Target Discovery and Validation

Alleyn Plowright obtained his PhD in organic chemistry with Professor Gerald Pattenden at the University of Nottingham, UK in 1999, and continued with postdoctoral studies in chemical biology with Professor Andrew Myers at Harvard University, USA. In 2002, he joined AstraZeneca and in 2008 he became Associate Director Medicinal Chemistry leading the Lead Optimisation section driving new programs to the clinic. In 2012, Dr. Alleyn took on the role of Senior Principal Scientist and Project Leader in the Cardiovascular and Metabolic Diseases Innovative Medicines unit leading multidisciplinary research, including phenotypic screening and driving new projects into and through the drug project portfolio. In 2017 he moved to Sanofi, taking on the position as Head Integrated Drug Discovery Germany leading a cross-disciplinary research unit driving projects from target validation through to pre-clinical development. He has been instrumental in delivering a number of compounds into the clinic, including two compounds currently in phase 2 clinical trials, and has authored more than 50 publications and patents.

• Preface xiiiA Personal Foreword xvii1 Chemical Strategies for Evaluating New Drug Targets 1Adrian J. Carter, Raina Seupel, Paul E. Brennan, Michael Sundström, Andrea Introini, and Anke Mueller-Fahrnow1.1 Introduction 11.2 Use Cases and Case Studies for Chemogenomic Compounds and Chemical Probes 51.2.1 Chemogenomic Libraries 51.2.2 Inactive Control 61.2.3 Use of Biological Target Panels and Profiling 81.3 Development of Chemical Probes 101.3.1 From BIX01294 to EPZ035544: Development and Improvement of G9a/GLP Inhibitors 101.3.2 Development of BRD9 Inhibitors 121.4 Compound-Based Target Evaluation with Patient-Derived Cells 141.4.1 Compound-Based Target Evaluation 141.4.2 Patient-Derived Cell Assays 161.4.3 Target Evaluation Approach 161.4.4 Case Story: Inflammatory Bowel Disease (IBD) Tissue Platform 181.5 Summary and Outlook 19References 202 Affinity-Based Chemoproteomics for Target Identification 25Annika Jenmalm Jensen and Ivan Cornella Taracido2.1 Introduction 252.2 Small Molecule Phenotypic Mechanism of Action Elucidation 292.3 Quantitative High-Resolution Mass Spectrometry as a Protein Detection Read-Out 302.4 In-Lysate Affinity-Based Chemical Proteomics 332.4.1 Design of the Affinity Probe 342.4.2 General Experimental Pulldown Workflow 362.4.3 Limitations 382.5 In-Cell Light-Activated Affinity-Based Chemoproteomics 392.5.1 Design of the Reactive Photoaffinity Probe (PAL Probe) 402.5.2 General Experimental Workflow 402.5.3 Limitations 432.6 Target Validation and Mode of Action 432.7 Concluding Remarks 45References 463 Activity-Based Protein Profiling 51Nattawadee Panyain, Cassandra R. Kennedy, Ryan T. Howard, and Edward W. Tate3.1 Introduction 513.2 Activity-Based Probe (ABP) and Affinity-Based Probe (AfBP) Design 533.2.1 Warheads (Reactive Groups) 533.2.1.1 Electrophilic Warheads 553.2.1.2 Photocrosslinking Warheads 553.2.2 Reporter Tags 563.2.3 Linkers 563.2.4 Bioorthogonal Ligation Chemistry 573.2.4.1 Staudinger Ligation 583.2.4.2 Copper(I)-Catalysed Azide-Alkyne Cycloaddition (CuAAC) 583.2.4.3 Strain-Promoted Azide-Alkyne Cycloaddition (SPAAC) 593.2.4.4 Diels–Alder Reaction 593.3 Chemical Proteomic Workflow 603.3.1 Quantitative Proteomics by Mass Spectrometry 613.3.1.1 Label-Free Quantification (LFQ) 613.3.1.2 Chemical Labelling Quantification 613.3.1.3 Metabolic Labelling Quantification 633.4 ABPP Applications and Case Studies 633.4.1 Case Study 1: Activity-Based Protein Profiling as a Robust Method for Enzyme Identification and Screening in Extremophilic Archaea 653.4.2 Case Study 2: Failed Clinical Trial of a Fatty Acid Amide Hydrolase (FAAH) Inhibitor 683.4.3 Case Study 3: Target Identification of Small Molecule Inhibitors 713.4.3.1 New Target Profiling for Sulforaphane 713.4.3.2 Profiling USP Inhibitors in Human Cell Lines as Potential Therapeutic Molecules 733.4.4 Case Study 4: Fragment-Based Ligand Discovery Aided by Photoaffinity Labelling 743.4.5 Case Study 5: Quenched Fluorescent Activity-Based Probe (qABP) Design and Application in Protein Localization 803.5 Summary 82References 834 Kinobeads: A Chemical Proteomic Approach for Kinase Inhibitor Selectivity Profiling and Target Discovery 97Maria Reinecke, Stephanie Heinzlmeir, Mathias Wilhelm, Guillaume Médard, Susan Klaeger, and Bernhard Kuster4.1 Kinase Inhibitor Target Deconvolution Using Chemical Proteomics 974.1.1 Polypharmacology of Small Molecule Kinase Inhibitors 974.1.2 Chemoproteomic Profiling of Kinase Inhibitors 1004.1.3 Tips and Tricks Regarding Chemoproteomic Assay Development 1034.2 Detailed Kinobeads Protocol 1054.2.1 Cell or Tissue Lysate 1074.2.2 Affinity Matrices 1074.2.3 Kinobeads Competition Assay 1104.2.4 Mass Spectrometry 1114.2.5 Peptide and Protein Identification and Quantification 1124.2.6 Data Analysis 1124.3 Application Examples for Kinobeads 1134.3.1 Expanding the Target Space of Kinobeads 1134.3.2 Target Space Deconvolution of Small Molecule Kinase Inhibitors 1164.3.3 Opportunities Arising from Inhibitor Polypharmacology: Drug Repositioning 1204.3.4 Chemoproteomic-Guided Medicinal Chemistry 1214.4 Kinobeads, Inhibitors, and Drug Discovery: Where are We Heading? 1234.4.1 What is a Good Drug? 1234.4.2 How Can We Discover New Drugs in the Future? 1244.4.3 The Yin and Yang of Chemoproteomic-Guided Drug Discovery 124Acknowledgments 125References 1255 Label-Free Techniques for Target Discovery and Validation 131Daniel Martinez Molina and Michael Dabrowski5.1 Introduction 1315.2 CETSA: How It All Began 1325.3 The CETSA Formats 1365.3.1 CETSA Classics 1365.3.2 CETSA HT 1385.3.3 CETSA MS 1405.4 Target Discovery 1425.4.1 Generation of Active Hit Molecules 1425.4.2 Tool Generation (Small Screens to Identify Tool Compounds) 1435.4.3 Target Classes That are In and Out of Scope and Difficult Targets 1435.4.4 Focused or Iterative Library Screening 1445.4.5 Fragment Library Screening 1445.4.6 Hit Confirmation 1455.4.7 Phenotypic Hit Deconvolution to Discover Targets 1455.5 Target Validation 1475.5.1 Binding Modes 1475.5.2 Selectivity, Specificity, and Safety 1485.5.3 Translation Bench to Bedside (via Animals) 1495.6 Conclusion 150References 1516 Reverse Translation to Support Efficient Drug Target Selection and Stratified Medicine 153Lauren Drowley and Martin Armstrong6.1 Introduction: the Challenge 1536.2 Genetics to Date in Drug Discovery 1546.3 Genetic Strategies for Target Discovery 1566.3.1 GWAS 1586.3.2 Rare Disease Genetics 1606.3.2.1 Rare Mutation→Rare Disease Drug Discovery 1616.3.2.2 Rare Mutation→Common Disease Drug Discovery 1616.3.3 Somatic Mutations 1626.3.4 Analytical Approaches 1636.4 Functional Validation 1646.4.1 Prioritization of Putative Mutations 1656.4.2 Determining Functional Consequence of Mutation 1656.4.2.1 Publicly Available Data 1656.4.2.2 Systems Biology 1666.4.2.3 Model Systems: ‘The Tissue is the Issue’ 1686.4.3 Druggability: From Validation of a Gene to a Druggable Target 1696.5 Forward-Looking Perspectives 1706.5.1 Molecular Taxonomy of Disease 1716.5.2 Precision Medicine 1716.5.3 Data Integration 1726.6 Conclusion 173References 1737 Elucidating Target Biology and Drug Mechanism of Action Across Human Cell-Based Model Systems 179John C. Dawson and Neil O. Carragher7.1 Introduction 1797.2 Advances in Human Cell-Based Model Development 1827.2.1 Next-Generation Sequencing (NGS) 1837.2.2 CRISPR Genome Editing 1847.2.3 Induced Pluripotent Stem Cell Biology 1847.2.4 3D Cell and Organoid Models 1857.2.5 Microfluidic and Organ-on-a-Chip Devices 1867.2.6 In Vivo Imaging 1887.2.7 High-Content Imaging 1907.3 Multiparametric High-Content Phenotypic Profiling of Target Biology and Drug Mechanism of Action 1917.3.1 High-Content Cell Painting in Functional Genomics 1937.3.2 Integration of Multiparametric High-Content Imaging with Chemoinformatics 1957.3.3 Guiding Chemical Design and Target Selectivity from Multiparametric High-Content Analysis 1957.4 Target-Annotated Compound Libraries for Phenotypic Screening and MOA Determination 1967.5 Quantitative Pathway Profiling Across New Model Systems 1977.5.1 Pathway Profiling at the Gene Transcription Level 1987.5.2 Dynamic Post-Translational Pathway Profiling Across Dose–Response and Time-Series Studies 1997.6 Conclusions 202References 2038 Cell Biology Methods in Target Validation 211Manfred Koegl and Simon Wöhrle8.1 Introduction 2118.2 Biomarkers 2118.2.1 Direct Target Engagement Biomarkers 2128.2.2 Indirect Target Engagement Biomarkers and Pathway Biomarkers 2138.2.3 Response Biomarkers 2148.2.4 Correlation of Biomarkers 2148.3 Direct Evidence to Show That Modulation of a Target Leads to a Cellular Response 2198.4 Direct Evidence That Target Modulation is Responsible for Cellular Responses by Mutations Conferring Sensitivity to Existing Drugs 2198.4.1 The ‘Bump-and-Hole’ Approach to Generate Sensitivity to Small Molecule Inhibitors 2198.4.2 Chemogenomic Approaches for Inducible Degradation of Protein Targets 2228.5 Resistance Conferring Mutations 226References 2299 Genetic Manipulation/Modulation for Target Discovery and Validation 233Christophe Lanneau, Georges Kalouche, Xinming Cai, Francois Lo-Presti, and Christoph Potting9.1 Introduction 2339.2 Overview of the Development of Leading Genetic Manipulation Technologies 2349.2.1 RNAi, ZFNs, and TALENs 2349.2.2 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) 2379.3 Considerations for Designing and Interpreting CRISPR Experiments 2389.3.1 Methodological Considerations for Genetic Manipulation by the CRISPR/Cas Technology 2389.3.2 Choosing a Cellular Model: Biological and Genomic Aspects 2399.3.3 gRNA Design 2429.3.3.1 Identification of Target Locations 2429.3.3.2 Selection of Spacer Sequences 2459.3.3.3 Predictive Tools 2479.3.4 Successful Application of the CRISPR/Cas Technology 2499.3.4.1 Delivering CRISPR Reagents to Target Cells 2499.3.4.2 Check for Anticipated Knockout/Knock-In 2529.4 Further Developments of the CRISPR/Cas Technology Facilitates Additional Modes of Genetic Perturbation 2539.4.1 CRISPRi 2539.4.2 CRISPRa 2539.4.3 Base Editing 2549.5 The CRISPR/Cas Technology in Target Discovery and Validation 2549.5.1 CRISPR/Cas Technology for Early Target Validation 2549.5.2 CRISPR Screens and Use for Target Discovery 2559.5.3 CRISPR Screens: General Principle and Considerations 2569.5.4 Selected Examples of Target Discovery Using CRISPR Screens to Illustrate the Breadth of Applications 2589.6 Application of CRISPR Genome Editing in Immunology Studies 2609.7 Concluding Remarks 262References 26310 Computational Approaches for Target Inference 277Gerhard Hessler, Christoph Grebner, and Hans Matter10.1 Introduction 27710.2 Data Annotation for Target Identification 27810.3 In Silico Methods for Target Identification 28010.3.1 2D Similarity Methods for Target Inference 28310.3.2 3D Similarity Methods for Target Inference 28910.3.3 Fragment-Based Approaches 29010.3.4 QSAR Models and Machine Learning 29210.3.5 Experimentally Derived Molecular Descriptors 29710.3.6 Structure-Based Screening 29910.3.7 Protein–Protein and Ligand–Target Networks 30210.4 Practical Considerations 30410.5 Conclusion 307References 30811 Bioinformatic Approaches in the Understanding of Mechanism of Action (MoA) 323Maria-Anna Trapotsi, Ian Barrett, Ola Engkvist, and Andreas Bender11.1 Bioinformatics: Introduction 32311.1.1 Some Definitions: Mechanism Versus Mode of Action 32311.1.2 Importance of MoA and Target Prediction in the Drug Discovery Process 32411.1.3 Different Levels of Information in Mechanism of Action and Target Prediction 32511.2 Transcriptomics Data and Databases 32611.2.1 Biological Background of the Transcription Process 32611.2.2 Connectivity Map: CMap 32711.2.2.1 Applications of CMap in MoA Deconvolution 32811.2.3 Library of Integrated Network-Based Cellular Signatures (LINCS) 33111.2.3.1 LINCS L1000 Data Exploration 33211.2.3.2 Applications of L1000 Data in MoA Understanding 33311.3 Pathway Data and Databases 33911.3.1 What is a Pathway? 33911.3.2 Process of Pathway Analysis 34111.3.3 Pathways in the Understanding of MoA 34511.3.3.1 Methodology 1: MoA Analysis by Annotating Predicted Compounds’ Targets with Pathways 34511.3.4 Combination of Gene Expression and Pathway Data 34611.3.4.1 Methodology 2: Construction of Drug Networks (DNs) with Gene Expression Data and Pathway Annotations 34611.3.4.2 Methodology 3: Link Drug Target and Pathway Activation to Understand MoA 34711.4 Image-Based Data 34811.4.1 Image Data and Where to Extract Them From 34811.4.2 Application of Image-Based Data in Target Prediction and Better Understanding of MoA 35011.4.2.1 Methodology 1: Clustering of Compounds Based on Cell Morphology 35011.4.2.2 Methodology 2: Use of Image-Based Data in the Development of a Cell Morphology Database That Can Facilitate Drug Target Identification 35011.4.2.3 Methodology 3: Use of Image Data in Drug Repositioning and Biological Activity Prediction 35311.4.2.4 Methodology 4: Association of Genes with Context-Dependent Morphology Alterations from Cells Exposed to Chemical or Genetic Perturbations for MoA Elucidation 35411.5 Conclusions 357Acknowledgement 357References 357Index 365