A new vision of strategic collaborations

However, there is a new vision where strategic

collaborations between pharmaceutical

companies, government agencies, venture

capital-backed biotechnology companies and

academic medical centres will create a new

breadth of approaches that have a good chance

of increasing the innovation that has been

stagnant in recent years5-8. Government funding

agencies are increasing the emphasis on

translational research, even in a period of

funding pressures. Academic medical centres

are also increasing their efforts in translational

science, while continuing to create new tech -

nologies and basic knowledge. Life sciences

and diagnostic companies have recognised

the emergence of personalised or precision

medicine and have begun commercialising

the ‘omics’ platforms required for realising

personalised (precision) medicine. There is also a

heightened focus on coupling diagnostic /

prognostic tests with new therapeutics to

identify the patient sub-populations that will

benefit from a treatment. This evolution of the

pharmaceutical industry offers opportunities

and challenges to the potential partners.

It is necessary to harness the expertise of the

collaborating partners and to be innovative in

focused efforts in order to gain the advantage of

collaboration. All potential partners must be

willing to negotiate terms of agreements that

benefit all partners. In many cases, this will

require modifications of institutional policies

created in a very different era. In a new

environment of collaboration, each potential

partner must also identify and optimise the

strengths of their organisation to make it a good

complementary fit for potential partners.

Academic medical centres are in a great position

to take part in the collaborative business model

of drug discovery and development, clinical

trials and diagnostics, if they are willing to

negotiate mutually beneficial terms, including

ownership / licensing of intellectual property

arising from the collaboration.

The importance of adopting the collabor -

ative business model to increase the potential

for innovation is reflected in the fact that the

dominant paradigm in drug discovery for

the last 25 years has been the target-centric

model or the one gene, one target, one New

Chemical Entity (NCE) approach. Most

pharmaceutical companies embraced this

philosophy due to the expectation of a near-

term impact of the human genome project,

technological advances in screening auto -

mation, creation of large chemical libraries,

increases in the number of solutions to protein

The pharmaceutical industry has experienced a decade of turbulence driven by the

‘patent cliff’ as major revenue generators are lost to generic status, coupled to

the absence of a sustainable pipeline of drug candidates in development that have

a good chance of being approved and launched1,2

. It is generally agreed that the

lowest hanging drug discovery ‘fruit’ has been harvested and the industry is

addressing diseases that are more complex. The current one target, one drug

discovery and development paradigm continues to exhibit more than 90 per cent

attrition mainly due to the lack of success in translating preclinical efficacy and

safety data into successful human trials1

. It has also become clear that efficient

drug discovery and development requires a deeper understanding of the complexity

of human biology early in the process3

. The high attrition rates increase the costs

and with the science indicating that precision therapeutics will replace the

blockbuster model4

, the challenge of drug discovery and development is even

greater. The traditional business model of pharmaceutical companies working in

silos is no longer sustainable.

A new vision of drugdiscovery and development

D. Lansing Taylor

Director, University of Pittsburgh Drug Discovery Institute and Allegheny Foundation Professor of Computational and Systems Biology, University of Pittsburgh School of Medicine

DRUG DISCOVERY

European Pharmaceutical Review

Volume 17 | Issue 6 | 2012 20

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structures and the creation of in silico software tools. A major

challenge has been the slow and incomplete determination of the

relationship between the disease genotype and the phenotype. It is

now clear that putting most of the intellectual and experimental

‘eggs’ in this one basket has not produced the predicted productivity1.

A road to higher innovation

High Throughput Screening (HTS) had become the standard in

target-centric discovery programs by the early 1990s, but in the late

1990s, High Content Screening (HCS) was introduced as an

alternative or at least a complement to HTS9,10. The concept of HCS

was to fully automate fluorescence microscopic analysis of cells and

later small experimental organisms, allowing the creation of deeper

biological knowledge based on the physiological disease relevance

to better guide decisions on the progression of compounds.

HCS offered the opportunity to initiate discovery based on a disease

phenotype and not a specific molecular target. However, most

discovery programs in the early days used HCS only in secondary

assays and to follow a specific molecular target activity, since the

speed of reading primary assays using HTS was viewed as paramount.

However, given the late-stage failure of drug candidates, especially in

phase II clinical trials1, the speed of reading a plate in discovery is

insignificant compared to the time to make good decisions on

progressing candidates based on the necessary biological and

chemical knowledge11. Importantly, it has been demonstrated

that phenotypic discovery was the most successful approach for

first-in-class small molecule drugs approved by the US Food and Drug

Administration (FDA) between 1999 and 200812,13.

Combining the application of multiplexed HCS to more disease

relevant (and more complex) biological screening models such as

mixed cell cultures, 3D human tissue engineered models of organs

and small experimental organisms such as yeast, C. elegans, and

zebrafish should have a major impact in the early discovery

process14,15. Especially promising for the future is the potential of

human induced pluripotent stem cells (iPSCs) from patients with a

range of genomic backgrounds to serve as the source for disease

relevant human tissue models to be used in discovery and early

development16. Success here could have a major impact in optimising

efficacy, as well as pharmacodynamics (PD) and pharmacokinetics

(PK) and could minimise animal studies in pre-clinical trials after

proper validation. It will also be critical to expand the use of

computational chemistry and biology together with systems biology,

in parallel with the improvement of the experimental systems used in

phenotypic discovery17-19. Generation of complex data sets

integrating the available ‘omics’ information requires computational

methods to reduce the data to knowledge.

A critical role for quantitative systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging field that

integrates experimentation, including screening data, with

computational tools to define the actions of drugs across multiple

scales of biological complexity from molecules to cells, tissues, organs

and organisms for a ‘systems’ level perspective20-22. QSP is the merger

of pharmacology, especially pharmacokinetics (PK) and

pharmacodynamics (PD) with the field of systems biology23.

European Pharmaceutical Review

Volume 17 | Issue 6 | 2012

DRUG DISCOVERY

taylor_Layout 1 03/12/2012 09:12 Page 2

The promise of QSP also includes the ability

to understand the interaction of drugs in indi -

vidual patients.

We view QSP as involving Inputs, as well as

the application of the elements of experi -

mentation and computational methods of QSP

and finally Outputs that result from the

approach (Figure 1). The Input (disease-

associated genotypes and phenotypes) of the

QSP approach includes all of the available

‘omics’ generated on the disease model systems

and patient samples including multiplexed

fluorescence-based pathology. Disease relevant

pathways are inferred from the ‘omics’ data to

guide the development of the optimal

phenotypic screen. The best possible pheno -

typic disease model is then validated as a High

Content Screen (HCS) assay. The disease

model/assay can range from multiplexed cells to

engineered human tissue to experimental

organisms. QSP integrates the data from the

phenotypic discovery approach (see Figure 3,

page 23) and applies a range of computational

chemistry, computational biology and systems

biology software tools to reduce the large

screening/experimental data sets to functional

knowledge, including polypharmacology24,25.

Furthermore, a number of machine learning

approaches have the potential to reduce the

amount of screening measurements required26.

Mathematical models of refined pathways, as

well as PK and PD, are developed and then

extended to define the complex processes of

living systems23,27. The Output from this

approach is expected to yield advanced

functional knowledge on the pathways,

including potential feedback loops between

pathways, as well as physiological processes

interrogated experimentally and compu -

tationally. Additionally, the output will identify

any ‘emergent properties’ or properties of

the system not predicted from investigating the

components, such as heterogeneity of system

responses22. Another important output should

be improved and predictive drug efficacy and

safety as a ‘knowledgebase’ of information as

QSP is applied. The major output should be

novel drugs, including drug combinations

working on connected pathways28. There

will be ‘feedback loops’ between input,

QSP and output. For example, the QSP results

will help build new hypotheses in regards

to pathways and MOA that could feedback to

input to perform new assays. Likewise,

the outputs (e.g. drugs, combination therapies

or polypharma cological strategies) would also

be tested against HCS assays and newly

collected omics data (e.g. from patients

that have received treatment). QSP will also

help to understand side effects, or mechanisms

of drug resistance.

European Pharmaceutical Review

Volume 17 | Issue 6 | 2012 22

DRUG DISCOVERY

Figure 1 QSP in drug discovery and development in the UPDDI begins with Inputs of ‘omics’ data, the developmentof inferred pathways involved in the disease and a phenotypic disease model for the screen. The application of QSPinvolves the phenotypic discovery approach using HCS and computational and systems biology, as well asmathematical modeling. The outputs of QSP include functional knowledge including any emergent properties,improved drug efficacy and safety and most importantly, novel drugs

Figure 2 Starting discovery campaigns with computational chemistry and biology allows the integration of existingknowledge of the targeted pathway(s) and the identification of predicted binding of existing drugs to pathwaycomponents that could identify useful ‘probes’ of pathway modulations, as well as starting points for rational drugdesign. Some of the databases used in target prediction include: STITCH – chemical-protein interactions,http://stitch.embl.de/ . DrugBank – approved/experimental drugs with target information from literature,http://www.drugbank.ca/ ,SIDER – drug side effects database, http://sideeffects.embl.de/ , ChEMBL – chemical-protein interactions from bioassays/literature, https://www.ebi.ac.uk/chembl/ , Target Hunter-identify possibletargets of small molecules, http://www.cbligand.org/targethunter, and MDDR – chemical bioactivity databasehttp://accelrys.com/products/databases/bioactivity/mddr.html

‘‘It is necessary to harness the expertiseof the collaborating partners and to beinnovative in focused efforts in order to

gain the advantage of collaboration’’

taylor_Layout 1 03/12/2012 09:13 Page 3

The University of Pittsburgh

Drug Discovery Institute (UPDDI)

as an example

The University of Pittsburgh Drug Discovery

Institute (UPDDI) has embraced a two-fold

strategy in creating an environment for the new

vision of drug discovery and development

(www.upddi.pitt.edu): Collaboration – through

integrated activities across internal depart -

ments, centres and institutes, along with

collaborations with other academic centres,

government agencies and commercial partners;

and Innovation – by developing and practicing

new approaches and technologies to comple -

ment the present paradigm of discovery.

Technologically, the UPDDI has key strengths in

HCS and phenotypic discovery, chemistry /

medicinal chemistry and molecular biophysics

applied to target-centric discovery and is

investing heavily in the implementation of QSP.

The focus of this discussion is on QSP coupled

to phenotypic discovery, but the power of

molecular biophysics in target-centric programs

is evident when applied to the right target and

can be performed subsequent to an initial

phenotypic discovery step when a target is

identified29. The University of Pittsburgh has the

depth and breadth to support a major effort

in drug discovery and development and in

cooperation with the University of Pittsburgh

Medical Center (UPMC), the ability to translate

basic research to clinical trials and clinical

practice. Ready access to clinician scientists,

patients and patient samples is crucial to the

success of this new paradigm.

A major advance in computational

chemistry and computational biology in recent

years has been the harnessing of genomics and

proteomics databases and sophisticated

algorithms to infer pathways responsible for cell

functions as a starting point for defining the

actual pathways (Figure 2, page 22). It is also

now possible to harness drug and target protein

databases to predict drug interactions with

specific target proteins. Positive ‘hits’ from the

in silico profiling can be used to identify useful

‘chemical probes’ of pathways and cellular

functions, as well as a starting point to design

a phenotypic screening strategy for drug

discovery and/or as a reference point for

pursuing a molecular biophysics / target-centric

approach to drug discovery30,31.

Critical role of HCS in QSP

The UPDDI is applying QSP in the drug discovery

and development process starting with the

application of phenotypic screening using

HCS11,14,15. Figure 3 shows the steps in performing

a phenotypic screen. In phenotypic drug

discovery, the detailed, basic and clinical science

is a critical component, as in target-centric

discovery. However, a functional, disease

relevant phenotype is selected, validated

and profiled by HCS rather than selecting and

validating a single molecular target. This

broadens the potential molecular targets that

can be modulated by compounds and puts the

initial focus on functions and pathways. This is

very different from applying hypothesis driven

research to follow a single molecular target, but

allows an unbiased, physiological profile to

define the cellular functions and pathways

directly involved in the disease. Optimally, one

or more biomarkers can be identified in the

process, quantified in the HCS assay and carried

through the whole discovery-development

process. A critical step is the selection of

compound libraries based on both diversity and

novelty. In addition, exploring existing drugs for

repurposing and natural products for added

diversity has great promise. The screening

campaign is performed identifying ‘hits’ and lead

generation and lead optimisation is subse -

quently performed. An optimally designed HCS

assay can be used to guide the structure activity

relationship (SAR) in lead optimisation15,32,33.

In fact, the FDA does not require molecular

target identification for approval13. Extensive

data mining on 890 approved drugs demon -

strated that on average, each of the analysed

drugs interact with six known molecular

targets34. This latter fact gives promise to the

drug repurposing efforts now underway35.

However, when optimised leads are

identified, it is important to apply chemical

proteomics approaches to try to identify

potential molecular target(s)36,37. Identification

of molecular target(s) allows the application of

mechanism of action (MOA) studies that might

involve multiple molecular targets across

multiple pathways. Furthermore, one or a

combination of identified molecular targets

could be useful as biomarkers. In vitro early

safety assessment can also be performed using

HCS at this stage to help guide the prioritisation

of leads38,39. The pre-clinical and clinical stages

are the same functionally, as with the target-

centric approach, but with the addition of

computational and systems biology that

permits quantification and modelling of

the data. The phenotypic drug discovery-

development approach allows the application

of QSP from early discovery on a range of

scales of biological complexity from molecules

to mammalian models to human patient

DRUG DISCOVERY

European Pharmaceutical Review

www.europeanpharmaceuticalreview.com 23 Volume 17 | Issue 6 | 2012

‘‘The phenotypic drug discovery-development approach allows

the application of QSP from earlydiscovery on a range of scales of biological complexity from

molecules to mammalian models to human patient cohorts’’

Figure 3 Phenotypic drug discovery and development using HCS is a major commitment of the UPDDI. Theimplementation of QSP involves the best experimental methods, including HCS screening data, coupled withcomputational chemistry and biology, as well as systems biology. Although not required by the FDA, theidentification of the molecular target(s) is investigated by chemical proteomics. The goal is to integrate and toanalyse data across the complete discovery-development continuum from early discovery to clinical data

taylor_Layout 1 03/12/2012 09:13 Page 4

cohorts. The phenotypic approach can be

extended from early discovery through pre-

clinical and clinical trials and represents a natural

path to the development and use of biomarkers

using multiscale imaging modalities40.

Conclusion

A new vision of drug discovery and develop -

ment is based on collaborations between

pharmaceutical companies, government

agencies, venture-backed biotechnology

companies and academic medical centres to

expand the potential for innovation, while

sharing the complexity and technologies

involved in discovering and developing novel

therapeutics. Quantitative systems pharma -

cology coupled with high content screening is

being developed as one innovative path at the

University of Pittsburgh Drug Discovery

Institute. Personalised / precision medicine is

dependent of the development of diagnostic

tests to guide the optimal delivery of thera -

peutics to patient sub-populations.

Acknowledgements

I would like to thank my colleagues for valuable

discussions on how to harness the new vision of

drug discovery and development including

Mark Schurdak, Bert Gough, Andy Stern,

Andreas Vogt, Dutch Boltz, Ivet Bahar, Jeremy

Berg, Ahmet Bakan, Tim Lezon and Bob Murphy.

European Pharmaceutical Review

Volume 17 | Issue 6 | 2012 24

DRUG DISCOVERY

Dr. D. Lansing Taylor began his academiccareer at Harvard University developing andapplying novel fluorescence-based reagentsand imaging technologies to investigatefundamental cellular processes. He thenmoved to Carnegie Mellon University andbecame the Director of the National Science

Foundation, Center for Light Microscope Imaging and Biotechnology,and in 1995 was named Vice Dean of CMU’s Division of MolecularSciences. Together with his students, post-docs and collaborators, Dr. Taylor developed reagent and imaging technologies includingfluorescent analog cytochemistry, ratio imaging, fluorescenceanisotropy imaging microscopy, the multimode light microscope andstanding wave fluorescence microscopy. Dr. Taylor co-founded and led a series of biotechnology companies including BiologicalDetection Systems (cyanine dyes with Alan Waggoner), Cellomics(High Content Screening), Cellumen (predictive early safetyassessment) and Cernostics (tissue systems pathology). Dr. Taylor is aco-inventor on more than 25 US patents, including six focused oncell-based imaging. He was the recipient of an NIH MERIT award anda Pioneer Award from the NSF. Dr. Taylor returned to academia at theend of 2010 to continue his academic interests which now link large-scale cell, tissue and experimental organism profiling withcomputational and systems biology to optimise drug discovery anddevelopment. As the Director of the Drug Discovery Institute, he isfocusing on the application of Quantitative Systems Pharmacology inorder to change the paradigm in drug discovery and development.

Biography

1. Paul S, Mytelka D, Dunwiddie C, Persinger C, Munos B,Lindborg S, Schacht A. How to improve R&Dproductivity: the pharmaceutical industry's grandchallenge. Nature Reviews Drug Discovery.2010;9:203-14

2. Munos B. Lessons from 60 years of pharmaceuticalinnovation. Nature Reviews Drug discovery.2009;8(12):959-68

3. Butcher E. Can cell systems biology rescue drugdiscovery? Nature Reviews Drug Discovery2005;4:461-7

4. Eichler HG, Abadie E, Breckenridge A, Flamion B,Gustafsson LL, Leufkens H, et al. Bridging the efficacy-effectiveness gap: a regulator's perspective onaddressing variability of drug response. NatureReviews Drug Discovery. 2011;10(7):495-506

5. Vallance P, Williams P, Dollery C. The future is muchcloser collaboration between the pharmaceuticalindustry and academic medical centers. ClinPharmacol Ther. 2010;87(5):525-7

6. Frye S, Crosby M, Edwards T, Juliano R. US academicdrug discovery. Nature Reviews Drug Discovery.2011;10(6):409-10

7. Kaitin KI. Deconstructing the drug developmentprocess: the new face of innovation. Clin PharmacolTher. 2010;87(3):356-61. PMCID: 2953249

8. Edmondson G, Valigra L, Kenward M, Hudson R,Belfield H. Making industry-university partnershipswork: Science/Business Innovation Board, AISBL; 2012Contract No.: Document Number|

9. Taylor DL. A personal perspective on high-contentscreening (HCS): from the beginning. J Biomol Screen.2010;15(7):720-5

10. Taylor D. Past, Present, and Future of High ContentScreening and the Field of Cellomics. In: Taylor D,Haskins J, Giuliano K, editor. Methods in MolecularBiology, 2007; 356: 3-18. High Content Screening: APowerful Approach to Systems Cell Biology and DrugDiscovery. Totowa, NJ: Humana Press

11. Alcock P, Bath C, Blackett C, Simpson P. High contentcell based primary screening for oncology targets--aperspective. European Pharmaceutical Review. 2010;3

12. Swinney D, Anthony J. How were new medicinesdiscovered? Nature Reviews Drug Discovery. 2011;10

13. Lee J, Uhlik M, Moxham C, Tomandl D, Sall D. Modernphenotypic drug discovery is a viable, neoclassicpharma strategy. Journal of Medicinal Chemistry.2012;55:4527-38.

14. Gough A, Taylor D, et al. High Content Screening. TheMolecular Basis of Cancer: Elsevier; In Press

15. Zanella F, Lorens JB, Link W. High content screening:seeing is believing. Trends Biotechnol. 2010;28(5):237-45

16. Grskovic M, Javaherian A, Strulovici B, Daley GQ.Induced pluripotent stem cells--opportunities fordisease modelling and drug discovery. Nature ReviewsDrug Discovery. 2011;10(12):915-29

17. Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ. Multidimensional drug profiling by automated microscopy. Science.2004;306(5699):1194-8

18. Giuliano KA, Chen YT, Taylor DL. High-contentscreening with siRNA optimizes a cell biologicalapproach to drug discovery: defining the role of P53activation in the cellular response to anticancer drugs.J Biomol Screen. 2004;9(7):557-68

19. Loo LH, Lin HJ, Steininger RJ, 3rd, Wang Y, Wu LF,Altschuler SJ. An approach for extensibly profiling themolecular states of cellular subpopulations. NatMethods. 2009;6(10):759-65. PMCID: 2818727

20. Yang R, Niepel M, Mitchison TK, Sorger PK. Dissectingvariability in responses to cancer chemotherapythrough systems pharmacology. Clin Pharmacol Ther.2010;88(1):34-8. PMCID: 2941986

21. Zhao S, Iyengar R. Systems pharmacology: network

analysis to identify multiscale mechanisms of drugaction. Annu Rev Pharmacol Toxicol. 2012;52:505-21

22. Sorger P, Allerheiligen S. Quantitative and systemspharmacology in the post-genomic era: newapproaches to discovering drugs and understandingtherapeutic mechanisms. National Institutes of HealthWhite Paper. 2011

23. van der Graaf PH, Benson N. Systems pharmacology:bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery anddevelopment. Pharm Res. 2011;28(7):1460-4

24. Pujol A, Mosca R, Farres J, Aloy P. Unveiling the role ofnetwork and systems biology in drug discovery.Trends Pharmacol Sci. 2010;31(3):115-23

25. Hopkins AL. Network pharmacology: the nextparadigm in drug discovery. Nat Chem Biol.2008;4(11):682-90

26. Murphy RF. An active role for machine learning in drugdevelopment. Nat Chem Biol. 2011;7(6):327-30

27. Faeden J, Blinov M, Hlavacek W. Rule-based modelingof biochemical systems with BioNetGen. In: Maly I,editor. Methods in Molecular Biology: SystemsBiology. Totowa, NJ: Humana Press; 2009

28. Bernards R. A missing link in genotype-directed cancertherapy. Cell. 2012;151(3):465-8.

29. Molina G, Vogt A, Bakan A, Dai W, Queiroz de Oliveira P,Znosko W, et al. Zebrafish chemical screening revealsan inhibitor of Dusp6 that expands cardiac celllineages. Nat Chem Biol. 2009;5(9):680-7. PMCID:2771339

30. Liu Z, Fang H, Reagan K, Xu X, Mendrick DL, Slikker W,Jr., et al. In silico drug repositioning – what we need toknow. Drug Discov Today. 2012;00(00):13-6

31. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI,Hufeisen SJ, et al. Predicting new molecular targets forknown drugs. Nature. 2009;462(7270):175-81. PMCID:2784146

32. Link W, Oyarzabal J, Serelde BG, Albarran MI, Rabal O,Cebria A, et al. Chemical interrogation of FOXO3anuclear translocation identifies potent and selectiveinhibitors of phosphoinositide 3-kinases. J Biol Chem.2009;284(41):28392-400. PMCID: 2788888

33. Zanella F, Rosado A, Garcia B, Carnero A, Link W.Chemical genetic analysis of FOXO nuclear-cytoplasmic shuttling by using image-based cellscreening. Chembiochem. 2008;9(14):2229-37

34. Mestres J, Gregori-Puigjane E, Valverde S, Sole RV. Thetopology of drug-target interaction networks: implicitdependence on drug properties and target families.Mol Biosyst. 2009;5(9):1051-7

35. Mullard A. Drug repurposing programmes get lift off.Nature Reviews Drug Discovery. 2012;11(7):505-6

36. Cong F, Cheung AK, Huang SM. Chemical genetics-based target identification in drug discovery. AnnuRev Pharmacol Toxicol. 2012;52:57-78

37. Laggner C, Kokel D, Setola V, Tolia A, Lin H, Irwin JJ, et al.Chemical informatics and target identification in azebrafish phenotypic screen. Nat Chem Biol.2012;8(2):144-6. PMCID: 3262069

38. Vernetti L, Irwin W, Giuliano K, Gough A, Johnston K,Taylor D. Cellular Systems Biology Applied to Pre-Clinical Safety Testing: A Case Study of CellCiphr™Cytotoxicity Profiling. In: Xu J, Ekins S, editor. DrugEfficacy, Safety, and Biologics Discovery: EmergingTechnologies and Tools. New Jersey: John Wiley andSons; 2008. p. 53-73

39. Giuliano K, Gough A, Taylor D, Vernetti L, Johnston K.Early Safety Assessment Using Cellular SystemsBiology Yields Insights into Mechanisms of Action. JBiomol Screen. 2010;15:783-97

40. Prescher JA, Contag CH. Guided by the light:visualizing biomolecular processes in living animalswith bioluminescence. Curr Opin Chem Biol.2010;14(1):80-9

References

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