compound), partial inhibition (incomplete inhibition is observed even in thepresence of saturating concentrations of a second substrate/inhibitor) and substratedependent inhibition (Korzekwa et al. 1998; Hutzler and Tracy, 2002; Kramer and Tracy,2008). Atypical kinetics have been characterised more extensively in vitro than in vivo andthat raises a question about the choice of in vivo probe drugs (Wienkers and Heath, 2005).Extrapolations from in vitro to in vivo have been made and the qualitatively more potentinhibitors in vitro tend to be those drugs that cause DDIs in vivo, but potency alone is initself not quantitatively predictive (Obach et al. 2005).

Polymorphic expression of several CYP enzymes introduces another stumbling blockinto investigations of DDIs. CYP2D6 is by far the best characterized CYP enzyme thatdemonstrates polymorphic expression in humans (Linder et al. 1997). The polymorphismof the CYP2D6 enzyme results in poor, intermediate, efficient or ultrarapid metabolizers of

16

drugs (Ingelman-Sundberg, 2005). When compared to reversible inhibition in which themagnitude of the DDI diminishes as the inhibitor is cleared from the body, mechanism-based inhibitors of CYP enzymes cause more often unfavourable DDIs, since theinactivated CYP enzymes have to be replaced by newly synthesized proteins (Kalgutkar etal. 2007; Zhou ZF et al. 2009). This can take a longer time compared to the rate at which theinhibitor is eliminated. For example, 14 out of 19 identified clinically significant CYP3A4inhibitors are either established or putative mechanism-based inactivators of the enzyme.

2.8.3 Methods in CYP-mediated DDI researchCYP inhibition assays typically consist of an incubation mixture of fixed concentration ofthe test compound (inhibitor), recombinant CYP enzymes or human liver microsomes anda probe substrate which is metabolized to a product that can be measured (figure 4)(Wienkers and Heath, 2005). The potency of inhibition can be assessed by thedetermination of the Ki or IC50 values. The half maximal inhibitory concentration (IC50) is ameasure of the effectiveness of a compound in inhibiting biological or biochemicalfunction; it represents the concentration of a drug that is required for 50% inhibition invitro. Ki is the inhibition constant for a drug. An IC50 is determined at a single substrateconcentration with multiple inhibitor concentrations whereas assessment of the Ki valuedemands multiple substrate concentrations with multiple inhibitor concentrations. Thusthe IC50 value is enzyme- and substrate concentration-dependent unlike the Ki (Kramer andTracy, 2008). A general relationship between IC50 and Ki, where [S] is the concentration ofsubstrate used in vitro IC50 determination, can be summarized as follows (Wienkers andHeath, 2005):

Eq. 6

IC50 = Ki � �1 + [S]Km�

The apparent IC50 value approximately equals 2*Ki, when IC50 values are determined underadequate conditions with relevant probe substrates at a concentration near to the Km valuesfor competitive or uncompetitive inhibitors. For a noncompetitive inhibitor, the IC50 valueis closer to Ki (Bjornsson et al. 2003; Bell et al. 2008).

Figure 4. A typical in vitro CYP inhibition assay (modified from Wienkers and Heath, 2005)

Test compound(inhibitor)+microsomesor rhCYP+probesubstrate

+co-factor(NADPH)

Measureproductformation(fluorescenceor LC-MS)

17

2.8.3.1 In vitro techniquesFluorometric assays represent the most common approach used to test compounds as CYPinhibitors in early drug discovery and the use of recombinant CYP enzymes withfluorescent probes (substrates) gives the highest throughput levels when compared toother possibilities (Wienkers and Heath, 2005; Foti et al. 2010). In this method, a pro-fluorescent substrate is metabolized into fluorescent products which are then detected by afluorometer. Substrates have been developed particularly to form fluorescent products, butthey mostly are not specific for any CYP form (Bjornsson et al. 2003). They are easy to useand generally provide a high level of reproducibility; speed and low cost are otheradvantages (Miller et al 2000; Crespi et al. 2002; Trubetskoy et al. 2005, Wienkers andHeath, 2005). The drawbacks of this method are disturbance due to test inhibitors whichcan cause fluorescence quenching or fluoresce itself; despite the fact these are not verycommon (Fowler and Zhang, 2008).

It might be practical to use a fluorescence method for the robust screening and further ordetailed assays LC-MS method, but there is not always a good agreement between thesetwo methods (Bell et al. 2008; Wang, 2009). For example, the IC50 value is not constantwithin a particular CYP enzyme, being completely substrate-dependent. LC-MS hasbecome the standard measurement technology for in vitro DDI experiments (Fowler andZhang, 2008). This approach offers some advantages over fluorometric assays e.g.simultaneous evaluation of CYP isoform activities, the high selectivity of probe substratesfor their respective CYP enzymes and the use of clinically relevant probe substrates.

There are many variables which need to be taken into account in measuring CYPinhibition potency. The choice of buffer strength and pH, divalent metals and organicsolvents can all have effects on the outcome of in vitro drug interaction studies. Thesubstrate concentration should be chosen where CYP catalysis follows Michaelis-Mentenkinetics and it is usually equal to its apparent Km for the given CYP enzyme (Wienkersand Heath, 2005). In addition, preferably less than 20% of the substrate should beconsumed during the assay. The incubations should be performed using conditions wherelinearity can be assured with respect to time and enzyme concentration. In particular manyearly lead compounds are highly lipophilic causing solubility problems. The concentrationof organic solvent must be as low as possible due to potential inhibitory effects (e.g. <0.5%final (v/v)), but this may cause precipitation of the inhibitor (or the substrate). The testinhibitor might be metabolized and there is the possibility that the metabolites can be morepotent inhibitors than the parent substance. It is also advisable to use the lowest possibleamount protein in the incubation (below 0.5 mg of microsomal protein · ml -1 is suggested)in order to avoid protein binding (Bjornsson et al. 2003). In some cases, the inhibitorypotency may depend on the substrate. This is best known with CYP3A4 (Kenworthy et al.1999). One explanation is that CYP3A4 has three different binding sites or the binding siteis sufficiently large to allow multiple substrates to bind. It is recommended that twosubstrates are used with this enzyme (Houston and Kenworthy, 2000).

Expressed CYP enzymesNowadays many manufacturers have commercially available cDNA-expressed CYPenzymes. The specific enzyme is usually expressed in either baculovirus-insect cell

18

expressed human enzymes or an E. coli –based expression system, allowing for a virtuallyinexhaustible supply of reagents. There is only a single CYP enzyme present, so the needfor a highly selective probe substrate is not essential (Foti et al. 2010). Recombinantenzymes are valuable in high throughput screening (HTS), they can be used with HTSsubstrates and the role of individual CYPs is easily studied in the metabolism of an NCE(Raunio et al. 2004; Pelkonen et al. 2005). The disadvantage of expressed microsomal CYPsis that only a single enzyme at a time can be studied and the metabolic contribution ofother enzymes is not represented (Raunio et al. 2004; Pelkonen et al. 2005; Kramer andTracy, 2008).

Human liver microsomesHuman liver microsomes are also very convenient, low in cost and easy to use. They areextremely popular and a widely used in vitro system for studying CYP kinetics (Kramerand Tracy, 2008). They contain a more complete complement of hepatic drug metabolizingenzymes when compared to recombinant enzymes and thus represent a morephysiologically relevant system in which to evaluate CYP inhibition (Foti et al. 2010). Livermicrosomes consist of vesicles of the hepatocyte endoplasmic reticulum and are preparedby differential centrifugation. The selectivity of probe substrates for a particular CYPisoform is more important when multiple CYP enzymes are being evaluatedsimultaneously (Wienkers and Heath, 2005). The disadvantage is the incompleterepresentation of the in vivo situation, because only CYP and UGT enzymes are represent(Brandon et al. 2003).

HepatocytesHepatocytes can also be used to assess drug interactions, but they are less commonly usedfor studies of human enzymes, because they are relatively difficult to obtain and difficult topreserve for later use. They are highly differentiated cells and do not normally divide inculture (Boobis et al. 2009). They should be the most representative of the situationoccurring in vivo, because they contain the full complement of phase I and phase IIenzymes. The inhibition potential of compounds in hepatocytes versus human livermicrosomes has assessed to display in good agreement between the two techniques(Brown et al. 2007).

Human liver S9 fractionsLiver S9 fractions are subcellular fractions that contain drug-metabolizing enzymesincluding the CYPs, flavin monooxygenases, and UDP-glucuronyltransferases. It is a majoradvantage that they contain both phase I and phase II activity. One disadvantage is theoverall lower enzyme activity, 20-24 lower enzyme activity than in microsomes andexpressed enzymes (Brandon et al. 2003; Kramer and Tracy, 2008).

Liver slicesLiver slices resemble most closely the in vivo environment, because they contain the entirecomplement of metabolizing enzymes and all cell types from the organ tissue and all theconnections between the cells are present (Lerche-Langrand and Toutain, 2000; Kramer

19

and Tracy, 2008). Unfortunately liver slices are relatively difficult to obtain and themaintenance is demanding. In addition, loss of CYP catalytic activity is can occur, thusmetabolism studies are best performed using freshly cut slices (VandenBranden et al. 1998;Kramer and Tracy, 2008).

2.8.3.2 In silico techniquesIn silico is an expression used to mean "performed on computer or via computersimulation." The chemical space of drug-like compounds is too large to screen by in vitrotests and the amount of synthesized drug is low and syntheses are slow. Thus there is anincreasing demand for computational tools that can identify and analyze active sites andidentify molecules that can bind to the active sites. Computational approaches can beroughly divided into two main categories: ligand-based approaches and structure-based(protein-based) approaches (de Graaf et al. 2005; Arimoto, 2006; de Groot, 2006). Ligand-based approaches include pharmacophore modelling, quantitative structure-activityrelationship (QSAR), three-dimensional (3D) QSAR and similarity searches (Wang JF et al.2009). All of these models provide indirect information about a protein´s active sitewithout prior knowledge about the protein structure. These models are based on theshape, electronic properties and conformations of substrates, inhibitors or metabolicproducts and they use experimental data (de Groot et al. 2006).

In structure-based drug design, the three-dimensional structure of a drug targetinteracting with molecules is used (figure 5). Structure-based approaches includehomology modelling, docking and molecular dynamic simulations; these provide directstructural information of the target proteins (Wang JF et al. 2009). If the shape and chemicalnature of the binding site are known and the interactions between ligands and the proteinwithin its active site have been identified, this can be applied for the identification of newligands and optimization of lead compounds. In that case, so-called hits can be identifiedthrough the docking of molecules into the active site (Andricopulo et al. 2009). Moleculardocking is a technique for predicting whether a small molecule, ligand, will bind to a hostmacromolecule, typically to a protein. It is done by predicting energetically favourablebinding conformations and orientations of the molecules within the active site of targetprotein and modelling the interaction between the protein and a ligand. The ligand willpotentially bind to the protein in vitro or in vivo if the geometry of the ligand-protein pair iscomplimentary and involves favourable biochemical interactions. With cytochrome P450s,the most common docking algorithms applied include GOLD, FlexX, DOCK andAutoDock (reviewed by de Graaf et al. 2005).

20

Figure 5. The principal steps in structure-based drug design using crystallography (modifiedfrom Neidle and Thurston, 2005)

2.8.3.3 Quantitative structure-activity relationshipQSAR is a mathematical and statistical model that tries to detect relationships between themolecular properties of molecules and biological activity. Lipophilicity, polarizability,electric properties and steric parameters are used to describe different size of substituents.This method has been known for almost 50 years and it was first introduced by Hanschand Fujita in 1964. It is often run in parallel with homology modelling to enableinterpretation and understanding of the active site through superimposition of a set ofstructurally different compounds (Chohan et al. 2006). QSAR studies use supervisedmethods often classified into the following categories: linear methods such as multiplelinear regression (MLR), principal components regression/analysis (PCR/PCA), partial leastsquares (PLS) and non-linear methods such as artificial and Bayesian neural networks(ANN and BNN) and classification and regression trees (CART) (Chohan et al. 2006;Verma et al. 2010). Developing a good quality QSAR model depends on many factors, suchas the quality and accuracy of biological data, the choice of descriptors and statisticalmethods. Although there are many problems with QSAR and sometimes they fail, QSARdoes possess several advantages. Conventional synthetic methods and biological assays areexpensive and time-consuming and may require the use of animals. Even more serious,drug failures due to poor ADMET profiles at later stages of development are extremelyexpensive and thus estimations are needed to allow prioritization of synthesis andscreening (Verma et al. 2010). 3D-QSAR refers to the application of force field calculations

Define the targetmacromolecule

-Isolate target molecule directlyfrom cells, by cloning the gene,or by protein expression andpurification (if a protein orenzyme-Crystallize molecule

Determine three-dimensional structureof the native target

-Find a plausibleinhibitor from a knownstarting point or fromscreening- Model its interactionswith target

Co-crystallizeinhibitor andtarget- Determine itsstructure

-Model used to suggestimproved inhibitor-Synthesize improvedinhibitor

-Optimal compoundobtained-Undertake cell-based andother assays

21

requiring three-dimensional structures, e.g. based on protein crystallography or moleculesuperimposition.

CoMFACramer and co-workers published a method called comparative molecular field analysis(CoMFA) in 1988 (Cramer et al. 1988). This method is an advanced QSAR analysis whichcorrelates the property fields of sets of molecules (steric and electrostatic fields) with theiractivity (e.g. IC50 value). CoMFA is the most widely used 3D QSAR approach for theprediction of biological activity (Andricopulo et al. 2009). In CoMFA, a set of molecules isneeded and those molecules should interact in the same manner with the same kind ofreceptor. Molecules are subdivided into a training and a test set: the training set moleculesare diverse with respect to binding affinity and structural features and the properties of themolecules in the test set are similar to those of the training set. The crucial step inconstructing a CoMFA model is the alignment of the training set molecules. This has amajor impact on the outcome of the 3D statistical analysis (Andricopulo et al. 2009). Theinteraction energies are calculated using probes such as small molecule like water or achemical fragment such as a methyl group (Verma et al. 2010). Interactions are representedby the standard potential energy fields in which the differences are related to differences inbiological activity.

Relationships between the interaction fields and biological activity are calculated usingPartial Least Square (PLS) (Cramer et al. 1998). PLS models are validated to ensure thequality and predictive power of the model. The validation of the model is done by Leave-one-out cross-validation-method or Standard leave-some-out cross-validation method. Thefirst calculates as many models as there are compounds in the data set, and then leaveseach compound out of the model and predicts its activity. The latter technique divides thecompounds into a specified number of groups leaving each one out, one at a time. Mostoften the results are presented as a 3D coefficient contour map, which shows thefavourable and unfavourable steric regions around the molecules and the favourable andunfavourable regions for electropositive or electronegative substituents in certain positions(Höltje, 2003).

22

3 Aims of the study

The general objective of this study was to develop novel in vitro and in silico methods toassess the metabolic features of xenobiotics. The metabolic feature covered in this thesiswork is inhibition of CYP enzymes. The specific aims were as follows:

1. To compare three CYP in vitro inhibition screening tests

2. To create new inhibition structure-activity databases for human CYP enzymes(1A2, 2B6, 2E1)

3. To construct QSAR models of inhibitors of selected CYP enzymes

4. To identify more potent and selective inhibitors of CYP enzymes.

23

4 General experimental procedures

4.1 Materials

4.1.1 ChemicalsFluoxetine was a kind gift from Solvay Pharma (Brussels, Belgium). Sotalol, propranolol,citalopram, oxazepam, diazepam, �-heptalactone, �-caprolactone, �-decanolactone, �-dodecanolactone, �-caprolactone, 4,6-dimethyl-�-pyrone, 1,3-dimethylnaphthalene, 1,4-dimethylnaphthalene, 4-methoxybenzaldehyde, 1-naphthol, nicotine, cotinine and 7-ethoxyresorufin were obtained from Sigma Chemical Company (St. Louis, MO). Bupropionand hydroxybupropion were generous donations from Glaxo SmithKline (ResearchTriangle, NC), midazolam and �-hydroxymidazolam from F. Hoffmann-La Roche (Basel,Switzerland) and omeprazole, omeprazole sulphone and 5-hydroxyomeprazole from AstraZeneca (Mölndal, Sweden). The metabolite standards dextromethorphan, 6-hydroxychlor-zoxazone, hydroxytolbutamide and 6�-hydroxytestosterone were purchased fromUltrafine Chemical Company (Manchester, U.K.). Formic acid, LichroSolv GG methanoland acetonitrile were obtained from Merck KGaA (Darmstadt, Germany). Undecanoic-�-lactone, �-valerolactone, �-decanolactone, �-phenyl-�-butyrolactone, (+/-)-undecaonic-�-lactone, �-nonanoic-lactone, 2,3-dihydrobenzofuran, 4-methoxyfuran2(5H)-one, 2-benzoxazolinone, butylbenzyl, 2-indanone, 2-coumarone, indan, butylcyclohexane, 2,7-di-methylnaphthalene, 2-methoxynaphthalene, 2-(p-tolyl)ethylamine, 1-methylnaphthalene,quinaldine, 2-ethylnaphthalene, 1,5-dimethylnaphthalene, 2,6-dimethylquinoline, 2-methylnaphthalene, 1,6-dimethylnaphthalene, 3-methylisoquinoline, 2,6-dimethylnaphtha-lene, 2,7-dimethylquinoline, 3-methylquinoline, 1,2-dimethylnaphthalene, 2,4-dimethylquinoline, 2-naphthol and naphthalene, 4-(4-chlorobenzyl)pyridine (CBP), 4-ben-zylpyridine (BP), 2-benzylaniline, 4-hydroxydiphenylmethane, 4-chlorobenzophenone, 3,4-dihydroxybenzaldehyde and 2-(4-aminophenyl)-ethanol were purchased from Aldrich-Chemie GmbH & Co. (Heidenheim, Germany). Biphenyl and 1,7-dimethylnaphthalenewere purchased from Fluka Chemie AG (Buchs, Switzerland). 2-Chlorobiphenyl, 4-chlorobiphenyl, 1,4-dichloronaphthalene, 1,5-dichloronaphthalene and 1-chloronaphtha-lene were from Promochem (Boras, Sweden). 4-Benzoylpyridine, 4-(4-chloroben-zoyl)pyridine, 4-(4-nitrobenzyl)pyridine (NBP), 4,4-DDM, bromodiphenylmethane, 4-bromo-benzaldehyde, 4-hydroxybenzophenone, 4-bromobenzylbromide, 4-hydroxybenzaldehyde, were purchased from Acros Organics (New Jersey, USA). 2-Fluoronaphthalene was from Supelco (St. Louis, MO, U.S.A.). NADP was from RocheDiagnostic GmbH (Mannheim, Germany). All other chemicals used were from SigmaChemical Company (St. Louis, MO) and Boehringer (Ingelheim, Germany) and were of thehighest purity available. Water was in-house freshly prepared with Simplicity 185 (Milli-pore S.A., Molsheim, France) water purification system and was UP grade (ultra pure, 18.2M�).

24

4.1.2 Biological material

4.1.2.1 Human liver microsomesThe human liver samples used in this study were obtained from the University Hospital ofOulu as a surplus from transplantation donors. The collection of surplus tissue wasapproved by the Ethics Committee of the Medical Faculty of the University of Oulu,Finland. The livers were transferred to ice immediately after surgical excision, cut intopieces, snap-frozen in liquid nitrogen and stored at -80 �C until the microsomes wereprepared by standard differential ultracentrifugation. A weight-balanced microsomal poolof seven liver microsomal preparations which have been extensively characterized forprimary screening (sufficient model activities, no known polymorphisms, expected effectsof model inhibitors, quantification of CYPs by Western blotting) was employed. The finalmicrosomal pellet was suspended in 100 mM phosphate buffer pH 7.4. The protein contentwas determined by the method of Bradford (1976).

4.1.2.2 cDNA expressed human P450sBaculovirus-insect cell expressed human CYP enzymes were purchased from BDBiosciences Discovery Labware (Bedford, MA) and used according to the manufacturer’sinstructions.

4.2 Methods

4.2.1 Fluorescent probe assaysIncubations were performed in 96-well plates. The experimental conditions aresummarized in Table 4. In each well, the 150 μl incubation volume contained 100 mM Tris-HCl buffer (pH 7.4), 4.2 mM MgCl2, 1-100 μM substrate and cDNA expressed CYP. Thereaction was initiated by adding the NADPH-generating system after 10 min preincubationat 37 °C. After a given incubation time the reactions were terminated by the addition of110 μl 80% acetonitrile / 20% 0.5 M Tris Base. For CYP2A6 in each well, a 100 μl incubationvolume contained 50 mM Tris-HCl buffer (pH 7.4) and 5.0 mM MgCl2. The reaction wasinitiated by addition of NADPH and terminated by adding 60 μl 10% TCA. Immediatelybefore the measurement 140 μl 1.6 M glycine-NaOH buffer (pH 10.4) was added. ForCYP2C8, the reaction was terminated by 2 M NaOH. The formed fluorescence wasmeasured with a Victor2 plate counter (Perkin Elmer Life Sciences Wallac, Turku, Finland).For assays using DBF, the signal to noise ratio was greatly increased when the plate wasincubated (37 °C) approximately two hours after the addition of NaOH. The linearity of thereaction with respect to incubation time and protein concentration was determined.Several control incubations were carried out to determine the effect of quenching by theinhibitors and other interfering factors. All IC50 values were determined in duplicate. TheIC50 values were calculated using non-linear regression analysis with Prism 4.0TM software(San Diego, California, U.S.A.) from the results of two separate experiments.

25

Table 4. Experimental conditions of fluorescent probe assays in recombinant human CYPenzymes

CYP isoformSubstrate andconcentration (μM)

Fluorescentmetabolite

Incubationtime (min) Enzyme (pmol)

1A2 7-ethoxyresorufin (1) Resorufin 20 0.5

2A6 Coumarin (3) 7-Hydroxycoumarin 15 0.75

2B6 EFC (2.5) HFC 30 0.75

2C8 DBF (1) Fluorescein 30 3

2C9 MFC (75) HFC 45 0.75

2C19 BFC (25) HFC 45 2.5

2D6 MAMC (7.5) HAMC 60 1.5

2E1 MFC (100)(10)* HFC 45, 45* 1.5, 0.5*

3A4 BFC (50) HFC 30 0.75

* Used in Chapter 8

4.2.2 Comparative Molecular Field Analysis (CoMFA)CoMFA is a ligand-based predictive software with the ability to derive three-dimensionalquantitative structure-activity relationships (3D-QSARs). Using regression analysis on thecharges for each atom of each molecule in a 3D aligned library, CoMFA can correlateelectrostatic and steric properties with activities (e.g. IC50 values) obtained by biologicalexperiments. Two utilities of CoMFA analysis are its use as a predictive tool to predict IC50s(or other properties) of other ligands and contour plots that allow the visualization ofpredicted favourable and unfavourable properties of the ligands in 3D. Generally, theseplots can be directly translated to interactions with the enzyme, allowing a more directedroute of ligand design that exploits the desired properties (Cramer et al. 1988).

The construction of the molecules, superimposition and CoMFA modelling wereperformed using the Sybyl 6.9 (TRIPOS Associates Inc., St. Louis, MO) molecularmodelling software (Clark et al. 1989). The biological data was transformed to pIC50 (-logIC50) values. Atomic point charges were calculated using the MMFF94 method (Halgren,1996). Standard CoMFA analysis included steric and electrostatic fields which werecalculated using the sp3 carbon probe atom with a +1 charge and a 2 Å grid spacing. Thestandard derivation threshold for exclusion of columns from the PLS analysis was set at 2kcal/mol. The PLS method with 5 random group cross-validations was used for statisticalanalyses and this calculation was repeated 20 times to verify the stability of the obtained q2

values. The optimum number of components for the non-validated analyses was chosenaccording to the validation results: concerning the lowest SPRESS (standard error ofprediction) or SDEP0* (corresponding value from progressive scrambling), highest q2 orQ0*2 (corresponding value from progressive scrambling). The predictive capacity of thegenerated model was tested using an external set of compounds.

26

27

5 Comparison of cytochrome P450 inhibition screeningtests 1

Abstract: There are several different experimental systems for screening of in vitroinhibitory potency of drugs under development. In this study we compared three differenttypes of cytochrome P450 (CYP) inhibition tests: the traditional single substrate assays, thefluorescent probe method with recombinant human CYPs, and a novel n-in-one technique.All major hepatic drug-metabolizing CYPs were included (1A2, 2A6, 2B6, 2C8, 2C9, 2C19,2D6, 2E1, 3A4). Six compounds (sotalol, propranolol, citalopram, fluoxetine, oxazepam anddiazepam) were selected for detailed comparisons. The IC50 values of each of thesecompounds were measured using the three assay types. The inhibitory potencies of thesemodel drugs were generally within the same order of magnitude and followed similarinhibition profiles in all the assay types. Clinically observed inhibitory interactions, or lackthereof, were predictable with all three assays. Comparison of potencies of ‘diagnostic’inhibitors revealed also some notable differences between the assays, especially regardingCYP2E1. The potency of inhibitors towards CYP3A4 was dependent on the substrate andreaction measured. Generally all three assays gave reasonably comparable results,although some unexplained differences were also noted.

1 Adapted with permission of Elsevier LTD from: Turpeinen M, Korhonen LE, Tolonen A, Uusitalo J, Juvonen R,Raunio H and Pelkonen O. Cytochrome P450 (CYP) inhibition screening: comparison of tests. Eur J Pharm Scien. 29:130–138, 2006

28

5.1 Introduction

Metabolism tests most suitable for high throughput screening usually include metabolicstability and drug interaction (CYP inhibition) assays (Li et al. 2001; White, 2000), and alsomore elaborate test systems are available. However, none of the in vitro systems currentlyavailable for drug metabolism is yet validated and comprehensive controlled data basesare rare or include only a modest number of drugs (Pelkonen and Raunio 2005; Pelkonen etal. 2005; Hutzler et al. 2005).

The aim of this work was to systematically compare the performance of three basic typesof in vitro metabolism inhibition tests. These tests are 1) a conventional single substrateassay, 2) fluorescence based technology and 3) a newer, n-in-1 technique, all developed formeasuring the inhibition of major drug-metabolizing cytochrome P450 (CYP) activities. Sixcompounds (sotalol, propranolol, citalopram, fluoxetine, oxazepam and diazepam) fromthree different therapy classes (-adrenergic receptor antagonists, selective serotoninreuptake inhibitors and benzodiazepines) were selected on the basis of previous literaturereports on their CYP inhibition potential. Each compound pair represents drugs withdifferent inhibition profiles.

5.2 Materials and methods

The materials and general methods (fluorescent probe assay) used are described in Chapter4.

5.2.1 Single substrate assaysThe incubation conditions and instrumentation used to assess the enzyme activities ofCYP1A1/2 (ethoxyresorufin O-deethylation), CYP2A6 (coumarin 7-hydroxylation),CYP2C9 (tolbutamide hydroxylation), CYP2D6 (dextromethorphan O-demethylation),CYP2E1 (chlorzoxazone 6-hydroxylation), CYP3A4/5 (midazolam 1´-hydroxylation) havebeen described previously in detail in Taavitsainen et al. (2001) and the assay for CYP2B6(bupropion hydroxylation) in Turpeinen et al. (2004). Each reaction mixture waspreincubated for 2 minutes at +37 �C in a shaking incubator block (EppendorfThermomixer 5436, Hamburg, Germany). The reaction was started by adding NADPH andterminated by adding equal volume of ice-cold acetonitrile and subsequently cooled in anice bath to precipitate the proteins. The mixture was vortex mixed and spun at 10,000g for15 minutes. Supernatants were collected and stored at -20 �C until analyzed.

The amodiaquine N-deethylation assay for CYP2C8 was a modification from Li et al.(2002): incubation mixtures contained 0.5 mg protein/ml and 30 μM amodiaquine and theincubation time was 20 min. Omeprazole 5-hydroxylation and sulphoxidation assays forCYP2C19 and CYP3A4, respectively, were adapted from Äbelö et al (2000): incubationmixtures contained 0.5 mg microsomal protein /ml and 40 μM omeprazole and incubationperiod was 20 min. Otherwise the incubations contained buffer and NADPH and wereperformed as described above.

29

5.2.2 N-in-one assayConduction of the n-in-one assay was described previously in detail in Turpeinen et al.(2005). Shortly, each incubation mixture contained 0.5 mg microsomal protein/ml, 0.1 Mphosphate buffer (pH 7.4), 1 mM NADPH and all the ten probe substrates. Substrates andtheir concentrations for the incubations were: melatonin (0.4 mM), coumarin (0.2 mM),bupropion (0.1 mM), amodiaquine (0.2 mM), tolbutamide (0.4 mM), omeprazole (0.2 mM),dextromethorphan (0.02 mM), chlorzoxazone (0.6 mM), midazolam (0.04 mM) andtestosterone (0.1 mM). Reaction mixture, in a final volume of 200 μl, was preincubated for 2minutes at +37 �C in a shaking incubator block (Eppendorf Thermomixer 5436, Hamburg,Germany) before the reaction was initiated by addition of NADPH. Each reaction wasterminated after 30 minutes by adding 100 μl of ice-cold acetonitrile containing warfarin(1.5 μM) as an internal standard (ISTD). Samples were subsequently cooled in an ice bathto precipitate the proteins. Supernatants were collected into clean tubes and stored at -20�C until analyzed. For analysis all incubation samples were thawed at room temperature,shaken and centrifuged for 10 min at 10,000*g. The supernatants were transferred to aWaters Total Recovery vial (Waters Corporation, Milford, Massachusetts, USA) to awaitLC/TOF-MS analysis.

5.2.3 HPLC conditionsA Waters Alliance 2695 chromatographic system with autosampler, vacuum degasser andcolumn oven was used. The analytical column was a Waters XTerra MS C18, 2.1*50 mm,3.5 m with Phenomenex Luna C18(2) precolumn, 4.0*2.0 mm, 3.0 m (Phenomenex,Torrance, California, USA). The flow rate was 0.3 ml/min and the column oventemperature was 30 ºC. The eluents used were A: aqueous 0.1% formic acid (pH 2.75) andB: methanol. Gradient elution from 15% B to 60% B in 10 min was used. The flow was splitpost-column with Acurate Post-Column Stream Splitter (LC Packings, Amsterdam, TheNetherlands). The split ratio was 1:4 to MS and waste, respectively.

5.2.4 LC/ESI/MS conditionsLC/MS data were recorded with a Micromass LCT time-of-flight (TOF) high resolutionmass spectrometer (Micromass Ltd., Manchester, England) equipped with Z-Sprayelectrospray ionization source. A generic positive electrospray ionization method was usedfor all substrates and metabolites including internal standard. Basic parameter conditionswere capillary, sample cone and extraction cone voltages 3000, 20 and 3 V, desolvation andnebulization N2 flows 600 and 70 l/h, and desolvation and source temperatures 220 and 150�C, respectively. The mass spectrometer and HPLC system were operated underMicromass MassLynx 3.4 software, which also was used to process the measured LC/MSdata. Chromatographic traces of protonated metabolites of CYP specific substrates wereextracted from total ion chromatograms or traces of appropriate molecular ions(protonated and/or Na+ or K+-adducts) and/or in-source fragments were combined. Masschromatograms were then integrated and peak areas were normalized to ISTD. Noquantification was performed. The peak areas of metabolites in different incubations werecompared as such after internal standard corrections were made. The standard deviationsof warfarin (ISTD) peak areas were typically below 10%.

30

5.2.5 In vitro inhibition and reference inhibitorsFor determinations of the IC50 values the study compounds were added into the incubationmixture in five different concentrations (0.01, 0.1, 1, 10 and 100 μM) in a small volume of anappropriate solvent. The solvents used were water (sotalol and citalopram), methanol(diazepam) and DMSO (propranolol, fluoxetine and oxazepam). The performance oftraditional CYP-selective model inhibitors (for references see Turpeinen et al. 2005) wascharacterized with fluorescent probe assay also as described above. Montelukast was usedfor inhibition of CYP2C8 activity (Walsky et al. 2005) in all assay types. All data pointsrepresent the average of duplicate incubations. The enzyme activities in the presence ofinhibitors were compared with the control incubations (incubations with the solvent butwithout an inhibitor).

5.3 Results

5.3.1 Sotalol and propranololWith sotalol, no inhibition was observed in any model activity in any of the three assayseven at the highest concentration used (100 μM). With propranolol, a strong inhibition ofCYP2D6 with IC50 values of 5.8 μM, 2.7 μM and 2.5 μM for single substrate, fluorescentprobe and n-in-one assays were observed, respectively. Moderate inhibition of CYP1A2with IC50 values ranging from 26 μM to 47 μM and CYP2C9 (IC50 values 40.0 μM to 88 μM)were observed with all three assay types. In addition, inhibition of CYP2B6 activity with anIC50 value of 41 μM was observed when the n-in-one assay was used (Table 5).

5.3.2 Citalopram and fluoxetineCitalopram was found to inhibit CYP2D6 with IC50 values of 12 μM, 3.3 μM and 4.6 μM insingle substrate, fluorescent probe and n-in-one assays, respectively (Table 6). The CYP3A4associated model reactions testosterone 6�-hydroxylation and omeprazole 3-hydroxylationshowed inhibition with IC50 values of 32 μM and 10 μM, respectively, for citalopram whenthe n-in-one test was used. Fluoxetine inhibited CYP2D6 equally well in all three assays,with IC50 values of 0.3 μM, 0.3 μM and 0.2 μM in single substrate, fluorescent probe and n-in-one assay, respectively. Fluoxetine was a relatively potent inhibitor towards CYP2C19and CYP2C9, with IC50 values of 0.33 μM and 3.6 μM in the fluorescent assay, respectively.The IC50 values were considerably higher in single substrate and n-in-one assays, with IC50

values of 33 μM and 22 μM (CYP2C19) and 49 and 12 μM (CYP2C9), respectively.Fluoxetine inhibited CYP3A4 moderately, but the IC50 value was dependent on the assaytype and the model reaction used. The values obtained with the single substrate assayranged from 9.9 μM (testosterone 6-hydroxylation) to 79 μM (midazolam 1´-hydroxylation) and with the n-in-one assay from 7.4 μM (testosterone 6-hydroxylation) to65 μM (midazolam 1´-hydroxylation). The fluorescence probe assay gave a value of 17 μM.CYP2B6 was inhibited by fluoxetine in all three assays (IC50 values between 22 μM and 85μM). CYP2C8 was inhibited only in the fluorescence probe assay, with an IC50 value of 16μM.

31

Table 5. IC50 values (μM) of sotalol and propranolol as measured by traditional single substrateassay, fluorescent probe assay and n-in-one approach

Sotalol Propranolol

CYP Single Fluorescent n-in-one Single Fluorescent n-in-one

1A2 > 100 > 100 > 100 31 47 26

2A6 > 100 > 100 > 100 > 100 > 100 > 100

2B6 > 100 > 100 > 100 > 100 > 100 41.1

2C8 > 100 > 100 > 100 > 100 > 100 > 100

2C9 > 100 > 100 > 100 > 100 > 100 > 100

2C19 > 100 > 100 > 100 88 40 61

2D6 > 100 > 100 > 100 5.8 2.65 2.5

2E1 > 100 > 100 > 100 > 100 > 100 > 100

3A4 > 100 > 100

�-OH-MDZ > 100 > 100 > 100 > 100

6�-TES > 100 > 100 > 100 > 100

SO-OME > 100 > 100 > 100 > 100

3-OH-OME n.d. > 100 n.d. > 100

n.d. = not determined

Table 6. IC50 values (μM) of citalopram and fluoxetine as measured by traditional singlesubstrate assay, fluorescent probe assay and n-in-one approach

Citalopram Fluoxetine

CYP Single Fluorescent n-in-one Single Fluorescent n-in-one

1A2 > 100 > 100 > 100 > 100 > 100 > 100

2A6 > 100 > 100 > 100 > 100 > 100 > 100

2B6 > 100 > 100 > 100 62 85 22

2C8 > 100 > 100 > 100 > 100 16 > 100

2C9 > 100 > 100 > 100 49 3.6 12

2C19 > 100 > 100 > 100 33 0.33 22

2D6 12 3.3 4.6 0.3 0.3 0.2

2E1 > 100 > 100 > 100 > 100 > 100 > 100

3A4 > 100 17

�-OH-MDZ > 100 > 100 79 65

��-TES > 100 32 9.9 7.4

SO-OME > 100 > 100 52 24

3-OH-OME n.d. 10.0 n.d. 20

n.d. = not determined

32

5.3.3 Oxazepam and diazepamWith oxazepam, moderate inhibition of CYP1A2 (IC50 = 32) and CYP2D6 (IC50 = 92 μM) wasseen with the n-in-one assay (Table 7). Other assay types showed no inhibition. Diazepamwas found to inhibit CYP2D6 with IC50 values of 97 μM, 82 μM and 40 μM in singlesubstrate, fluorescent probe and n-in-one assays, respectively. Moderate inhibition ofCYP2C19 by diazepam was seen in all assay types with IC50 values ranging from 40 μM to46 μM. In addition, in the n-in-one assay diazepam inhibited CYP2B6 activity with an IC50

value of 58 μM.

Table 7. IC50 values (μM) of oxazepam and diazepam as measured by traditional singlesubstrate assay, fluorescent probe assay and n-in-one approach

Oxazepam Diazepam

CYP Single Fluorescent n-in-one Single Fluorescent n-in-one

1A2 > 100 > 100 31.6 > 100 > 100 > 100

2A6 > 100 > 100 > 100 > 100 > 100 > 100

2B6 > 100 > 100 > 100 > 100 > 100 58

2C8 > 100 > 100 > 100 > 100 > 100 > 100

2C9 > 100 > 100 > 100 > 100 > 100 > 100

2C19 > 100 > 100 > 100 40 46 41

2D6 > 100 > 100 92 97 82 40

2E1 > 100 > 100 > 100 > 100 > 100 > 100

3A4 > 100 > 100

�-OH-MDZ > 100 > 100 > 100 > 100

��-TES > 100 > 100 > 100 > 100

SO-OME > 100 > 100 > 100 > 100

3-OH-OME n.d. > 100 n.d. > 100

n.d. = not determined

5.3.4 Effect of CYP-specific reference inhibitors in the fluorescent probe assayTo further compare the three types of tests with each other, the effects of well establishedreference inhibitors were evaluated in the single substrate, fluorescent probe and n-in-oneassays. The effects of well established reference inhibitors were measured in the fluorescentprobe assay and compared with the results obtained from our previous study with thesingle substrate and n-in-one assays (Turpeinen et al. 2005).The results are summarized inTable 8. Each diagnostic inhibitor was found to inhibit the model activities in a very similarfashion. There were, however, some notable differences. Ketoconazole was at least 8-foldmore potent in the fluorescence probe assay than in the single substrate or n-in-one assays.In the latter two assays, there were large differences in the IC50 values between differentCYP3A4 substrates. CYP2E1 model inhibitor pyridine displayed IC50 values of 2.7 μM, 6.2μM and 37 μM for single substrate, fluorescent probe and n-in-one assay, respectively.

33

Table 8. IC50 values (μM) of CYP-specific diagnostic inhibitors in traditional single substrateassay, N-in-1 technique and fluorescent probe assay. Data for single and n-in-1 assaysexcluding montelukast is adapted from Turpeinen et al. 2005.

CYP Inhibitor Single n-in-one Fluorescent

1A2 Fluvoxamine 0.1 0.1 0.1

2A6 Tranylcypromine 0.4 1.0 0.3

2B6 Ticlopidine 0.3 0.1 0.4

2C8 Montelukast 0.4 0.9 0.2

2C9 Sulphaphenazole 0.4 0.2 0.3

2C19 Fluconazole 24 5.7 c, 6.4 d 8.2

2D6 Quinidine 0.1 0.03 0.004

2E1 Pyridine 2.8 6.2 37

3A4Ketoconazole 0.07 a, 1.8 b

1.8 a, 0.07 b, 0.08 e,0.1 f

0.01

IC50 values for a midazolam 1´-hydroxylation, b testosterone 6�-hydroxylation, c omeprazole

demethylation, d omeprazole 5-hydroxylation, e omeprazole 3-hydroxylation and f omeprazole

sulphoxidation.

5.4. Discussion

In this study, three different types of tests were developed and adapted for determiningthe inhibition capacities of chemical compound towards human drug-metabolizing CYPenzymes. Nine distinct CYP forms were included in each of the assays, including CYP2B6and CYP2C8, two forms that have recently been identified as significant contributors todrug metabolism. The performance of the three assays was first assessed using wellestablished in vitro inhibitors of CYPs. To cross-validate the assays, the inhibitorycapacities of six selected drugs in clinical use were analyzed. The results show that thethree assay types yield remarkably similar results with the majority of tested CYP forms.

Of the two �-receptor antagonists studied, sotalol has negligible first-pass metabolismand the majority of oral dose is excreted unchanged in urine (Sotaniemi et al. 1979). Noinhibition of any hepatic CYP form by sotalol has been reported previously. A clear lack ofinhibition was observed also in this study in all the three assay types. Propranolol isknown to be a potent CYP2D6 inhibitor (Rowland et al. 1994) with moderate inhibitionaffinity also against CYP1A2 (Gillam and Reilly, 1988). In the present study, all threeassays predicted these inhibitions and their magnitude correctly. Also moderate inhibitionof CYP2C19 ranging from 40 μM to 88 μM was observed in all the assays. The n-in-one

34

technique suggested a moderate inhibition of CYP2B6 by propranolol with IC50 value ofapproximately 40 μM, but this was not observed in the other two assay types.

The two serotonin selective antidepressants studied, fluoxetine and citalopram, wereselected because they have widely different CYP inhibition profiles. Fluoxetine is known toundergo extensive hepatic biotransformation mainly to the metabolite norfluoxetine (Fulleret al. 1991), with both having a strong inhibition capacity against CYP2D6 (Stevens andWrighton, 1993), and notably low IC50 values for CYP3A4 (Greenblatt et al. 1996), CYP2C9(Shader et al. 1994) and CYP2B6 (Hesse et al. 2000). In the current study, the magnitude ofinhibition towards CYP forms by fluoxetine was consistent with previous literature results.The assay utilizing the fluorescent probe technique predicted also an inhibition againstCYP2C8, which was not observed in the other tests. To our knowledge, there are noliterature reports about inhibition of CYP2C8 by fluoxetine. With fluoxetine, the IC50 valuesconcerning CYP2C9 varied from approximately 0.5 μM to 33 μM depending on the assayused.

Citalopram is considered to be one of the compounds least prone to metabolicinteractions among serotonin selective antidepressants. After continuous treatment withcitalopram, the in vivo activity of CYP2D6 has been found to be slightly reduced (Gram etal. 1993; Baumann and Rochat, 1995), but inhibition of other CYP forms has not beenreported. In agreement with these data, citalopram showed an inhibition of CYP2D6 whichwas observed in all three tests, while no inhibition of the other CYP forms was observed.

The benzodiazepine diazepam and its metabolite oxazepam were selected because ofclose structural similarity (Figure 6) but drastically different metabolic profiles. Oxazepambelongs structurally to 3-hydroxybenzodiazepines, which undergo direct conjugation byphase II enzymes and excretion to urine, while diazepam undergoes hepatic metabolism(Chouinard et al. 1999). In the present study, the results with both oxazepam and diazepamwere in agreement with literature information.

Reference inhibitors were found to perform mainly as expected and the IC50 values werewithin the same order of magnitude with all the three screening tests. The largestdiscrepancy was observed with the CYP2E1 inhibitor pyridine, which yielded IC50 valuesranging from 2.8 μM to 37 μM in the single substrate and n-in-one assays, respectively. Thereason for this remains open and is discussed previously in detail in a paper by Turpeinenet al. (2005). Inhibition of CYP3A4 was tested with ketoconazole using multiple probesubstrates. It was not unexpected that the inhibition potency of ketoconazole varieddepending on the specific reaction measured. Previously, inhibition of CYP3A4 has beenshown to be substrate-dependent (Harlow and Halpert, 1998). A drug molecule can bind toan allosteric site of CYP3A4 and change the affinity of the substrate-binding site (Wang etal. 2000). Molecular modelling and metabolism studies suggest that the active site ofCYP3A4 has the capacity to accommodate large molecules and more than one substrate atthe same time (Shou et al. 1994; Lewis et al. 1996). Because of the unique properties ofCYP3A4, the reactions catalyzed do not always follow typical enzyme kinetics. Thus,CYP3A4 enzyme kinetics is unpredictable and depends on many different factors such asthe substrates and inhibitors used (Galetin et al. 2005).

35

Figure 6. Chemical structures of study compounds: a) sotalol, b) propranolol, c) citalopram,d) fluoxetine, e) oxazepam and f) diazepam

In this study this effect was observed especially with citalopram, which showed amoderate CYP3A4 inhibition in both the single substrate and n-in-1 assays whentestosterone 6�-hydroxylation was measured, but no inhibition occurred when midazolam1´-hydroxylation assay was carried out. The use of multiple CYP3A4 substrates is thereforerecommended when screening the CYP3A4 inhibition potential of new chemical entities.

In conclusion, two in vitro assays with higher throughput, the n-in-one technique andthe fluorescent probe assay, were found to be useful and suitable for measuring theinhibition of major drug-metabolizing CYP activities compared to a conventional singlesubstrate assay. The rationale behind using more efficient and robust methods in earlystage of drug development is the cost-effectiveness of screening methods and the aim torank lead candidates for selection purposes. These tests provide tentative inhibition valuesand point out the CYP forms potentially inhibited. If detailed and more accurateinformation is wanted, the traditional single substrate technique would still be therecommended choice.

NH

OH

NH CH3

CH3S

O

O

CH3

a) b)

O

OH

NH

CH3

CH3

c)O

NC

NCH3

CH3

Fd)

e) f)

CF3

ONHCH3

ClN

NHO

OH

ClN

NO

CH3

36

37

6 Inhibition studies and 3D-QSAR of CYP1A2Inhibitors 2

Abstract: The purpose of this study was to determine the CYP1A2 inhibition potencies ofstructurally diverse compounds, to create a comprehensive 3D-QSAR model of CYP1A2inhibitors, and to use this model to predict the inhibition potencies of an external set ofcompounds. Fifty two compounds including naphthalene, lactone and quinolinederivatives were assayed in a 96-well plate format for CYP1A2 inhibition activity using 7-ethoxyresorufin-O-dealkylation as the probe reaction. The IC50 values of the testedcompounds varied from 2.3 M to over 40 000 M. Based on this data set, a comparativemolecular field analysis (CoMFA) and GRID/GOLPE models were created which yieldednovel structural information about the interaction between inhibitory molecules and theCYP1A2 active site. The created CoMFA model was able to accurately predict inhibitorypotencies of several structurally unrelated compounds, including selective inhibitors ofother cytochrome P450 forms.

2 Adapted with permission of American Chemical Society from: Korhonen LE, Rahnasto M, Mähönen NJ, Wittekindt C,Poso A, Juvonen RO and Raunio H. Predictive three-dimensional quantitative structure-activity relationship ofcytochrome P450 1A2 inhibitors. J Med Chem. 48: 3808–15, 2005

38

6.1 Introduction

Several computational approaches, especially QSARs, have been used to characterize themolecular properties of substrates and inhibitors of the CYP1A2 enzyme. The overall resultof these studies is that substrates of CYP1A2 are moderately basic and planar moleculeswith medium volume and low total interaction energies (E value), but there is no clearcorrelation between compound lipophilicity and inhibitory effect. (Fuhr et al. 1993; Sanz etal. 1994; Lee et al. 1998; De Rienzo et al 2000; Lozano et al. 2000; Lewis, 2000; Moon et al.2000)

The aims of this study were to determine the CYP1A2 inhibition potencies ofstructurally diverse compounds, to create a comprehensive 3D-QSAR model of CYP1A2inhibitors, and to use this model to predict the inhibition potencies of an external set ofcompounds. To achieve this goal, the abilities of 52 compounds including naphthalene,lactone and quinoline derivatives to inhibit CYP1A2 catalyzed 7-ethoxyresorufin-O-dealkylation reaction were examined. Based on this data set, a comparative molecular fieldanalysis (CoMFA) and a GRID/GOLPE analysis were carried out.

6.2 Materials and methods

The materials and general methods used are described in Chapter 4.

6.2.1 Determination of inhibition potencyThe 7-ethoxyresorufin O-dealkylation assay is based on the detection of fluorescenceemitted by resorufin (Burke et al. 1985). The assay was adapted to the 96-well plate format(Chapter 4). All inhibitors were dissolved in ethanol, and the final concentration of ethanolwas 2% in all incubations. Each inhibitor was pre-screened using pooled human livermicrosomes at inhibitor concentrations ranging from 0.1 M to 40 000 M. The actual IC50

values were determined using expressed human CYP1A2 enzyme and seven inhibitorconcentrations.

6.2.2 GRID/GOLPE modelIn addition to the standard CoMFA model (Chapter 4), another model was created usingthe GRID/GOLPE method. GRID fields were created using a phenolic hydroxyl probe with1 Å grid spacing and the positive cut-off was set to 5 kcal/mol. The phenolic hydroxylprobe was chosen because it represents a hydrogen bond donor or acceptor which wasfound to be suitable for examining aromatic systems. GRID fields of the molecules wereanalyzed with GOLPE (Wade et al. 1993; Baroni et al. 1993).

Initially the GRID/GOLPE analysis included several variables that did not describeinteractions between inhibitory molecules and the GOLPE hydroxyl probes. Therefore theSRD and the F.Factorial Selection procedures were used to reduce the number of variablesin the final GOLPE model. Data pre-treatment was done using the zeroing optioncorrection. To diminish the amount of noise in the data, positive values smaller than 0.10and negative values greater than -0.01 were zeroed. The SRD analysis was performed toselect important variables for the interaction using a maximum dimensionality of 5 with a

39

critical distance cut-off value of 1 Å and collapsing distance cut-off value of 2 Å. TheF.Factorial Selection procedure was used with a maximum dimensionality of 2 and theRandom Groups Method with 5 groups to separate a variable that significantly improvespredictability. The final model was validated by using PLS crossvalidation with 5 randomgroups.

6.3 Results

6.3.1 Inhibition of CYP1A2 activityCYP1A2 substrates and inhibitors are usually planar small volume molecules that areneutral or weakly basic. Fifty two naphthalene and lactone derivatives as well as othercompounds partially fitting these criteria were tested for inhibitory potency towardCYP1A2 enzyme. The structures of the compounds are shown in Figures 7 and 8.

40

Figure 7. Structures of naphthalene type compounds used in inhibition assays

Naphthalene

OHCl

F OH O

Cl

Cl

Cl

Cl

N CH3 N

CH3

N CH3

CH3

N CH3

CH3

N CH3CH3

N

CH3

1-Methylnaphthol 1-Chloronaphthalene 1-Naphthol 2-Methylnaphthalene

2-Ethylnaphthalene 2-Fluoronaphthalene 2-Naphthol 2-Methoxynaphthalene

1,2-Dimethylnaphthalene 1,3-Dimethylnaphthalene 1,4-Dimethylnaphthalene 1,4-Dichloronaphthalene

1,5-Dichloronaphthalene 1,5-Dimethylnaphthalene 1,6-Dimethylnaphthalene 1,7-Dimethylnaphthalene

2,6-Dimethylnaphthalene 2,7-Dimethylnaphthalene Quinaldine 3-Methylquinoline

2,4-Dimethylquinoline 2,6-Dimethylquinoline 2,7-Dimethylquinoline 3-Methylisoquinoline

41

Figure 8. Structures of lactones and other compounds used in inhibition assays

O OO O O O O O

�-Caprolactone �-Valerolactone �-Caprolactone �-Heptalactone

O O O O O O

� -Nonanoic-lactone �-Decanolactone Undecanoic-�-lactone

O O O O

O O

�-Dodecanolactone �-Decanolactone (+/-)-Undecaonic-�-lactone

O O

NH

O O

2-Coumarone 2-Indanone 2,3-Dihydrobenzofuran Indan 2-Benzoxazolinone

Cl

Cl OO

O O

O

CH3

CH3 O

O O

H3CO

NN

NN

ONH2

Biphenyl 2-Chlorobiphenyl 4-Chlorobiphenyl 4-Methoxybenzaldehyde

Butylcyclohexane Butylbenzyl �-Phenyl-�-butyrolactone 4-Methoxyfuran2(5H)-one

4,6-Dimethyl-�-pyron 2-(p-tolyl)ethylamine Nicotine Cotinine

O O

42

The IC50 values of these compounds were measured using 7-ethoxyresorufin O-dealkylation as the probe reaction. Recombinant human CYP1A2 was used as the enzymesource to exclude any interference from other cytochrome P450 forms present in humanliver microsomes. The enzymatic activity was assayed in constant conditions using sevendifferent concentrations of each inhibitor. Figure 9 represents an example of concentrationdependent inhibition of the enzyme activity by two structurally different compounds (2,6-dimethylquinoline and 2-methylnaphthalene).

Figure 9. Inhibition of CYP1A2 activity by two selected compounds. 2,6-dimethylquinoline (�)

and 2-methylnaphthalene (�) were used as inhibitors.

The lowest IC50 values were obtained with 1,4-dichloronaphthalene and 2,4-dimethylquinoline (<3 M) and the highest values with -caprolactone and 4-methoxyfuran2(5H)-one (>40 mM) (Tables 9 and 10). Most of the naphthalene derivativeswere more potent inhibitors than the lactones and other compounds tested.

The IC50 values of all naphthalene derivatives were lower than the IC50 value ofnaphthalene as methyl, chloro, ethyl, fluoro and hydroxyl substitutions decreased the IC50

value. In general, disubstituted naphthalenes or quinolines were more potent thanmonosubstituted or unsubstituted ones. Especially electronegative substitutions at position1 of naphthalene increased the inhibitory potency. The IC50 values of quinoline derivativeswere lower than the corresponding naphthalene derivatives, indicating that a heterocyclicnitrogen atom decreases the IC50 value (Table 9).

The inhibitory potencies of 11 lactones were tested (Table 10). The simple lactone ringstructures were weak inhibitors, �-caprolactone being the least potent. With alkylsubstituted �- and �-lactones, the inhibition potency increased with increasing side chainlength. The optimum length of the side chain was 7 carbons. The IC50 value decreased morethan 300-fold when the side chain length increased from methyl to hexyl in five ringlactones. The most potent lactone inhibitor was undecanoic-�-lactone (IC50 = 45 M).

10 -2 10-1 10 0 101 102 103 10 40

25

50

75

100

Inhibitor (µM)

Inhi

bitio

n pe

rcen

t

43

Table 9. Inhibition potencies of naphthalene type compounds for CYP1A2

Compound IC50 value (M) 95% confidenceintervals

pIC50 value

1,4-Dichloronaphthalene 2.3 0.9–3.7 5.64

2,4-Dimethylquinoline 2.4 1.5–3.2 5.62

1-Naphthol 3.2 2.4–4.0 5.49

2,6-Dimethylquinoline 3.3 2.7–3.9 5.48

1,4-Dimethylnaphthalene 3.6 2.8–4.2 5.44

2,7-Dimethylquinoline 4.4 3.5–5.3 5.36

1,2-Dimethylnaphthalene 5.5 3.4–7.6 5.26

1,7-Dimethylnaphthalene 7.5 4.5–11 5.12

1,3-Dimethylnaphthalene 7.9 5.9–9.8 5.10

2-Methoxynaphthalene 13 11–15 4.89

3-Methylquinoline 13 9.7–17 4.89

1,5-Dichloronaphthalenea 14 8.0–19 4.85

2-Naphthol 17 15–18 4.77

2-Ethylnaphthalene 19 12–26 4.72

3-Methylisoquinoline 21 18–24 4.68

1,6-Dimethylnaphthalene 25 17–33 4.60

Quinaldine 40 34–45 4.40

1,5-Dimethylnaphthalenea 42 29–56 4.38

2-Fluoronaphthalene 49 34–64 4.31

1-Chloronaphthalene 50 44–56 4.30

2,6-Dimethylnaphthalene 52 33–72 4.28

2,7-Dimethylnaphthalene 65 36–91 4.19

1-Methylnaphthalene 110 81–150 3.96

2-Methylnaphthalene 120 106–130 3.92

Naphthalene about 700 3.15

a Used only in the external set of test compounds

44

Table 10. Inhibition potencies of lactones and other compounds for CYP1A2

Compound IC50 value (M) 95% confidenceintervals

pIC50 value

2-(p-Tolyl)ethylaminea 14 12–17 4.85

Undecanoic-�-lactone 45 34–56 4.35

4-Chlorobiphenyla 49 29–69 4.30

Butylcyclohexane 55 45–66 4.26

�-Dodecanolactone 58 49–68 4.24

(+/-)-Undecaonic-�-lactone 72 53–90 4.14

2-Indanone 80 51–110 4.10

�-Decanolactone 110 75–150 3.96

Biphenyl 160 74–240 3.80

�-Decanolactone 230 190–270 3.64

2-Chlorobiphenyla 230 150–310 3.64

2-Coumarone 260 170–340 3.59

4-Methoxybenzaldehydea 270 180–350 3.57

�-Nonanoic-lactone 310 230–390 3.51

2-Benzoxazolinone 370 230–510 3.43

Indan 550 430–660 3.26

2,3-Dihydrobenzofuran 1200 880–1500 2.92

�-Phenyl-�-butyrolactone 2300 1600–3000 2.64

Butylbenzyl 3700 2400–4900 2.43

Nicotine 4100 2600–5600 2.39

4,6-Dimethyl-�-pyron 4500 3300–5600 2.35

�-Heptalactone 4500 3300–5700 2.35

Cotinine 5400 2400–8400 2.27

�-Caprolactone 9900 5600–14000 2.00

�-Valerolactone 15000 11000–20000 1.82

�-Caprolactone > 40000 >1.40

4-Methoxyfuran2(5H)-one > 40000 >1.40

a Used only in the external set of test compounds

45

In addition to lactone and naphthalene derivatives, selected other compounds weretested for CYP1A2 inhibition (Table 10). When the cyclohexane ring of butylcyclohexanewas replaced by phenyl, the IC50 value increased about 60-fold. 2-Coumarone and 2-indanone, which have a carbonyl group, were more potent inhibitors than indan and 2,3-dihydrobenzofuran. A halogen group at position 4 of the biphenyl increased and atposition 2 decreased inhibitory potency.

6.3.2 CoMFA and GRID/GOLPE analysisForty six compounds listed in Tables 9 and 10 were analyzed using CoMFA and

GRID/GOLPE methods. Also listed in Table 9 and Table 10 are six compounds used in theexternal test set. After data pre-treatment, GRID/GOLPE analysis gave an r2 value of 0.86,followed by a q2 value of 0.73 with 3 components and SDEP of 0.60. In the final model, thesmart region definition (SRD) method with F.Factorial Selection was used to removevariables that did not correlate with the inhibition potency. The obtained statistics of themodels are summarized in Table 11, and the plots for the training set of both models areshown in Figure 10.

Table 11. Statistics from the CoMFA and GRID/GOLPE analyses

Models q2 r2 SPRESS/SDEP N

CoMFA 0.69 0.87 0.67 3

GRID/GOLPE 0.79 0.90 0.52 3

q2 = the crossvalidated correlation coefficient with 5 random groups; r2 = correlation coefficient; SPRESS

(Sybyl), SDEP (GOLPE) = standard deviations for the error of predictions; N = number of PLS (partialleast squares) components.

Figure 10. Plots for the training set (actual/predicted IC50 values) of the CoMFA model (r2 =0.87, left panel) and the GOLPE/GRID model (r2 = 0.90, right panel)

1 3 5 70

2

4

6

Actual

Pred

icte

d

1 3 5 70

2

4

6

Actual

Pred

icte

d

46

The CYP1A2 CoMFA model is represented as 3D contour maps with 2,7-dimethylquinoline as the reference structure (Figure 11). The contours of the steric map areshown in green and yellow regions, in which bulky groups enhance or decrease inhibitionpotency, respectively. Those of the electrostatic map are shown in red and blue areas inwhich a more negative charge enhance or decrease inhibition potency, respectively.Negative charge favoured areas which were near to a nitrogen atom and position 6 and 8of the quinoline ring indicate that a partial negative charge here tended to increase theinhibition potency. The broad negative charge disfavoured area shown in blue wasdisplayed near position 3 of the ring. The sterically favoured green area was locatedaround substitution at position 7 of quinoline in the CoMFA model.

A stereo figure of the GOLPE coefficient map (Figure 12) displays large continuousregions, where the interaction with the hydroxyl probe is favourable (cyan) orunfavourable (purple). The favourable region is around the nitrogen atom and partlyabove and under the aromatic ring structure. These regions of the inhibitor can act ashydrogen bond acceptors or donors. The electronic configuration of the OH probe isdefined so that it interacts with the �–electron of the conjugated system like planararomatic ring or double bond. It makes strong hydrogen-bonding interactions which mayaccount for the shape of the interaction regions with the molecular structures (Pastor et al.1997). The unfavourable regions reside in the plane of the ring and are spread all aroundthe ring except to the region nearby the nitrogen atom.

The prediction power of the CoMFA model was evaluated by estimating pIC50 values foran external test set of compounds, including the six compounds tested in this study (Table9 and Table 10). These six compounds were chosen on the basis that they resemble instructure the ones in the training set but also contain features that differ from the trainingset compounds. The values for five additional compounds that are well-known substratesand inhibitors of CYP enzymes were obtained from Turpeinen et al. 2005 (Figure 13). Assummarized in Table 12, the predictions of pIC50 values for the external set of moleculesdiffered by less than 0.1 log units from the actual values for biphenyls, 1,5-dichloronaphthalene and 4-methoxybenzaldehyde. The predictions of pIC50 values for theother molecules were within 0.13–0.67 log units of the actual values.

47

Figure 11. Stereo figure of colour contour maps of CYP1A2 CoMFA. Red and green representareas where more negative partial charge and bulkier groups increase inhibition potency,respectively. Blue and yellow represent areas where more negative partial charge and bulkiergroups decrease inhibition potency, respectively. The reference structure is 2,7-dimethylquinoline.

Figure 12. Stereo figure of GOLPE coefficient map with 2,7-dimethylquinoline as the referencestructure. The cyan regions describe favourable interactions and the purple regionsunfavourable interactions between the hydroxyl probe and the molecules.

48

Figure 13. Structures of the external set of test compounds

Table 12. Validation of CYP1A2 model

pIC50

Inhibitor Experimental Predicted Residual

2-(p-Tolyl)ethylamine 4.85 4.53 0.32

4-Chlorobiphenyl 4.30 4.30 0

2-Chlorobiphenyl 3.64 3.59 0.05

4-Methoxybenzaldehyde 3.57 3.51 0.06

1,5-Dichloronaphthalene 4.85 4.80 0.05

1,5-Dimethylnaphthalene 4.38 4.80 0.42

Ketoconazolea 4.12 4.30 0.18

Quercetina 5.18 5.05 0.13

Quinidinea 4.59 3.92 0.67

Sulfaphenazolea >4 4.05 -

Tranylcyprominea 4.01 4.30 0.29

a Experimental data from Turpeinen et al. 2005

O

O

N

N

ONNO

Cl Cl

O

OH

OH

OH O

OHOH

Ketoconazole Quercetin

N

O

HNH

H

OH

NH2

S NH

NN

OO

Quinidine Sulfaphenazole Tranylcypromine

NH2

49

6.4 Discussion

The main purpose of this study was to create a QSAR model to understand in greaterdetail the structural properties of inhibitors of the human CYP1A2 enzyme. The inhibitionpotency of a series of 52 chemicals was determined against the CYP1A2 enzyme. With thisdata set, CoMFA and GRID/GOLPE models were created which yielded novel structuralinformation about the interaction between inhibitory molecules and the CYP1A2 activesite. In addition, the developed CYP1A2 CoMFA model accurately predicted the inhibitionpotencies of several structurally unrelated compounds.

Some disubstituted naphthalene and quinoline derivatives were found to be potentinhibitors of CYP1A2. Quinoline derivatives had lower IC50 values than the correspondingnaphthalene derivatives, indicating that a heterocyclic nitrogen atom increases inhibitionpotency. Also hydrophobic substituents at position 1 of the naphthalene ring increaseinhibition potency. Hydroxyl, halogen and methyl substitutions of the naphthalene ringdecreased the IC50 values. The IC50 values of molecules decreased as the molecular volumeof lactones and the length of alkyl side chain increased.

Previously published QSAR studies on the CYP1A2 enzyme were based on a limitednumber of structurally diverse compounds and have not been used to predict theinhibition potency of new compounds. One of the first human CYP1A2 3D-QSAR studieswas carried out on quinolone antibacterials (Fuhr et al. 1993). Four pharmacophorefeatures were suggested to be involved in binding of quinolone-type CYP1A2 inhibitors inthe active site. No correlation between lipophilicity and inhibitory effect could be observedin this study (Fuhr et al. 1993). Based on the experimental finding that a methyl group atposition 8 of the xanthine ring is important for CYP1A2 inhibition, a subsequent theoreticalmodel was created using a molecular electrostatic potential approach (Sanz et al. 1994). AQSAR analysis using inhibition data of 16 naturally occurring flavonoids showed thatplanar molecules with a small volume to surface area were the most potent inhibitors, thenumber of hydroxyl groups being inversely proportional to inhibition potency, andglycosylation of the free hydroxyls could reduce the inhibition potency (Lee et al. 1998)

Two different studies docked selected substrates into the active site of homology modelsof the CYP1A2 protein and analyzed the binding mode of the ligand-enzyme complex interms of molecular mechanics calculations (De Rienzo et al. 2000; Lozano et al. 2000).Lozano et al. used the COMBINE method for energy partition and adjacent 3D-QSARanalysis. Subsequently they performed a GRID/GOLPE analysis using the phenylic OH-group. The combination of both methods provided useful insights into the positions oflikely hydrogen bonding or placement of hydrophobic or bulky groups. A previous studysuggested that CYP1A2 substrates are generally neutral or protonated molecules (Lewis,2000).

The correlation between electrostatic interactions and pIC50 values appeared in theCoMFA maps as negative charge favored areas which are near the nitrogen atom of thequinoline ring. Also electronegative substituents at position 1 of naphthalene ring areimportant for inhibition. This explains the finding that 1,4-dichloronaphthalene and 1-naphthol are quite potent inhibitors compared with the naphthalenes with halogen orother electronegative substituent at position 2. In addition, there is an attractive interaction

50

between the hydroxyl probe and an inhibitor favourable area located around position 1 ofthe quinoline ring near to the nitrogen atom. The hydroxyl probe is capable of donatingand accepting one hydrogen bond with a hydrogen acceptor or donor atom or with the �–system of the aromatic ring. GRID/GOLPE maps can describe interactions betweennitrogen and hydroxyl group or dipole-induced dipole interaction between aromatic ringand the probe (Pastor et al. 1997). The nitrogen atom can act as hydrogen bonding acceptoratom which indicates that hydrogen bonding is an important interaction betweenquinoline type inhibitors and the CYP1A2 enzyme.

Most of the CYP1A2 substrates are hydrophobic with a high log P value, suggesting thathydrophobic interactions play an important role in binding to CYP1A2. The createdCoMFA model confirms this, because the CoMFA model shows that alkyl substitutions atposition 7 increase the inhibitory potency. The alkyl side chain of lactones can interact withhydrophobic amino acids in the CYP1A2 protein. A large steric favoured region is locatednear the 7-position in the CoMFA map. Also substitution of naphthalene with hydrophobicsubstituents like halogen or methyl groups increases the inhibition potency. This datasuggests that the binding pocket of the CYP1A2 enzyme is composed of mostlyhydrophobic and aromatic amino acids with polar amino acids for hydrogen bondingbeing present near to the heme centre (Dai et al. 2000). In addition to using steric andelectrostatic fields, a CoMFA model was created using log P values as an additionalvariable. This did not improve in the model.

The created CoMFA model predicted very well the inhibitory potencies of the test set ofcompounds. The best predictions were obtained for biphenyls and 1,5-dichloronaphthalenewith predicted pIC50 values being within 0.05 log units of the actual values. The model alsopredicted well the CYP1A2 pIC50 values of several well known inhibitors of othercytochrome P450 forms - ketoconazole (CYP3A4), tranylcypromine (CYP2A6), quinidine(CYP2D6), quercetin (CYP2C8), and sulfaphenazole (CYP2C9) (Pelkonen et al. 1998;Taavitsainen et al. 2001). This is remarkable because the structures of these compoundsdiffer considerably from the ones used in the training set.

The current CoMFA model takes into account both steric and electrostatic interactions.The CoMFA approach described in this report represents a robust method to screen forinhibitory properties of compounds toward CYP1A2. A similar approach will be applicableto other xenobiotic metabolizing cytochrome P450 as well. We have shown earlier thatCoMFA modelling can predict reasonably well the actual inhibition characteristics oflactones and naphthalenes toward the human CYP2A6 and mouse CYP2A5 enzymes, andthat naphthalene is metabolized by these CYP forms (Juvonen et al. 2000; Asikainen et al.2003). A CoMFA method similar to the one described here has been used for modelling theCYP2A6 enzyme (Rahnasto et al. 2005). In the present CYP1A2 QSAR study, theGRID/GOLPE analysis was also applied which improved the precision of the modelthrough exclusion of variables that were not significant.

The model presented here is clearly superior to previously published QSAR models ofCYP1A2. The current training set is large and contains structurally diverse compounds. Allbiological data used in the training set is generated with internally fully consistentmethods. The model is reasonably predictive even for compounds that differ in structurefrom the ones in the training set. Using the hydroxyl probe yielded information about the

51

importance of dipole-induced dipole interaction in the active site of CYP1A2. TheGRID/GOLPE analysis gave insight into the shape of the interaction regions. The presentedCoMFA and GRID/GOLPE models are in agreement with previous studies concerningareas of H-bonds and electrostatic field (Fuhr et al. 1993; Sanz et al. 1994; Lee et al. 1998; DeRienzo et al 2000; Lozano et al. 2000). However, due to the statistical analysis of a largetraining set with structurally diverse compounds the present model is more general andpredictive than the previous ones. Especially the spatial orientation in the regions whereH-bonds are likely to occur and the regions where bulky groups enhance the inhibitorypotential could be determined both in a qualitative and quantitative way.

There are two major reasons for studying inhibitory potencies of drug candidates andother xenobiotics: First, potent inhibition by a drug candidate gives an early warning aboutpotential DDIs during its clinical use. Second, evidence of inhibition capabilities suggeststhat the compound may be a substrate for the cytochrome P450 studied. This appliesespecially to competitive inhibitors. Because competitive inhibitors are usually alsosubstrates of cytochrome P450, screening of inhibitory properties serves as a good startingpoint for substrate specificity studies for any chemical compound. Inhibitor orientation inthe CoMFA model provides clues on how it is coordinated toward the cytochrome P450heme iron if the oxidation pattern of at least one molecule in the training set is known.Thus, a CoMFA model based on inhibition data can represent a starting point to the muchmore complex task of predicting the metabolism of novel compounds.

52

53

7 Inhibition studies and 3D-QSAR of CYP2B6Inhibitors 3

Abstract: The cytochrome P450 2B6 (CYP2B6) enzyme metabolises a number of clinicallyimportant drugs. Drug-drug interactions resulting from inhibition or induction of CYP2B6activity may cause serious adverse effects. The aims of this study were to construct a three-dimensional structure-activity relationship (3D-QSAR) model of the CYP2B6 protein andto identify novel potent and selective inhibitors of CYP2B6 for in vitro research purposes.The inhibition potencies (IC50 values) of structurally diverse chemicals were determinedwith recombinant human CYP2B6 enzyme. Two successive models were constructed usingComparative Molecular Field Analysis (CoMFA). Three compounds proved to be verypotent and selective competitive inhibitors of CYP2B6 in vitro (IC50<1 μM): 4-(4-chlorobenzyl)pyridine (CBP), 4-(4-nitrobenzyl)pyridine (NBP), and 4-benzylpyridine (BP).A complete inhibition of CYP2B6 activity was achieved at a 0.1 μM concentration of 4-(4-chlorobenzyl)pyridine, whereas other CYP related activities were not affected. Forty-onecompounds were selected for further testing and construction of the final CoMFA model.The created CoMFA model was of high quality and predicted accurately the inhibitionpotency of a test set (n=7) of structurally diverse compounds. Two CoMFA models werecreated which revealed the key molecular characteristics of inhibitors of the CYP2B6enzyme. The final model accurately predicted the inhibitory potencies of severalstructurally unrelated compounds. CBP, BP and NBP were identified as novel potent andselective inhibitors of CYP2B6. Especially CBP is a suitable inhibitor for in vitro screeningstudies.

3 Adapted with permission of John Wiley and Sons from: Korhonen LE, Turpeinen M, Rahnasto M, Wittekindt C, PosoA, Pelkonen O, Raunio H and Juvonen RO. New potent and selective cytochrome P450 2B6 (CYP2B6) inhibitors basedon three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. Br J Pharmacol. 150: 932–42,2007

54

7.1 Introduction

Several inhibitors of CYP2B6 have been described including ticlopidine, clopidogrel,thioTEPA, memantine, and 2-phenyl-2-(1-piperidinyl)propane, but they do not adequatelydistinguish between different CYP forms (Turpeinen et al. 2006). Thus, more selectiveCYP2B6 inhibitors are needed for in vitro drug development and drug interaction studies.The aims of this study were (1) to determine the CYP2B6 inhibition potencies ofstructurally diverse compounds to create a database for structure-activity relationshipanalysis (2) to construct a comprehensive 3D-QSAR (CoMFA) model, and (3) to identifynovel, potent and selective inhibitors of CYP2B6.

In this study we describe a 3D-QSAR model for CYP2B6 inhibitors using the IC50 valuesgenerated with cDNA-expressed CYP2B6. A total of forty-one chemicals were screened.Some of the chemicals were selected from our extensive database and further expandedwith structures related to CYP2B6 substrates and inhibitors. Three potent inhibitors werefound and two of these compounds were also highly CYP2B6 selective. The CoMFA modelcreated was also used to accurately predict the IC50 values of a test set of seven compoundsnot present in the training set.

7.2 Materials and methods

The materials and general methods used are described in Chapter 4.

7.2.1 Determination of inhibition potencyThe 7-ethoxy-4-trifluoromethylcoumarin O-deethylation activity assay is based on thedetection of fluorescence emitted by the metabolite 7-hydroxy-4-trifluoromethylcoumarin.The incubations were performed in 96-well plates (Chapter 4). 7-ethoxy-4-trifluoromethylcoumarin varied between 1 and 17 μM in the determination of inhibitiontype and the inhibition constant Kic. All inhibitors were dissolved in DMSO, and to ensurecomplete solubility of the compounds, a final concentration of 2% DMSO was used in theincubations. This concentration of DMSO caused a 17% inhibition in CYP2B6 catalyticactivity, and this effect was taken into account in the experiments. Each inhibitor wasprescreened using inhibitor concentrations ranging from 0.001 to 1000 μM. The actual IC50

values were determined using a narrower range of seven concentrations. All IC50 valueswere determined in two separate experiments. The apparent Km and Vmax of 7-ethoxy-4-trifluoromethylcoumarin was also determined. The IC50, the apparent Km and the apparentVmax values were calculated using non-linear regression analysis with Prism 4.0TM

software (San Diego, CA, USA).

7.2.2 Enzyme assays using human liver microsomesTo assess CYP selectivity of novel inhibitors, a CYP screen was carried out using CYPspecific substrates and human liver microsomes as the enzyme source. The incubationconditions, analysis and instrumentation used to assess the enzyme activities of CYP1A1/2(ethoxyresorufin O-deethylation), CYP2A6 (coumarin 7-hydroxylation), CYP2C9(tolbutamide hydroxylation), CYP2D6 (dextromethorphan O-demethylation), CYP2E1

55

(chlorzoxazone 6-hydroxylation), CYP3A4/5 (midazolam 1´-hydroxylation) and multipleCYPs (7-ethoxycoumarin O-deethylation) have been described previously in detail inTaavitsainen et al. (2001). The bupropion hydroxylation assay and the analytical methodsfor CYP2B6 were previously described by Turpeinen et al. (2004). The amodiaquine N-desethylation assay for CYP2C8 was a modification from Li et al. (2002): incubationmixtures contained 0.5 mg protein/ml and 30 μM amodiaquine and the incubation timewas 20 min. Omeprazole 5-hydroxylation and sulfoxidation assays for CYP2C19 andCYP3A4, respectively, were adapted from Äbelö et al. (2000): incubation mixturescontained 0.5 mg microsomal protein/ml and 40 μM omeprazole and the incubation periodwas 20 min. Otherwise the incubations contained buffer and NADPH and were performedas described above.

Each reaction mixture was preincubated for 2 minutes at +37 �C in a shaking incubatorblock (Eppendorf Thermomixer 5436, Hamburg, Germany). The reaction was started byadding NADPH and terminated by adding 200 μl of ice-cold acetonitrile and subsequentlycooled in an ice bath to precipitate the proteins. The mixture was vortex mixed and spun at10,000g for 15 minutes. Supernatants were collected and stored at -20 �C until analyzed. Inexperiments directed at revealing mechanism-based inhibition, the inhibitors were pre-incubated with NADPH for 15 min before the reaction was started by the addition ofsubstrate.

7.3 Results

7.3.1 Inhibition of CYP2B6 activity in vitro and initial CoMFA modelFirst, 25 compounds were analyzed for CYP2B6 inhibition potency (IC50 values) (Table 13).The IC50 values of these compounds were measured using recombinant CYP2B6 catalyzed7-ethoxy-4-trifluoromethylcoumarin O-deethylation as the probe reaction. The Km value of7-ethoxy-4-trifluoromethylcoumarin was 4.0 μM (1.6-6.4 μM 95% confidence interval) andthe Vmax value 2.4 nmol/(min x nmol CYP) [1.8 - 3.0 nmol/(min x nmol CYP)]. Theenzymatic activity was assayed under constant conditions using seven differentconcentrations of each inhibitor. These compounds were selected from our own structuraldatabase. This database consists of compounds used in our previous studies on CYP2A6and CYP1A2 (Rahnasto et al. 2005; Chapter 6). Based on this series of compounds, aninitial CoMFA model was created to find out the key structural features which increaseinhibition potency. This CoMFA model revealed the location of a hydrophobic bulkygroup and an acceptor atom that affect inhibition potency. Based on this information andthe 2D structure of a known potent CYP2B6 inhibitor, ticlopidine, 16 new pyridine andphenyl derivatives were selected and tested.

The lowest IC50 values were obtained with pyridine and diphenylmethane derivatives(Table 13). The heterocyclic nitrogen of benzylpyridine and benzoylpyridine increased theinhibitory potency. Benzylpyridines were found to be more potent than benzoylpyridines,as the sp2 oxygen decreased inhibitory potency. Of the polyaromatic compounds, 1-naphthol was the most potent inhibitor (IC50 = 9.1 μM). In contrast, 2-naphthol was a morethan 7-fold weaker inhibitor.

56

Table 13. Inhibition potencies of compounds towards CYP2B6

Compound

IC50

(μM)

95 %Confidenceintervals

pIC50

(M)

4-(4-Chlorobenzyl)pyridine (CBP) 0.3 0.2-0.4 6.52

4-Benzylpyridine (BP) 0.4 0.3-0.6 6.40

4-(4-Nitrobenzyl)pyridine (NBP) 0.4 0.4-0.5 6.40

4-(4-Chlorobenzoyl)pyridine 1.5 1.2-1.8 5.82

2-Benzylaniline 4.4 2.7-6.2 5.36

4-Hydroxydiphenylmethane 5.0 2.5-7.5 5.30

4-Benzoylpyridine 5.2 3.8-6.5 5.28

4,4-DDM 6.8 3.9-9.8 5.17

1-Naphthola 9.1 6.6-12 5.04

4-Chlorobenzophenone 13 8.3-17 4.89

�-Dodecanolactonea 22 14-31 4.66

Undecanoic-�-lactonea 35 27-44 4.46

4-Methoxybenzaldehydea 37 27-47 4.43

(Table continues)

NCl

N

NNO2

N

O

Cl

NH2

OH

N

O

NH2 NH2

OH

O

Cl

O O

O O

OO

57

Table 13 (continued)

2-Methoxynaphthalenea 37 29-45 4.43

Bromodiphenylmethane 39 29-49 4.41

2-(p-Tolyl)ethylaminea 45 29-60 4.35

4-Bromo-benzaldehyde 50 34-65 4.30

2-Naphthola 67 41-96 4.17

4-Hydroxybenzophenone 95 49-140 4.02

4-Bromobenzylbromide 96 67-130 4.02

2,4-Dimethylquinolinea 140 94-180 3.85

�-Tetralonea 150 118-170 3.82

�-Decanolactonea 150 130-180 3.82

Quinaldinea 310 210-410 3.51

2-Indanonea 350 240-450 3.46

4-Hydroxybenzaldehyde 370 300-430 3.43

3-Methylisoquinolinea 390 240-540 3.41

(Table continues)

O

Br

NH2

Br

O

OH

OH

O

Br

Br

N CH3

CH3

O

O O

N CH3

O

OH

O

N

CH3

58

Table 13 (continued)

3-Methylquinolinea 420 250-580 3.38

2,7-Dimethylquinolinea 420 320-510 3.38

3,4-Dihydroxybenzaldehydea 440 290-580 3.36

2,6-Dimethylquinolinea 480 350-620 3.32

2-(4-Aminophenyl)-ethanola 630 220-1000 3.20

2,3-Dihydrobenzofurana 670 330-1000 3.17

Butylbenzenea 820 610-1000 3.09

�-Phenyl-�-butyrolactonea 1100 750-1500 2.96

2-Coumaronea 1100 610-1700 2.96

2-Benzoxalinonea 1600 880-2400 2.80

�-Heptalactonea 2400 1600-3100 2.62

�-Hexalactonea 4700 3800-5700 2.33

�-Caprolactonea 6400 4900-8000 2.19

�-Caprolactonea 28 000 26 000-30000

1.55

a Compounds used in the initial CoMFA model

N

CH3

N CH3CH3

OH

OH

O

N CH3

CH3

OH

NH2

O

O O

O O

NH

O O

O O

O O

O O

O O

59

The other polyaromatic compounds were even weaker, 2-benzoxalinone having thehighest IC50 value (1600 μM). The inhibitory potencies of 11 lactones were also tested. Thesimple lactone ring structures were very weak inhibitors, �-caprolactone being the leastpotent (IC50 value = 28 mM). An increase in the length of side chain in lactone correlatedwith an increase in the inhibitory potency.

The inhibition by 4-benzylpyridine (BP), 4-(4-chlorobenzyl)pyridine (CBP) and 4-(4-nitrobenzyl)pyridine (NBP) was studied in more detail due to their high potency (Figure14). According to these experiments they all are competitive inhibitors for deethylation of7-ethoxy-4-trifluoromethylcoumarin by CYP2B6 since the apparent Vmax was not affectedand the apparent Km increased in the presence of these compounds. The apparent Km

values correlated linearly and significantly with the concentration of inhibitors. Accordingto this analysis, a Kic value of 0.15 μM (0.01-0.67 μM 95% confidence interval) was obtainedfor BP, 0.002 μM (0-0.11 μM) for CBP and 0.04 μM (0-0.10 μM) for NBP.

Figure 14. Competitive inhibition of CYP2B6 by 4-benzylpyridine (BP) (A), 4-(4-chlorobenzyl)pyridine (CBP) (B) and 4-(4-nitrobenzyl)pyridine (NBP) (C). 7-ethoxy-4-trifluoromethylcoumarin deethylation to 7-hydroxy-4-trifluoromethylcoumarin via recombinantCYP2B6 was inhibited at different concentrations of the substrate and the inhibitors. The upperpanels show non-linear regression analysis of one out of 3-5 experiments with each inhibitor.The lower panels show the effect of the inhibitor concentrations on the apparent Km values. TheKm values were obtained from experiments shown in the upper panels. The Vmax value was notaffected by the inhibitor concentration.

7.3.2 The final CoMFA modelForty-one compounds listed in Table 13 were analyzed using the CoMFA method(including the compounds used in the initial CoMFA model). The created CoMFA modelwas of high quality with the following statistical values with 2 components: q2=0.71,SPRESS=0.64, r2=0.85, predictive r2=0.80 (Figure 15). The CYP2B6 CoMFA model isrepresented as 3D contour maps with CBP as the reference structure (Figure 16). Red andgreen maps represent areas where more negative partial charge and bulkier groups

60

increase inhibitory potency, respectively. Blue and yellow maps represent areas wheremore negative partial charge and bulkier groups decrease inhibition potency, respectively.The sterically favoured green area was located around substitution at carbon 4 of thebenzyl ring of benzylpyridine in the CoMFA model. A broad partial negative charge nearto the nitrogen atom and above and below the plane of the benzyl ring tended to increasethe inhibitory potency.

Figure 15. Plot for the training set (actual/predicted IC50 values) of the CoMFA model (r2 =0.85).

Figure 16. Colour contour maps of final CYP2B6 CoMFA model. Red and green represent areaswhere more negative partial charge and bulkier groups increase inhibition potency,respectively. Blue and yellow represent areas where more negative partial charge and bulkiergroups decrease inhibition potency, respectively. The reference structure is 4-(4-chlorobenzyl)pyridine (CBP).

61

The prediction power of the CoMFA model was evaluated by estimating pIC50 values foran external test set of seven compounds (Table 14). Two of these compounds are knownsubstrates of CYP2B6 (selegiline and bupropion) and one is an inhibitor of this enzyme(ThioTEPA). Four compounds were chosen on the basis that they possess structuralsimilarities to compounds in the training set but also contain features that differ from thetraining set compounds (piperonylalcohol, 4-chlorobenzylamine, p-dimethylamino-benzaldehyde and anisalcohol). As summarised in Table 14, the predictions of pIC50 valuesfor the external test set of molecules agreed well with the actual values. This is evidencethat the created CoMFA exhibits a high predictive power.

Table 14. Validation of CYP2B6 model.

pIC50 (M)

Compound Experimental Predicted Residual

Selegiline 5.41 5.33 0.08

ThioTEPA 5.00 4.3 0.70

Piperonylalcohol 4.70 4.1 0.60

Bupropion 4.55 4.23 0.32

4-Chlorobenzylamine 4.43 4.1 0.33

p-Dimethylamino-benzaldehyde 3.96 3.8 0.16

Anisalcohol 2.85 3.36 0.51

7.3.3 CYP2B6 selectivityThe CYP selectivity of the three most potent inhibitors, BP, CBP, and NBP were furthercharacterized using the CYP2B6 selective bupropion as the substrate and human livermicrosomes as the enzyme source. The IC50 values for the CYP2B6 mediated bupropionhydroxylation reaction were 0.02, 0.03 and 0.03 μM for CBP, NBP and BP, respectively(Table 15). No changes of IC50 values were observed with any of these three inhibitorswhen the experiments were carried out to reveal mechanism based (suicide) inhibition.

CH3

NCH3

CH

N P NN

S

O

OOH

NH

Cl

O

Cl

NH2

H

O

N

OH

O

62

With CBP and BP, the IC50 values with other CYPs were > 1 μM. The next sensitive CYPform for CBP and BP inhibition was CYP1A2 with IC50 values of 3.8 μM and 1.2 μM,respectively. In addition to inhibiting CYP2B6, NBP was also a potent inhibitor of CYP1A2with an IC50 value of 0.02 μM (Table 15). At a 0.1 μM concentration of CBP, completeinhibition of CYP2B6 activity was observed. At this concentration, CBP did not affect anyother CYP related activities, and less than 20% inhibition of CYP1A2, CYP2C9 and CYP2E1was observed at 1.0 μM CBP (Figure 17A). Both NBP and BP were less selective, inhibitingalso CYP1A2 at both 0.1 and 1.0 μM (Figure 17B-C).

Table 15. IC50 values of CBP, NBP and BP on CYP-specific model activities in human livermicrosomes in vitro. Mechanism-based inhibition experiments for ethoxycoumarin O-deethylation (multiple CYPs) and bupropion hydroxylation (CYP2B6) are also presented (2 min /15 min pre-incubation). Substrate concentrations for each model reaction in parenthesis.

IC50 (�M)

CYP Substrate CBP NBP BP

Multiple 7-Ethoxycoumarin (10 M) 7.8 / 7.8 0.05 / 0.06 2.8 / 2.8

1A2 7-Ethoxyresorufin (1 M) 3.8 0.02 1.2

2A6 Coumarin (10 M) 31.3 24.3 3.0

2B6 Bupropion (50 M) 0.02 / 0.02 0.03 / 0.025 0.03 / 0.02

2C8 Amodiaquine (30 μM) > 100 > 100 > 100

2C9 Tolbutamide (200 M) 5.4 6.8 7.2

2C19 Omeprazole (40 M) 24.0 31.0 64.0

2D6 Dextromethorphan (10 M) 17.5 13.3 5.1

2E1 Chlorzoxazone (100 M) > 100 3.8 8.5

3A4 Midazolam (10μM) > 100 > 100 > 100

3A4 Testosterone (100 μM) > 100 > 100 > 100

3A4 Omeprazole (40 μM) > 100 > 100 > 100

63

Figure 17. CYP inhibition selectivity of 4-(4-chlorobenzyl)pyridine (CBP) (A), 4-(4-nitrobenzyl)pyridine (NBP) (B), and 4-benzylpyridine (BP) (C). CYP screen was carried outusing CYP specific substrates and human liver microsomes as the enzyme source.

64

7.4 Discussion

The main achievement of this study was to find two novel potent and selective inhibitorsof CYP2B6: CBP and BP. For this purpose the inhibition potencies of 41 compounds weredetermined and a comprehensive 3D-QSAR (CoMFA) model was created with this dataset. In this way a precise view of the key molecular characteristics of CYP2B6 inhibitorswere obtained. The prediction power of this CoMFA model was evaluated by estimatingthe pIC50 values for an external test set of compounds. The CoMFA model yielded novelstructural information about CYP2B6 inhibitors. The developed model accuratelypredicted the inhibitory potencies of several structurally unrelated compounds.

An important aim of this study was to find new potent and selective inhibitors of theCYP2B6 enzyme. Ticlopidine is a potent mechanism-based inhibitor of CYP2B6 (IC50 value= 0.32 μM), but it is also an inhibitor of CYP2C19 and it also impairs activities associatedwith CYP1A2 and CYP2D6 (Ha-Duong et al. 2001; Richter et al. 2004; Turpeinen et al.2004). ThioTEPA has been shown to be a very selective CYP2B6 inhibitor, but it is not aspotent as ticlopidine (Rae et al. 2002; Turpeinen et al. 2004). In this study we demonstratedthat CoMFA modelling can be used to identify new inhibitors. Three novel potentinhibitors were found: CBP, NBP, and BP, all displaying an IC50 value of less than 1 μM.CBP was the most selective inhibitor. It was 190 times more potent towards CYP2B6 thantowards the next sensitive form, namely CYP1A2. For BP, the next sensitive CYP form wasalso CYP1A2, but the difference in potency was only 40-fold. Therefore, the chlorosubstituent in the benzyl ring in CBP must influence the selectivity. In addition toinhibiting CYP2B6, NBP was also a potent inhibitor of CYP1A2. The NO2 group in the NBPbenzyl ring maybe the reason for its inhibitory properties. Mechanism-based enzymeinhibition, by definition, requires metabolism of a substrate into a reactive intermediatethatcan bind to the enzyme irreversibly, resulting in the loss of enzyme activity (Kent et al.2001). Ticlopidine is a mechanism-based inhibitor and its structure bears resemblances tothese new potent molecules. However, none of the three new compounds act as amechanism-based inhibitor.

The substrates of CYP2B6 are usually non-planar molecules, neutral or weakly basic,fairly lipophilic with one or two hydrogen bond acceptors (Lewis, 2000). CYP2B6 has beenstudied using homology modelling, protein docking and molecular dynamics approaches(de Graaf et al. 2005). The 3D-QSAR approach is mostly used i.e. descriptors of essentialfunctional groups and topologies of the molecules are superimposed. Ekins et al. (1999b)published two different 3D-QSAR models based on 16 CYP2B6 substrates. Apharmacophore model was built using the program Catalyst, which was comparedwith apartial least-squares (PLS) model using molecular surface-weighted holistic invariantmolecular (MS-WHIM) descriptors. The pharmacophore model obtained with Catalystconsisted of three hydrophobes and one hydrogen bond acceptor region. The cross-validated PLS MS-WHIM model gave a good q2 value of 0.607. Molecular size, positiveelectrostatic potential, hydrogen bonding acceptor capacity, and hydrophobicity werefound to be the most relevant descriptors for the model. Wang and Halpert (2002)published a homology model of CYP2B6 based on the crystal structure of CYP2C5. Also3D-QSAR models were generated for 16 structurally diverse CYP2B6 substrates with

65

Catalyst. Both include two hydrophobes and one hydrogen bond acceptor. Thepharmacophores were combined with the CYP2B6 model by docking the substrates in theactive site of CYP2B6 and the pharmacophores were used to predict the Km value for testset molecules in conjunction with the CYP2B6 model.

To our knowledge there are no reports on the application of pharmacophore or QSARtechniques in order to find new inhibitors or in order to predict the inhibitory potency ofcompounds. The models described in the previous studies are based on the Km valuesobtained from the literature and were based on a limited number of structurally diversecompounds. The model presented here is based on large training set which was generatedwith internally fully consistent methods. The results in our present 3D-QSAR model areconsistent with previous studies with the CYP2B6 enzyme since the importance of thehydrophobic and electronic characteristic in the binding of inhibitors was demonstrated. Inthe present study, both electrostatic and steric interactions were found to account for thedifferences in inhibitory potencies. In CoMFA, electrostatic fields described the areaswhere hydrogen bond acceptors or donors should be located in an inhibitor. Thesignificance of hydrophobicity for inhibitory potency was evident in the CoMFA stericfields. The final CoMFA model pinpointed the location of the partial negative charge thatincreases the inhibition potency. In practice, an acceptor atom correctly located in thestructure yields a more potent inhibitor. The CoMFA model also predicted accurately theinhibitory potencies of structurally diverse compounds. The predictions for selegiline,bupropion, 4-chlorobenzylamine and p-dimethylamino-benzaldehyde were verysuccessful, because the residuals between predicted and tested pIC50 values were 0.08 - 0.33log units.

The CoMFA electrostatic and steric fields of CYP2B6 are consistent with the structure ofbenzylpyridine, which contains the optimal steric, hydrophobic, and acceptor features. Inparticular the benzyl structure and nitrogen acceptor atom render benzylpyridines morepotent inhibitors than e.g. lactones. It is interesting to compare the structural features ofticlopidine and CBP, since these two compounds act via different inhibitory mechanisms.Figure 18 shows the electrostatic fields for these two structures. The red colour in thecolour contour map of electrostatic fields means that this region has a partial negativecharge, whereas the blue colour describes a partial positive charge. The most notabledifference between these two structures is that CBP includes a partial negative region nearto the electronegative chlorine atom in the benzyl ring. Also the nitrogen acceptor atomand the sulphur atom in ticlopidine exhibited a partial negative charge in the colourcontour map.

66

Figure 18. Electrostatic fields and 2D structures of 4-(4-chlorobenzyl)pyridine (CBP) (left) andticlopidine (right). The electrostatic map are shown in red and blue areas in which morenegative charge increase and decrease inhibition potency, respectively.

In conclusion, the CYP2B6 CoMFA model was created which revealed the key molecularcharacteristics of inhibitors of the CYP2B6 enzyme. Three novel potent inhibitors werediscovered. Of these, especially CBP was very potent and selective. Thus, CBP is a suitableagent for in vitro studies directed at defining the relative contribution of CYP2B6 in themetabolism of chemical compounds, including novel drugs.

67

8 Inhibition studies and 3D-QSAR of CYP2E1Inhibitors 4

Abstract: CYP2E1 is an important enzyme oxidizing ethanol as well as several drugs andother xenobiotics in the human liver. We determined the inhibition potency of structurallydiverse compounds against human CYP2E1, and analyzed their interactions with theenzyme active site by molecular docking and 3D-QSAR approaches. The IC50 values for thetested compounds varied from 1.4 M for �-undecanolactone to over 46 mM for glycerol.This data set was used to create a comparative molecular field analysis (CoMFA) model.The most important interactions for binding of inhibitors were identified by docking andkey features for inhibitors were characterized via the COMFA model. Since the active siteof CYP2E1 is flexible, long chain lactones and alkyl alcohols fitted best into the larger openform while the other compounds fitted better in the smaller closed form of the active site.Electrostatic interactions near the Thr303 residue proved to be important for inhibition of theenzyme activity. Thus, docking analysis and the predictive CoMFA model proved to beefficient tools for revealing interactions between inhibiting compounds and CYP2E1. Theseapproaches can be used to analyze CYP2E1-mediated metabolism and drug interactions inthe development of new chemical entities.

Martikainen LE, Rahnasto-Rilla M, Neshybova S, Lahtela-Kakkonen M, Raunio H and Juvonen RO. Interactions ofinhibitor molecules with the human CYP2E1 enzyme active site. Manuscript

68

8.1 Introduction

CYP2E1 is an important member of the cytochrome P450 (CYP) enzyme superfamily.Although several variant alleles of the human CYP2E1 gene have been discovered, none ofthese totally abolish the activity of the enzyme, suggesting that the enzyme possesses someperhaps many, crucial endogenous properties (Ingelman-Sundberg, 2004b). In addition toethanol, CYP2E1 catalyzes also the metabolism of a wide variety of endogenoussubstances. Although CYP2E1 does not metabolize as many different drugs as some otherCYP enzymes e.g. CYP2D6 or CYP3A4, it is known to be involved in the metabolism ofseveral commonly used drugs such as paracetamol and chlorzoxazone (Rendic, 2002). Ingeneral, the majority of CYP2E1 substrates are neutral, small molecular weight compoundswith relatively low log P (octanol/water partition coefficient) values (Anzenbacher andAnzenbacherova, 2001). To date a number of CYP2E1 inhibitors have been described, butunfortunately, they are mostly not selective (Rendic, 2002; Khojasteh et al. 2011)). Theseinhibitors include the metabolite of disulfiram, diethyldithiocarbamate (Pratt-Hyatt et al.2010), dilinoleylphosphatidylcholine, the major component of polyunsaturatedphosphatidylcholines from soy beans (Aleynik and Lieber, 2001), and diallylsulphide, amechanism-based inhibitor found in Allium vegetables (Loizou and Cocker, 2001).

The structures of the human CYP2E1 protein complexed with indazole and 4-methylpyrazole have been solved (Porubsky et al. 2008). Porubsky and co-workers havealso recently co-crystallized CYP2E1 with �-imidazolyl-octanoic fatty acid, �-imidazolyl-decanoid fatty acid, and �-imidazolyl-dodecanoid fatty acid (Porubsky et al. 2010) andDeVore and co-workers reported a CYP2E1 structure complexed with pilocarpine (Devoreet al. 2011). To date QSAR studies of CYP2E1 have been carried out with limited number ofstructural analogues or with data obtained from different studies in the literature protein(Wang et al. 1995; Lewis et al. 2000; Lewis et al. 2003a; Lewis et al. 2003b; Park and Harris,2003; Spatzenegger et al. 2003; Ohashi et al. 2005; Yamazoe et al. 2011; Wu et al. 2011).

In this study we established a convenient fluorometric multiwell plate assay to measureinhibition of CYP2E1. Using the data generated with this method, the aims of this studywere: (1) to determine the inhibition potency of a large set of compounds against humanCYP2E1 and to create a new inhibition structure-activity database and (2) to analyse theinteractions between these inhibiting compounds and the CYP2E1 active site by moleculardocking and 3D-QSAR methods.

8.2 Materials and methods

The materials and general methods used are described in Chapter 4.

8.2.1 Determination of inhibition potencyThe 7-methoxy-4-trifluoromethylcoumarin (MFC) O-demethylation activity assay is basedon the detection of fluorescence emitted by its metabolite 7-hydroxy-4-trifluoromethylcoumarin (HFC). The incubations were performed in 96-well plates(Chapter 4). MFC and the inhibitors were dissolved into water to prevent all disturbingeffects of organic solvents.

69

8.2.2 Protein structure of CYP2E1Two crystal structures of CYP2E1 were obtained from the Protein Data Bank (PDB)database; the one co-crystallized with omega-imidazolyl octanoid acid (PDB: 3KOH) whichrepresents the open form, and the one co-crystallized with indazole (PDB: 3E6I),representing the closed form of crystallized CYP2E1 protein. The quality of the structureswas assessed using the internal evaluation program PROCHECK (Laskowski et al. 1993).The minimisation technique was used to refine the structures of CYP2E1, as previouslydescribed (Rahnasto et al. 2011).

8.2.3 Molecular docking and CoMFA modelTo create the CoMFA model (Chapter 4), the training set of the inhibitors was aligned bydocking the 9 compounds to the open form and 29 compounds to the closed form of theactive site of the enzyme. Only structurally similar molecules (alkyl alcohols, lactones,phenols/aromatic alcohols, quinolines and other compounds) were included in the trainingset (n=32) or the test set (n=6) (Table 16). Molecules with IC50 values of >1000 μM are notconsidered as inhibitors (White, 2000) and were omitted from the model. To achieve asufficiently homogeneous training set, all structurally very different molecules (N,N-diethyldithiocarbamate, paracetamol and fluoxetine) were omitted from the training set.The tested lactone derivatives were racemates and the S-(-)-enantiomers were used for themodel.

For docking the genetic optimization for ligand docking (GOLD) method (v.3.2) (Joneset al. 1997, Verdonk et al. 2003) was used with the metal coordination parameters of theChemScore (Kirton et al. 2005). GOLD is a docking program that evaluates solutions ofconformers at the protein active site using scoring functions to estimate changes in freeenergy (G) during binding. The GOLD program produced different possible dockingposes for each inhibitor, which were then ranked based on a scoring function (eitherChemScore or the CScore (Tripos Associates) consensus scoring ranking values) thatestimate the free energy change during binding.

8.3 Results

8.3.1 Inhibition of CYP2E1The IC50 values of 47 compounds were determined based on their ability to inhibitoxidation by CYP2E1 of MFC to HFC (Table 16). Recombinant human CYP2E1 was used asthe enzyme source to exclude any interference from other CYP forms. The effects of theamount of CYP2E1 enzyme and incubation time on the oxidation of MFC to HFC weredetermined. The fluorescence increased linearly with 0.5 – 1.5 pmol CYP2E1 for 40 min at10 μM MFC concentration (data not shown). The IC50 value for each compound wasestimated at 10 μM MFC in constant conditions using seven different concentrations ofeach inhibitor. Figure 19 presents an example of concentration dependent inhibition of theenzyme activity by two structurally similar compounds.

70

Figure 19. Inhibition of CYP2E1 activity by 2-naphtol (�) and 2-hydroxy-methyl-naphthalene

(�). Inhibition of MFC (10 μM) oxidation to HFC by recombinant human CYP2E1 was

determined.

Overall, the IC50 values varied from 1.4 μM for �-undecanolactone to 46 mM forglycerol. With primary alcohols the inhibition potency depended on the length of thehydrocarbon chain, as the IC50 decreased from methanol to 1-pentanol (Figure 20A). Theposition of the hydroxyl group was also important, as the IC50 of 2-butanol was ~3 timeshigher than that of 1-butanol where as the IC50 of 1-propanol was also lower than that of 2-propanol. Interestingly, the IC50 of acetone was 16 times higher than that of 2-propanol,indicating that a carbonyl oxygen in the hydrocarbon chain decreased inhibition potency.The inhibitory potency of some non-alcohol solvents was also determined (Table 16).Acetonitrile was the weakest inhibitor of the tested solvents. The IC50 values of acetone andDMSO were practically the same (150 μM and 170 μM, respectively). Several compoundscontaining a lactone ring were included in the analysis (Table 16). The weakest lactone-type inhibitor was -caprolactone (IC50 51 μM), and the most potent was �-undecanolactone (IC50 1.4 μM). The IC50 decreased when the length of the alkyl side-chainincreased in position 5 of �-lactones, however, �-dodecanolactone was not more potentthan undecanolactone (Figure 20B). A total of 20 aromatic compounds were also examined(Table 16). Some aromatic compounds were quite potent CYP2E1 inhibitors with 2-naphthol, 1-naphthol and 2-methoxynaphthalene being the most potent. The IC50 values ofquinolines varied from 7.6 μM of 3-methylquinoline to 48 μM of 2,6-dimethylquinolineindicating that both the number and the position of methyl substitutions affect inhibitionpotency. The known CYP2E1 ligands N,N-diethyldithiocarbamate, paracetamol andfluoxetine displayed IC50 values of 4.5 μM, 490 μM and 490 μM, respectively.

0.01 0.1 1 10 100 10000

25

50

75

100

2-hydroxy-methyl-naphthalene

2-naphthol

Inhibitor (µM)

Inhi

bitio

n pe

rcen

t

71

Figure 20. Effect of size of primary alcohols and �-lactones on inhibition potency againstCYP2E1 activity. Panel A illustrates inhibition of MFC (10 μM) oxidation by methanol, ethanol,1-propanol, 1-butanol and 1-pentanol, and panel B inhibition by �-valerolactone, �-caprolactone, �-heptalactone, �-undecanolactones and �-dodecanolactone.

Table 16. IC50 values of compounds towards CYP2E1

Alcohols IC50 (μM)

1-Pentanol 2.0a

1-Butanol 2.2a

2-Butanol 6.0a

1-Propanol 8.5b

2-Propanol 9.2a

Ethanol 35b

Methanol 1900

Glycerol 46000

Non-alcohol solvents IC50 (μM)

Acetone 150a

(Table continues)

72

Table 16 (continued)

DMSO 170a

Formaldehyde 560

Acetonitrile 6000

Lactones IC50 (μM)

�-Undecanolactone 1.4b

�-Decanolactone 3.8a

�-Dodecanolactone 3.9a

�-Valerolactone 8.9a

4,6-Dimethyl-�-pyrone 14 a

�-Heptalactone 19 a

�-Valerolactone 23 a

�-Caprolactone 25 a

4-Methoxy-2(5H)furanone 26 a

(Table continues)

73

Table 16 (continued)

�-Hexalactone 35 b

�-Caprolactone 51 a

Aromatic compounds IC50 (μM)

2-Naphthol 1.8 a

1-Naphthol 1.9 a

2-Methoxynaphthalene 2.0 a

2-(p-Tolyl)-ethylamine 4.0 a

Butylbenzene 5.5 a

Indan 5.8 a

3-Methylquinoline 7.6 a

2,3-Dihydrobenzofuran-5-carboxaldehyde

7.7 a

(Table continues)

74

Table 16 (continued)

2,3-Dihydrobenzofuran 9.1 a

�-Tetralone 9.1 a

Chlorzoxazone 9.2 a

2-Hydroxy-methyl-naphthalene

10 a

3-Methylisoquinoline 15 a

Quinaldine 21 a

2,7-Dimethylquinoline 24 a

Pyridine 36 a

2,6-Dimethylquinoline 48 b

2-Methylbenzofuran 50 b

Paracetamol 490

(Table continues)

75

Table 16 (continued)

Other compounds IC50 (μM)

N,N-Diethyldithiocarbamate 4.5

Pyrazole 20 a

Fluoxetine 490

Caffeine 5100

Theophylline 12000

a Training set molecules

b Test set molecules

8.3.2 Docking of the training set moleculesThe cavity of CYP2E1 above I helix interacts with the inhibiting compounds and isconnected via a channel to the protein surface. A single amino acid, Phe298, can close (PDBID: 3E6I) or open (PDB ID: KOH) the connection between this cavity and the channel.Therefore the volume of the active site increased in the open form in comparison to theclosed one. Both of these two forms were used in the docking studies, because theorientation of the studied molecules in the active site depended on the structural form ofthe crystal structure used. These two different crystal structures (open and closed forms ofthe active site) took into account the flexibility of CYP2E1 protein. The selection of thedocking orientations was based on the ranked values of the scoring function. First allmolecules were docked into both open and closed forms. Then the most favourableorientation of an inhibitor in the active site was selected for the alignment and constructionof the CoMFA model (Figure 21A). All of the alkyl alcohols coordinated towards the

76

pyridine ring of heme (Figure 21B). Most of the lactones (Figure 21C), phenols/aromaticalcohols (Figure 21D) and solvents such as DMSO and acetone formed a hydrogen bondwith the Thr303 residue. The long-chain lactones were orientated in the same way as thefatty acids in 3E61 crystal and formed a hydrogen bond with the Asn206 residue (Figure21C).

Figure 21. Docking orientation of inhibitors in the active site of human CYP2E1. The open form(blue) and the closed form (magenta) of the active site of the CYP2E1 are shown. Hydrogenbonds are shown by dashed line. The reference molecules are: (A) all inhibitors overlaid; (B) 1-pentanol (green) and 2-butanol (red); (C) �-undecanolactone (green) and �-valerolactone(red); (D) 2-naphthol.

8.3.3 CoMFA modelThirty two structurally similar compounds were included in the CoMFA training set (Table1). The created CoMFA model with four components was of high quality, as the crossvalidated q2 was 0.62, SPRESS was 0.32 and the correlation coefficient (r2) was 0.96. Aprogressive scrambling test slope (dq2/dryy2) of more than 1.1 can lead to overfitting.Therefore, the number of components in the final model was chosen so that the slope was <1.1 (dq2/dryy2=0.9).

Six compounds structurally similar to the training set molecules were included in thetest set. The model accurately predicted the inhibitory potencies (pIC50) of the test setcompounds, as the correlation coefficient of the prediction was 0.83 (Figure 22). The

77

inhibitory potency of the known CYP2E1 substrates paracetamol and fluoxetine and theinhibitor N,N-diethyldithiocarbamate was also predicted. The model predicted correctlythe inhibition potency of N,N-diethyldithiocarbamate but not that of paracetamol andfluoxetine (data not shown). The CYP2E1 CoMFA model is shown as 3D contour maps inFigure 23 with 2-naphthol as the reference structure. In the red and green areas, a morenegative partial charge and bulkier groups increase inhibition potency, respectively.

Figure 22. Plot for the training set (�) and test set (�) (actual/predicted IC50 values) of theCoMFA model (r2pred = 0.83).

Figure 23. Colour contour maps of CYP2E1 CoMFA model. The reference structure is 2-naphthol. Red and green represent areas where more negative partial charge and bulkiergroups increase inhibition potency, respectively. Blue and yellow represent areas where morenegative partial charge and bulkier groups decrease inhibition potency, respectively.

78

In the blue and yellow areas, a more negative partial charge and bulkier groups decreaseinhibition potency, respectively. In the CYP2E1 CoMFA model, electrostatic interactions(red/blue contours) near the Thr303 residue proved to be important for inhibition. Stericallyfavoured (green) regions surrounded the active site cavity near Phe116, Asp295 and Phe298.

8.4 Discussion

In this study, the IC50 values of 47 compounds were determined against humanrecombinant CYP2E1 enzyme by a convenient multiwell plate assay. A comprehensiveinhibition data set containing alcohols, other solvents, lactones and small molecular weightaromatic compounds was created for this enzyme. Molecular docking and a predictive 3D-QSAR (CoMFA) analysis revealed that CYP2E1 can interact with these compounds eitherin the open or in the closed form. These analyses are in good agreement with the data fromcrystallized CYP2E1 and indicate that electrostatic interactions near the Thr303 residue areimportant for the interaction between inhibitor molecules and the enzyme active site.

Alcohols are well known substrates and also inhibitors of CYP2E1 (Bolt et al. 2003). Inthis study, the IC50 values of series of primary alcohols and secondary alcohols weredetermined. Secondary alcohols were less potent inhibitors than the correspondingprimary alcohols. The IC50 values depended on the length of the hydrocarbon chain, as itdecreased with the length of the carbon chain from methanol (IC50 1.9 mM) to pentanol(IC50 2.0 μM). These findings are in good agreement with the study of Wang et al. (1995).They monitored the inhibitory effects of alcohols and acids on CYP2E1-specific oxidationof N-nitrosodimethylamine. An increase in the alcohol carbon chain length up to sevencarbons yielded a binding energy of 0.28 kcal/mol CH2. Acetone is an inducer and asubstrate of CYP2E1 (Song et al. 1989; Bondoc et al. 1999). This study demonstrates that it isalso a CYP2E1 inhibitor (IC50 150 μM). DMSO, containing a sulphur atom instead ofcarbon, has about the same IC50 value (170 μM). In contrast, the carbonyl oxygen of acetonedecreases the potency, i.e. 2-propanol is 16 times more potent than acetone.

Several QSAR approaches have been applied to the CYP2E1 protein (Wang et al. 1995;Lewis et al. 2000; Lewis et al. 2003a; Lewis et al. 2003b; Park and Harris 2003; Spatzeneggeret al. 2003; Ohashi et al. 2005; Yamazoe et al. 2011; Wu et al. 2011). In addition severalcrystal structures of CYP2E1 protein has been published (Porubsky et al. 2008; Porubsky etal. 2010; Devore et al. 2011). These structures have emphasized that the CYP2E1 protein isvery flexible. The CYP2E1 active site is in the closed form with the low molecular massligands indazole or 4-methylpyrazole, and in the open form with high molecular massligands, such as fatty acid substrate analogs of various chain lengths (Porubsky et al. 2008;Porubsky et al. 2010; Devore et al. 2011). Our docking results also showed that lowmolecular mass ligands such as short-chain lactones were orientated differently in theactive site compared with high molecular mass ligands such as long-chain lactones.

Hydrogen bonding, ��� stacking and desolvation of the heme environment are thoughtto play important roles in CYP2E1 substrate binding affinity (Lewis et al. 2003a). Thepresent docking analysis showed that the active site of CYP2E1 consists of two hydrogen-bonding sites (Thr303 and Asn206). The interaction of larger inhibitors with Asn206 was

79

important only in the open form of the active site. Most of the lactones, aromatic alcoholsand solvents such as DMSO and acetone formed a hydrogen bond with Thr303, which isseen also in the crystal structure with imidazole and 4-methylpyrazole. Mutational studieshave identified Thr303 as important distal binding site feature (Fukuda et al. 1993; Imai et al.1994; Moreno et al. 2001).

The created CoMFA model supported the docking results. The CYP2E1 CoMFA fieldsshowed that hydrogen-bond acceptor/donor interactions are important near Thr303. Thesterically favoured and disfavoured regions provided indications about the optimalvolume and shape of a potent inhibitor.

The created CoMFA model supported the docking results. The CYP2E1 CoMFA fieldsshowed that hydrogen-bond acceptor/donor interactions are important near the Thr303

residue. The sterically favoured and disfavoured regions provided indications about theoptimal volume and shape of a potent inhibitor. The predictive power of a CoMFA modelis highly dependent on the training and test set molecules having similar chemical features(Höltje, 2003). The predictive power of this CYP2E1 CoMFA model was ascertained byomitting the few structurally different compounds from the analysis. Consequently, thecurrent model was able to predict the inhibition potency of compounds possessing similarchemical features as the training set compounds. The model did not predict accurately theinhibition potency of the CYP2E1 substrates paracetamol and fluoxetine. These moleculeswere structurally very different from the training set compounds and therefore the modelwas not predictive for them. In addition, paracetamol and fluoxetine are CYP2E1substrates rather than inhibitors and may possess different binding modes with theenzyme active site.

In conclusion, a sensitive and rapid assay to measure IC50 values of compounds forCYP2E1 was established and inhibition potencies of 47 compounds were determined. Themolecular docking and CoMFA analyses revealed the most important interactions ofinhibitors with the CYP2E1 active site. The CoMFA model accurately predicted theinhibition potency of a test set of molecules. Thus, the combination of CoMFA and dockinganalyses is a good approach to rationally design potent and selective inhibitors of CYP2E1.

80

9 General discussion

The discovery and development of drugs demands huge investments, much labour andtechnological expertise (Dimasi et al. 2003). The process of drug discovery anddevelopment involves the identification of leads, synthesis, characterization, screening,toxicological tests, in the route towards a of randomised clinical trial program. Thisdiscovery and development process can take more than a decade before NCE ultimatelyreceives marketing authorisation (Figure 24). In most countries, there is some sort of formaldrug approval process. The main points are similar regardless of the country, although theactual regulation process may vary. In Europe, EMA is responsible for the scientificevaluation of applications for European marketing authorisations for both human andveterinary medicines. In the centralised procedure, companies submit a single marketingauthorisation application to the EMA. Once granted by the European Commission, acentralised marketing authorisation is valid in all European Union (EU) and EEA-EFTAstates (Iceland, Liechtenstein and Norway). Generally, documentation is required aboutproof of safety and efficacy as well as the high quality of the product.

Poor pharmacokinetic properties, severe ADRs, major DDIs, animal toxicity or lack ofefficacy can lead to the withdrawal of NCE from clinical trials. The evaluation of ADMETprofiles of drug candidates during pre-clinical development represents one of the crucialparts of the drug discovery process. Efficient profiling operations are now run in parallel topotency screening during lead optimization. In vitro assays are used during early drugdevelopment and high-throughput ADMET screens are available. The advantage of usingin silico approaches over in vitro assays is that less investment is needed in resources, timeand technology.

Figure 24. Drug discovery and development process (modified from Ashburn and Thor, 2004).

81

The general aim of this study was to develop novel in vitro and in silico methods toassess the metabolic features of xenobiotics. These techniques will be further refined withthe ultimate aim of developing the capability to predict inhibition properties across thehuman CYP spectrum of novel drugs and other compounds.

Comparison of in vitro tests

In the first part of the present study, three CYP in vitro inhibition screening tests werecompared: the traditional single substrate assays, the fluorescent probe method withrecombinant human CYPs, and a novel n-in-one technique (Chapter 5). The resultsrevealed that the three assay types yielded very similar results with the majority of testedCYP forms. In the subsequent studies the fluorescent probe method was used formeasuring the inhibition potency. This method possesses the advantages of speed and highthroughput. Currently, LC/MS methods have evolved into effective tools for the analysis ofdrug metabolism; they are fast, reliable and robust analytical means which are applicablein various in vitro and in vivo situations (Kostiainen et al. 2003; Pelkonen et. al. 2008). Thisapproach offers some advantages over fluorometric assays e.g. simultaneous evaluation ofseveral CYP isoform activities, the high selectivity of the probe substrates for theirrespective CYP enzymes and the use of clinically relevant probe substrates (Fowler andZhang, 2008). It also allows for the direct quantification of the marker substrate or product(Foti et al. 2010) LC with time-of-flight (TOF) or quadrupole (Q)TOF mass spectrometersare the instruments of choice for screening (metabolite screening and tentativeidentification) and the use of TOF-MS is clearly the most time-efficient way to screen andtentatively identify metabolites from various in vitro sources (Pelkonen et al. 2008).Nevertheless, as LC/MS techniques require expensive instruments and highly specializedskills, the much simpler and cheaper methods such as fluorometric assays still play animportant role as screening methods.

CYP1A2

Three CYP enzymes were selected (CYP1A2, CYP2B6, CYP2E1) for further evaluation, andnew databanks of inhibitors were created. The inhibition potencies of large series ofchemicals were determined against these enzymes. All data used in the training sets wasgenerated with internally fully consistent methods. Some disubstituted naphthalene andquinoline derivatives were found to be potent inhibitors of CYP1A2 (Chapter 6). Theresults indicated that substitutions at the C1 position of naphthalene with hydrophobicsubstituents such as halogen or methyl groups increased the inhibitory potency. The IC50

values of molecules decreased as the molecular volume of lactones and the length of alkylside chain increased. In addition a large steric-favoured region was located near the C7position in the CoMFA map. This data suggests that the binding pocket of the CYP1A2enzyme is composed of mostly hydrophobic and aromatic amino acids with the polaramino acids for hydrogen bonding being present near to the heme centre. The createdCoMFA model predicted very accurately the inhibitory potencies of the test set ofcompounds. Later the crystal structure CYP1A2 in a complex with the inhibitor �-naphthoflavone has been published (Sansen et al. 2007). The structure revealed a compact,

82

closed active site cavity of CYP1A2, which can fit well with planar compounds such as �-naphthoflavone and typical small-molecular-weight CYP1A2 substrates such astheophylline, caffeine, tizanidine, melatonin, tacrine, and clozapine. CYP1A2 substratesgenerally contain planar a ring that can fit into the narrow and planar active site of theenzyme. Thus the structure explains the substrate specificity of CYP1A2 (Zhou SF et al.2009c).

CYP2B6

For CYP2B6, both electrostatic and steric interactions were found to account for thedifferences in inhibitory potencies (Chapter 7). In CoMFA, electrostatic fields described theareas where hydrogen bond acceptors or donors need to be located in an inhibitor. Thesignificance of hydrophobicity for inhibitory potency was evident in the CoMFA stericfields. The CoMFA model accurately predicted the inhibitory potencies of severalstructurally unrelated compounds. This study demonstrated that CoMFA modelling couldbe used to identify new inhibitors. Three novel potent inhibitors were found: CBP, NBP,and BP, all displaying IC50 values of less than 1 μM. Recently the crystal structures ofCYP2B6 in complex with BP and NBP were resolved (Shah et al. 2011). The crystalstructures revealed a closed active site comprising amino acid residues that can vary theconformation of their side chains to allow binding of diverse ligands; particularly those ofphenylalanine residues (Phe206 and Phe297 side chains, along with the movement of Phe108)within the hydrophobic cavity. In addition, the highly conserved Ser304 may play animportant role in substrate binding by forming a hydrogen-bonding network near theactive site (Shah et al. 2011).

CYP2E1

For CYP2E1, a sensitive and rapid assay to measure IC50 values of compounds wasestablished and inhibition potencies of 47 compounds were determined and in addition toCoMFA, molecular docking was also carried out (Chapter 8). Docking results and CoMFAshowed that CYP2E1 could interact with these inhibiting compounds either in the open orin the closed form and that the active site of CYP2E1 consisted of two hydrogen-bondingsites (Thr303 and Asn206). The CoMFA model predicted accurately the inhibition potency of atest set of molecules. Several crystal structures of CYP2E1 protein have been published(Porubsky et al 2008; Porubsky et al. 2010; Devore et al. 2011). The CYP2E1 active site is inthe closed form with the low molecular mass ligands and in the open form with highmolecular mass ligands. Furthermore, these docking results showed that low molecularmass ligands such as short-chain lactones were orientated differently in the active sitecompared with high molecular mass ligands such as long-chain lactones.

83

Future perspectives

Pharmaceutical companies are currently under immense pressure to reduce the cost ofdrug discovery and development. In the early phase of drug discovery and development,in vitro HTS and in silico studies can be applied for the prediction of the possibleinvolvement of CYP enzymes in the metabolism of drugs or drug candidates. These studiescould potentially make drug design more efficient, less costly and improve drug safety andpossibly replace some of the in vivo experiments.

In the future, one can foresee that in silico models will become very efficient tools forpredicting the structural requirements of inhibitors of CYP enzymes. In particular, thecombination of QSAR and protein models represents a potentially very powerful approachfor predicting CYP metabolism. Unfortunately, the development of in silico models is notstraightforward and there are various reasons why these models may fail. For example,failure can be due to the poor quality of original data. Some endpoints are complexincreasing the difficulty of creating a useful model. In addition, reliable in silico-in vitro-invivo correlation is still very difficult because of the multiplicity of mechanisms involved.

84

10 ConclusionsThe main conclusions of the present study are as follows:

1. Three assay types, the traditional single substrate assays, the fluorescent probemethod with recombinant human CYPs, and a novel n-in-one technique, yieldedsimilar results with the majority of tested CYP forms. The fluorescent probe assaywas found to be useful and suitable for measuring the inhibition of major drug-metabolizing CYP activities compared to a conventional single substrate assay.

2. For CYP1A2, CYP2B6 and CYP2E1, new inhibition structure-activity databases werecreated. These were large and composed of structurally diverse compounds.

3. The created CoMFA models revealed the key molecular characteristics of inhibitorsof the CYP1A2, CYP2B6 and CYP2E1 and were also able to accurately predict theinhibitory potencies of the test set of compounds.

4. For CYP2B6 three novel potent inhibitors were discovered, CBP, BP and NBP. CBPis particularly a suitable inhibitor for use in in vitro screening studies.

85

References

Äbelö A, Andersson TB, Antonsson M, Naudot AK, Skanberg I and Weidolf L (2000) Stereoselectivemetabolism of omeprazole by human cytochrome P450 enzymes. Drug Metab Dispos 28:966-972.

Adas F, Berthou F, Picart D, Lozac'h P, Beauge F and Amet Y (1998) Involvement of cytochrome P450 2E1 inthe (omega-1)-hydroxylation of oleic acid in human and rat liver microsomes. J Lipid Res 39:1210-1219.

Aleynik MK and Lieber CS (2001) Dilinoleoylphosphatidylcholine decreases ethanol-induced cytochromeP4502E1. Biochem Biophys Res Commun 288:1047-1051.

Andricopulo AD, Salum LB and Abraham DJ (2009) Structure-based drug design strategies in medicinalchemistry. Curr Top Med Chem 9:771-790.

Androutsopoulos VP, Tsatsakis AM and Spandidos DA (2009) Cytochrome P450 CYP1A1: wider roles incancer progression and prevention. BMC Cancer 9:187.

Anzenbacher P and Anzenbacherova E (2001) Cytochromes P450 and metabolism of xenobiotics. Cell MolLife Sci 58:737-747.

Arimoto R (2006) Computational models for predicting interactions with cytochrome p450 enzyme. Curr TopMed Chem 6:1609-1618.

Ashburn TT and Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs.Nat Rev Drug Discov 3:673-683.

Asikainen A, Tarhanen J, Poso A, Pasanen M, Alhava E and Juvonen RO (2003) Predictive value ofcomparative molecular field analysis modelling of naphthalene inhibition of human CYP2A6 and mouseCYP2A5 enzymes. Toxicol In Vitro 17:449-455.

Baldwin SJ, Clarke SE and Chenery RJ (1999) Characterization of the cytochrome P450 enzymes involved inthe in vitro metabolism of rosiglitazone. Br J Clin Pharmacol 48:424-432.

Baroni M, Constantino G, Cruciani G, Riganelli D, Valigi R and Clementi S (1993) Generating optimal linearPLS estimations (GOLPE): an advanced chemometric tool for handling 3D-QSAR problems. Quant Struct-ActRelat 9-10-20.

Baumann P and Rochat B (1995). Comparative pharmacokinetics of selective serotonin reuptake inhibitors: alook behind the mirror. Int Clin Psychopharmacol 10:15-21.

Bell L, Bickford S, Nguyen PH, Wang J, He T, Zhang B, Friche Y, Zimmerlin A, Urban L and Bojanic D (2008)Evaluation of fluorescence- and mass spectrometry-based CYP inhibition assays for use in drug discovery. JBiomol Screen 13:343-353.

Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN and Bourne PE (2000) TheProtein Data Bank. Nucleic Acids Res 28:235-242.

86

Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, Grimm S, Kao J, King SP, Miwa G, Ni L, Kumar G,McLeod J, Obach RS, Roberts S, Roe A, Shah A, Snikeris F, Sullivan JT, Tweedie D, Vega JM, Walsh J andWrighton SA, Pharmaceutical Research and Manufacturers of America (PhRMA) Drug Metabolism/ClinicalPharmacology Technical Working Group and FDA Center for Drug Evaluation and Research (CDER) (2003)The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research andManufacturers of America (PhRMA) perspective. Drug Metab Dispos 31:815-832.

Bolt HM, Roos PH and Thier R (2003) The cytochrome P-450 isoenzyme CYP2E1 in the biological processingof industrial chemicals: consequences for occupational and environmental medicine. Int Arch Occup EnvironHealth 76:174-85.

Bondoc FY, Bao Z, Hu WY, Gonzalez FJ, Wang Y, Yang CS and Hong JY (1999) Acetone catabolism bycytochrome P450 2E1: studies with CYP2E1-null mice. Biochem Pharmacol 58:461-463.

Boobis A, Watelet JB, Whomsley R, Benedetti MS, Demoly P and Tipton K (2009) Drug interactions. DrugMetab Rev 41:486-527.

Bradford MM, (1976). A rapid and sensitive method for the quantitation of microgram quantities of proteinutilizing the principle of protein-dye binding. Anal Biochem 72:248-254.

Brandon EF, Raap CD, Meijerman I, Beijnen JH and Schellens JH (2003) An update on in vitro test methods inhuman hepatic drug biotransformation research: pros and cons. Toxicol Appl Pharmacol 189:233-246.

Brockmoller J, Kirchheiner J, Meisel C and Roots I (2000) Pharmacogenetic diagnostics of cytochrome P450polymorphisms in clinical drug development and in drug treatment. Pharmacogenomics 1:125-151.

Brown HS, Chadwick A and Houston JB (2007) Use of isolated hepatocyte preparations for cytochrome P450inhibition studies: comparison with microsomes for Ki determination. Drug Metab Dispos 35:2119-2126.

Burke MD, Thompson S, Elcombe CR, Halpert J, Haaparanta T and Mayer RT (1985) Ethoxy-, pentoxy- andbenzyloxyphenoxazones and homologues: a series of substrates to distinguish between different inducedcytochromes P-450. Biochem Pharmacol 34:3337-3345.

Cai X, Wang RW, Edom RW, Evans DC, Shou M, Rodrigues AD, Liu W, Dean DC and Baillie TA (2004)Validation of (-)-N-3-benzyl-phenobarbital as a selective inhibitor of CYP2C19 in human liver microsomes.Drug Metab Dispos 32:584-586.

Chohan KK, Paine SW and Waters NJ (2006) Quantitative structure activity relationships in drug metabolism.Curr Top Med Chem 6:1569-1578.

Chouinard G, Lefko-Singh K and Teboul E, (1999) Metabolism of anxiolytics and hypnotics: benzodiazepines,buspirone, zoplicone, and zolpidem. Cell Mol Neurobiol 19:533-552.

Clark, M, Cramer, RD and Vanopdenbosch, N (1989) Validation of the General-Purpose Tripos 5.2 Force-Field. Journal of Computational Chemistry 10(8):982-1012.

Cramer RI, Patterson D and Bunce J (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shapeon binding of steroids to carrier proteins. J Am Chem Soc 110:5959-5960.

Crespi CL, Miller VP and Stresser DM (2002) Design and application of fluorometric assays for humancytochrome P450 inhibition. Methods Enzymol 357:276-284.

87

Dai R, Pincus MR and Friedman FK (2000) Molecular modeling of mammalian cytochrome P450s. Cell MolLife Sci 57:487-499.

Daly AK (2006) Significance of the minor cytochrome P450 3A isoforms. Clin Pharmacokinet. 45:13-31.

de Graaf C, Vermeulen NP and Feenstra KA (2005) Cytochrome p450 in silico: an integrative modelingapproach. J Med Chem 48:2725-2755.

de Groot MJ (2006) Designing better drugs: predicting cytochrome P450 metabolism. Drug Discov Today11:601-606.

De Rienzo F, Fanelli F, Menziani MC and De Benedetti PG (2000) Theoretical investigation of substratespecificity for cytochromes P450 IA2, P450 IID6 and P450 IIIA4. J Comput Aided Mol Des 14:93-116.

Denisov IG, Makris TM, Sligar SG and Schlichting I (2005) Structure and chemistry of cytochrome P450.Chem Rev 105:2253-2277.

Devore NM, Meneely KM, Bart AG, Stephens ES, Battaile KP and Scott EE (2011) Structural comparison ofcytochromes P450 2A6, 2A13, and 2E1 with pilocarpine. FEBS J

DiMasi JA, Hansen RW and Grabowski HG (2003) The price of innovation: new estimates of drugdevelopment costs. J Health Econ 22:151-185.

DiMasi JA and Grabowski HG (2007) The cost of biopharmaceutical R&D: is biotech different? Manage DecisEcon 28: 469–479.

Du L, Hoffman SM and Keeney DS (2004) Epidermal CYP2 family cytochromes P450. Toxicol ApplPharmacol 195:278-287.

Eaton DL, Gallagher EP, Bammler TK and Kunze KL (1995) Role of cytochrome P4501A2 in chemicalcarcinogenesis: implications for human variability in expression and enzyme activity. Pharmacogenetics5:259-274.

Ekins S and Wrighton SA (1999a) The role of CYP2B6 in human xenobiotic metabolism. Drug Metab Rev31:719-754.

Ekins S, Bravi G, Ring BJ, Gillespie TA, Gillespie JS, Vandenbranden M, Wrighton SA and Wikel JH (1999b)Three-dimensional quantitative structure activity relationship analyses of substrates for CYP2B6. J PharmacolExp Ther 288:21-29.

The European Medicines Agency (EMA) (2010) Guideline on the Investigation of Drug Interactions (draft)http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/05/WC500090112.pdf

Faber MS, Jetter A and Fuhr U (2005) Assessment of CYP1A2 activity in clinical practice: why, how, andwhen? Basic Clin Pharmacol Toxicol 97:125-134.

FDA, Guidance for Industry (2006) Drug interaction studies: study design, data analysis and implications fordosing and labeling (draft)http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM072101.pdf

88

Fernandez-Salguero P and Gonzalez FJ (1995) The CYP2A gene subfamily: species differences, regulation,catalytic activities and role in chemical carcinogenesis. Pharmacogenetics 5:123-8.

Foti RS, Wienkers LC and Wahlstrom JL (2010) Application of cytochrome P450 drug interaction screening indrug discovery. Comb Chem High Throughput Screen 13:145-158.

Fowler S and Zhang H (2008) In vitro evaluation of reversible and irreversible cytochrome P450 inhibition:current status on methodologies and their utility for predicting drug-drug interactions. AAPS J 10:410-424.

Fuhr U, Strobl G, Manaut F, Anders EM, Sorgel F, Lopez-de-Brinas E, Chu DT, Pernet AG, Mahr G and SanzF (1993) Quinolone antibacterial agents: relationship between structure and in vitro inhibition of the humancytochrome P450 isoform CYP1A2. Mol Pharmacol 43:191-199.

Fukuda T, Imai Y, Komori M, Nakamura M, Kusunose E, Satouchi K and Kusunose M (1993) Replacement ofThr-303 of P450 2E1 with serine modifies the regioselectivity of its fatty acid hydroxylase activity. J Biochem113: 7-12.

Fuller RW, Wong DT and Robertson DW (1991) Fluoxetine, a selective inhibitor of serotonin uptake. Med ResRev 11:17-34.

Galetin A, Clarke SE and Houston JB (2003) Multisite kinetic analysis of interactions between prototypicalCYP3A4 subgroup substrates: midazolam, testosterone, and nifedipine. Drug Metab Dispos 31:1108-1116.

Galetin A, Ito K, Hallifax D and Houston B (2005). CYP3A4 substrate selection and substitution in theprediction of potential drug-drug interactions. J Pharm Exp Ther 314:180-190.

Gillam EM and Reilly PE (1988) Phenacetin O-deethylation by human liver microsomes: kinetics andpropranolol inhibition. Xenobiotica 18:95-104.

Goldstein JA and de Morais SM (1994) Biochemistry and molecular biology of the human CYP2C subfamily.Pharmacogenetics 4:285-299.

de Graaf C, Vermeulen NPE and Feenstra KA (2005) Cytochrome P450 in silico: An integrative modelingapproach. J Med Chem. 48:2725-2755

Gram LF, Hansen MG, Sindrup SH, Brosen K, Poulsen JH, Aaes-Jorgensen T and Overo KF (1993)Citalopram: interaction studies with levomepromazine, imipramine, and lithium. Ther Drug Monit 15:18-24.

Greenblatt DJ, von Moltke LL, Schmider J, Harmatz JS and Shader RI (1996) Inhibition of human cytochromeP450-3A isoforms by fluoxetine and norfluoxetine: in vitro and in vivo studies. J Clin Pharmacol 36:792-798.

Guengerich FP (1993) Cytochrome-P450 Enzymes. Am Sci 81:440-447.

Guengerich FP and Johnson WW (1997) Kinetics of ferric cytochrome P450 reduction by NADPH-cytochromeP450 reductase: rapid reduction in the absence of substrate and variations among cytochrome P450 systems.Biochemistry 36:14741-14750.

Guengerich FP (2000) Metabolism of chemical carcinogens. Carcinogenesis 21:345-351.

Guengerich FP (2001) Common and uncommon cytochrome P450 reactions related to metabolism andchemical toxicity. Chem Res Toxicol 14:611-650.

89

Gunes A and Dahl ML (2008) Variation in CYP1A2 activity and its clinical implications: influence ofenvironmental factors and genetic polymorphisms. Pharmacogenomics 9:625-637.

Ha-Duong NT, Dijols S, Macherey AC, Dansette PM and Mansuy D (2001) Inhibition by ticlopidine and itsderivatives of human liver cytochrome p450. Mechanism-based inactivation of CYP 2C19 by ticlopidine. AdvExp Med Biol 500:145-148.

Halgren TA (1996) Merck molecular force field II. MMFF94 van der Waals and electrostatic parameters forintermolecular interaction. J comput chem 17: 520-552

Hansch C and Fujita T (1964) �� �� analysis: a method for the correlation of biological activity and chemicalstructure. J Am Chem Soc 86:1616-1626.

Harlow GR and Halpert JR (1998) Analysis of human cytochrome P450 3A4 cooperativity: construction andcharacterization of a site-directed mutant that displays hyperbolic steroid hydroxylation kinetics. Proc NatlAcad Sci USA 95:6636-6641.

Haufroid V, Ligocka D, Buysschaert M, Horsmans Y and Lison D (2003) Cytochrome P4502E1 (CYP2E1)expression in peripheral blood lymphocytes: evaluation in hepatitis C and diabetes. Eur J Clin Pharmacol59:29-33.

Hesse LM, Venkatakrishnan K, Court MH, von Moltke LL, Duan SX, Shader RI and Greenblatt DJ (2000)CYP2B6 mediates the in vitro hydroxylation of bupropion: potential drug interactions with otherantidepressants. Drug Metab Dispos 28:1176-1183.

Höltje H-D, Sippl W, Rognan D and Folkers G (2003) Molecular modeling. Basic principles and applications.2nd Edition. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany

Honkakoski P and Negishi M (1997) The structure, function, and regulation of cytochrome P450 2A enzymes.Drug Metab Rev 29:977-996.

Houston JB and Kenworthy KE (2000) In vitro-in vivo scaling of CYP kinetic data not consistent with theclassical Michaelis-Menten model. Drug Metab Dispos 28:246-254.

Hukkanen J, Jacob P,3rd and Benowitz NL (2005) Metabolism and disposition kinetics of nicotine. PharmacolRev 57:79-115.

Hutzler JM and Tracy TS (2002) Atypical kinetic profiles in drug metabolism reactions. Drug Metab Dispos30:355-362.

Hutzler M, Messing DM and Wienkers LC (2005) Predicting drug-drug interactions in drug discovery: whereare we now and where are we going? Curr Opin Drug Discov Devel 8:51-58.

Imai Y, Fukuda T, Komori M and Nakamura M (1994) Comparison of heme environment at the putativedistal region of P-450s utilizing their external and internal nitrogenous ligand bound forms. Biochim BiophysActa 1207:49-57.

Ingelman-Sundberg M (2004a) Pharmacogenetics of cytochrome P450 and its applications in drug therapy:the past, present and future. Trends Pharmacol Sci 25:193-200.

Ingelman-Sundberg M (2004b) Human drug metabolising cytochrome P450 enzymes: properties andpolymorphisms. Naunyn Schmiedebergs Arch Pharmacol 369:89-104.

90

Ingelman-Sundberg M (2005) Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinicalconsequences, evolutionary aspects and functional diversity. Pharmacogenomics J 5:6-13.

Jones G, Willett P, Glen RC, Leach AR and Taylor R (1997) Development and validation of a geneticalgorithm for flexible docking. J Mol Biol 267:727-748.

Juvonen RO, Gynther J, Pasanen M, Alhava E and Poso A (2000) Pronounced differences in inhibitionpotency of lactone and non-lactone compounds for mouse and human coumarin 7-hydroxylases (CYP2A5and CYP2A6). Xenobiotica 30:81-92.

Kalgutkar AS, Obach RS and Maurer TS (2007) Mechanism-based inactivation of cytochrome P450 enzymes:chemical mechanisms, structure-activity relationships and relationship to clinical drug-drug interactions andidiosyncratic adverse drug reactions. Curr Drug Metab 8:407-447.

Kapucuoglu N, Coban T, Raunio H, Pelkonen O, Edwards RJ, Boobis AR and Iscan M (2003)Immunohistochemical demonstration of the expression of CYP2E1 in human breast tumour and non-tumourtissues. Cancer Lett 196:153-159.

Kent UM, Juschyshyn MI and Hollenberg PF (2001) Mechanism-based inactivators as probes of cytochromeP450 structure and function. Curr Drug Metab 2:215-243.

Kent UM, Mills DE, Rajnarayanan RV, Alworth WL and Hollenberg PF (2002) Effect of 17-alpha-ethynylestradiol on activities of cytochrome P450 2B (P450 2B) enzymes: characterization of inactivation ofP450s 2B1 and 2B6 and identification of metabolites. J Pharmacol Exp Ther 300:549-558.

Kenworthy KE, Bloomer JC, Clarke SE and Houston JB (1999) CYP3A4 drug interactions: correlation of 10 invitro probe substrates. Br J Clin Pharmacol 48:716-727.

Khojasteh SC, Prabhu S, Kenny JR, Halladay JS and Lu AY (2011) Chemical inhibitors of cytochrome P450isoforms in human liver microsomes: a re-evaluation of P450 isoform selectivity. Eur J Drug MetabPharmacokinet 36:1-16.

Kinonen T, Pasanen M, Gynther J, Poso A, Jarvinen T, Alhava E and Juvonen RO (1995) Competitiveinhibition of coumarin 7-hydroxylation by pilocarpine and its interaction with mouse CYP 2A5 and humanCYP 2A6. Br J Pharmacol 116:2625-2630.

Kirton SB, Murray CW, Verdonk ML and Taylor RD (2005) Prediction of binding modes for ligands in thecytochromes P450 and other heme-containing proteins. Proteins 58:836-844.

Kitada M and Kamataki T (1994) Cytochrome P450 in human fetal liver: significance and fetal-specificexpression. Drug Metab Rev 26:305-323.

Ko JW, Desta Z, Soukhova NV, Tracy T and Flockhart DA (2000) In vitro inhibition of the cytochrome P450(CYP450) system by the antiplatelet drug ticlopidine: potent effect on CYP2C19 and CYP2D6. Br J ClinPharmacol 49:343-351.

Korzekwa KR, Krishnamachary N, Shou M, Ogai A, Parise RA, Rettie AE, Gonzalez FJ and Tracy TS (1998)Evaluation of atypical cytochrome P450 kinetics with two-substrate models: evidence that multiple substratescan simultaneously bind to cytochrome P450 active sites. Biochemistry 37:4137-4147.

91

Koskela S, Hakkola J, Hukkanen J, Pelkonen O, Sorri M, Saranen A, Anttila S, Fernandez-Salguero P,Gonzalez F and Raunio H (1999) Expression of CYP2A genes in human liver and extrahepatic tissues.Biochem Pharmacol 57:1407-1413.

Kostiainen R, Kotiaho T, Kuuranne T and Auriola S (2003) Liquid chromatography/atmospheric pressureionization-mass spectrometry in drug metabolism studies. J Mass Spectrom 38:357–372

Kramer MA and Tracy TS (2008) Studying cytochrome P450 kinetics in drug metabolism. Expert Opin DrugMetab Toxicol 4:591-603.

Kremers P (2002) In vitro tests for predicting drug-drug interactions: the need for validated procedures.Pharmacol Toxicol 91:209-217.

Lang T, Klein K, Fischer J, Nussler AK, Neuhaus P, Hofmann U, Eichelbaum M, Schwab M and Zanger UM(2001) Extensive genetic polymorphism in the human CYP2B6 gene with impact on expression and functionin human liver. Pharmacogenetics 11:399-415.

Laskowski RA, Macarthur MW, Moss DS and Thornton JM (1993) Procheck - a Program to Check theStereochemical Quality of Protein Structures. Journal of Applied Crystallography 26:283-291.

Lee H, Yeom H, Kim YG, Yoon CN, Jin C, Choi JS, Kim BR and Kim DH (1998) Structure-related inhibition ofhuman hepatic caffeine N3-demethylation by naturally occurring flavonoids. Biochem Pharmacol 55:1369-1375.

Lee CR, Goldstein JA and Pieper JA (2002) Cytochrome P450 2C9 polymorphisms: a comprehensive review ofthe in-vitro and human data. Pharmacogenetics 12:251-263.

Lerche-Langrand C and Toutain HJ (2000) Precision-cut liver slices: characteristics and use for in vitropharmaco-toxicology. Toxicology 153:221-253.

Lewis DF, Eddershaw PJ, Goldfarb PS and Tarbit MH (1996) Molecular modelling of CYP3A4 from analignment with CYP102: identification of key interactions between putative active site residues and CYP3A-specific chemicals. Xenobiotica 26:1067-1086.

Lewis DF and Pratt JM (1998) The P450 catalytic cycle and oxygenation mechanism. Drug Metab Rev 30:739-786.

Lewis DF (2000) On the recognition of mammalian microsomal cytochrome P450 substrates and theircharacteristics: towards the prediction of human p450 substrate specificity and metabolism. BiochemPharmacol 60:293-306.

Lewis DF, Bird MG, Dickins M, Lake BG, Eddershaw PJ, Tarbit MH and Goldfarb PS (2000) Molecularmodelling of human CYP2E1 by homology with the CYP102 haemoprotein domain: investigation of theinteractions of substrates and inhibitors within the putative active site of the human CYP2E1 isoform.Xenobiotica 30:1-25.

Lewis DF, Sams C and Loizou GD (2003a) A quantitative structure-activity relationship analysis on a series ofalkyl benzenes metabolized by human cytochrome p450 2E1. J Biochem Mol Toxicol 17:47-52.

92

Lewis DF, Lake BG, Bird MG, Loizou GD, Dickins M and Goldfarb PS (2003b) Homology modelling ofhuman CYP2E1 based on the CYP2C5 crystal structure: investigation of enzyme-substrate and enzyme-inhibitor interactions. Toxicol In Vitro 17:93-105.

Lewis DF, Jacobs MN and Dickins M (2004) Compound lipophilicity for substrate binding to human P450s indrug metabolism. Drug Discov Today 9:530-537.

Li AP (2001) Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today 6:357-366.

Li XQ, Bjorkman A, Andersson TB, Ridderstrom M and Masimirembwa CM (2002) Amodiaquine clearanceand its metabolism to N-desethylamodiaquine is mediated by CYP2C8: a new high affinity and turnoverenzyme-specific probe substrate. J Pharmacol Exp Ther 300:399-407.

Lieber CS (1999) Microsomal ethanol-oxidizing system (MEOS): the first 30 years (1968-1998)--a review.Alcohol Clin Exp Res 23:991-1007

Lin HL, Zhang H and Hollenberg PF (2009) Metabolic activation of mifepristone [RU486; 17beta-hydroxy-11beta-(4-dimethylaminophenyl)-17alpha-(1-propynyl)-estra-4,9-dien -3-one] by mammalian cytochromesP450 and the mechanism-based inactivation of human CYP2B6. J Pharmacol Exp Ther 329:26-37.

Linder MW, Prough RA and Valdes R,Jr (1997) Pharmacogenetics: a laboratory tool for optimizingtherapeutic efficiency. Clin Chem 43:254-266.

Locuson CW 2nd, Wahlstrom JL, Rock DA, Rock DA and Jones JP (2003) A new class of CYP2C9 inhibitors:probing 2C9 specificity with high-affinity benzbromarone derivatives. Drug Metab Dispos 31:967-971.

Loizou GD and Cocker J (2001) The effects of alcohol and diallyl sulphide on CYP2E1 activity in humans: aphenotyping study using chlorzoxazone. Hum Exp Toxicol 20:321-327.

Lozano JJ, Pastor M, Cruciani G, Gaedt K, Centeno NB, Gago F and Sanz F (2000) 3D-QSAR methods on thebasis of ligand-receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series ofCYP1A2 ligands. J Comput Aided Mol Des 14:341-353.

Lucas D, Ferrara R, Gonzales E, Albores A, Manno M and Berthou F (2001) Cytochrome CYP2E1phenotyping and genotyping in the evaluation of health risks from exposure to polluted environments.Toxicol Lett 124:71-81.

Mancy A, Dijols S, Poli S, Guengerich P and Mansuy D (1996) Interaction of sulfaphenazole derivatives withhuman liver cytochromes P450 2C: molecular origin of the specific inhibitory effects of sulfaphenazole onCYP 2C9 and consequences for the substrate binding site topology of CYP 2C9. Biochemistry 35:16205-16212.

Mestres J (2005) Structure conservation in cytochromes P450. Proteins 58:596-609.

Miller VP, Stresser DM, Blanchard AP, Turner S and Crespi CL (2000) Fluorometric high-throughputscreening for inhibitors of cytochrome P450. Ann N Y Acad Sci 919:26-32.

Miners JO and Birkett DJ (1998) Cytochrome P4502C9: an enzyme of major importance in human drugmetabolism. Br J Clin Pharmacol 45:525-538.

Mo SL, Zhou ZW, Yang LP, Wei MQ and Zhou SF (2009a) New insights into the structural features andfunctional relevance of human cytochrome P450 2C9. Part I. Curr Drug Metab 10:1075-1126.

93

Mo SL, Liu YH, Duan W, Wei MQ, Kanwar JR and Zhou SF (2009b) Substrate specificity, regulation, andpolymorphism of human cytochrome P450 2B6. Curr Drug Metab 10:730-753.

Moon T, Chi MH, Kim D, Yoon CN and Choi Y (2000) Quantitative structure-activity relationship (QSAR)study of flavonoid derivatives for inhibition of cytochrome P450 1A2. Quant Struct-Act Relat 19:257-263.

Moreno RL, Goosen T, Kent UM, Chung FL and Hollenberg PF (2001) Differential effects of naturallyoccurring isothiocyanates on the activities of cytochrome P450 2E1 and the mutant P450 2E1 T303A. ArchBiochem Biophys 391:99-110.

Murdock DJ, Clarke J, Flatt PR, Barnett YA and Barnett CR (2004) Role of CYP2E1 in ketone-stimulatedinsulin release in pancreatic B-cells. Biochem Pharmacol 67:875-884.

Murray GI, Melvin WT, Greenlee WF and Burke MD (2001) Regulation, function, and tissue-specificexpression of cytochrome P450 CYP1B1. Annu Rev Pharmacol Toxicol 41:297-316.

Nebert DW, Roe AL, Dieter MZ, Solis WA, Yang Y and Dalton TP (2000) Role of the aromatic hydrocarbonreceptor and [Ah] gene battery in the oxidative stress response, cell cycle control, and apoptosis. BiochemPharmacol 59:65-85.

Nebert DW and Russell DW (2002) Clinical importance of the cytochromes P450. Lancet 360:1155-1162.

Nebert DW, Dalton TP, Okey AB and Gonzalez FJ (2004) Role of aryl hydrocarbon receptor-mediatedinduction of the CYP1 enzymes in environmental toxicity and cancer. J Biol Chem 279:23847-23850.

Neidle S and Thurston DE (2005) Chemical approaches to the discovery and development of cancertherapies. Nat Rev Cancer 5:285-296.

Nelson DR, Koymans L, Kamataki T, Stegeman JJ, Feyereisen R, Waxman DJ, Waterman MR, Gotoh O, CoonMJ, Estabrook RW, Gunsalus IC and Nebert DW (1996) P450 superfamily: update on new sequences, genemapping, accession numbers and nomenclature. Pharmacogenetics 6:1-42.

Nelson, DR (2009) The Cytochrome P450 Homepage. Human Genomics 4:59-65

Obach RS, Zhang QY, Dunbar D and Kaminsky LS (2001) Metabolic characterization of the major humansmall intestinal cytochrome p450s. Drug Metab Dispos 29:347-352.

Obach RS, Walsky RL, Venkatakrishnan K, Houston JB and Tremaine LM (2005) In vitro cytochrome P450inhibition data and the prediction of drug-drug interactions: qualitative relationships, quantitativepredictions, and the rank-order approach. Clin Pharmacol Ther 78:582-592.

Ogawa R and Echizen H (2010) Drug-drug interaction profiles of proton pump inhibitors. ClinPharmacokinet 49:509-533.

Ohashi Y, Yamada K, Takemoto I, Mizutani T and Saeki K (2005) Inhibition of human cytochrome P450 2E1by halogenated anilines, phenols, and thiophenols. Biol Pharm Bull 28:1221-1223.

Otyepka M, Skopalik J, Anzenbacherova E and Anzenbacher P (2007) What common structural features andvariations of mammalian P450s are known to date? Biochim Biophys Acta 1770:376-389.

94

Paris BL, Ogilvie BW, Scheinkoenig JA, Ndikum-Moffor F, Gibson R and Parkinson A (2009) In vitroinhibition and induction of human liver cytochrome p450 enzymes by milnacipran. Drug Metab Dispos37:2045-2054.

Park JY and Harris D (2003) Construction and assessment of models of CYP2E1: predictions of metabolismfrom docking, molecular dynamics, and density functional theoretical calculations. J Med Chem 46:1645-1660.

Pastor M, Cruciani G and Clementi S (1997) Smart region definition: a new way to improve the predictiveability and interpretability of three-dimensional quantitative structure-activity relationships. J Med Chem40:1455-1464.

Pelkonen O, Mäenpää J, Taavitsainen P, Rautio A and Raunio H (1998) Inhibition and induction of humancytochrome P450 (CYP) enzymes. Xenobiotica 28:1203-1253.

Pelkonen O and Raunio H (2005) In vitro screening of drug metabolism during drug development: can wetrust the predictions? Expert Opin Drug Metab Toxicol 1:49-59.

Pelkonen O, Turpeinen M, Uusitalo J, Rautio A and Raunio H (2005) Prediction of drug metabolism andinteractions on the basis of in vitro investigations. Basic Clin Pharmacol Toxicol 96:167-175.

Pelkonen O, Turpeinen M, Hakkola J, Honkakoski P, Hukkanen J and Raunio H (2008) Inhibition andinduction of human cytochrome P450 enzymes: current status. Arch Toxicol 82:667-715.

Pelkonen O, Turpeinen M and Raunio H (2011) In vivo-in vitro-in silico pharmacokinetic modelling in drugdevelopment: current status and future directions. Clin Pharmacokinet 50:483-491

Pirmohamed M and Park BK (2003) Cytochrome P450 enzyme polymorphisms and adverse drug reactions.Toxicology 192:23-32.

Poloyac SM, Tortorici MA, Przychodzin DI, Reynolds RB, Xie W and Frye RF (2004) The effect of isoniazid onCYP2E1- and CYP4A-mediated hydroxylation of arachidonic acid in the rat liver and kidney. Drug MetabDispos 32:727-733.

Porubsky PR, Battaile KP and Scott EE (2010) Human cytochrome P450 2E1 structures with fatty acid analogsreveal a previously unobserved binding mode. J Biol Chem 285:22282-22290.

Porubsky PR, Meneely KM and Scott EE (2008) Structures of human cytochrome P-450 2E1. Insights into thebinding of inhibitors and both small molecular weight and fatty acid substrates. J Biol Chem 283:33698-33707.

Pratt-Hyatt M, Lin HL and Hollenberg PF (2010) Mechanism-based inactivation of human CYP2E1 bydiethyldithocarbamate. Drug Metab Dispos 38:2286-92.

Rae JM, Soukhova NV, Flockhart DA and Desta Z (2002) Triethylenethiophosphoramide is a specific inhibitorof cytochrome P450 2B6: implications for cyclophosphamide metabolism. Drug Metab Dispos 30:525-530.

Rahman A, Korzekwa KR, Grogan J, Gonzalez FJ and Harris JW (1994) Selective biotransformation of taxol to6 alpha-hydroxytaxol by human cytochrome P450 2C8. Cancer Res 54:5543-5546.

Rahnasto M, Raunio H, Poso A, Wittekindt C and Juvonen RO (2005) Quantitative structure-activityrelationship analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. J Med Chem 48:440-449.

95

Rahnasto MK, Raunio HA, Wittekindt C, Salminen KA, Leppanen J, Juvonen RO, Poso A and Lahtela-Kakkonen MK (2011) Identification of novel CYP2A6 inhibitors by virtual screening. Bioorg Med Chem19:7186-7193.

Raucy JL, Schultz ED, Wester MR, Arora S, Johnston DE, Omdahl JL and Carpenter SP (1997) Humanlymphocyte cytochrome P450 2E1, a putative marker for alcohol-mediated changes in hepatic chlorzoxazoneactivity. Drug Metab Dispos 25:1429-1435.

Raucy JL, Mueller L, Duan K, Allen SW, Strom S and Lasker JM (2002) Expression and induction of CYP2CP450 enzymes in primary cultures of human hepatocytes. J Pharmacol Exp Ther 302:475-482.

Raunio H, Rautio A, Gullsten H and Pelkonen O (2001) Polymorphisms of CYP2A6 and its practicalconsequences. Br J Clin Pharmacol 52:357-363.

Raunio H, Taavitsainen P, Honkakoski P, Juvonen R and Pelkonen O (2004) In vitro methods in the predictionof kinetics of drugs: focus on drug metabolism. Altern Lab Anim 32:425-430.

Rendic S (2002) Summary of information on human CYP enzymes: human P450 metabolism data. DrugMetab Rev 34:83-448.

Rendic S and Guengerich FP (2010) Update information on drug metabolism systems--2009, part II: summaryof information on the effects of diseases and environmental factors on human cytochrome P450 (CYP)enzymes and transporters. Curr Drug Metab 11:4-84.

Richter T, Murdter TE, Heinkele G, Pleiss J, Tatzel S, Schwab M, Eichelbaum M and Zanger UM (2004) Potentmechanism-based inhibition of human CYP2B6 by clopidogrel and ticlopidine. J Pharmacol Exp Ther308:189-197.

Rowland K, Yeo WW, Ellis SW, Chadwick IG, Haq I, Lennard MS, Jackson PR, Ramsay LE and Tucker GT(1994) Inhibition of CYP2D6 activity by treatment with propranolol and the role of 4-hydroxy propranolol. BrJ Clin Pharmacol 38:9-14.

Sachse C, Brockmoller J, Bauer S and Roots I (1997) Cytochrome P450 2D6 variants in a Caucasianpopulation: allele frequencies and phenotypic consequences. Am J Hum Genet 60:284-295.

Sansen S, Yano JK, Reynald RL, Schoch GA, Griffin KJ, Stout CD and Johnson EF (2007) Adaptations for theoxidation of polycyclic aromatic hydrocarbons exhibited by the structure of human P450 1A2. J Biol Chem282:14348-14355.

Sanz F, Lopez-de- Brinas E, Rodriques J and Manaut F (1994) Theoretical study on the metabolism of caffeineby cytochrome P-450 1A2 and its inhibition. Quant Struct -Act Relat 281-284.

Sergentanis TN and Economopoulos KP (2010) Four polymorphisms in cytochrome P450 1A1 (CYP1A1) geneand breast cancer risk: a meta-analysis. Breast Cancer Res Treat 122:459-469.

Shader RI, Greenblatt DJ, von Moltke LL (1994) Fluoxetine inhibition of phenytoin metabolism. J ClinPsychopharmacol 14:375-376.

Shah MB, Pascual J, Zhang Q, Stout CD and Halpert JR (2011) Structures of cytochrome P450 2B6 bound to 4-benzylpyridine and 4-(4-nitrobenzyl)pyridine: insight into inhibitor binding and rearrangement of active siteside chains. Mol Pharmacol 80:1047-1055.

96

Shebley M, Kent UM, Ballou DP and Hollenberg PF (2009) Mechanistic analysis of the inactivation ofcytochrome P450 2B6 by phencyclidine: effects on substrate binding, electron transfer, and uncoupling. DrugMetab Dispos 37:745-752.

Shi X, Zhou S, Wang Z, Zhou Z and Wang Z (2008) CYP1A1 and GSTM1 polymorphisms and lung cancerrisk in Chinese populations: a meta-analysis. Lung Cancer 59:155-163.

Shimada T, Yamazaki H, Mimura M, Inui Y and Guengerich FP (1994) Interindividual variations in humanliver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studieswith liver microsomes of 30 Japanese and 30 Caucasians. J Pharmacol Exp Ther 270:414-423.

Shou M, Grogan J, Mancewicz JA, Krausz KW, Gonzalez FJ, Gelboin HV and Korzekwa KR (1994) Activationof CYP3A4: evidence for the simultaneous binding of two substrates in a cytochrome P450 active site.Biochemistry 33:6450-6455.

Shou M, Dai R, Cui D, Korzekwa KR, Baillie TA and Rushmore TH (2001) A kinetic model for the metabolicinteraction of two substrates at the active site of cytochrome P450 3A4. J Biol Chem 276:2256-2262.

Siu EC and Tyndale RF (2008) Selegiline is a mechanism-based inactivator of CYP2A6 inhibiting nicotinemetabolism in humans and mice. J Pharmacol Exp Ther 324:992-999.

Song BJ, Veech RL, Park SS, Gelboin HV and Gonzalez FJ (1989) Induction of rat hepatic N-nitrosodimethylamine demethylase by acetone is due to protein stabilization. J Biol Chem 264:3568-3572.

Sonne J, Loft S, Dossing M, Vollmer-Larsen A, Olesen KL, Victor M, Andreasen F and Andreasen PB (1988)Bioavailability and pharmacokinetics of oxazepam. Eur J Clin Pharmacol 35:385-389.

Sotaniemi EA, Anttila M, Pelkonen RO, Järvensivu P and Sundquist H (1979) Plasma clearance of propranololand sotalol and hepatic drug-metabolizing enzyme activity. Clin Pharmacol Ther 26:153-161.

Spatzenegger M, Liu H, Wang Q, Debarber A, Koop DR and Halpert JR (2003) Analysis of differentialsubstrate selectivities of CYP2B6 and CYP2E1 by site-directed mutagenesis and molecular modeling. JPharmacol Exp Ther 304:477-487.

Stevens JC and Wrighton SA (1993) Interaction of the enantiomers of fluoxetine and norfluoxetine withhuman liver cytochromes P450. J Pharmacol Exp Ther 266:964-971.

Stockley IH (2004) Drug Interactions, 6th Edition. The Pharmaceutical Press, London.

Suzuki H, Kneller MB, Haining RL, Trager WF and Rettie AE (2002) (+)-N-3-Benzyl-nirvanol and (-)-N-3-benzyl-phenobarbital: new potent and selective in vitro inhibitors of CYP2C19. Drug Metab Dispos 30:235-239.

Taavitsainen P, Juvonen R and Pelkonen O (2001) In vitro inhibition of cytochrome P450 enzymes in humanliver microsomes by a potent CYP2A6 inhibitor, trans-2-phenylcyclopropylamine (tranylcypromine), and itsnonamine analog, cyclopropylbenzene. Drug Metab Dispos 29:217-222.

Takahashi H, Sato T, Shimoyama Y, Shioda N, Shimizu T, Kubo S, Tamura N, Tainaka H, Yasumori T andEchizen H (1999) Potentiation of anticoagulant effect of warfarin caused by enantioselective metabolicinhibition by the uricosuric agent benzbromarone. Clin Pharmacol Ther 66:569-581.

97

Tracy TS (2003) Atypical enzyme kinetics: their effect on in vitro-in vivo pharmacokinetic predictions anddrug interactions. Curr Drug Metab 4:341-346.

Trubetskoy OV, Gibson JR and Marks BD (2005) Highly miniaturized formats for in vitro drug metabolismassays using vivid fluorescent substrates and recombinant human cytochrome P450 enzymes. J BiomolScreen 10:56-66.

Turpeinen M, Nieminen R, Juntunen T, Taavitsainen P, Raunio H and Pelkonen O (2004) Selective inhibitionof CYP2B6-catalyzed bupropion hydroxylation in human liver microsomes in vitro. Drug Metab Dispos32:626-631.

Turpeinen M, Uusitalo J, Jalonen J and Pelkonen O (2005). Multiple 450 substrates in single run: rapid andcomprehensive in vitro interaction assay. Eur J Pharm Sci 24:123-132.

Turpeinen M, Raunio H and Pelkonen O (2006) The functional role of CYP2B6 in human drug metabolism:substrates and inhibitors in vitro, in vivo and in silico. Curr Drug Metab 7:705-714.

VandenBranden M, Wrighton SA, Ekins S, Gillespie JS, Binkley SN, Ring BJ, Gadberry MG, Mullins DC,Strom SC and Jensen CB (1998) Alterations of the catalytic activities of drug-metabolizing enzymes incultures of human liver slices. Drug Metab Dispos 26:1063-1068.

VandenBrink BM and Isoherranen N (2010) The role of metabolites in predicting drug-drug interactions:focus on irreversible cytochrome P450 inhibition. Curr Opin Drug Discov Devel 13:66-77.

Varela-Lema L, Taioli E, Ruano-Ravina A, Barros-Dios JM, Anantharaman D, Benhamou S, Boccia S, BhiseyRA, Cadoni G, Capoluongo E, Chen CJ, Foulkes W, Goloni-Bertollo EM, Hatagima A, Hayes RB, Katoh T,Koifman S, Lazarus P, Manni JJ, Mahimkar M, Morita S, Park J, Park KK, Pavarino Bertelli EC, de SouzaFonseca Ribeiro EM, Roy B, Spitz MR, Strange RC, Wei Q and Ragin CC (2008) Meta-analysis and pooledanalysis of GSTM1 and CYP1A1 polymorphisms and oral and pharyngeal cancers: a HuGE-GSEC review.Genet Med 10:369-384.

Verdonk ML, Cole JC, Hartshorn MJ, Murray CW and Taylor RD (2003) Improved protein-ligand dockingusing GOLD. Proteins 52:609-623.

Verma J, Khedkar VM and Coutinho EC (2010) 3D-QSAR in drug design--a review. Curr Top Med Chem10:95-115.

Wade RC, Clark KJ and Goodford PJ (1993) Further development of hydrogen bond functions for use indetermining energetically favorable binding sites on molecules of known structure. 1. Ligand probe groupswith the ability to form two hydrogen bonds. J Med Chem 36:140-147.

Wade RC, Motiejunas D, Schleinkofer K, Sudarko, Winn PJ, Banerjee A, Kariakin A and Jung C (2005)Multiple molecular recognition mechanisms. Cytochrome P450--a case study. Biochim Biophys Acta1754:239-244.

Walsky RL, Obach RS, Gaman EA, Gleeson JP and Proctor WR (2005) Selective inhibition of humancytochrome P4502C8 by montelukast. Drug Metab Dispos 33:413-418.

Wang B, Wang J, Huang SQ, Su HH and Zhou SF (2009) Genetic polymorphism of the human cytochromeP450 2C9 gene and its clinical significance. Curr Drug Metab 10:781-834.

98

Wang J (2009) Comprehensive assessment of ADMET risks in drug discovery. Curr Pharm Des 15:2195-2219.

Wang JF, Zhang CC, Chou KC and Wei DQ (2009) Structure of cytochrome p450s and personalized drug.Curr Med Chem 16:232-244.

Wang JS, Neuvonen M, Wen X, Backman JT and Neuvonen PJ (2002) Gemfibrozil inhibits CYP2C8-mediatedcerivastatin metabolism in human liver microsomes. Drug Metab Dispos 30:1352-1356.

Wang MH, Wade D, Chen L, White S and Yang CS (1995) Probing the active sites of rat and humancytochrome P450 2E1 with alcohols and carboxylic acids. Arch Biochem Biophys 317:299-304.

Wang RW, Newton DJ, Liu N, Atkins WM and Lu AY (2000) Human cytochrome P-450 3A4: in vitro drug-drug interaction patterns are substrate-dependent. Drug Metab Dispos 28:360-366.

Wang Q and Halpert JR (2002) Combined three-dimensional quantitative structure-activity relationshipanalysis of cytochrome P450 2B6 substrates and protein homology modeling. Drug Metab Dispos 30:86-95.

Wen X, Wang JS, Neuvonen PJ and Backman JT (2002) Isoniazid is a mechanism-based inhibitor ofcytochrome P450 1A2, 2A6, 2C19 and 3A4 isoforms in human liver microsomes. Eur J Clin Pharmacol 57:799-804.

White RE (2000) High-throughput screening in drug metabolism and pharmacokinetic support of drugdiscovery. Annu Rev Pharmacol Toxicol 40:133-157.

Wienkers LC, Wurden CJ, Storch E, Kunze KL, Rettie AE and Targer WE (1996). Formation of (R)-8-hydroxywarfarin in human liver microsomes. A new metabolic marker for the (S)-mephenytoin hydoxylase,P4502C19. Drug Metab Dispos 24: 610–614.

Wienkers LC and Heath TG (2005) Predicting in vivo drug interactions from in vitro drug discovery data. NatRev Drug Discov 4:825-833.

Wrighton SA and Stevens JC (1992) The human hepatic cytochromes P450 involved in drug metabolism. CritRev Toxicol. 22:1-21.

Wrighton SA, Schuetz EG, Thummel KE, Shen DD, Korzekwa KR and Watkins PB (2000) The human CYP3Asubfamily: practical considerations. Drug Metab Rev 32:339-361.

Wu B, Sun J, Cheng SP, Gu JD, Li AM and Zhang XX (2011) Comparative analysis of binding affinitiesbetween styrene and mammalian CYP2E1 by bioinformatics approaches. Ecotoxicology 20:1041-1046.

Yamazoe Y, Ito K and Yoshinari K (2011) Construction of a CYP2E1-template system for prediction of themetabolism on both site and preference order. Drug Metab Rev 43:409-439.

Yano JK, Wester MR, Schoch GA, Griffin KJ, Stout CD and Johnson EF (2004) The structure of humanmicrosomal cytochrome P450 3A4 determined by X-ray crystallography to 2.05-A resolution. J Biol Chem279:38091-38094.

Yoo HH, Lee MW, Kim YC, Yun CH and Kim DH (2007) Mechanism-based inactivation of cytochrome P4502A6 by decursinol angelate isolated from Angelica Gigas. Drug Metab Dispos 35:1759-1765.

99

Zaphiropoulos PG (1997) Exon skipping and circular RNA formation in transcripts of the human cytochromeP-450 2C18 gene in epidermis and of the rat androgen binding protein gene in testis. Mol Cell Biol 17:2985-2993.

Zhang L, Reynolds KS, Zhao P and Huang SM (2010) Drug interactions evaluation: an integrated part of riskassessment of therapeutics. Toxicol Appl Pharmacol 243:134-145.

Zhou SF (2008) Drugs behave as substrates, inhibitors and inducers of human cytochrome P450 3A4. CurrDrug Metab 9:310-322.

Zhou SF, Zhou ZW, Yang LP, and Cai JP (2009a) Substrates, inducers, inhibitors and structure-activityrelationships of human Cytochrome P450 2C9 and implications in drug development. Curr Med Chem16:3480-675.

Zhou SF, Liu JP and Lai XS (2009b) Substrate specificity, inhibitors and regulation of human cytochrome P4502D6 and implications in drug development. Curr Med Chem 16:2661-2805.

Zhou SF, Yang LP, Zhou ZW, Liu YH and Chan E (2009c) Insights into the substrate specificity, inhibitors,regulation, and polymorphisms and the clinical impact of human cytochrome P450 1A2. AAPS J 11:481-94.

Zhou ZW and Zhou SF (2009) Application of mechanism-based CYP inhibition for predicting drug-druginteractions. Expert Opin Drug Metab Toxicol 5:579-605.

Zhuo WL, Zhang YS, Wang Y, Zhuo XL, Zhu B, Cai L and Chen ZT (2009) Association studies of CYP1A1and GSTM1 polymorphisms with esophageal cancer risk: evidence-based meta-analyses. Arch Med Res40:169-179.

Publications of the University of Eastern Finland

Dissertations in Health Sciences

isbn 978-952-61-0754-7

Publications of the University of Eastern FinlandDissertations in Health Sciences

dissertatio

ns | 10

8 | Lau

ra M

ar

tika

inen

| In Vitro an

d in Silico M

ethods to P

redict Cytochrom

e P450 E

nzym

e Inhibition

Laura MartikainenIn Vitro and in Silico Methods

to Predict Cytochrome P450 Enzyme Inhibition Laura Martikainen

In Vitro and in Silico Methodsto Predict Cytochrome P450 Enzyme Inhibition

Cytochrome P450 (CYP) enzymes are

important enzymes involved in drug

metabolism. In the present study, the

overall goal was to develop novel in

vitro and in silico methods to assess

the metabolic features of xenobiotics.

In vitro high throughput screening

and in silico studies can provide early

prediction of the possible involve-

ment of CYP enzymes in the metabo-

lism of drugs or drug candidates.

Therefore these studies could poten-

tially make drug design more effec-

tive, less costly and improve drug

safety and replace some of the in vivo

experiments.