Small Molecule Medicinal Chemistry

Werngard Czechtizky is the Head of Medicinal Chemistry of the German Hub of Sanofi, based in Frankfurt, Germany.  She has wide experience in lead generation and lead optimization for central nervous system, cardiovascular and diabetes targets, and her teams have been responsible for a number of leads and clinical candidates in these areas over the last years. She was educated at ETH Zürich and Harvard University, USA. Peter Hamley is the global head of External Innovation and Sourcing for chemistry, computational chemistry, and screening technologies at Sanofi, based in Frankfurt, Germany.  He spent ten years at AstraZeneca in the United Kingdom, and then moved to Sanofi as a medicinal chemistry leader, building their automated chemistry capabilities and natural product technology. He was educated at Imperial College, London, the University of Cambridge and the University of Pennsylvania.

List of Contributors xiii

Introduction 1
Werngard 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 13
Edgar Jacoby

1.1 Introduction 13

1.2 Chemical Space for Drug Discovery 14

1.3 Molecular Properties for Drug Discovery 17

1.4 Major Compound Classes 21

1.5 Chemical Design Approaches to Expand Bioactive Chemical Space 25

1.6 Conclusion 28

Acknowledgments 29

References 29

2 The European Lead Factory 37
Christopher Kallus, Jörg Hüser, Philip S. Jones, and Adam Nelson

2.1 Introduction 37

2.1.1 Background 37

2.1.2 The European Lead Factory 38

2.2 Building the Joint European Compound Library 43

2.2.1 Definition of Criteria and an Approach for the Review and Selection of Library Proposals 46

2.2.2 Collation, Review, and Selection of an Initial Wave of Library Proposals 47

2.2.3 A Web]Based Tool to Support the Collation, Review, and Selection of Proposals 49

2.2.4 Synthetic Validation of Library Proposals and Library Production 49

2.3 Qualified Hit Generation 54

2.3.1 Capabilities of the ESC 54

2.3.2 Target Selection and Generation of Qualified Hits 56

2.3.3 Exploitation of Qualified Hit List 58

2.4 Future Perspectives 58

Acknowledgments 59

References 59

3 Access to Compound Collections: New Business Models for Compound Acquisition and Sharing 61
Peter ten Holte

3.1 Introduction 61

3.1.1 Vertical Disintegration and the Quest for Innovation 61

3.1.2 Innovative Chemistry 63

3.1.3 Access to Supplementary Compound Collections 63

3.2 Risk]Sharing Approaches 64

3.2.1 Overview 64

3.2.2 Blinded Screening 65

3.2.3 Follow]Up of Blinded Screening: Various Models 65

3.3 Library Exchange 69

3.3.1 Partners with Different Scientific Interests 70

3.3.2 Partners with Similar Scientific Interests 70

3.3.3 Compound Selection: Use and Potential Risks 71

3.4 Sharing Collections for External Screening 72

3.4.1 Rationale 72

3.4.2 Academic Drug Discovery Consortium (Addc) 72

3.4.3 Eu]Openscreen 73

3.4.4 N IH Roadmap 73

3.5 Conclusion 74

Acknowledgments 74

References 75

Part II Exploring Biological Space: Access to New Chemistries 77

4 New Advances in Diversity]Oriented Synthesis 79
Warren R. J. D. Galloway, Jamie E. Stokes, and David R. Spring

4.1 Introduction: Small Molecules and Biology 79

4.2 The Need for Structural Diversity in Synthetic Small Molecule Screening Collections 80

4.3 Diversity]Oriented Synthesis of New Structurally Diverse Compound Collections 82

4.3.1 General Principles of Diversity]Oriented Synthesis 82

4.3.2 Achieving Structural Diversity: The Importance of Scaffold Diversity 83

4.3.3 Synthetic Principles in DOS 83

4.3.4 Scaffold Diversity and Molecular Type 86

4.3.5 Examples of DOS Campaigns 86

4.4 Concluding Remarks 97

References 98

5 Solid]Phase Combinatorial Chemistry 103
Marcel Patek, Martin Smrcina, Eric Wegrzyniak, Victor Nikolaev, and Andres Mariscal

5.1 Introduction 103

5.2 Chapter Outline 104

5.3 Combinatorial Chemistry in Retrospect 104

5.4 Foundations of Solid]Phase Synthesis of Combinatorial Chemistry 107

5.4.1 Ingredients of Solid]Phase Chemistry 109

5.4.2 Library Development and Production 117

5.4.3 Analytical Chemistry and Solid]Phase Synthesis of Libraries 129

5.5 The Outcome of Tucson Combinatorial Chemistry at Sanofi 132

5.5.1 Overall Strategy 132

5.5.2 Drug Discovery Outcomes 134

5.5.3 Key Parameters of Combichem Productivity 134

5.6 Conclusions and Outlook 135

References 136

6 Recent Advances in Multicomponent Reaction Chemistry: Applications in Small Molecule Drug Discovery 145
Christopher Hulme, Muhammad Ayaz, Guillermo Martinez]Ariza, Federico Medda, and Arthur Shaw

6.1 Introduction 145

6.2 Classical Multi-Component Reactions (MCRs) 147

6.3 The Passerini Reaction (Mario Passerini 1921) 147

6.4 Ugi Reaction 147

6.4.1 The Ugi-deprotect-cyclize (UDC) strategy 152

6.4.2 Bi-functional approach (BIFA) 153

6.4.3 Miscellaneous Post]Ugi Condensations 154

6.5 Van Leusen Reaction 154

6.6 Petasis Reaction 155

6.7 Groebke–Blackburn–Bienaymé (GBB) Reaction 155

6.8 Recently Discovered Novel MCRs 155

6.8.1 Cyclic Anhydride]Based MCRs 155

6.8.2 1]Azadiene]Based MCRs 156

6.8.3 Recent IMCRs and Secondary Reactions 157

6.8.4 Miscellaneous MCRs 159

6.9 Asymmetric MCRs 159

6.10 Applications of MCRs in Medicinal Chemistry 160

6.10.1 Kinase Inhibitors 161

6.10.2 Protease Inhibitors 163

6.10.3 Ion Channel Inhibitors 165

6.10.4 Protein–Protein Interaction Inhibitors 165

6.10.5 Tubulin Polymerization Inhibitors 166

6.10.6 G]Protein]Coupled Receptors 168

6.11 Summary 171

References 171

Part III Screening Strategies 189

7 Computational Techniques to Support Hit Triage 191
Douglas B. Kitchen and Hélène Y. Decornez

7.1 Lead Finding Process: Overview and Challenges 191

7.1.1 The Need for Triage 191

7.1.2 The Lead Generation Process 191

7.1.3 Hit Triage: From Actives to Hits to Hit Series 193

7.1.4 Challenges to Successful Lead Finding 194

7.1.5 Frequent Hitters 195

7.1.6 Implications of Human Decision]Making 195

7.2 Chemical Structure Analysis of Hit Lists 196

7.2.1 Similarity]Based Clustering 197

7.2.2 Scaffold]Based Clustering 198

7.2.3 Application of Clustering Classification Methods 201

7.3 Rules and Filters 201

7.3.1 Computational Descriptors for Property Assessment 202

7.3.2 Lipophilicity and Other Physicochemical Descriptors 205

7.3.3 Structural and Shape Descriptors 205

7.3.4 Multiparameter Calculations: MPO and QED 206

7.3.5 Frequent]Hitter Analysis 207

7.3.6 Reactive Group Analysis 209

7.4 Triage Systems 210

7.5 Ligand Efficiency Indices 210

7.6 Hit Series Analysis 211

7.6.1 Latent Hit Series and Singletons 211

7.6.2 Rapid Hit Exploration and Compound Set Enrichment 211

7.6.3 SAR Analysis 212

7.6.4 Data Volume, Integration, Retrieval, and Visualization 213

7.7 Summary 214

References 214

8 Fragment]Based Drug Discovery 221
Jean]Paul Renaud, Thomas Neumann, and Luc Van Hijfte

8.1 Introduction 221

8.2 Fragment Libraries 223

8.3 Biophysical Screening Technologies 223

8.3.1 Surface Plasmon Resonance (SPR) 224

8.3.2 Nuclear Magnetic Resonance (NMR) 231

8.3.3 X]Ray Crystallography 234

8.3.4 Noncovalent Mass Spectrometry 235

8.3.5 Differential Scanning Fluorimetry (DSF) 237

8.3.6 Biophysical Techniques for Fragment Screening against Membrane Proteins 238

8.3.7 Biophysical Techniques for Fragment Screening against PPIs 238

8.4 Fragment Evolution Strategies 239

8.5 Fbdd Case Studies 240

8.5.1 Aurora Kinase Inhibitors 240

8.5.2 Tackling PPIs: Fragment]Based Discovery of Bromodomain Inhibitor Leads 241

8.6 The Future 243

References 244

9 Virtual Screening 251
Karl]Heinz Baringhaus and Gerhard Hessler

9.1 Introduction 251

9.1.1 Goals of Virtual Screening 252

9.2 Databases and Database Preparation 254

9.3 Validation of the Virtual Screening Strategy 256

9.4 Ligand]Based Virtual Screening 258

9.4.1 2D Approaches 259

9.4.2 3D Ligand]Based Approaches 261

9.5 Structure]Based Virtual Screening 263

9.6 Other Virtual Screening Applications 266

9.7 Conclusion 268

References 269

10 Phenotypic Screening 281
Michelle Palmer

10.1 Introduction 281

10.2 History and Past Successes 282

10.3 Impact of Phenotypic Screening 282

10.4 Model Systems for Phenotypic Assays 285

10.4.1 Cell Lines 285

10.4.2 Primary and Stem Cells 285

10.4.3 Cocultures 286

10.4.4 3D Cell Models 287

10.5 Assays 287

10.5.1 Assay Technologies 287

10.5.2 Assay Development Considerations 290

10.5.3 Example 1: Selective Killing of Breast Cancer Stem Cells 291

10.5.4 Example 2: CFTR Potentiator Drug 291

10.6 Deorphaning 292

10.6.1 Affinity]Based Proteomics 292

10.6.2 Genetic Profiling 295

10.6.3 Target Profiling 296

10.6.4 Comodifier Profiling 296

10.6.5 Target Engagement 297

10.6.6 Example 3: Elucidating MOA for a Regulator of Polyploidization 297

10.7 Summary 298

References 299

Part IV Technologies for Medicinal Chemistry Optimization 305

11 Advances in the Understanding of Drug Properties in Medicinal Chemistry 307
Peter Hamley and Patrick Jimonet

11.1 Introduction 307

11.2 Properties and Origins of Marketed Drugs 308

11.2.1 The Consistent Properties of Oral Drugs 308

11.2.2 The Changing Origins of Oral Drugs 308

11.3 Drug Properties and Attrition in Clinical Development 310

11.4 The Rule of Five 312

11.4.1 The Concept 312

11.4.2 Druggability 313

11.5 The Concept of Lead]Likeness 313

11.5.1 The Consequences on Screening and Collections 314

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

11.7 Building on the Ro5: New Guidelines for Compound Design 316

11.7.1 Ligand Efficiency 316

11.7.2 Ligand Lipophilicity Efficiency and Other Indices 317

11.7.3 Chemical Beauty 318

11.8 Alternatives, Criticisms, and Exceptions 318

11.9 Conclusions 320

References 320

12 Recent Developments in Automated Solution Phase Library Production 323
Thomas C. Maier and Werngard Czechtizky

12.1 Introduction 323

12.1.1 Introduction and Definitions 323

12.1.2 Library Types 324

12.1.3 Chemotypes 326

12.2 Library Production 327

12.2.1 The Library Production Process 327

12.2.2 Process Optimization 330

12.3 New Technologies in Automated Liquid]Phase Library Synthesis 334

12.3.1 Provision of Starting Materials: Automated Reagent Dispensaries 334

12.3.2 Microwave 335

12.3.3 Library Purification: Automated RP]HPLC and SFC as Orthogonal Methods 336

12.4 Flow Chemistry and Gas]Phase Reactions 342

12.4.1 Reactive Gases in Flow 344

12.5 Conclusion 345

References 345

13 A DME Profiling: An Introduction for the Medicinal Chemist 353
Katharina Mertsch, Martin Will, Werngard Czechtizky, Niels Griesang, Alexander Marker, and Jacob Olsen

13.1 Introduction 353

13.2 Compound Profiling in H2L Optimization 354

13.2.1 Intestinal Absorption 354

13.2.2 Drug Metabolism and Inhibition of CYP450 Enzymes 355

13.2.3 Protein Binding 356

13.2.4 En Route to a Lead Series: In Vivo PK Studies 358

13.3 Compound Profiling in Lead Optimization 359

13.3.1 Extended CYP Inhibition Studies 359

13.3.2 Mechanism]Based CYP Inhibition 359

13.3.3 Inhibition of Transport Proteins 360

13.3.4 Biopharmaceutical Classification of a Clinical Candidate (Classification of Potential Drugs into Biopharmaceutical Classification System or Biopharmaceutical Drug Disposition and Classification System) 360

13.4 Integration of Medicinal Chemistry, Biology, Physicochemical, and ADME Profiling: Strategies Toward Cycle Time Reductions 362

13.4.1 Planning Phase 363

13.4.2 Sample Preparation and Distribution 364

13.4.3 Compound QC 365

13.4.4 Determination of Physicochemical Properties 367

13.4.5 ADME Profiling: General Remarks 369

13.4.6 Metabolic Lability Profiling 369

13.4.7 Permeability Testing 370

13.4.8 CYP Inhibition Profiling 372

13.5 Summary 372

References 373

Part V Medicinal Chemistry beyond Small Molecules 379

14 The Role of Natural Products in Drug Discovery: Examples of Marketed Drugs 381
Lars Ole Haustedt and Karsten Siems

14.1 Natural Products and Natural Product Derivatives in Commercial Drugs 381

14.2 Hit to Lead Optimization of Natural Product Hits 397

14.3 Case Study 1: Taxol 397

14.4 Case Study 2: Epothilone 406

14.5 Case Study 3: Eribulin 407

14.6 Case Study 4: Geldanamycin 413

14.7 Case Study 5: Ingenol Mebutate (Picato) 417

14.8 Summary 422

References 423

15 Peptidomimetics of α]Helical and β]Strand Protein Binding Epitopes 431
Nina Bionda and Rudi Fasan

15.1 Protein–Protein Interactions as Therapeutic Targets 431

15.2 Peptidomimetics of α]Helical Protein Binding Epitopes 433

15.2.1 α]Helix]Mediated PPIs 433

15.2.2 Side]Chain Cross]Linked α]Helices 435

15.2.3 Hydrogen]Bond Surrogate]Stabilized α]Helices 442

15.2.4 Other Type I α]Helix Peptidomimetics 443

15.2.5 Type III α]Helix Peptidomimetics 445

15.3 Peptidomimetics of β]Strand Protein Binding Epitopes 446

15.3.1 β]Strand]Mediated PPIs 446

15.3.2 Type I β]Strand Peptidomimetics 447

15.3.3 Type III β]Strand Peptidomimetics 449

15.4 Conclusion 452

References 453

16 In Vivo Imaging of Drug Action 465
Oliver Plettenburg and Matthias Löhn

16.1 Introduction 465

16.2 Overview of Imaging Methods 466

16.2.1 Fluorescence]Based Methods 466

16.2.2 MRI 470

16.2.3 CT 470

16.2.4 PET/SPECT 471

16.3 Imaging of Therapeutic Effects 476

16.3.1 Cancer 476

16.3.2 Diabetes 483

16.3.3 CNS Disorders 486

16.4 Conclusion and Outlook 490

References 491

Index 503