thumbnail...7.1.3 hit triage: from actives to hits to hit series, 193 7.1.4 challenges to successful...

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Small molecule medicinal chemiStry


Strategies and technologies

Edited by

Werngard czechtizkyPeter hamley

Copyright copy 2016 by John Wiley amp Sons Inc All rights reserved

Published by John Wiley amp Sons Inc Hoboken New Jersey

Published simultaneously in Canada

No part of this publication may be reproduced stored in a retrieval system or transmitted in any form or by any means electronic mechanical photocopying recording scanning or otherwise except as permitted under Section 107 or 108 of the 1976 United States Copyright Act without either the prior written permission of the Publisher or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center Inc 222 Rosewood Drive Danvers MA 01923 (978) 750‐8400 fax (978) 750‐4470 or on the web at wwwcopyrightcom Requests to the Publisher for permission should be addressed to the Permissions Department John Wiley amp Sons Inc 111 River Street Hoboken NJ 07030 (201) 748‐6011 fax (201) 748‐6008 or online at httpwwwwileycomgopermissions

Limit of LiabilityDisclaimer of Warranty While the publisher and author have used their best efforts in preparing this book they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages including but not limited to special incidental consequential or other damages

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Library of Congress Cataloging‐in‐Publication Data

Small molecule medicinal chemistry strategies and technologies edited by Werngard Czechtizky Peter Hamley pages cm Includes bibliographical references and index ISBN 978-1-118-77160-0 (cloth) 1 Pharmaceutical chemistry 2 Drug development I Czechtizky Werngard editor

II Hamley Peter editor RS403S62 2015 6151prime9ndashdc23

2015020801

Cover image courtesy of adempercemiStockphoto

Set in 1012pt Times by SPi Global Pondicherry India

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

1 2016

Contents

List of Contributors xiii

introduction 1Werngard Czechtizky and Peter Hamley

Part i exPLoring bioLogiCaL sPaCe aCCess to new CoLLeCtions 11

1 elements for the Development of strategies for Compound Library enhancement 13Edgar Jacoby

11 Introduction 1312 Chemical Space for Drug Discovery 1413 Molecular Properties for Drug Discovery 1714 Major Compound Classes 2115 Chemical Design Approaches to Expand Bioactive Chemical Space 2516 Conclusion 28Acknowledgments 29References 29

2 the european Lead factory 37Christopher Kallus Joumlrg Huumlser Philip S Jones and Adam Nelson

21 Introduction 37211 Background 37212 The European Lead Factory 38

vi ConTEnTS

22 Building the Joint European Compound Library 43221 Definition of Criteria and an Approach for the Review and Selection

of Library Proposals 46222 Collation Review and Selection of an Initial Wave of Library Proposals 47223 A Web‐Based Tool to Support the Collation Review

and Selection of Proposals 49224 Synthetic Validation of Library Proposals and Library Production 49

23 Qualified Hit Generation 54231 Capabilities of the ESC 54232 Target Selection and Generation of Qualified Hits 56233 Exploitation of Qualified Hit List 58

24 Future Perspectives 58Acknowledgments 59References 59

3 access to Compound Collections new business Models for Compound acquisition and sharing 61Peter ten Holte

31 Introduction 61311 Vertical Disintegration and the Quest for Innovation 61312 Innovative Chemistry 63313 Access to Supplementary Compound Collections 63

32 Risk‐Sharing Approaches 64321 overview 64322 Blinded Screening 65323 Follow‐Up of Blinded Screening Various Models 65

33 Library Exchange 69331 Partners with Different Scientific Interests 70332 Partners with Similar Scientific Interests 70333 Compound Selection Use and Potential Risks 71

34 Sharing Collections for External Screening 72341 Rationale 72342 Academic Drug Discovery Consortium (ADDC) 72343 EU‐oPEnSCREEn 73344 nIH Roadmap 73

35 Conclusion 74Acknowledgments 74References 75

Part ii exPLoring bioLogiCaL sPaCe aCCess to new CheMistries 77

4 new advances in Diversity‐oriented synthesis 79Warren R J D Galloway Jamie E Stokes and David R Spring

41 Introduction Small Molecules and Biology 7942 The need for Structural Diversity in Synthetic Small Molecule Screening

Collections 80

ConTEnTS vii

43 Diversity‐oriented Synthesis of new Structurally Diverse Compound Collections 82431 General Principles of Diversity‐oriented Synthesis 82432 Achieving Structural Diversity The Importance of Scaffold Diversity 83433 Synthetic Principles in DoS 83434 Scaffold Diversity and Molecular Type 86435 Examples of DoS Campaigns 86

44 Concluding Remarks 97References 98

5 solid‐Phase Combinatorial Chemistry 103Marcel Patek Martin Smrcina Eric Wegrzyniak Victor Nikolaev and Andres Mariscal

51 Introduction 10352 Chapter outline 10453 Combinatorial Chemistry in Retrospect 10454 Foundations of Solid‐Phase Synthesis of Combinatorial Chemistry 107

541 Ingredients of Solid‐Phase Chemistry 109542 Library Development and Production 117543 Analytical Chemistry and Solid‐Phase Synthesis of Libraries 129

55 The outcome of Tucson Combinatorial Chemistry at Sanofi 132551 overall Strategy 132552 Drug Discovery outcomes 134553 Key Parameters of Combichem Productivity 134

56 Conclusions and outlook 135References 136

6 recent advances in Multicomponent reaction Chemistry applications in small Molecule Drug Discovery 145Christopher Hulme Muhammad Ayaz Guillermo Martinez‐Ariza Federico Medda and Arthur Shaw

61 Introduction 14562 Classical Multi-Component Reactions (MCRs) 14763 The Passerini Reaction (Mario Passerini 1921) 14764 Ugi Reaction 147

641 The Ugi-deprotect-cyclize (UDC) strategy 152642 Bi-functional approach (BIFA) 153643 Miscellaneous Post‐Ugi Condensations 154

65 Van Leusen Reaction 15466 Petasis Reaction 15567 GroebkendashBlackburnndashBienaymeacute (GBB) Reaction 15568 Recently Discovered novel MCRs 155

681 Cyclic Anhydride‐Based MCRs 155682 1‐Azadiene‐Based MCRs 156683 Recent IMCRs and Secondary Reactions 157684 Miscellaneous MCRs 159

69 Asymmetric MCRs 159

viii ConTEnTS

610 Applications of MCRs in Medicinal Chemistry 1606101 Kinase Inhibitors 1616102 Protease Inhibitors 1636103 Ion Channel Inhibitors 1656104 ProteinndashProtein Interaction Inhibitors 1656105 Tubulin Polymerization Inhibitors 1666106 G‐Protein‐Coupled Receptors 168

611 Summary 171References 171

Part iii sCreening strategies 189

7 Computational techniques to support hit triage 191Douglas B Kitchen and Heacutelegravene Y Decornez

71 Lead Finding Process overview and Challenges 191711 The need for Triage 191712 The Lead Generation Process 191713 Hit Triage From Actives to Hits to Hit Series 193714 Challenges to Successful Lead Finding 194715 Frequent Hitters 195716 Implications of Human Decision‐Making 195

72 Chemical Structure Analysis of Hit Lists 196721 Similarity‐Based Clustering 197722 Scaffold‐Based Clustering 198723 Application of Clustering Classification Methods 201

73 Rules and Filters 201731 Computational Descriptors for Property Assessment 202732 Lipophilicity and other Physicochemical Descriptors 205733 Structural and Shape Descriptors 205734 Multiparameter Calculations MPo and QED 206735 Frequent‐Hitter Analysis 207736 Reactive Group Analysis 209

74 Triage Systems 21075 Ligand Efficiency Indices 21076 Hit Series Analysis 211

761 Latent Hit Series and Singletons 211762 Rapid Hit Exploration and Compound Set Enrichment 211763 SAR Analysis 212764 Data Volume Integration Retrieval and Visualization 213


8 fragment‐based Drug Discovery 221Jean‐Paul Renaud Thomas Neumann and Luc Van Hijfte

81 Introduction 22182 Fragment Libraries 223

ConTEnTS ix

83 Biophysical Screening Technologies 223831 Surface Plasmon Resonance (SPR) 224832 nuclear Magnetic Resonance (nMR) 231833 X‐Ray Crystallography 234834 noncovalent Mass Spectrometry 235835 Differential Scanning Fluorimetry (DSF) 237836 Biophysical Techniques for Fragment Screening against

Membrane Proteins 238837 Biophysical Techniques for Fragment Screening against PPIs 238

84 Fragment Evolution Strategies 23985 FBDD Case Studies 240

851 Aurora Kinase Inhibitors 240852 Tackling PPIs Fragment‐Based Discovery of Bromodomain

Inhibitor Leads 24186 The Future 243References 244

9 Virtual screening 251Karl‐Heinz Baringhaus and Gerhard Hessler

91 Introduction 251911 Goals of Virtual Screening 252

92 Databases and Database Preparation 25493 Validation of the Virtual Screening Strategy 25694 Ligand‐Based Virtual Screening 258

941 2D Approaches 259942 3D Ligand‐Based Approaches 261

95 Structure‐Based Virtual Screening 26396 other Virtual Screening Applications 26697 Conclusion 268References 269

10 Phenotypic screening 281Michelle Palmer

101 Introduction 281102 History and Past Successes 282103 Impact of Phenotypic Screening 282104 Model Systems for Phenotypic Assays 285

1041 Cell Lines 2851042 Primary and Stem Cells 2851043 Cocultures 2861044 3D Cell Models 287

105 Assays 2871051 Assay Technologies 2871052 Assay Development Considerations 2901053 Example 1 Selective Killing of Breast Cancer Stem Cells 2911054 Example 2 CFTR Potentiator Drug 291

x ConTEnTS

106 Deorphaning 2921061 Affinity‐Based Proteomics 2921062 Genetic Profiling 2951063 Target Profiling 2961064 Comodifier Profiling 2961065 Target Engagement 2971066 Example 3 Elucidating MoA for a Regulator of

Polyploidization 297107 Summary 298References 299

Part iV teChnoLogies for MeDiCinaL CheMistry oPtiMization 305

11 advances in the understanding of Drug Properties in Medicinal Chemistry 307Peter Hamley and Patrick Jimonet

111 Introduction 307112 Properties and origins of Marketed Drugs 308

1121 The Consistent Properties of oral Drugs 3081122 The Changing origins of oral Drugs 308

113 Drug Properties and Attrition in Clinical Development 310114 The Rule of Five 312

1141 The Concept 3121142 Druggability 313

115 The Concept of Lead‐Likeness 3131151 The Consequences on Screening and Collections 314

116 Influence of Drug Properties on Absorption Distribution Metabolism Excretion and Toxicity 314

117 Building on the Ro5 new Guidelines for Compound Design 3161171 Ligand Efficiency 3161172 Ligand Lipophilicity Efficiency and other Indices 3171173 Chemical Beauty 318

118 Alternatives Criticisms and Exceptions 318119 Conclusions 320References 320

12 recent Developments in automated solution Phase Library Production 323Thomas C Maier and Werngard Czechtizky

121 Introduction 3231211 Introduction and Definitions 3231212 Library Types 3241213 Chemotypes 326

ConTEnTS xi

122 Library Production 3271221 The Library Production Process 3271222 Process optimization 330

123 new Technologies in Automated Liquid‐Phase Library Synthesis 3341231 Provision of Starting Materials Automated Reagent

Dispensaries 3341232 Microwave 3351233 Library Purification Automated RP‐HPLC and SFC

as orthogonal Methods 336124 Flow Chemistry and Gas‐Phase Reactions 342

1241 Reactive Gases in Flow 344125 Conclusion 345References 345

13 aDMe Profiling an introduction for the Medicinal Chemist 353Katharina Mertsch Martin Will Werngard Czechtizky Niels Griesang Alexander Marker and Jacob Olsen

131 Introduction 353132 Compound Profiling in H2L optimization 354

1321 Intestinal Absorption 3541322 Drug Metabolism and Inhibition of CYP450 Enzymes 3551323 Protein Binding 3561324 En Route to a Lead Series In Vivo PK Studies 358

133 Compound Profiling in Lead optimization 3591331 Extended CYP Inhibition Studies 3591332 Mechanism‐Based CYP Inhibition 3591333 Inhibition of Transport Proteins 3601334 Biopharmaceutical Classification of a Clinical Candidate

(Classification of Potential Drugs into Biopharmaceutical Classification System or Biopharmaceutical Drug Disposition and Classification System) 360

134 Integration of Medicinal Chemistry Biology Physicochemical and ADME Profiling Strategies Toward Cycle Time Reductions 3621341 Planning Phase 3631342 Sample Preparation and Distribution 3641343 Compound QC 3651344 Determination of Physicochemical Properties 3671345 ADME Profiling General Remarks 3691346 Metabolic Lability Profiling 3691347 Permeability Testing 3701348 CYP Inhibition Profiling 372


xii ConTEnTS

Part V MeDiCinaL CheMistry beyonD sMaLL MoLeCuLes 379

14 the role of natural Products in Drug Discovery examples of Marketed Drugs 381Lars Ole Haustedt and Karsten Siems

141 natural Products and natural Product Derivatives in Commercial Drugs 381142 Hit to Lead optimization of natural Product Hits 397143 Case Study 1 Taxol 397144 Case Study 2 Epothilone 406145 Case Study 3 Eribulin 407146 Case Study 4 Geldanamycin 413147 Case Study 5 Ingenol Mebutate (Picato) 417148 Summary 422References 423

15 Peptidomimetics of α‐helical and β‐strand Protein binding epitopes 431Nina Bionda and Rudi Fasan

151 ProteinndashProtein Interactions as Therapeutic Targets 431152 Peptidomimetics of α‐Helical Protein Binding Epitopes 433

1521 α‐Helix‐Mediated PPIs 4331522 Side‐Chain Cross‐Linked α‐Helices 4351523 Hydrogen‐Bond Surrogate‐Stabilized α‐Helices 4421524 other Type I α‐Helix Peptidomimetics 4431525 Type III α‐Helix Peptidomimetics 445

153 Peptidomimetics of β‐Strand Protein Binding Epitopes 4461531 β‐Strand‐Mediated PPIs 4461532 Type I β‐Strand Peptidomimetics 4471533 Type III β‐Strand Peptidomimetics 449

154 Conclusion 452References 453

16 In Vivo imaging of Drug action 465Oliver Plettenburg and Matthias Loumlhn

161 Introduction 465162 overview of Imaging Methods 466

1621 Fluorescence‐Based Methods 4661622 MRI 4701623 CT 4701624 PETSPECT 471

163 Imaging of Therapeutic Effects 4761631 Cancer 4761632 Diabetes 4831633 CnS Disorders 486

164 Conclusion and outlook 490References 491

inDex 503

List of Contributors

Muhammad Ayaz University of Arizona Tucson AZ USA

Karl‐Heinz baringhaus Sanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

nina bionda University of Rochester Rochester NY USA

Werngard Czechtizky Sanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

Heacutelegravene Y Decornez Albany Molecular Research Inc (AMRI) Albany NY USA

rudi fasan University of Rochester Rochester NY USA

Warren r J D Galloway University of Cambridge Cambridge UK

niels Griesang Sanofi RampD Frankfurt am Main Germany

Peter Hamley Sanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

Lars ole Haustedt AnalytiCon Discovery GmbH Potsdam Germany

Gerhard Hessler Sanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

Christopher Hulme University of Arizona Tucson AZ USA

Joumlrg Huumlser Bayer Pharma AG Wuppertal Germany

Edgar Jacoby Janssen Research amp Development Beerse Belgium

Patrick Jimonet Sanofi-Aventis RampD Vitry-sur-Seine France

Philip s Jones European Screening Centre Newhouse Lanarkshire UK

Christopher Kallus Sanofi RampD Frankfurt am Main Germany

xiv LIST oF CoNTRIBUToRS

Douglas b Kitchen Albany Molecular Research Inc (AMRI) Albany NY USA

Matthias Loumlhn Sanofi Deutschland GmbH Frankfurt am Main Germany

thomas C Maier Sanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

Andres Mariscal Tucson Research Center Sanofi US Tucson AZ USA

Alexander Marker Sanofi RampD Frankfurt am Main Germany

Guillermo Martinez‐Ariza University of Arizona Tucson AZ USA

federico Medda University of Arizona Tucson AZ USA

Katharina Mertsch Sanofi RampD Frankfurt am Main Germany

Adam nelson Astbury Centre for Structural Molecular Biology and School of Chemistry University of Leeds Leeds UK

thomas neumann NovAliX BioParc Illkirch Cedex France

Victor nikolaev Tucson Research Center Sanofi US Tucson AZ USA

Jacob olsen Sanofi RampD Frankfurt am Main Germany

Michelle Palmer Broad Institute of Harvard and MIT Cambridge MA USA

Marcel Patek Tucson Research Center Sanofi US Tucson AZ USA

oliver Plettenburg Sanofi Deutschland GmbH Frankfurt am Main Germany

Jean‐Paul renaud NovAliX BioParc Illkirch Cedex France

Arthur shaw University of Arizona Tucson AZ USA

Karsten siems AnalytiCon Discovery GmbH Potsdam Germany

Martin smrcina Tucson Research Center Sanofi US Tucson AZ USA

David r spring University of Cambridge Cambridge UK

Jamie E stokes University of Cambridge Cambridge UK

Peter ten Holte Janssen Research amp Development LLC San Diego CA USA

Luc Van Hijfte NovAliX BioParc Illkirch Cedex France

Eric Wegrzyniak Tucson Research Center Sanofi US Tucson AZ USA

Martin Will Sanofi RampD Frankfurt am Main Germany

Small Molecule Medicinal Chemistry Strategies and Technologies First Edition Edited by Werngard Czechtizky and Peter Hamley copy 2016 John Wiley amp Sons Inc Published 2016 by John Wiley amp Sons Inc

IntroductIon

Werngard Czechtizky and Peter HamleySanofi‐Aventis Deutschland GmbH Frankfurt am Main Germany

I1 MEdIcInAL cHEMIStrY A dEFInItIon

The science of medicinal chemistry emerged in a recognizable form toward the end of the nineteenth century as a discipline exploring relationships between chemical structure and observed biological activity via chemical modification and structural mimicry of naturersquos materials Its roots have been said to be in the fertile mix of ancient folk medicine and early awareness of the properties of natural products hence the name [1] A more recent definition is that it is a ldquotraditional scientific discipline rooted in organic chemistry concerning the discovery development identification and interpretation of the mode of action of biologically active compounds at the molecular and cellular levelrdquo [2] It has also been stated that ldquomedicinal chemistry uses physical organic principles to understand the interaction of smaller molecular displays with the biological realmrdquo [1]

I2 tHE roLE oF A MEdIcInAL cHEMISt

Medicinal chemistry is pivotal to the process of discovering medicines The goal is seemshyingly simplemdashthe design and synthesis of new biologically active molecules with a new and useful medical advantage along with a safety profile good enough to obtain approval to reach the global pharmaceutical market However to achieve this is immensely chalshylenging and in order to have a chance of succeeding a successful medicinal chemist must operate at the boundaries of many disciplines [3] to interact in and understand areas far outside organic chemistry and to analyze and understand a significant amount of data from various biological sources such as cell biology molecular biology and

2 InTroduCTIon

pharmacology In addition the medicinal chemist must constantly take the right decisions using analytical creative and teamworking skills to advance toward the goal

Medicinal chemists are continuously working against the odds [4 5]mdashthe rate of molshyecules making it all the way to market approval is nowadays estimated to be 110000 [6]mdashin iterations of compound design and synthesis often referred to as designndashmakendashtest cycles In order to increase the likelihood of success what was once a process involving much trial and error has become more predictive over the last decade Ideally one would only consider the synthesis of molecules with a high chance of biological potency a reasonable physicochemical and pharmacokinetic behavior and an absence of properties predicted to lead to safety issues To this end medicinal chemists no longer rely on their own experience but access new molecules in collaboration with biologists chemoinformashyticians [7] and drug designers [8] structural biologists specialists for physicochemical and pharmacokinetic [9] profiling and toxicologists The creative forces within an indishyvidual medicinal chemistry project come together in a project team to give rise to a new chemical entity (nCE) [10] with a unique biological activity in a highly collaborative proshycess it requires a number of scientists to contribute their individual expertise and ideas The investigation of the data associated with an emerging chemical series with computational models of drugndashtarget interactions and the simulation andor testing of the seriesrsquo physicoshychemical and pharmacokinetic properties has become crucial for any drug discovery program

The modern medicinal chemist must maintain an awareness of new developments in this constantly evolving field otherwise there is a risk of following unproductive parashydigms and pathways that have been shown to be contributors to poor productivity of the pharmaceutical industry in the recent past [4 5 11] We know now that successful proshyductive medicinal chemistry must go beyond ldquosyntheses typically consisting of six steps predominantly composed of amine deprotections to facilitate amide formation reactions and Suzuki couplings to produce insoluble biaryl derivatives resulting in large flat achishyral derivatives destined for screening cascadesrdquo [12] new technologies and new stratshyegies are continuously brought to bear to better enable the discovery of medicines The landscape the understanding and the techniques involved in the chemistry aspects of drug discovery are very different now than they were even 10 years ago and it is necessary to keep up to date with these new aspects in order to be effective and competitive when engaged in the field That is the goal of this book

I3 tHE StAtE oF tHE Art

I31 the drug discovery Value chain

The phases of drug discovery and development ordered by time are relatively distinct and universal [6 13] This is known as the value chain of research and development (rampd) (Fig I1)

The value chain consists of a series of individual steps that sum up a time period of normally between 10 and 15 years between the initial target hypothesis and the market launch of the drug [6] Steps ldquotargetrdquo to ldquopreclinicalrdquo are parts of the typical research activities within a drug discovery program leading to a clinical candidate (see also Fig I2) Franz Hefti [14] nicely describes the properties of a clinical candidate as follows ldquoA drug candidate suitable for clinical testing is expected to bind selectively to

THE STATE oF THE ArT 3

the receptor site on the target to elicit the desired functional response of the target molecule and to have adequate bioavailability and biodistribution to elicit the desired responses in animals and humans it must also pass formal toxicity evaluation in animalsrdquo

Clinical phases IndashIII [15] comprise the phases of a clinical drug development program culminating in the filing for approval followed (ideally) by market launch of a new drug (or nCE) In clinical phase I researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety determine a safe dosage range and identify side effects [15] normally a small group of 20ndash100 healthy volunteers will be recruited In phase II [15] the drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety Phase II trials are usually performed on larger groups (100ndash300) and are designed to assess how well the drug works They are someshytimes divided into phase IIA and phase IIB Phase IIA is specifically designed to assess dosing requirements (how much drug should be given) while phase IIB is specifically designed to study efficacy (how well the drug works at the prescribed dose(s)) drug development for a new drug often fails during phase II trials when the drug is discovered not to work as planned or to have toxic effects In phase III [15] the drug or treatment is given to even larger groups of patients (up to 10000) to confirm its effectiveness monitor side effects compare it to commonly used treatments and collect information that will allow the drug or treatment to be used safely

I32 the origin of a drug discovery Project

drug discovery begins with a physiological or pharmacological hypothesis involving amplification or inhibition of a specific biological mechanism [1] This is often a hyposhythesis involving a single protein target (Fig I2) along with its proposed mechanism

Target Lead Preclinical Phase IPhase IIaPhase IIb

Phase IIIFile forapproval

Launch

10ndash15 Years

FIgurE I1 Sketch of the drug discovery and development value chain consisting of target hypothesis lead identification and optimization to a clinical candidate preclinical testing phase IndashIII studies approval and launch

Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection

Lead to candidate (L2C)Lead optimization

Hit to lead (H2L)Hit optimizationScreen to hit

Target validationAssay development

Prospectiveresearch

FIgurE I2 The value chain process focusing on the research phase from target hypothesis to identification of a clinical candidate

4 InTroduCTIon

of action (in this context the term biological target describes the native protein in the body whose activity is modified by a drug resulting in a therapeutic effect [16]) However it could also be a simple phenotypic response such as modulation of a bioshymarker [17] A biomarker is a biological molecule found in the blood other body fluids or tissues and is a sign of a normal or abnormal process or of a condition or disease [17]

A clear trend in drug discovery pipelines today is a focus on portfolios around targets or phenotypes that are validated in the context of human disease in an effort to reduce costly failure rates (attrition) at the proof‐of‐concept stage in humans rather than the hisshytoric reliance on animal models of disease that are often artificially induced and have poor translatability to the species of interest that is human Chemistry has a major role to play in the validation process by contributing chemical probes for target identification once medicinal chemistry had a strong voice in target selection but this is generally no longer the case since the ldquolow‐hanging fruitrdquo of readily druggable targets has already been picked [18] and fast‐follower or me‐too drugs (ones that are close to marketed drugs and offer little or no advantage) are rarely approved these days [18] Instead biologists and pharmacologists select a target (or phenotype) that has a strong likelihood of efficacy in the clinic readily druggable targets (targets that are likely to be modulated with a small‐molecule drug [19]) such as kinases GPCrs enzymes etc are becoming a smaller part of a modern portfoliomdashreplaced by more challenging targets such as proteinndashprotein interactions transcription factors or epigenetic targets Because these target classes have proven more difficult to modulate with small molecules the assessment of target ldquodrugshygabilityrdquo is becoming an important early step in delineating the likely challenges and hence approaches needed for a successful generation of useful hits [19]

The identification of biomarkers and the analysis of biological networks [20] and biochemical pathways [21] around the target of interest are nowadays further integral parts for the preparation of a drug discovery program deciphering biological signaling networks and the quantification of information flux through these networks has become one of the challenges of fundamental basic research for drug discovery Systems biology the computational and mathematical modeling of complex biological systems [22] is increasingly important for the development and detailed validation of highly selective tool compounds to perturb complex networks in order to discover nodes that can be targeted with innovative new drugs [2]

I33 target Validation and Assay development

Target selection is followed by target validation as the next crucial step before assay development and the start of the hit finding campaign Target validation [23] is the proshycess by which the predicted molecular target is verified Target validation can include determining the structurendashactivity relationship (SAr) of analogues of the small molecule generating a drug‐resistant mutant of the presumed target knockdown or overexpression of the presumed target and monitoring the known signaling systems downstream of the presumed target [23] However in recent years there has been more emphasis on using human patient data generated in the clinic or using epidemiological studies and these sources are particularly powerful if this data is genetic in origin In case the target validity is considered sufficient assay development typically leads to the setup of biochemical


andor cellular assays to investigate the interaction of chemical compounds that amplify or attenuate the hypothesis‐related biological target

I34 the generation of Hits

once appropriate assays are in place the discovery campaign can start The initial challenge is to generate chemical matter that has some promising level of activity against the target or phenotype in question although issues of selectivity and physical properties are at least as important

There are many methods that can be used to generate these hit structures and this subshyject is a central domain of medicinal chemistry While once approaches such as modificashytion of substrates or ligands were often used the predominant form of lead generation technique in the last two decades has been high‐throughput screening (HTS [24 25]) whereby a large number of compounds are robotically screened in miniaturized assays More recently fragment screening [26] (using collections of compounds that have reduced complexity typically with molecular weights under 300 da) has become popular and for targets for which structural information can be derived the technique of virtual screening [27] in silico can be used When resources are not an issue these techniques are sometimes used in parallel to increase the chance of success Alternative forms of screenshying such as dnA‐encoded library screening [28] have been introduced recently and these can offer significant advantages in certain cases

Screening nowadays utilizes screening collections from many sources The classical big pharma screening collections built up through many years of medicinal chemistry efforts and rounds of mergers and acquisitions and usually enriched with so‐called rule‐of‐5‐compliant compounds [29] are no longer the preserve of the major pharmaceutical companies The advent of academic drug discovery and the proliferation of small biotech companies have led to the evolution of new models for access to quality collections such as risk sharingpartnership approaches or from international consortia

Small molecules have intrinsic advantages such as oral bioavailability accessibility of cellular compartments simple manufacturing and low cost of goods However they are also associated with high rates of attrition despite the improvements in understanding of compound properties and this has led to a revival of interest in peptides peptidomimetshyics oligonucleotides novel protein formats and natural products In addition the limits of chemical space exploration imposed by Lipinskirsquos rule of 5 [29] have led to a greater emphasis on accessing more of the infinity of chemical space resulting in new chemical collections using fundamentally different choices of chemical reactions (diversity‐oriented synthesis (doS) [30]) collections derived from multicomponent reactions (MCrs [31]) natural product‐derived collections or peptidomimetics and macrocycles Such complex molecules are often richer in sp3‐configured carbons which distinguish them from standard drug‐like molecules from classical medicinal chemistry approaches [2]

I35 Hit to Lead

After screening the prioritization of compounds from large hit lists derived from HTS (HTS triage [32]) for further follow‐up is an especially challenging task for medicinal chemists during this step of drug discovery and in addition to biological in vitro efficacy

6 InTroduCTIon

and drug‐likeness [33] multiple parameters such as target specificity physicochemical and AdME (absorption distribution metabolism and excretion [34]) parameters must be considered simultaneously (ldquomultiparameter optimizationrdquo) during the last 10 years the industry has come to the realization that control of physicochemical and AdME propshyerties is critical to improve success rates in delivering effective new drugs to patients Most medicinal chemists nowadays have access to predictive AdME software and models that support compound design but the accuracy of these models is still a limiting factor Improving these models is an important challenge for medicinal chemists experts in pharmacokinetics and computational chemists and relies on access to experimental data available for model building

From the filtered pool of most promising compounds the medicinal chemist will select so‐called hit series These almost always must be further elaborated to generate a structureshyactivity relationship (SAr [35])mdashthe relationship between the chemical structure of the molecule and its biological activitymdashand an improved physicochemical and pharmacological profile Parallel (or high‐throughput) medicinal chemistry (either in solution or on solid phase) is routinely used as a tool allowing the medicinal chemist to prosecute multiple structurally distinct series concurrently and to develop rich SAr quickly It allows the design team to draw conclusions based on data associated with a matrix of compounds instead of single compounds The systems used are nowadays far more than just bench equipment tied together via robotics an extensive infrastructure of databases and software has been built to facilitate interactive use of the systems someshytimes even remotely from around the world

I36 Lead optimization

The hit optimization resp hit‐to‐lead (H2L) phase of the drug discovery program is crushycial to select a lead which usually has a suitable overall compound profile to showmdashfor the first timemdashan in vivo efficacy of the compound series at the target of interest in animal disease models After lead selection an often resource‐intensive lead optimizashytion (or lead‐to‐candidate (L2C)) program is required to identify the endpoint of a disshycovery program that is a clinical candidate with suitable biological potency and physicochemical and pharmacological profile which is then profiled in toxicity and dose‐finding studies in animals during preclinical testing This phase uses much of the same techniques as the H2L phase but the number of compounds and series tends to decrease dramatically until just one candidate drug is identified A more careful study of the properties of the reduced set of synthesized compounds needs to be made for example to assess behavior in vivo both in animal models and in terms of pharmacokishynetic properties (how quickly the drug is cleared from the body how it is metabolized and distributed etc) These studies usually necessitate preparation of more material therefore efficient synthetic routes need to be devised ideally in partnership with development (process) chemists Closer to the clinic the compounds of highest interest will be assessed for a suitable physical form to enable reproducible manufacture and often to increase solubility typically by selecting an optimal salt form If all results are acceptable the final compound is tested for animal toxicity usually in several species at ascending doses and if there are no adverse effects it is transferred into the clinic to be tested in humans

CurrEnT And FuTurE CHALLEnGES For MEdICInAL CHEMISTrY 7

I4 currEnt And FuturE cHALLEngES For MEdIcInAL cHEMIStrY

drug discovery has undergone major strategic changes in the last decade which affect both the setting and the practice of the discipline The regulatory environment has become more stringent with safety requirements ever more challenging while the industry faces substantial cost increases in tandem with declining rampd success rates often due to lack of clinical efficacy in humans or unexpected toxicity [18 5] This has resulted in a proshyductivity gap and although there are many factors contributing to this some techniques practiced by chemists in drug discovery in the past have been associated with this Commonly cited examples include the advent of combinatorial chemistry and the associshyated inflation of molecular weight the need for a large number of compounds to feed HTS leading to a lack of imagination in synthetic protocols and ultimately to ldquoflatrdquo molshyecules the phasing out of natural product collections and skills associated with them a race for potency rather than multidimensional optimization and the list goes on [36] What is exciting about recent developments in the field is that they are often at least in part answers to these particular criticismsmdashoften associated with a greater awareness of chemical structure the coverage of chemical space and the properties required to make a successful drug

other challenges and insights remain to be satisfactorily tackled Target occupancy and drugndashtarget residence times are seen as crucial for a drugrsquos final efficacy in vivo [37] but there is still a lack of understanding how they can be optimized and even less is known about how they can be designed into a given chemical series A better undershystanding of the energetic and kinetic aspects of proteinndashligand interactions is likely to have a great impact in this area unexpected toxicities furthermore require an increase in drug selectivity and a shift of the equilibrium between the desired effect on target and unwanted side effects However the tendency to increase lipophilicity within H2L and L2C optimization to improve potency on the target of interest often counteracts selecshytivity as nonpolar proteinndashligand interactions are often less specific and lead to toxic side effects [36] Since proteinndashprotein interactions and other difficult targets are becoming more prevalent the ability to optimize interactions while maintaining optimal levels of lipophilicity will become more important

The identification of highly validated targets has become more difficult and healthshycare providers worldwide are trying to reduce costs and demanding more accountshyability Medicinal chemists find themselves sandwiched between target discovery and the identification of clinical compounds the need to focus more and more on target identification and validation has become critical for the success of many drug discovery programs [2] recent approaches toward more disease relevant mechanisms using polyshypharmacology [38]mdashtackling a disease with two or more compounds with different modes of action or with one compound showing different modes of action in parallelmdashwill not lead to a reduction of complexity of the task

The era of large pharmaceutical companies with huge internal and inward‐looking departments of medicinal chemistry and expensive associated staff is over So‐called big pharma has made sustained efforts to reduce cost (often through layoffs and site closhysures) but in parallel growing capabilities at many contract research organizations offer the opportunity to build an effective lower‐cost global network while maintaining quality and efficiency A notable globalization and outsourcing of research and innovation away

8 InTroduCTIon

from the traditional bastions of the united States Europe and Japan is another obvious sign of approaches toward cost reduction At the same time we see increased investment in lean small biotechs and academia establishing their own efficient drug discovery facilshyities often using highly innovative approaches to therapies and technologies

It is the aim of the following chapters to cast light on these major challenges and to describe strategic and technological solutions that represent a panoramic snapshot of the status of the chemical aspects of drug discovery today

rEFErEncES

[1] Erhardt P W Pure Appl Chem 2002 74(5) 703ndash785

[2] Brenk r rauh d Bioorg Med Chem 2012 20 3695ndash3697

[3] Hart T 2006 Medicinal chemistry progress through innovation Summer 2006 httpwww ddw‐onlinecomchemistryp97059‐medicinal‐chemistry‐progress‐through‐innovation summer‐06html (accessed May 25 2015)

[4] Munos B Nat Rev Drug Discov 2009 8 959ndash968

[5] Paul S M Mytelka d S dunwiddie C T Persinger C C Munos B H Lindborg S r Schacht A L Nat Rev Drug Discov 2010 9(3) 203ndash214

[6] Castner M Hayes J Shankle d 2007 Global value chains shifts in the configuration of the industry from 1995 until present The Global Pharmaceutical Industry httpswebduke edusoc142team2shiftshtml (accessed May 27 2015)

[7] Brown F K Annu Rep Med Chem 1998 33 375

[8] Madsen u Krogsgaard‐Larsen P Liljefors T 2002 Textbook of Drug Design and Discovery Washington dC Taylor amp Francis

[9] ruiz‐Garcia A Bermejo M Moss A Casabo V G J Pharm Sci 2008 97(2) 654ndash690

[10] Branch S K Agranat I J Med Chem 2014 57(21) 8729ndash8765

[11] Hann M M Keseruuml G M Nat Rev Drug Discov 2012 11 355ndash365

[12] roughley S d Jordan A M J Med Chem 2011 54 3451ndash3479

[13] Walker S M davies B J Drug Discov Today 2011 16(11ndash12) 467ndash471

[14] Hefti F F BMC Neurosci 2008 9(Suppl 3) S7

[15] nIH 2008 FAQ ClinicalTrialsgovmdashClinical Trial Phases httpwwwnlmnihgovservices ctphaseshtml (accessed May 27 2015)

[16] rang H P dale M M ritter J M Flower r J Henderson G (eds) 2012 How drugs act general principles In Rang and Dalersquos Pharmacology Edinburghnew York Elsevier Churchill Livingstone pp 6ndash19

[17] Strimbu K Tavel J A Curr Opin HIV AIDS 2010 5(6) 463ndash466

[18] Scanell J W Blanckley A Boldon H Warrington B Nat Rev Drug Discov 2012 11 191ndash200

[19] Cheng A C et al Nat Biotechnol 2007 25 71ndash75

[20] Proulx S r Promislow d E L Phillips P C Trends Ecol Evol 2005 20(6) 345ndash353

[21] Krauss G 2008 Biochemistry of Signal Transduction and Regulation Weinheimnew York Wiley‐VCH p 15

[22] Alberghina L Westerhoff H V 2005 Systems Biology Definitions and Perspectives Topics in Current Genetics 13 Berlin Springer‐Verlag pp 357ndash451

rEFErEnCES 9

[23] Fishman M 2012 Target validation nature Publishing Group httpwwwnaturecom subjectstarget‐validation (accessed May 27 2015)

[24] Mayr L M Bojanic d Curr Opin Pharmacol 2009 9 580ndash588

[25] Hertzberg r P Pope A J Curr Opin Chem Biol 2000 4 445ndash451

[26] rees d C Congreve M Murray C W Carr r Nat Rev Drug Discov 2004 3 661ndash672

[27] drwal M Griffith r Drug Discov Today Technol 2013 10(3) 395ndash401

[28] Clark M A et al Nat Chem Biol 2009 5 647 ndash 654

[29] Lipinski C A Drug Discov Today Technol 2004 1(4) 337ndash341

[30] (a)Tan d S Nat Chem Biol 2005 1 74ndash84(b)Spring d r Org Biomol Chem 2003 1 3867ndash3870

[31] ugi I Pure Appl Chem 2001 73(1) 187ndash191

[32] Cox P B Gregg r J Vasudevan A Bioorg Med Chem 2012 20(14) 4564ndash4573

[33] Murcko M A Patrick Walters W Adv Drug Deliv Rev 2002 54(3) 255ndash271

[34] (a)Cruciani G Milletti F Storchi L Sforna G Goracci L Chem Biodivers 2009 6(11) 1812ndash1821(b)Yu H Adedoyin A Drug Discov Today 2003 8(18) 852ndash861

[35] Cherkasov A et al J Med Chem 2014 57 4977minus5010

[36] (a)Leeson P Springthorpe B Nat Rev Drug Discov 2007 6 881ndash890(b)Hann M Keseruuml G M Nat Rev Drug Discov 2012 11 355ndash365

[37] Copeland r A Pompliano d L Meek T d Nat Rev Drug Discov 2006 5 730ndash739

[38] Anighoro A Bajorath J rastelli G J Med Chem 2014 57 7874minus7887

Part I

ExPlorIng BIologIcal SPacE accESS to nEw collEctIonS


1ElEmEnts for thE DEvElopmEnt of stratEgiEs for CompounD library EnhanCEmEnt

Edgar JacobyJanssen Research amp Development Beerse Belgium

11 introDuCtion

The main purpose of a small molecule compound collection that is sometimes considered to constitute the crown jewels of a drug discovery organization is to supply the discovery pipeline with hit‐to‐lead compounds for todayrsquos and the futurersquos portfolio of drug discovery programs and to provide tool compounds for the investigation of biological targets and path-ways [1ndash7] Independent of the followed discovery strategy relying on diversity or hypothesis‐based screening the automated access to high‐quality compounds constitutes a key asset [8] Accordingly all major organizations including the National Institutes of Health (NIH) and the European Union Innovative Medicines Initiative (EU IMI) have initiated over the last years dedicated compound collection enhancement projects [9] In alignment with the general paradigm shift observed in drug discovery going from quantity to quality the fundamental principle aims to select bothmdashat the chemical and the biological levelmdashthe best possible molecular starting points for lead discovery and development in the early drug discovery phases in order to reduce attrition at later preclinical and clinical stages

To be successful on the long‐term perspective such design strategy addresses the known target space and tries to expand into nonprecedented areas of chemical and biological spaces using diversity principles [5 6] Directing the molecular properties toward the lead‐like space is expected to improve overall success rates The application of absorption distribution metabolism excretion and toxicity (ADMET) property models and rules of thumb aims to reduce the attrition risk and can be front‐loaded into the design

14 STRATEGIES FOR COMPOUND LIBRARY ENHANCEMENT

of the collection On the other hand a screening collection should allow for serendipitous discovery going in hand with diversity designs

Drug discovery compound collections have evolved during recent history Up to the early 1990s when drug discovery was mainly conduced in phenotypic in vivo screening of corporate medicinal chemistry compounds the collections were limited to a few thousands of compounds that were carefully generated within the individual therapeutic programs With the advances of molecular and cell biology and the advent of high‐throughput chem-istry and screening the drug discovery world changed and compound collections were grown in the last 15 years to pass in a number of organizations beyond the one million number Today screening collections integrate design‐focused and diversity‐based compound sets from the synthetic and natural paradigms generated via corporate medicinal chemistry and combinatorial compound synthesis and external compound acquisition or merger projects [1ndash3] The compound collections serve diverse screening paradigms ranging from target‐based to phenotypic‐based screening from biochemical to cell‐based screening and from focused hypothesis‐based to diversity‐based screening opening a wide diversity of strategic choices for the future enhancement of the compound collection

Herein we review chemical biological and informatics elements for the development of strategies for compound library enhancement The interdisciplinary nature of the library design activity is emphasized

12 ChEmiCal spaCE for Drug DisCovEry

The chemical space is the ensemble of all possible molecules and comprises physically documented molecules available in the corporate and public databases as well as yet unknown virtual molecules [10] To delineate how many and which molecules populate unknown chemical space in total Jean‐Louis Reymondrsquos group at the University of Berne performed a systematic computational enumeration and assembled the so‐called chemical universe databasemdashFigure 11 [10] GDB‐11 lists 264 million molecules of up to 11 atoms of C N O and F GDB‐13 lists 977 million molecules up to 13 atoms of C N O Cl and S and GDB‐17 lists 166 billion molecules up to 17 atoms of C N O S and halogens [13] The number of molecules enumerated in GDB increases exponentially with the number of atoms such that the database will become impracticably large as molecular size increases For instance extrapolation from the numbers in GDB‐17 suggests that there would be approximately 1024 molecules up to 30 nonhydrogen atomsmdashtypically drug‐sized molecules include up to 35 nonhydrogen atoms with molecular weight (MW) lt 500 Da

Within a drug discovery context these astronomic numbers have to be placed in relation to the number of physically available chemicals and the actual number of around 1200 approved drugs satisfying stringent efficacy and safety criteria [14] The Elsevier Medicinal Chemistry and Chemical Abstracts Service (CAS) Registry databases which are up‐to‐date representatives of molecules described in the chemical literature list respectively 55 and 74 million compounds [15 16] The eMolecules and ChemNavigator iResearch libraries which are industry references for off‐the‐shelf compound acquisition list respec-tively five and six million unique commercially available compounds [17 18] The screen-ing collections of the major pharmaceutical companies include typically one to two million proprietary and nonproprietary compounds [7] Given the practically infinite possibilities




Edited by












2015020801




10 9 8 7 6 5 4 3 2 1

1 2016

Contents








vi ConTEnTS















Collections 80

ConTEnTS vii














viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion




















2015020801




10 9 8 7 6 5 4 3 2 1

1 2016

Contents








vi ConTEnTS















Collections 80

ConTEnTS vii














viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










Contents








vi ConTEnTS















Collections 80

ConTEnTS vii














viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










vi ConTEnTS















Collections 80

ConTEnTS vii














viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










ConTEnTS vii














viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










viii ConTEnTS













ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










ConTEnTS ix















x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










x ConTEnTS















ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










ConTEnTS xi













xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










xii ConTEnTS














inDex 503














































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion























































IntroductIon






2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










2 InTroduCTIon















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion

















Launch

10ndash15 Years


Targethypothesis

Targetselection

Start ofscreening

Hitselection

Leadselection

Candidateselection




Prospectiveresearch


4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










4 InTroduCTIon













I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion

















I35 Hit to Lead


6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










6 InTroduCTIon











8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion
















8 InTroduCTIon



rEFErEncES























rEFErEnCES 9

















Part I





11 introDuCtion










rEFErEnCES 9

















Part I





11 introDuCtion










Part I





11 introDuCtion













11 introDuCtion










thumbnail...7.1.3 hit triage: from actives to hits to hit series, 193 7.1.4 challenges to successful...

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