Lead Generation, 2 Volume Set

Joerg Holenz is a trained organic and medicinal chemist and acquired his PhD in Germany on the synthesis of alkaloids as antimalarial agents. After leading the preclinical activities of the marketed analgesic Tapentadol (Grunenthal Pharmaceuticals GmbH), he headed the medicinal chemistry department of Barcelona-based Laboratorios Esteve. He then moved to AstraZeneca's CNS/ pain research unit in Sweden to head lead generation chemistry. In 2012, Joerg was selected to join AZ's newly formed 'virtual' neuroscience unit in Boston as director of discovery and preclinical sciences. As a project leader he is responsible for pioneering a novel concept of driving research and development projects via increased use of academic and industry collaborative networks. In his career, Joerg worked predominantly with peripheral and central targets in the pain and neuroscience disease areas. He has edited, authored or contributed to more than 45 publications, 50 patent applications and several books and book chapters.

"it is certainly the most comprehensive and up-to-date resource currently available on the topic"......."is an excellent resource for any scientist working in lead generation"......."This is a tribute to rationaldrug discovery, which combined with very thorough up-to-date literature references and emphasis on emerging technologies, makes it a book we would readily suggest taking a look at" (Dr. Robert Webster, Dr. Nuria Ortega Hernandez Bayer Pharma AG, ChemMedChem, July 2017)

• Dedication VList of Contributors XXIPreface XXVIIA Personal Foreword XXXIVolume 68aPart I Introduction to Lead Generation 11 Introduction: Learnings from the Past – Characteristics of Successful Leads 3Mike HannAcknowledgments 10References 102 Modern Lead Generation Strategies 13Jörg Holenz and Dean G. Brown2.1 Lead Generation Greatly Influences Clinical Candidate Quality 142.2 Screening of Compound Libraries has Undergone a Major Paradigm Change 152.3 New Chemical Modalities are Available to Tackle Difficult Targets 152.4 As Demands have Increased, New Lead Generation Methods Emerged 162.5 How do Lead Generation Chemists Meet These Challenges and Subsequently Provide Their Lead Optimization Colleagues with High-Quality Lead Series? 172.5.1 Learnings can be Drawn from LG Project Failures 172.5.2 How Many Compounds to Screen to Generate High-Quality Leads? 182.5.3 Which Compounds to Screen to Generate High-Quality Leads? 192.5.4 Developing Project-Customized, Concerted, and Comprehensive Lead Generation Strategies will Increase LG Success Rates: the CREATION of Leads 202.5.5 Selecting the Target Defines LG Success Rates 212.5.6 Lead Generation should be Complemented by Auxiliary Technologies to Characterize Hits 212.5.7 Phenotypic Screens are Often Complemented by a Chemical Biology Arm 222.5.8 The Lead Generation Strategy is Defined by the Budget Allocated 222.5.9 Cost-Efficient but Information-Rich Lead Generation Strategies 232.5.10 The Revival of Potency as the Most Important Lead Criterion? 242.5.11 When has a LG Campaign Delivered Successfully? 27References 31Part II The Importance of Target Identification for Generating Successful Leads 353 “Ligandability” of Drug Targets: Assessment of Chemical Tractability via Experimental and In Silico Approaches 37Udo Bauer and Alexander L. Breeze3.1 Introduction 373.2 The Concept of Ligandability 393.2.1 General Characteristics of Ligandable Targets 393.3 The Intersection of Ligandability and Human Disease Target Space 403.3.1 Experimental Techniques for Assessing Target Ligandability 423.3.1.1 High-Throughput Screening and Subset/“Validation Set” Screening 433.3.1.2 Fragment Screening 443.4 Practical Examples of the Use of Fragment Screening for Ligandability Assessment 503.4.1 Chemical Tractability Assessment by in silico Approaches 543.4.1.1 Pocket-Finding Algorithms 543.4.1.2 Discrimination Functions and Validation Sets 553.4.1.3 Simulation-Based Methods for Identifying Interaction Potentials 563.5 Conclusions and Outlook 56References 584 Chemistry-Driven Target Identification 63Iván Cornella-Taracido, Ryan Hicks, Ola Engkvist, Adam Hendricks, Ronald Tomlinson, and M. Paola Castaldi4.1 Introduction 634.2 Chemistry-Driven Target Discovery: Enabling Biology 654.2.1 Biological Samples 654.2.2 Cells Cultured in 2D 664.2.3 Cells Cultured in 3D, Organoids, and Tissues 674.2.4 Nonhuman Cells and Whole-Organism Screening 684.2.5 Functional Assays and Readouts 684.3 Chemistry for Target Discovery 714.3.1 Screening Deck Selection 714.3.2 Triaging and Prioritization of Chemical Matter 724.3.3 SAR Expansion and Probe Synthesis for Target Deconvolution 734.4 Small-Molecule Target Identification Techniques 754.4.1 In Silico Target Deconvolution 754.4.2 Biochemical Profiling 774.4.3 Target Deconvolution Correlational Tools 784.4.4 Subcellular Localization 794.4.5 Chemical Genetics 794.4.6 Affinity Chemical Proteomics 814.4.7 Target Corroboration 844.5 Conclusions 86References 89Part III Hit Generation Methods 935 Lead Generation Based on Compound Collection Screening 95Dirk Weigelt and Ismet Dorange5.1 Introduction 955.2 Screening of Existing Collections: the General Workflow 965.2.1 High-Throughput Screening 965.2.2 Medium-Throughput Screening: Selection Methods 985.3 Generation of New Screening Compounds 995.3.1 Collection Enhancement Programs 1025.3.2 Library Design and Compound Selection 1025.3.2.1 Number of Dimensions 1035.3.2.2 Enumeration and Filtering 1045.3.2.3 Layout 1065.3.3 Focus on Synthetic Feasibility 1075.3.3.1 Multicomponent Reactions 1075.3.3.2 Click Chemistry 1085.3.3.3 Diversity-oriented Synthesis 1085.3.4 Structure-driven Approaches 1095.3.4.1 Privileged Structures 1105.3.4.2 Structure-driven Approaches Toward Unchartered Territory 1125.3.5 Target Focus 1145.3.5.1 Kinases 1145.3.5.2 G-Protein-Coupled Receptors 1155.3.5.3 Ion Channels 1165.3.5.4 Protein–Protein Interactions 1175.4 Other Concepts 1175.4.1 Natural Products 1185.4.2 DNA-Encoded Libraries 1195.4.3 Spatially Addressed Libraries 1205.4.4 On-bead Screening 1205.4.5 Dynamic Combinatorial Chemistry 1215.4.6 Cocktails and Mixtures 1215.5 Summary and Outlook 122References 1236 Fragment-Based Lead Generation 133Ivan V. Efremov and Daniel A. Erlanson6.1 Introduction 1336.2 Screening Methods 1356.3 Hit Validation 1376.4 Ligand Efficiency and Other Metrics 1386.5 Hit Optimization 1396.6 Fragment Growing 1406.7 Fragment Linking 1446.8 Protein–Protein Interactions 1476.9 GPCRs 1516.10 Computational Approaches 1526.11 Conclusions 153References 1547 Rational Hit Generation 159Bernd Wellenzohn and Alexander Weber7.1 Introduction 1597.2 Lead Generation: Transition State and Substrate Analogs 1617.3 Hit Generation by Rational Library Design 1657.4 Hit Generation by Virtual Screening 1677.4.1 Structure-based VS in Enumerated Molecules 1707.4.2 Ligand-based VS in Nonenumerated Virtual Chemical Spaces 1717.5 Hit Generation by Scaffold Replacement Technologies 1737.6 Hit Generation by Chemogenomics Approaches 1747.7 Summary 178References 1788 Competitive Intelligence–based Lead Generation and Fast Follower Approaches 183Yu Jiang, Ziping Liu, Jörg Holenz, and Hua Yang8.1 Introduction 1838.2 Competitive Intelligence-based Approach 1858.2.1 Example A: A Case Study for the Hybrid Strategy 1908.2.2 Example C: A Case Study for the Fused Strategy 1928.2.3 Example C: A Case Study for the Fused Strategy 1938.2.4 Example D: A Case Study for the Fused Strategy 1968.2.5 Example E: A Case Study for the Chimera Strategy 1978.3 Fast Follower Approach 2018.3.1 Salfanilamide-based Fast Follower Approaches 2028.3.2 Omeprazole-based Fast Follower Approaches 2038.3.3 Rimonabant-based Fast Follower Approach 210References 2149 Selective Optimization of Side Activities: An Alternative and Promising Strategy for Lead Generation 221Norbert Handler, Andrea Wolkerstorfer, and Helmut Buschmann9.1 Introduction 2219.1.1 Drug Selectivity and Unwanted or Desired Side Effects 2229.2 Definition, Rational, and Concept of the SOSA Approach 2239.2.1 Multiple Ligands and Polypharmacology 2249.2.2 Safety and Bioavailability 2259.3 Drugs in Other Drugs: Drug as Fragments 2259.4 Drug Repositioning and Drug Repurposing 2269.4.1 Old Drugs 2269.5 The SOSA Approach and Analog Design 2279.6 Patentability and Interference Risk of the SOSA Approach 2309.6.1 Analogization, Optimization, and Isosterism 2309.7 Case Studies and Examples 2319.7.1 Sulfonamides 2319.7.2 Morphine Analogs 2329.7.3 Warfarin 2329.7.4 Sildenafil (Viagra) 2329.7.5 Thalidomide Analogs 2339.7.6 Bupropion 2349.7.7 Chlorpromazine 2359.7.8 Chlorothiazide 2359.7.9 Propranolol 2359.7.10 Minaprine Analogs 2369.7.11 Viloxazine Analogs 2379.7.12 Methylation in the SOSA Strategy of Drug Design 2379.7.13 Discovery of New Antiplasmodial Compounds 2399.7.14 Drugs Acting on Central Nervous System Targets as Leads for Non-CNS Targets 2419.7.15 Mexiletine Derivatives as Orally Bioavailable Inhibitors of Urokinase-Type Plasminogen Activator 2429.7.16 Amiloride Analogs as Inhibitors of the Urokinase-type Plasminogen Activator 2459.7.17 Flavonoids with an Oligopolysulfated Moiety: A New Class of Anticoagulant Agents 2469.7.18 Clioquinol 2499.8 Conclusions 251References 25210 Lead Generation for Challenging Targets 259Jinqiao Wan, Dengfeng Dou, Hongmei Song, Xian-Hui Wu, Xuemin Cheng, and Jin Li10.1 Introduction 25910.2 DNA-Encoded Library Technology in Lead Generation 26010.2.1 Background 26010.2.2 DNA-Recorded Synthesis-Assisted Libraries 26210.2.3 DNA-Templated Synthesis-Assisted Libraries 26410.2.4 Encoded Self-Assembling Chemical Libraries 26610.2.5 Summary and Perspective 26710.3 Stapled Peptide 27610.3.1 Background 27610.3.2 Structure, Design, and Synthesis of Stapled Peptide 27810.3.2.1 Stapled Peptide Structure 27810.3.2.2 Stapled Peptide Design 28010.3.2.3 Stapled Peptide Synthesis 28210.3.3 Stapled Peptide Solution α-Helix Conversion Measurement 28310.3.4 Stapled Peptide Affinity Evaluation and α-Helix Content Correlation 28410.3.4.1 Surface Plasmon Resonance Binding Assays 28410.3.4.2 Fluorescence Polarization Assay 28410.3.4.3 Stapled Peptide Affinity and α-Helix Content Correlation 28510.3.5 Stapled Peptide Permeability 28610.3.6 Peptide Stability Assay 28810.3.7 Outlook 28810.4 Phenotypic Screening 28910.4.1 Introduction 28910.4.2 Basics for Establishing a Phenotypic Screen 29110.4.2.1 Identify a “Druggable” Phenotype and the Type of Readout 29110.4.2.2 Assay Design 29110.4.2.3 Hit Selection and Secondary Assay 29110.4.3 Typical Phenotypic Assays 29210.4.3.1 Cell-Viability Assay 29210.4.3.2 Fluorescent Imaging Plate Reader Technology 29310.4.3.3 High-Content Screening 29310.4.4 In Vitro Phenotypic Screening 29310.4.4.1 Classic Phenotypic Screening 29310.4.4.2 Patient-Derived Stem Cell in Drug Discovery 29410.4.4.3 Phenotypic Screening on iPSC-Derived Disease Models 29510.4.4.4 High-Content Cytotoxicity Screening by iPSC-Derived Hepatocytes 29610.5 Summary 297References 29811 Collaborative Approaches to Lead Generation 307Fabrizio Giordanetto, Anna Karawajczyk, and Graham Showell11.1 Introduction 30711.2 Creativity 30811.3 Speed 30811.4 Risk Sharing 30811.5 Intellectual Property 30911.6 Costs 30911.7 Management 31011.8 Lilly’s Open Innovation Drug Discovery 31011.9 Molecular Library Program 31211.10 EU Openscreen 31411.11 European Lead Factory 31511.12 Medicines for Malaria Venture 31711.13 Open Source Malaria Project 32011.14 Drugs for Neglected Diseases Initiative 32011.15 Open Lab Foundation 32111.16 Scientists Against Malaria 32211.17 Open Source Drug Discovery 32311.18 TB Alliance 32311.19 Summary 324References 325Volume 68bDedication VList of Contributors XXIPart IV Converting Hits to Successful Leads 32912 A Medicinal Chemistry Perspective on the Hit-to-Lead Phase in the Current Era of Drug Discovery 331Dean G. Brown12.1 Introduction 33112.2 Active to Hit Processes 33312.3 Target Potency: Energetics of Binding 33612.4 Addressing Vast Chemical Space: HtL Strategies 34512.5 Matched Pair Analysis 34812.6 The Role of Hydrophobicity and HtL 35112.7 Probing H-Bond Donors and Acceptors 35312.8 Structure Based DD in HtL 35612.9 Statistical Molecular Design 35812.10 Hit to Lead is not Lead Optimization 35912.11 Summary 362References 36313 Molecular Recognition and Its Importance for Fragment-Based Lead Generation and Hit-to-Lead 367Thorsten Nowak13.1 Introduction 36713.2 Brief Summary of the Main Factors that Govern Molecular Interactions 36813.3 Thermodynamics of Molecular Interactions and Impact on Hit Finding and Optimization 36913.4 Enthalpy as a Key Decision Tool in Medicinal Chemistry 37113.5 Importance of Enthalpic Interactions: Drivers of Selectivity and Specificity? 37313.6 Fragment Screening Hit Optimization: Fragment Linking 37413.7 Interstitial Waters and Their Usefulness: Case Studies on HSP-90 38113.8 Fragments to Find Hot Spots in Binding Pockets 38513.9 Nonclassical Hydrogen Bonds – Interactions of Halogen Atoms with Π-Systems and Carbonyl Groups: Factor Xa and Cathepsin L 38613.10 Binding Mode Dependency of the Experimental Conditions and Chemical Framework of Ligand 39013.11 Cooperativity in Binding: DAO or DAAO D-Amino Acid Oxidase 391References 39414 Affinity-Based Screening Methodologies and Their Application in the Hit-to-Lead Phase 401Stefan Geschwindner14.1 Introduction 40114.2 Nuclear Magnetic Resonance Spectroscopy 40214.3 Optical Biosensors: Surface Plasmon Resonance and Optical Waveguide Grating 40414.4 Isothermal Titration Calorimetry 40714.5 Thermal Shift Assay 41114.6 Mass Spectrometry Approaches 41214.7 Encoded Library Technologies 41414.8 Emerging Technologies: Microscale Thermophoresis and Backscattering Interferometry 417References 41815 Predictive Methods in Lead Generation 425Matthew D. Segall and Peter Hunt15.1 Introduction 42515.2 Compound Property Prediction 42715.3 Multiparameter Optimization: Identifying High-Quality Compounds 43015.3.1 Drug-like Properties 43015.3.2 Filters 43115.3.3 Desirability Functions and Probabilistic Scoring 43215.3.4 Pareto Optimization 43515.3.5 Example 43615.4 De Novo Design: Guiding the Exploration of Novel Chemistry 43915.4.1 Example Application 44215.5 Selection: Balancing Quality with Diversity 44315.6 Conclusions 445References 44716 Lead Quality 451J. Willem M. Nissink, Sebastien Degorce, and Ken Page16.1 Introduction 45116.2 Properties in Drug Design 45216.2.1 Primary Activity Assays 45316.2.2 Physicochemical Properties 45316.2.3 DMPK 45416.2.4 Safety 45416.2.5 Overall Profiles 45616.3 Optimizing Properties: Useful Rules, Guides, and Simple Metrics for Early-Stage Projects 45716.3.1 Rules for Potency: Ligand Efficiency Measures 45716.3.2 Rules for Safety 46216.3.3 Rules for DMPK and Mode of Administration: Early-Stage Structure-Based Profiling 46416.3.3.1 Simple Design Rules for Good DMPK 46416.3.3.2 Other DMPK Design Rules 46516.3.4 Multiobjective Optimization 46616.4 Predicted Dose to Man as a Measure of Early- and Late-Stage Lead Quality 46716.4.1 Introduction 46716.4.2 Description of Models and Data 46916.4.3 Data Supporting Technique 47116.4.3.1 Matching eD2M Doses with Normalized Observed Clinical Doses 47216.4.3.2 Matching Cmax Values from eD2M and Clinical Studies 47216.4.4 Flagging Potential Candidate Drugs Using eD2M 47316.4.5 Determining Properties that Drive eD2M Predictions for a Series 47416.5 Summary 480References 481Part V Hypothesis-driven Lead Optimization 48717 The Strategies and Politics of Successful Design, Make, Test, and Analyze (DMTA) Cycles in Lead Generation 489Steven S. Wesolowski and Dean G. Brown17.1 DMTA Cycles: Perspectives from History 49017.2 Test: What Assays, in What Order, and Why? 49417.3 Additional Advice for “Test” Component of DMTA 49617.4 Design: What to Make and Why? 49617.5 Additional Advice for “Design” Component of DMTA 50017.6 Make: Challenges and Strategies for Synthesis 50117.7 Additional Advice for the “Make” Component of DMTA 50217.8 Analyze: Making Sense of What’s Been Done and Formulating Sensible Plans for the Next Designs 50217.9 Additional Advice for “Analyze” Component of DMTA 50817.10 Results: Do Lead Optimization Teams Get What They Need? 508References 509Part VI Recent Lead Generation Success Stories 51318 Lead Generation Paved the Way for the Discovery of a Novel H3 Inverse Agonist Clinical Candidate 515Christophe Genicot and Laurent Provins18.1 Introduction 51518.2 Hit Identification 51718.3 Lead Generation 52118.3.1 Exploration of Oxazoline Substitution 52318.3.2 Rigidification of Propoxy Linker 53118.3.3 Oxazoline/Oxazole Surrogates: Lactams 53318.3.4 Conclusions 53618.4 Lead Optimization and Candidate Selection 53718.5 Conclusions 543Acknowledgments 544References 54419 Vorapaxar: From Lead Identification to FDA Approval 547Samuel Chackalamannil and Mariappan Chelliah19.1 Introduction 54719.2 Background Information on Antiplatelet Agents 54919.3 Thrombin Receptor (Protease-activated Receptor-1) Antagonists as a Novel Class of Antiplatelet Agents 55019.4 Mechanism of Thrombin Receptor Activation 55019.5 Preclinical Data Supporting the Antiplatelet Effect of Thrombin Receptor Antagonists 55119.6 Himbacine-derived Thrombin Receptor Antagonists 55219.6.1 Lead Identification 55219.6.2 Lead Generation of Himbacine-derived Thrombin Receptor Antagonist Hit 55319.6.2.1 Structure–Activity Relationship Studies 55519.6.2.2 First-Generation Thrombin Receptor Antagonists 55619.6.2.3 In vivo Metabolism of Himbacine Derivatives 55819.6.2.4 Generation of Aryl Himbacine Leads 56119.6.2.5 Second-Generation Leads that Incorporate Heteroatoms in the C-ring 56219.6.2.6 Identification of nor-seco Himbacine Lead 56419.6.3 Discovery of Vorapaxar (SCH 530348) 56519.6.3.1 Clinical Studies of Vorapaxar 56719.7 Conclusions 569Abbreviations 570Acknowledgments 570References 57120 Lead Generation Approaches Delivering Inhaled β2-Adrenoreceptor Agonist Drug Candidates 575Michael Stocks and Lilian Alcaraz20.1 Introduction 57520.2 Lead Generation Exercises to Discover β2AR Agonist Clinical Candidates 57720.3 AstraZeneca Lead Generation Exercises to Discover β2AR Agonist Clinical Candidates 58720.4 Summary 593References 59321 GPR81 HTS Case Study 597Eric Wellner and Ola Fjellström21.1 General Remarks 59721.2 The Target 59821.3 Screening Cascade 59921.4 Compound Selection (10 K Validation Set) 60221.5 HTS 60621.5.1 CSE 60821.5.2 Single-Concentration Counterscreen 61421.5.3 Clustering 61521.5.4 Cluster Expansion and Nearest Neighbours 61821.6 Hit Evaluation 61821.6.1 Potency, Efficacy, and Curves 61821.6.2 Binding Kinetics 62121.6.3 Concentration–Response Counterscreen 62221.6.4 Hit Assessment 62221.6.4.1 Size and Lipophilicity Efficiency Assessment 62221.6.4.2 Secondary Pharmacology Assessment 62621.6.5 Secondary Screening Cascade and Hit Expansion 63021.6.6 Biological Effect Assay 63421.7 Alternative Lead Generation Strategies 63821.7.1 Pepducins and Other Modified Peptides 64121.8 Conclusions 645References 64622 Development of Influenza Virus Sialidase Inhibitors 651Mauro Pascolutti, Robin J. Thomson, and Mark von Itzstein22.1 Introduction 65122.2 Targets for Anti-influenza Drug Development: Receptor Binding and Receptor Cleavage 65222.2.1 Targeting Receptor Binding by Haemagglutinin 65422.2.2 Targeting Receptor Destruction by Sialidase 65522.2.3 Influenza Virus Sialidase: Structure and Mechanism 65622.3 Development of Influenza Virus Sialidase Inhibitors 65822.3.1 The Development of Zanamivir: Proof of Concept and First-in-Class Sialidase Inhibitor Drug 65922.3.1.1 Template Selection 65922.3.1.2 Structure-based Inhibitor Design 66222.3.1.3 X-Ray Crystallographic Confirmation of Inhibitor Binding Mode 66522.3.1.4 Selectivity for Influenza Virus Sialidase over Human Sialidases 66622.3.1.5 Efficacy against Virus Replication 66722.3.1.6 Mode of Administration of the Highly Polar Drug 66722.3.1.7 Modifying the Presentation of Zanamivir: Prodrugs and Multivalency 66822.3.2 Sialidase Inhibitor Development on Noncarbohydrate Scaffolds 67122.3.2.1 A Sialidase Inhibitor Based on a Cyclohexene Scaffold: The Development of Oseltamivir 67122.3.2.2 A Sialidase Inhibitor Based on a Cyclopentane Scaffold: The Development of Peramivir 67322.3.3 Monitoring Resistance to Influenza Virus Sialidase Inhibitors 67522.4 Summary and Future Directions 676References 67623 The Discovery of Cathepsin A Inhibitors: A Project-Adapted Fragment Approach Based on HTS Results 687Sven Ruf, Christian Buning, Herman Schreuder, Wolfgang Linz, Dominik Linz, Hartmut Rütten, Georg Horstick, Markus Kohlmann, Katja Kroll, Klaus Wirth, and Thorsten Sadowski23.1 General Background 68723.2 Cathepsin A enzyme 68723.2.1 Structural Biology and Catalytic Mechanism 68723.2.2 Structural and Catalytic Functions of CatA 68923.2.3 Tissue Distribution and Substrates 68923.2.4 Natural Products and Synthetic Peptides as Inhibitors of CatA 69023.3 CatA and the Link to Cardiovascular Disease 69123.4 Lead Discovery 69223.4.1 High-Throughput Screening and Data Analysis 69223.4.2 Evaluation of Hit Series 69323.4.2.1 Covalent Inhibitor Series 69323.4.2.2 Malonamide Series 69723.4.2.3 Pyrazolone Hit Series 69823.4.3 Explorative Chemistry Delivers a Novel Lead Structure 69923.4.3.1 Crystal Structure of 9b Bound to CatA 70523.5 Lead Optimization 70523.6 Toward an in vivo Proof of Concept 71123.7 Summary and Conclusions 713References 71424 Lead Structure Discovery for Neglected Diseases: Product Development Partnerships Driving Drug Discovery 717Jeremy N. Burrows and Takushi Kaneko24.1 Introduction 71724.2 Malaria and Medicines for Malaria Venture 71924.3 Malaria Lead Generation Strategy 71924.4 Hit Identification Strategies 72224.5 Optimization of a Marketed Antimalarial Chemotype 72324.6 Target-Based Approaches 72324.7 Asexual Blood-Stage Phenotypic Screening 72424.8 Whole-Cell Screening: Results 72524.9 Repositioning of Clinical Candidates Developed for Other Indications 72624.10 Case Studies 72724.10.1 Dihydroorotate Dehydrogenase (DHODH) 72724.10.2 Whole-Cell Screening 72824.11 Screening for Malaria Eradication 72924.12 Tuberculosis and the Global Alliance for Tuberculosis Drug Development (TB Alliance) 72924.13 Target Product Profiles 73024.14 TB Alliance’s Mission 73024.15 Hit Generation Strategies for TB 73224.16 Examples of Phenotypic Screens 73324.17 Conclusions 741References 74125 A Fragmentation Enumeration Approach to Generating Novel Drug Leads 747Pravin S. Iyer and Manoranjan Panda25.1 Introduction 74725.2 Principle 74825.3 Research Methodology 74825.3.1 Fragmentation 74925.3.1.1 Origin of Parent Molecules 74925.3.1.2 Cores and Daughters 74925.3.1.3 Nonflat Cores 75125.3.2 Intelligent Recombination and Enumeration 75425.4 Evaluation 75425.4.1 Preliminary Experimental Evaluation 75525.4.2 In Silico Evaluation 75525.4.3 Virtual Screening Using Enzyme–Ligand Docking 75625.5 Summary 758References 759Index 761