Small Molecule Medicinal Chemistry E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1050

Veröffentlichungsjahr: 2015

Beschreibung

Stressing strategic and technological solutions to medicinal chemistry challenges, this book presents methods and practices for optimizing the chemical aspects of drug discovery. Chapters discuss benefits, challenges, case studies, and industry perspectives for improving drug discovery programs with respect to quality and costs.

•    Focuses on small molecules and their critical role in medicinal chemistry, reviewing chemical and economic advantages, challenges, and trends in the field from industry perspectives
•    Discusses novel approaches and key topics, like screening collection enhancement, risk sharing, HTS triage, new lead finding approaches, diversity-oriented synthesis, peptidomimetics, natural products, and high throughput medicinal chemistry approaches
•    Explains how to reduce design-make-test cycle times by integrating medicinal chemistry, physical chemistry, and ADME profiling techniques
•    Includes descriptive case studies, examples, and applications to illustrate new technologies and provide step-by-step explanations to enable them in a laboratory setting

Leseprobe

Table of Contents

COVER

TITLE PAGE

LIST OF CONTRIBUTORS

INTRODUCTION

I.1 MEDICINAL CHEMISTRY: A DEFINITION

I.2 THE ROLE OF A MEDICINAL CHEMIST

I.3 THE STATE OF THE ART

I.4 CURRENT AND FUTURE CHALLENGES FOR MEDICINAL CHEMISTRY

REFERENCES

PART I: EXPLORING BIOLOGICAL SPACE: ACCESS TO NEW COLLECTIONS

1 ELEMENTS FOR THE DEVELOPMENT OF STRATEGIES FOR COMPOUND LIBRARY ENHANCEMENT

1.1 INTRODUCTION

1.2 CHEMICAL SPACE FOR DRUG DISCOVERY

1.3 MOLECULAR PROPERTIES FOR DRUG DISCOVERY

1.4 MAJOR COMPOUND CLASSES

1.5 CHEMICAL DESIGN APPROACHES TO EXPAND BIOACTIVE CHEMICAL SPACE

1.6 CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

2 THE EUROPEAN LEAD FACTORY

2.1 INTRODUCTION

2.2 BUILDING THE JOINT EUROPEAN COMPOUND LIBRARY

2.3 QUALIFIED HIT GENERATION

2.4 FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

REFERENCES

3 ACCESS TO COMPOUND COLLECTIONS

3.1 INTRODUCTION

3.2 RISK-SHARING APPROACHES

3.3 LIBRARY EXCHANGE

3.4 SHARING COLLECTIONS FOR EXTERNAL SCREENING

3.5 CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

PART II: EXPLORING BIOLOGICAL SPACE: ACCESS TO NEW CHEMISTRIES

4 NEW ADVANCES IN DIVERSITY-ORIENTED SYNTHESIS

4.1 INTRODUCTION: SMALL MOLECULES AND BIOLOGY

4.2 THE NEED FOR STRUCTURAL DIVERSITY IN SYNTHETIC SMALL MOLECULE SCREENING COLLECTIONS

4.3 DIVERSITY-ORIENTED SYNTHESIS OF NEW STRUCTURALLY DIVERSE COMPOUND COLLECTIONS

4.4. CONCLUDING REMARKS

REFERENCES

5 SOLID-PHASE COMBINATORIAL CHEMISTRY

5.1 INTRODUCTION

5.2 CHAPTER OUTLINE

5.3 COMBINATORIAL CHEMISTRY IN RETROSPECT

5.4 FOUNDATIONS OF SOLID-PHASE SYNTHESIS OF COMBINATORIAL CHEMISTRY

5.5 THE OUTCOME OF TUCSON COMBINATORIAL CHEMISTRY AT SANOFI

5.6 CONCLUSIONS AND OUTLOOK

REFERENCES

6 RECENT ADVANCES IN MULTICOMPONENT REACTION CHEMISTRY

6.1 INTRODUCTION

6.2 CLASSICAL MULTI-COMPONENT REACTIONS (MCRs)

6.3 THE PASSERINI REACTION (MARIO PASSERINI, 1921)

6.4 UGI REACTION

6.5 VAN LEUSEN REACTION

6.6 PETASIS REACTION

6.7 GROEBKE–BLACKBURN–BIENAYMÉ (GBB) REACTION

6.8 RECENTLY DISCOVERED NOVEL MCRs

6.9 ASYMMETRIC MCRs

6.10 APPLICATIONS OF MCRs IN MEDICINAL CHEMISTRY

6.11 SUMMARY

REFERENCES

PART III: SCREENING STRATEGIES

7 COMPUTATIONAL TECHNIQUES TO SUPPORT HIT TRIAGE

7.1 LEAD FINDING PROCESS: OVERVIEW AND CHALLENGES

7.2 CHEMICAL STRUCTURE ANALYSIS OF HIT LISTS

7.3 RULES AND FILTERS

7.4 TRIAGE SYSTEMS

7.5 LIGAND EFFICIENCY INDICES

7.6 HIT SERIES ANALYSIS

7.7 SUMMARY

REFERENCES

8 FRAGMENT-BASED DRUG DISCOVERY

8.1 INTRODUCTION

8.2 FRAGMENT LIBRARIES

8.3 BIOPHYSICAL SCREENING TECHNOLOGIES

8.4 FRAGMENT EVOLUTION STRATEGIES

8.5 FBDD CASE STUDIES

8.6 THE FUTURE

REFERENCES

9 VIRTUAL SCREENING

9.1 INTRODUCTION

9.2 DATABASES AND DATABASE PREPARATION

9.3 VALIDATION OF THE VIRTUAL SCREENING STRATEGY

9.4 LIGAND-BASED VIRTUAL SCREENING

9.5 STRUCTURE-BASED VIRTUAL SCREENING

9.6 OTHER VIRTUAL SCREENING APPLICATIONS

9.7 CONCLUSION

REFERENCES

10 PHENOTYPIC SCREENING

10.1 INTRODUCTION

10.2 HISTORY AND PAST SUCCESSES

10.3 IMPACT OF PHENOTYPIC SCREENING

10.4 MODEL SYSTEMS FOR PHENOTYPIC ASSAYS

10.5 ASSAYS

10.6 DEORPHANING

10.7 SUMMARY

REFERENCES

PART IV: TECHNOLOGIES FOR MEDICINAL CHEMISTRY OPTIMIZATION

11 ADVANCES IN THE UNDERSTANDING OF DRUG PROPERTIES IN MEDICINAL CHEMISTRY

11.1 INTRODUCTION

11.2 PROPERTIES AND ORIGINS OF MARKETED DRUGS

11.3 DRUG PROPERTIES AND ATTRITION IN CLINICAL DEVELOPMENT

11.4 THE RULE OF FIVE

11.5 THE CONCEPT OF LEAD-LIKENESS

11.6 INFLUENCE OF DRUG PROPERTIES ON ABSORPTION, DISTRIBUTION, METABOLISM, EXCRETION, AND TOXICITY

11.7 BUILDING ON THE Ro5: NEW GUIDELINES FOR COMPOUND DESIGN

11.8 ALTERNATIVES, CRITICISMS, AND EXCEPTIONS

11.9 CONCLUSIONS

REFERENCES

12 RECENT DEVELOPMENTS IN AUTOMATED SOLUTION PHASE LIBRARY PRODUCTION

12.1 INTRODUCTION

12.2 LIBRARY PRODUCTION

12.3 NEW TECHNOLOGIES IN AUTOMATED LIQUID-PHASE LIBRARY SYNTHESIS

12.4 FLOW CHEMISTRY AND GAS-PHASE REACTIONS

12.5 CONCLUSION

REFERENCES

13 ADME PROFILING: AN INTRODUCTION FOR THE MEDICINAL CHEMIST

13.1 INTRODUCTION

13.2 COMPOUND PROFILING IN H2L OPTIMIZATION

13.3 COMPOUND PROFILING IN LEAD OPTIMIZATION

13.4 INTEGRATION OF MEDICINAL CHEMISTRY, BIOLOGY, PHYSICOCHEMICAL, AND ADME PROFILING: STRATEGIES TOWARD CYCLE TIME REDUCTIONS

13.5 SUMMARY

REFERENCES

PART V: MEDICINAL CHEMISTRY BEYOND SMALL MOLECULES

14 THE ROLE OF NATURAL PRODUCTS IN DRUG DISCOVERY: EXAMPLES OF MARKETED DRUGS

14.1 NATURAL PRODUCTS AND NATURAL PRODUCT DERIVATIVES IN COMMERCIAL DRUGS

14.2 HIT TO LEAD OPTIMIZATION OF NATURAL PRODUCT HITS

14.3 CASE STUDY 1: TAXOL

14.4 CASE STUDY 2: EPOTHILONE

14.5 CASE STUDY 3: ERIBULIN

14.6 CASE STUDY 4: GELDANAMYCIN

14.7 CASE STUDY 5: INGENOL MEBUTATE (PICATO)

14.8 SUMMARY

REFERENCES

15 PEPTIDOMIMETICS OF α-HELICAL AND β-STRAND PROTEIN BINDING EPITOPES

15.1 PROTEIN–PROTEIN INTERACTIONS AS THERAPEUTIC TARGETS

15.2 PEPTIDOMIMETICS OF α-HELICAL PROTEIN BINDING EPITOPES

15.3 PEPTIDOMIMETICS OF β-STRAND PROTEIN BINDING EPITOPES

15.4 CONCLUSION

REFERENCES

16

IN VIVO

IMAGING OF DRUG ACTION

16.1 INTRODUCTION

16.2 OVERVIEW OF IMAGING METHODS

16.3 IMAGING OF THERAPEUTIC EFFECTS

16.4 CONCLUSION AND OUTLOOK

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 02

TABLE 2.1 Description of Workpackages of the ELF Project

TABLE 2.2 Membership of the Library Selection Committee

TABLE 2.3 Criteria for the Review and Selection of Library Proposals

TABLE 2.4 Possible Outcomes Following the Review and Selection of Library Proposals

TABLE 2.5 Examples of Scores and Feedback from the Library Selection Committee

TABLE 2.6 Criteria for the Assessment of Library Proposals by the Library Selection Committee

Chapter 06

TABLE 6.1 Classical Multicomponent Reactions

Chapter 07

TABLE 7.1 Organizational Considerations and Decision Points for Successful Early Drug Discovery

TABLE 7.2 Rules and Filters for Compounds Assessment in HTS Triage

TABLE 7.3 Typical Simple Calculated Descriptors and Recommended Ranges in HTS Triage

Chapter 08

TABLE 8.1 Selected Clinical-Stage Beyond, Phase 2 and Beyond, Originating from FBDD

TABLE 8.2 Comparison between Different Biophysical Screening Technologies

TABLE 8.3 Key Features of “Channel-Based SPR” Compared to Those of “Chemical Microarray SPR”

Chapter 10

TABLE 10.1 Phenotypic Assay Technologies

TABLE 10.2 Proteomic Method and the Small Molecule Probe Type

Chapter 11

TABLE 11.1 From 323 Drug Candidates, 41 are Selected that Have Been Stopped in Development for Reasons Related Only to ADME/PK (Bioavailability/Exposure, Drug–Drug Interaction/CYP Inhibition or Induction) and Toxicity (All Types)

Chapter 12

TABLE 12.1 Different Types of Gas–Liquid Separators (GLS) and Their Properties

TABLE 12.2 Advantages and Disadvantages of SFC in High-Throughput Purification of Library Compounds Compared to HPLC

Chapter 13

TABLE 13.1 CYP450 Enzymes

TABLE 13.2 Recommended Transporters to be Tested According to Regulatory Demands [31, 32]

TABLE 13.3 The Biopharmaceutical Drug Disposition Classification System (BDDCS) Where Metabolism and Transporter Roles are Included [53]

TABLE 13.4 Comparison of Papp Values Measured in 24-Well and 96-Well Setup

TABLE 13.5 Evaluation of Known CYP3A4 Inhibitors in the Competitive Higher-Throughput CYP3A4 Inhibition Assay Using Midazolam as Probe Substrate

Chapter 14

TABLE 14.1 Drugs Directly Derived from Natural Products Introduced Into Market between 1980 and 2013

Chapter 16

TABLE 16.1 Properties of Different Radionuclides Used for Imaging

TABLE 16.2 Dissociation Constants for Selected DOTA and NOTA Complexes

List of Illustrations

Introduction

FIGURE I.1 Sketch of the drug discovery and development value chain consisting of target hypothesis, lead identification and optimization to a clinical candidate, preclinical testing, phase I–III studies, approval, and launch.

FIGURE I.2 The value chain process focusing on the research phase, from target hypothesis to identification of a clinical candidate.

Chapter 01

FIGURE 1.1 Example of visualization of chemical space via principal component analysis (PCA) [10–12]. Color-coded molecular quantum number (MQN) maps of the chemical space of PubChem compounds up to 60 heavy atoms and a subset of GDB-13 compounds in the (PC1, PC2) plane (total: 66,647,914 molecules). (a) Occupancy map color coded by the number of molecules per pixel. (b–d) Descriptor value maps color coded by the average descriptor value in each pixel. Saturation to gray is used to show standard deviation. (e) Category map for blue, fragments (rule of 3 (

vide infra

), 32.5 million compounds); green, lead-like (Teague’s NOT rule of 3 (

vide infra

), 2.7 million compounds) (note: in total 12.2 million structures follow Teague’s lead-likeness criteria); and cyan, Lipinski (rule of 5 (

vide infra

) NOT leads or rule of 3, 31.4 million compounds); and red, not matching any rule (1.6 million compounds). Color coding according to the majority category in each pixel except for leads (green), which were given priority to make them visible.

FIGURE 1.2 Analysis by Wenlock et al. of the evolution of molecular property distributions with progressing through development stages [51]. Phase I (PI); discontinued phase I (DI); phase II (PII); discontinued phase II (DII); phase III (PIII); discontinued phase III; preregistration (Prereg); marketed oral drugs. The analysis shows that the mean molecular weight of orally administered drugs in development decreases on passing through each of the different clinical phases and gradually converges toward the mean molecular weight of marketed oral drugs. It is also clear that the most lipophilic compounds are being discontinued from development.

FIGURE 1.3 Trelis plot of Hill and Young showing the distribution of water solubility as a function of computed LogD and # of aromatic rings [68]. Solubility classes—green, high (>200 μM); yellow, medium (30–200 μM); and red, low (<30 μM). The number above the pie charts corresponds to the number of compounds analyzed for each bin.

Chapter 02

FIGURE 2.1 Consortium members and their roles in the European Lead Factory.

FIGURE 2.2 Organization of the European Lead Factory in distinct workpackages (WPs).

FIGURE 2.3 Provenance of compounds in the Joint European Compound Library. The approximately 300,000 compounds contributed by the consortium’s EPFIA partners will be complemented by approximately 200,000 additional compounds produced as part of the project.

FIGURE 2.4 Molecular properties of the approximately 300,000 compounds contributed by EFPIA partners to the Joint European Compound Library. d, desirability functions; QED, quantitative estimate of drug-likeness.

FIGURE 2.5 Definition of a library proposal using the tools embedded within the consortium’s web-based tool. (a) The submitter sketches a scaffold (using Marvin Sketch), specifying the positions of variable substituents (as R1, R2, R3 etc.). (b) The submitter can then select alternative possible substituents from predefined lists.

FIGURE 2.6 Information presented to the submitter before submission of a library proposal (a) General information on proposed library e.g., size and fraction not passing structural filters. (b) Distribution of properties used in the assessment later on.

FIGURE 2.7 An illustration of scores and feedback that might be provided to the submitter after consideration of the library proposal by the consortium’s Library Selection Committee.

FIGURE 2.8 Selected library proposals undergo chemical validation work. The validation chemistry management team (VCMT) assigns a consortium academic group to each proposal submitted. After library refinement in the light of validation work, the VCMT will review the validation work and decide on the further progress. After approval, library production is undertaken.

FIGURE 2.9 Workflow of Library proposal, validation, design, and production.

FIGURE 2.10 Data workflow covered by TarosGate.

FIGURE 2.11 Process from HTS toward qualified hit lists and improved hit lists.

Chapter 03

FIGURE 3.1 Representation of a fully integrated and a partially disintegrated organization where four providers participate in different aspects of the R&D process.

FIGURE 3.2 Stepwise approach of a lease model, where there is full contractual commitment of the chemistry provider to support the partner in every step of the project.

FIGURE 3.3 The appetizer business model. Novel and blinded chemistry of a chemistry provider is exchanged for biological data from a chemistry acquirer in the first cycle. If the biological data suit the acquirer’s needs, the two parties can come to an agreement about further collaboration. If this is not the case, the chemistry provider is free to use the generated data and look for an alternative partner interested in the compounds and data generated.

FIGURE 3.4 Within a subscription model, several acquirers can sign up to get screening compound libraries from a chemistry provider. The up-front commitment from both the partner and the chemistry provider is high. The partner commits at the beginning of the deal to acquire the envisaged libraries, and the chemistry provider commits to the library design, synthesis, and timely delivery.

FIGURE 3.5 Library exchange. The biological screening spaces are generally well defined and may or may not be overlapping (partners with different or similar scientific interest).

Chapter 04

FIGURE 4.1 General approaches for generating scaffold diversity in DOS. (a) The “branching” approach. (b) The “folding” approach.

FIGURE 4.2 The build/couple/pair (B/C/P) synthetic algorithm, a common feature in DOS pathways.

SCHEME 4.1 DOS library subset generated from compound

1

. Conditions: (a) phenylacetylene, Rh

2

(OAc)

4

(1 mol%), CH

2

Cl

2

; (b)

p

-nitroiodobenzene, Pd(OAc)

2

(10 mol%), K

2

CO

3

, DMF; (c) styrene, Rh

2

(OAc)

4

(1 mol%), CH

2

Cl

2

; (d) allene, Rh

2

(OAc)

4

(1 mol%), CH

2

Cl

2

; (e)

N

-iodosuccinimide, MeCN–H

2

O (2:1), 50°C; (f) Bu

3

SnH, AIBN, PhH, 80°C.

SCHEME 4.2 DOS library subset generated from compound

2

. Conditions: (a) cyclopentadiene, Rh

2

(OAc)

4

(1 mol%), CH

2

Cl

2

; (b) mCPBA, CH

2

Cl

2

; (c) OsO

4

(2.5 mol%), NMO, acetone/H

2

O (9:1); (d) aldehyde/ketone, CSA (10 mol%), 3 Å molecular sieves, CH

2

Cl

2

; (e) SOCl

2

, CH

2

Cl

2

; (f) 2,6-lutidine, NMO, OsO

4

(2.5 mol%), PhI(OAc)

2

, acetone/H

2

O (10:1), then dimethylamine, NaBH(OAc)

3

, CH

2

Cl

2

; (g) 2,6-lutidine, NMO, OsO

4

(2.5 mol%), PhI(OAc)

2

, acetone/H

2

O (10:1), then primary amine, NaBH(OAc)

3

, CH

2

Cl

2

; (h) 2,6-lutidine, NMO, OsO

4

(2.5 mol%), PhI(OAc)

2

, acetone/H

2

O (10:1), then NaBH

4

, MeOH; (i) alkene, Hoveyda–Grubbs (II) catalyst (10 mol%), ethylene, PhMe, 100°C; (j) Pd(OAc)

2

(10 mol%), boronic acid, PPh

3

(15 mol%), 2N K

2

CO

3

, PhMe, 90°C.

FIGURE 4.3 Compounds with potent antimitotic activity identified through the DOS campaign of Ibbeson et al.

SCHEME 4.3 The 12-fold branching DOS strategy developed by Robbins et al. Conditions: (a) NH

2

OH.HCl, NaOAc, MeCN, then PhMe, 140°C, microwave; (b) NH

2

OH.HCl, NaOAc, MeCN, 60°C; (c) NH

2

OH.HCl, NaOEt, EtOH; (d) PhNH

2

, TiCl

4

, CH

2

Cl

2

; (e) NaBH

4

, NH

3

, EtOH, Ti(OEt)

4

, then AcOH; (f) DIPEA, H

2

NCH

2

CO

2

Et; (g) NH

2

NHTs, toluene, reflux; (h) SmI

2

(2 equivalents), THF, MeOH, −78°C; (i) NaH, THF; (j) SmI

2

(5 equivalents), THF, MeOH, −78°C; (k) MeMgBr, THF; (l) superhydride, THF.

SCHEME 4.4 DOS strategy to access diverse molecular scaffolds based around a so-called relay catalytic branching cascade (RCBC) sequence. Conditions: (a) Ph

3

PAuOTf (5 mol%), CH

2

Cl

2

, 100°C.

SCHEME 4.5 Folding DOS pathway developed by Nelson and coworkers. (a) General synthetic scheme. (b) Synthesis of one final library compound.

FIGURE 4.4 The complexity-to-diversity (Ctd) approach for small molecule library construction. The controlled application of ring distortion reactions (ring expansion, ring cleavage, ring fusion, or ring rearrangement) on a natural product starting material yields complex natural product-like compounds.

SCHEME 4.6 Application of the complexity-to-diversity (Ctd) approach to the natural product quinine. Reaction sequences of between one and five steps were used to convert quinine into the five structurally complex and diverse structures shown.

SCHEME 4.7 Overview of the strategy developed by Isidro-Llobet et al. for the DOS of macrocyclic peptidomimetics. CuAAC, copper-catalyzed azide–alkyne cycloaddition; RuAAC, ruthenium-catalyzed azide–alkyne cycloaddition. The shaded shapes represent major scaffold-defining elements.

FIGURE 4.5 Schematic comparison of the “couple” phase in a classical B/C/P strategy with the “multidimensional coupling” phase. In this example, one building block (represented by a gray-filled rectangle) can be coupled with two other different building blocks (represented by black-and-white-filled rectangles) via the formation of three possible different linking motifs (L1–L3). (a) In the classical B/C/P strategy, the building blocks are coupled together using only one linking motif (L1). (b) When multidimensional coupling is used, the three different building blocks are coupled using three different linking motifs (L1–L3).

SCHEME 4.8 Outline of the strategy developed by Beckmann et al. for the DOS of macrocycles using multidimensional coupling. The synthesis of two representative macrocyclic scaffolds is shown. The multidimensional coupling process is represented by multiple arrows, but only one reaction product shown. Major scaffold-defining parameters are the variation of the building block (highlighted with a rectangle), the generation of different linking motifs using multidimensional coupling (highlighted with a gray-filled circle), and divergent macrocyclization (highlighted with a circle).

Chapter 05

FIGURE 5.1 Historical overview of early combinatorial chemistry methods.

FIGURE 5.2 Annual number of publications related to combinatorial chemistry. Keywords used in CAS search were “combinatorial chemistry” (excluding materials), “solid phase AND combinatorial chemistry,” and “combinatorial chemistry AND drugs.”

FIGURE 5.3 Directed sorting approach to synthesis of a combinatorial library of nine compounds.

FIGURE 5.4 General structure of popular polymer supports.

FIGURE 5.5 General structures of most common linkers.

FIGURE 5.6 Linkers used for attachment of acids as esters.

FIGURE 5.7 Linkers used for attachment of acids as amides.

FIGURE 5.8 Linkers used for attachment of alcohols.

FIGURE 5.9 Linkers used for attachment of amines.

FIGURE 5.10 Examples of traceless linkers.

FIGURE 5.11 Synthesis and use of bromoacetal linker.

FIGURE 5.12 Structural motifs of first small molecule libraries.

FIGURE 5.13 Structures of privileged scaffolds and the corresponding libraries.

FIGURE 5.14 The concept of randomized scaffold illustrated on scaffold cores.

FIGURE 5.15 Library-from-library concept.

FIGURE 5.16 Examples of globular and

sp

3

-rich chemotypes.

FIGURE 5.17 Selected examples of DOS chemotypes.

FIGURE 5.18 Examples of chemistries utilizing bromoacetal linker.

FIGURE 5.19 General outline of library design and realization.

FIGURE 5.20 Example of library design and selection of the library set.

FIGURE 5.21 Physicochemical properties mapped to virtual libraries. (a and c) Visual comparison of the original 3D scatter plots and (b and d) the final discovery library indicates.

FIGURE 5.22 Custom 48-well reaction block for bottom filtration.

FIGURE 5.23 Custom resin washer.

FIGURE 5.24 Custom dry resin dispenser.

FIGURE 5.25 The NanoKan directed sorting technology as adapted in Irori synthesis platform.

FIGURE 5.26 Details of the half-filled TEFZEL frame.

FIGURE 5.27 Synthesis of a hypothetical 3 × 3 × 3 component library.

FIGURE 5.28 Correlation of purity profiles between rehearsal and production libraries.

FIGURE 5.29 Parallel approaches to lead discovery.

FIGURE 5.30 Compound flow and main stages of lead discovery process. Underlined ADME tests are provided only for key representatives of each series.

Chapter 06

FIGURE 6.1 Scaffold diversity generated by UDC strategies using 1–3 internal nucleophiles.

FIGURE 6.2 Example chemotypes generated via (i) post-Ugi modifications, (ii) the Ugi-bifunctional approach, (iii) the Petasis reaction, (iv) the Van Leusen reaction, and (v) the GBB reaction.

SCHEME 6.12 1,4-Benzodiazepines derived from the UDC strategy.

SCHEME 6.13 Synthesis of amino-indoloazepines via bifunctional approach.

SCHEME 6.14 Yadav 3CR to access isoquinoline carboxylic acids.

SCHEME 6.15 Synthesis of γ-lactams using Shaw’s 4CR.

FIGURE 6.3 Reactivity of 1-azadienes.

SCHEME 6.16 1-Azadienes in MCRs.

FIGURE 6.4 Recent isocyanide-based MCRs having applications in diversity generation.

SCHEME 6.17 Recent isocyanide-based three-component reactions.

SCHEME 6.18 3CR involving a cascade of 12 steps.

SCHEME 6.19 Novel 3CR involving

N

-1 alkylation of indoles with α-iminoketones.

SCHEME 6.20 Catalytic enantioselective Passerini reactions.

SCHEME 6.21 Catalytic enantioselective variant of the Ugi reaction.

SCHEME 6.22 Structure and synthesis of SB220025.

FIGURE 6.5 Structure of gefitinib

159

and Gewald-3CR products

158

and

160

.

SCHEME 6.23 Novel kinase inhibitors

164a–d

prepared via the Groebke GBB-3CR.

FIGURE 6.6 Structures of ATP-competitive Plk-1 inhibitors

165a–c

generated via the TMSN

3

-Ugi reaction.

SCHEME 6.24 PADAM methodology for the assembly of potential protease inhibitors

168

and

169

.

SCHEME 6.25 Hydroxymethyl-tetrazole protease inhibitors

172a–c

assembled via Passerini TMSN

3

reaction.

SCHEME 6.26 Assembly of the α-aminonitrile nucleus

174

of vildagliptin

173

.

SCHEME 6.27 Assembly of the precursor (

177)

to the piperazine core of Crixivan (

176

) via Ugi 4CR reaction.

FIGURE 6.7 Structures of potent ion channel blockers

178–180

prepared via known MCR-based methodologies.

SCHEME 6.28 UDC methodology toward benzodiazepines

184

and

185

via an Ugi 4CR (Scheme 6.29).

SCHEME 6.29 Compound

184

, assembled by Priaxon via a Shaw 4CR.

SCHEME 6.30 Synthesis of tubugi analogues

190a–c

.

SCHEME 6.31 Synthesis of podophyllotoxin analogues

199

.

SCHEME 6.32 Synthesis of pyrano[2,3-

c

]pyridones

200

and pyrano[2,3-

c

]quinolones

201

.

SCHEME 6.33 Synthesis of 7-deazaxanthine

204

via a one-pot 4CR.

SCHEME 6.34 Palladium-catalyzed synthesis of benzo[

b

]furans

207

.

SCHEME 6.35 Synthesis of the diketopiperazine (

181

) core of Aplaviroc (

182

) via Ugi 4CR.

SCHEME 6.36 1,2,3,4-Tetrahydroisoquinolines

212–213

as orexin receptor antagonists.

SCHEME 6.37 UDC methodology delivering the OT receptor antagonist GSK221149A.

SCHEME 6.38 CRF receptor antagonists

216

and

217

.

SCHEME 6.39 Tetrahydropyrido[3,2-

c

]pyrrole

218

as 5-HT

7

antagonist.

FIGURE 6.8 Synthesis of SNAP-7941 (

221

) via Biginelli and Mannich reactions.

Chapter 07

FIGURE 7.1 The various classification schemes used during hit identification to triage compounds and identify the most promising hits.

FIGURE 7.2 Schematic representation of early drug discovery using large sources of compound collections either from experimental high-throughput screens or through virtual screening. Due to the large amount of data generated, results invariably undergo a triage process to categorize, prioritize, and classify compounds first as potential actives and then as hits. The contrasting soft approach to decision-making during hit triage is compared to the hard decisions necessary in subsequent H2L and LO campaigns.

FIGURE 7.3 A schematic of a hit list and the possible classification methods that can be applied to identify chemical relationships among hits. Typically, 99% of HTS results are clearly inactive (bottom box), and approximately 1% have a positive response in the assay (top box). Some hits are borderline (middle gray boxes). Classification methods are grouped into two classes: scaffold based (common ring and substructures) and similarity based. For scaffolds, a large scaffold (S

c

) may entirely encompass other scaffolds (S

a

and S

b

). Similarity-based clustering methods are hierarchical (the dendrogram in this figure) or agglomerative (represented by the circles). Actives (+) and inactives (−) can fall into any of the clusters. In both clustering methods compounds may belong to overlapping clusters.

FIGURE 7.4 Examples of the scaffolding process.

FIGURE 7.5 Simplified representation of an indole ring as its corresponding carbonized scaffold and related kinase substructures interacting with a similar orientation to the hinge region of members of the kinase class of protein targets.

FIGURE 7.6 Visual representation of an MPO as designed by Wager et al. for CNS drugs. Red boxes indicate property values in unacceptable ranges, green boxes are completely acceptable, and yellow boxes indicate a favorability with a maximum contribution of the weight on the that property (typically 1.0) and linearly diminishing to 0.0.

FIGURE 7.7 Examples of aminothiazole compounds as approved drugs in DrugBank. DrugBank contains 63 compounds with an aminothiazole substructure, 20 of which are approved drugs. Aminothiazoles are often labeled frequent hitters, but the presence of this group in a hit is not necessarily indicative of a hit’s likelihood of success in later stages.

FIGURE 7.8 Examples of typically reactive or interfering groups.

FIGURE 7.9 Example taken of a protein kinase B inhibitor series and binding to the active site. The crystal structures of these inhibitors illustrate that the binding of a group of analogs has its SAR dictated by key binding interactions that are preserved in each analog. In this case, the pyrazole ring makes hydrogen bonds to the hinge of the kinase (backbone hydrogen bonding groups that are used frequently in kinase inhibitors).

Chapter 08

FIGURE 8.1 (a) Schematic of SPR systems comprising an optical readout system, the coupling interface (grating or prism coupling), and the sensor chip or array, including the surface chemistry, an immobilized binding partner, and flow channel. (b) The angle- or wavelength-dependent position of the SPR minimum shifts due to binding of the analyte to the immobilized ligand.

FIGURE 8.2 Comparison between two major direct assay principles applied to SPR fragment screening: (a) channel-based SPR, immobilization of the protein target to the SPR sensor chip surface and detection of soluble fragments; (b) CM-SPR, screening of proteins against fragment collections immobilized on chemical microarrays.

FIGURE 8.3 Generic channel-based SPR sensorgram as obtained from a typical binding experiment: baseline detection, association, and dissociation phase, followed by regeneration of the chip surface and recovery of the baseline signal.

FIGURE 8.4 (a) 3000 fragments are spotted on gold chips to construct chemical microarrays. (b) Fragment hits identified in one screening experiment are indicated by triplicate pattern on a relative color scale plot.

FIGURE 8.5 Binding site mapping by an on-array competition study: in the standard experiment, all available binding sites give rise to an SPR array signal upon binding. Blocking known sites with tool compounds enables the discrimination between, for example, active and alternative site binders.

FIGURE 8.6 Principle of the FAXS competition experiment.

FIGURE 8.7 Structures of ligands; X-ray crystal structure of 3 (green) complexed with Aurora A (blue) superimposed with the binding mode of fragment 1 (orange).

FIGURE 8.8 The fragment motifs 4 and 5, with (right) the binding of both compounds to BRD2, clearly demonstrate the position of the nitrogen in the oxazole moiety. Compounds 6 were obtained from a “SAR-by-catalog” approach.

Chapter 09

FIGURE 9.1 Virtual screening is a computational filtering procedure, which uses available knowledge to filter out compounds with a low probability to be biologically active against the target of interest. To this end, three-dimensional (3D) information is used from ligand and protein structures in the so-called 3D approaches, while 2D approaches rely on topological information from ligands, such as the chemical substructure. Often, 2D and 3D approaches are combined.

FIGURE 9.2 Illustration of different descriptors, which are used to calculate chemical similarity. (a) Fingerprints look for absence or presence of chemical fragments in the molecular structure and set a 1 or a 0 in bit string accordingly. (b) Chemical fragments are converted into a node while capturing molecular features like H-bond donors, H-bond acceptors, or size of the corresponding group. The connectivity of the nodes is described in a graph-like structure. (c) The CATS descriptor converts each atom into its corresponding pharmacophoric feature like H-bond donor, H-bond acceptor, lipophilic, etc. The frequency of occurrences of pairs of such features separated by the number of bonds in between is stored in a normalized vector (not shown).

FIGURE 9.3 Frequently used success criteria for virtual screening.

FIGURE 9.4 In an ROC plot, the true positive rate (sensitivity) is plotted against the false positive rate (1-specifity). In a successful virtual screening run, the curves quickly rise, if many TP are identified among only few false positives. The area under curve (AUC) becomes large. The diagonal corresponds to a random selection of compounds.

FIGURE 9.5 Generation of a pharmacophore model for a chemical series of glycogen phosphorylase inhibitors. A 3D structure of a complex between glycogen phosphorylase and an inhibitor is analyzed in the program LigandScout. Hydrogen bonds between ligand and protein are indicated by vectors. Lipophilic ligand features and excluded volumes, which cannot be occupied by the ligand, are represented as spheres. The data set of five different ligands was used to derive several ligand-based pharmacophores. The combination of both types of knowledge allows to select the final pharmacophore.

FIGURE 9.6 Representation of the Sanofi workbench for

in silico

profiling, which is based on large experimental data sets for typical antitargets.

Chapter 10

FIGURE 10.1 An example of a screening cascade. The screening cascade is analogous to a funnel filter with the top tier representing the primary screening assay with capacity to screen large number of compounds. Moving through the funnel, the assays provide the stringency to remove or permit passage of compounds to the next tier below. The assays used in the lower tiers may not have a high-throughput capacity and thus need to be placed appropriately in the cascade to support screening programs with large numbers of compounds.

FIGURE 10.2 Using AUC captures differences in dose–response curves not captured by EC50 statistics [13] The solid and dash/dot line curves achieve 50% reduction in cell effect at the same compound concentration, but the solid curve achieves 100% effect at higher compound concentrations. Both curves would yield the same IC50 value (dotted line at 50%), while the AUC captures the greater sensitivity of the assay to the sample represented by the solid curve. The dashed line curve illustrates a sample with less than 50% maximal reduction in cell effect at high compound concentrations. The EC50 statistic would be the same for the dashed and solid curves (vertical dotted line), while the AUC statistic captures the increased response of the solid curve.

FIGURE 10.3 Phenotypic screening requires an integrated technology approach. The schematic illustrates the integration of multiple technologies that are required to support phenotypic screening programs and target deconvolution.

Chapter 11

FIGURE 11.1 MW of FDA-approved drugs from 1983 to 2013. Regression on the MW log is shown as a green line. Ninety-five percent confidence intervals are shown as dotted green lines.

FIGURE 11.2 Average MW of 323 drug candidates in 2005–2009 at Sanofi. Development phase repartition.

FIGURE 11.3 1000 random selections of 41 compounds within the 153 marketed drugs are carried out, followed by 1000 logical analyses with a balanced set of 41 stopped and 41 marketed compounds: 76% of the stopped compounds and 67% of the marketed drugs were predicted well (99.9/100 significant and MW is 100/100 significant).

FIGURE 11.4 A MW 200 fragment binding at 10 µM has equivalent ligand efficiency to a MW 500 compound binding at 10 nM, but has more room for optimization by increasing MW. Values for Δ

g

based on average values for no. of non hydrogen atoms in Pfizer’s compound collection.

Chapter 12

FIGURE 12.1 Typical processes in library production.

FIGURE 12.2 Areas for cycle time optimization in library production.

FIGURE 12.3 Fully automated, maintenance-free water supply and TFA dosage for RP-HPLC.

FIGURE 12.4 Typical flow schemes of HPLC and SFC.

FIGURE 12.5 Impact/inertia gas–liquid separator (GLS) developed at Sanofi.

Chapter 13

FIGURE 13.1 Typical profiling tree for a small molecule hit-to-lead (H2L) optimization containing biological, physicochemical, and ADME profiling assays and an antitarget assay versus hERG blockade.

FIGURE 13.2 Sequential profiling of lead compounds.

FIGURE 13.3 Parallel profiling of lead compounds and effect on timelines.

FIGURE 13.4 Sample preparation and distribution scheme. Aliquots for

in vitro

biology are produced for all batches. Samples for physicochemical and ADME profiling are only produced if sample is pure by LC–UV/MS.

FIGURE 13.5 Comparison of aqueous solubility at pH 7,4 and 25°C obtained from amorphous and crystalline solids. Assay target solubility is 0,2 mg/ml. (The diamonds in the circle represent individual compounds.)

FIGURE 13.6 Comparison of results for around 400 compounds derived from the newly developed higher-throughput assay and the standard protocol for the determination of metabolic lability. Values are shown in % total metabolism.

FIGURE 13.7 Regression analysis—results obtained in 24-well and HTS 96-well setup.

Chapter 14

FIGURE 14.1 Natural product scaffolds related to active ingredients in commercial drugs (

n

 = 89).

FIGURE 14.2 262 drugs based on natural products (

n

 = 30) or natural product derivatives (

n

 = 232).

FIGURE 14.3 Class of producing organism of corresponding natural product scaffolds (

n

 = 89).

FIGURE 14.4 Examples for drugs developed by the metabolomics approach.

FIGURE 14.5 Taxines A and B.

FIGURE 14.6 Taxol.

FIGURE 14.7 Selective protection of deacetylbaccatin III by Poitier et al.

FIGURE 14.8 Side chain synthesis by Greene et al.

FIGURE 14.9 Potier–Greene semisynthesis of Taxol.

FIGURE 14.10 Side chain synthesis of Taxotere by Greene et al.

FIGURE 14.11 Semisynthesis of Taxotere by Potier et al.

FIGURE 14.12 10-Deacetylbaccatin III and

Taxus baccata

(background).

FIGURE 14.13 Holton semisynthesis of Taxol.

FIGURE 14.14 Second generation semisynthesis of Taxol by Holton.

FIGURE 14.15 Side chain synthesis by Ojima et al.

FIGURE 14.16 Semisynthesis of Taxol by Ojima et al.

FIGURE 14.17 Taxol biosynthesis

FIGURE 14.18 Synthesis of a C7–C8 cyclopropyl analogue.

FIGURE 14.19 Synthesis of larotaxel.

FIGURE 14.20 Semisynthesis of cabazitaxel.

FIGURE 14.21 Taxanes on the market or in clinical trials.

FIGURE 14.22 Four different hit-to-lead strategies starting with natural products illustrated with anticancer compounds based on the lead epothilone A.

FIGURE 14.23 Structure of okadaic acid.

FIGURE 14.24 Structures of halichondrin A and norhalichondrin A.

FIGURE 14.25 Structures of homohalichondrin B and norhalichondrin B.

FIGURE 14.26 Total synthesis of halichondrin B by Kishi et al.

FIGURE 14.27 Structure of the macrolactone diol part of halichondrin B.

FIGURE 14.28 Convergent key intermediates for the synthesis of

2.22.

FIGURE 14.29 Structures of the drug candidates ER-077349 and ER-086526 (ER-7389, eribulin).

FIGURE 14.30 Synthesis of late stage intermediate

2.22

.

FIGURE 14.31 Final steps in the synthesis of eribulin mesylate.

FIGURE 14.32 Eribulin analogues showing improved MDR cell line activities or PK properties.

FIGURE 14.33 Structures of ansamycins geldanamycin, herbimycin, and macbecins I and II.

FIGURE 14.34 Synthesis of 17-AAG (17-allylamino geldanamycin).

FIGURE 14.35 Synthesis of the 17-AAG hydroquinone salt.

FIGURE 14.36 Synthesis of 17-DMAG.

FIGURE 14.37 Phenol analogues of geldanamycin and macbecin I.

FIGURE 14.38 Geldanamycin anlogues isolated by Kyowa Hakko.

FIGURE 14.39 Post-PKS modifications in the macbecin biosynthesis.

FIGURE 14.40 Semisynthesis of ingenol-3-angelate.

FIGURE 14.41 (+)-Ingenol bearing an in-out-[4.4.1]-undecane ringsystem (blue).

FIGURE 14.42 Key steps in the Winkler group total synthesis of (+)-ingenol.

FIGURE 14.43 Key steps in the Tanino group synthesis of ingenol.

FIGURE 14.44 Key steps of the Wood group total synthesis of ingenol.

FIGURE 14.45 En route to Taxol—cyclase and oxidase phase.

FIGURE 14.46 Baran group total synthesis of (+)-ingenol.

FIGURE 14.47 Regions accessible for SAR (lightgrey) and unaccessible for SAR (darkgrey).

Chapter 15

FIGURE 15.1 Representative α-helix-mediated protein–protein interactions. (a) Complex between p53 transactivation domain (blue) and HDM2 (pdb 1YCR); (b) Bcl-xL protein in complex with BIM-derived peptide (blue) (pdb 1PQ1); (c) DNA-bound ternary complex between Notch ICN1 (gray), CSL (tan), and MAML1 (blue) (pdb 2F8X); (d) side view of HIV gp41 hexameric coiled-coil fusion complex (pdb 1AIK). In (a) and (b), the interfacial “hot spot” residues are highlighted in yellow.

FIGURE 15.2 Representative side-chain cross-linking strategies for α-helix stabilization, involving interside-chain bridges (a–d) and non-peptidic linkers (e–h).

FIGURE 15.3 All-hydrocarbon stapled peptides. The cross-linking α-methyl-α-alkenyl glycine amino acids are incorporated during solid-phase peptide synthesis, followed by in-solution or on-resin cyclization via ring-closing metathesis (RCM) with Grubb’s first-generation catalyst. Three types of stapled peptides are schematically illustrated along with the preferred size of the linker and stereochemistry of the α-methylated amino acids.

FIGURE 15.4 Crystal structure of α-helix peptidomimetics bound to their target proteins. (a) Crystal structure of estrogen receptor α (gray) in complex with disulfide cross-linked PERM-1 (purple, PDB 1pcg); (c–d) structure of Mcl-1 (gray) in complex with (c) stapled peptide Mcl-1 SAHB

D

(orange, PDB 3mk8) [54] and (d) biphenyl-bridged peptide Bph-NOXA-2 (cyan, PDB 4g35) [55]; (d) crystal structure of complex between HDM2 (gray) and beta-hairpin inhibitor 78A (yellow, PDB 2axi) [12]. The residues in closest contact with the protein surface are displayed as stick models and highlighted in the corresponding structures.

FIGURE 15.5 Hydrogen-bond surrogates for α-helix nucleation and stabilization. The N-terminal (b–d) or internal (e) backbone hydrogen bond (C∙O···H–N) between the

i

th and

i

 + 4th residue of an α-helix (a) is replaced with a covalent bond.

FIGURE 15.6 Type III α-helix peptidomimetics. The topographical features of an α-helix (a) are mimicked using nonpeptidic scaffolds based on (b) 3,2′,2″-terphenyl, (c) terephthalamide, (d) biphenyl-4,4-dicarboxamide, (e) oligobenzoylurea, (f) oligopyridylcarboxamide, (g)

N

-alkylated oligobenzamide, and (h) oxopiperazine.

FIGURE 15.7 Representative protein complexes involving β-strand binding epitopes. (a) Crystal structure of bovine trypsin (gray) in complex with β-hairpin cyclic peptide inhibitor from sunflower seeds (SFTI, cyan) (PDB 1SFI); (b) structure of α

1

-syntrophin PDZ domain in complex with single β-strand heptapeptide recognition motif (yellow, PDB 2PDZ); (c) structure of human IgG Fc region (gray) in complex with disulfide-constrained β-hairpin peptide binder (Fc III, purple) isolated by phage display (PDB 1DN2).

FIGURE 15.8 Template-stabilized β-hairpin peptidomimetics. (a) General description of the design strategy for stabilizing β-hairpin (or β-sheet) motifs via the use of turn-mimicking templates; (b–i) selected templates used for β-hairpin/β-sheet stabilization of β-hairpin and β-sheet conformations based on phenoxathiin ring (b), 2,2′-substituted biphenyl (c), dibenzofuran (d), peptide/oligourea/azapeptide hybrid (e), (

l

)-ornithine (f), heterochiral nipecotic acid dimer (g), bicyclic proline derivative (h), and

d

Pro–

l

Pro unit (i).

FIGURE 15.9 β-hairpin peptidomimetics. (a) Chemical structure of protegrin I-derived cyclic peptidomimetic L27-11 [178] and NMR structure of its analog LB-01 [179]; (b) general structure of amyloid β-sheet mimics (ABSMs) and X-ray crystal structure of ABSM1r, an ABSM incorporating the heptapeptide sequence AIIGLMV from the amyloid-β peptide, Aβ [162].

FIGURE 15.10 (a) Representative type III peptidomimetics of peptide β-strand motifs, (b) oligopyrrolinones, (c) triazolamers, (d) dihydropyridinone/dihydropyrazinone hybrids, (e) imidazo[1,2-a]pyridine, and (f) “kinked” bis-triazoles.

Chapter 16

SCHEME 16.1 Luciferase reaction.

FIGURE 16.1 Frequently employed cyanine dyes.

FIGURE 16.2 Exemplary structures of BODIPY and Alexa dyes.

FIGURE 16.3 To allow fluorescence imaging

in vivo

, a wavelength window between 680 and 1300 nm could be envisaged.

FIGURE 16.4 Structures of frequently used chelators for MRI and PET.

FIGURE 16.5 Illustrative representation of a Gd

3+

-DOTAM complex. The Gd

3+

ion is buried in a cage-like complex. Modelled with Schrödinger™ software, illustration kindly provided by L.-H. Wang.

SCHEME 16.2 Novel rapid methods for incorporation of

18

F into aryl and heteroaryl systems.

FIGURE 16.6 Images from Chapman [64]. Example of dual

in vivo

99m

Tc-pentetate and Na

18

F tracer imaging showing individual modality displays A, CT; B, PET; C, SPECT; D, a merged dual modality SPECT/PET display; E, a merged trimodal PET/SPECT/CT display; F, unit scale bars for PET (kBq/cc) and SPECT (cps/voxel, voxel size = 250 µm isotropic).

FIGURE 16.7 Images from Pomper [90]. Sequential SPECT/CT (top row) and optical (bottom row) imaging of dual SPECT–NIRF reagent using PSMA-positive (PIP) and PSMA-negative (flu) tumors in a male SCID mouse.

FIGURE 16.8 Position of introduced radiolabels is indicated by gray circles.

FIGURE 16.9 Design of activity-based protease probes. In both cases, the spatial proximity of the fluorophore–quencher pair is abrogated, either by (a) irreversibly tagging and inactivating the target protease or (b) selective cleavage of the connecting linker using turnover-based protease probes, preserving the activity of the target enzyme.

SCHEME 16.3 Protease inactivation by acyloxymethylketones (AOMKs).

FIGURE 16.10 (a–c) Right facial nerve and its arborizations in a thy1-YFP mouse treated with Cy5-NP41. (d–f) Left sciatic nerve (arrow) and its arborization in a mouse with a syngeneic 8119 mammary tumor graft. Picture from Whitney [133]. Simultaneous tumor (green) and neural staining (cyan, near infrared, and yellow (YFP)) to assist nerve-sparing tumor resection during surgery.

FIGURE 16.11 Representative examples of SUR1 targeting moieties.

FIGURE 16.12 DTBZ-derived radioligands targeting VMAT2.

FIGURE 16.13 Points of modification for currently explored exendin derivatives.

FIGURE 16.14 Structure of MRK-409 and

11

C-flumazenil.

FIGURE 16.15 Structures of some successfully applied PET amyloid imaging agents.

FIGURE 16.16 Representative figure comparing

18

F-FDG and PIB imaging in control subjects, patients with nonamnestic mild cognitive impairment (naMCI), amnestic mild cognitive impairment (aMCI), and Alzheimer’s disease (AD).

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SMALL MOLECULE MEDICINAL CHEMISTRY

Strategies and Technologies

Edited by

WERNGARD CZECHTIZKYPETER HAMLEY

Copyright © 2016 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & 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, [phone], fax [phone], or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

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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. RS403.S62 2015 615.1′9–dc23

       2015020801

Cover image courtesy of adempercem/iStockphoto

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

Hélène 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 R&D, 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

Jörg Hüser, Bayer Pharma AG, Wuppertal, Germany

Edgar Jacoby, Janssen Research & Development, Beerse, Belgium

Patrick Jimonet, Sanofi-Aventis R&D, Vitry-sur-Seine, France

Philip S. Jones, European Screening Centre Newhouse, Lanarkshire, UK

Christopher Kallus, Sanofi R&D, Frankfurt am Main, Germany

Douglas B. Kitchen, Albany Molecular Research Inc. (AMRI), Albany, NY, USA

Matthias Löhn, 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 R&D, Frankfurt am Main, Germany

Guillermo Martinez-Ariza, University of Arizona, Tucson, AZ, USA

Federico Medda, University of Arizona, Tucson, AZ, USA

Katharina Mertsch, Sanofi R&D, 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 R&D, 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 & 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 R&D, Frankfurt am Main, Germany

INTRODUCTION

Werngard Czechtizky and Peter Hamley

Sanofi‐Aventis Deutschland GmbH, Frankfurt am Main, Germany

I.1 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 nature’s 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 “traditional 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 level” [2]. It has also been stated that “medicinal chemistry uses physical organic principles to understand the interaction of smaller molecular displays with the biological realm” [1].

I.2 THE ROLE OF A MEDICINAL CHEMIST

Medicinal chemistry is pivotal to the process of discovering medicines. The goal is seemingly simple—the 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 challenging, 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 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]—the rate of molecules making it all the way to market approval is nowadays estimated to be 1:10,000 [6]—in iterations of compound design and synthesis, often referred to as design–make–test 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, chemoinformaticians [7] and drug designers [8], structural biologists, specialists for physicochemical and pharmacokinetic [9] profiling, and toxicologists. The creative forces within an individual 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 process; 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 drug–target interactions and the simulation and/or testing of the series’ physicochemical 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 paradigms 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, productive medicinal chemistry must go beyond “syntheses 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, achiral derivatives destined for screening cascades” [12]. New technologies and new strategies 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.

I.3 THE STATE OF THE ART

I.3.1 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 (R&D) (Fig. I.1).

Figure I.1 Sketch of the drug discovery and development value chain consisting of target hypothesis, lead identification and optimization to a clinical candidate, preclinical testing, phase I–III studies, approval, and launch.

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 “target” to “preclinical” are parts of the typical research activities within a drug discovery program leading to a clinical candidate (see also Fig. I.2). Franz Hefti [14] nicely describes the properties of a clinical candidate as follows: “A drug candidate suitable for clinical testing is expected to bind selectively to 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 animals.”

Figure I.2 The value chain process focusing on the research phase, from target hypothesis to identification of a clinical candidate.

Clinical phases I–III [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 20–100 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 (100–300) and are designed to assess how well the drug works. They are sometimes 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 10,000) 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.

I.3.2 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 hypothesis involving a single protein target (Fig. I.2) along with its proposed mechanism 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 biomarker [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].