Fragment-based Drug Discovery E-Book

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Serie: Methods and Principles in Medicinal Chemistry
• Sprache: Englisch

Seitenzahl: 978

Veröffentlichungsjahr: 2015

Beschreibung

The secret of success in drug discovery written by the pioneers in the field with unrivaled experience in fragment-based methods. In this handbook, the first-hand knowledge imparted by the world's leading experts provides a comprehensive overview of current methods and applications of fragment-based discovery. The first part discusses basic considerations concerning when to use such methods, how to select targets, and how to build libraries in the chemical fragment space. The second part describes established, novel, and emerging techniques for fragment screening, including empirical as well as computational approaches, while also discussing such special cases as complex target systems and covalent inhibitors. The third and final part presents a number of successful real-world studies from recent and on-going drug discovery projects relating to a variety of target classes, from kinases and phosphatases to beta-secretase and epigenetic targets. With its discussion of future developments and potential novel applications, this will remain a valuable reference source for years to come.

Leseprobe

CONTENTS

Cover

Methods and Principles in Medicinal Chemistry

Title Page

Copyright

Contributors

Preface

A Personal Foreword

Part I: The Concept of Fragment-based Drug Discovery

Chapter 1: The Role of Fragment-based Discovery in Lead Finding

1.1 Introduction

1.2 What is FBLD?

1.3 FBLD: Current Practice

1.4 What do Fragments Bring to Lead Discovery?

1.5 How did We Get Here?

1.6 Evolution of the Methods and Their Application Since 2005

1.7 Current Application and Impact

1.8 Future Opportunities

References

Chapter 2: Selecting the Right Targets for Fragment-Based Drug Discovery

2.1 Introduction

2.2 Properties of Targets and Binding Sites

2.3 Assessing Druggability

2.4 Properties of Ligands and Drugs

2.5 Case Studies

2.6 Conclusions

References

Chapter 3: Enumeration of Chemical Fragment Space

3.1 Introduction

3.2 The Enumeration of Chemical Space

3.3 Using and Understanding GDB

3.4 Fragments from GDB

3.5 Conclusions and Outlook

Acknowledgment

References

Chapter 4: Ligand Efficiency Metrics and their Use in Fragment Optimizations

4.1 Introduction

4.2 Ligand Efficiency

4.3 Binding Thermodynamics and Efficiency Indices

4.4 Enthalpic Efficiency Indices

4.5 Lipophilic Efficiency Indices

4.6 Application of Efficiency Indices in Fragment-Based Drug Discovery Programs

4.7 Conclusions

References

Part II: Methods and Approaches for Fragment-based Drug Discovery

Chapter 5: Strategies for Fragment Library Design

5.1 Introduction

5.2 Aims

5.3 Progress

5.4 Future Plans

5.5 Summary

5.6 Key Achievements

References

Chapter 6: The Synthesis of Biophysical Methods In Support of Robust Fragment-Based Lead Discovery

6.1 Introduction

6.2 Fragment-Based Lead Discovery on a Difficult Kinase

6.3 Application of Orthogonal Biophysical Methods to Identify and Overcome an Unusual Ligand: Protein Interaction

6.4 Direct Comparison of Orthogonal Screening Methods Against a Well-Characterized Protein System

6.5 Conclusions

References

Chapter 7: Differential Scanning Fluorimetry as Part of a Biophysical Screening Cascade

7.1 Introduction

7.2 Theory

7.3 Practical Considerations for Applying DSF in Fragment-Based Approaches

7.4 Application of DSF to Fragment-Based Drug Discovery

7.5 Concluding Remarks

Acknowledgments

References

Chapter 8: Emerging Technologies for Fragment Screening

8.1 Introduction

8.2 Emerging Technologies

8.3 Conclusions

Acknowledgments

References

Chapter 9: Computational Methods to Support Fragment-based Drug Discovery

9.1 Computational Aspects of FBDD

9.2 Detection of Ligand Binding Sites and Binding Hot Spots

9.3 Assessment of Druggability

9.4 Generation of Fragment Libraries

9.5 Docking Fragments and Scoring

9.6 Expansion of Fragments

9.7 Outlook

References

Chapter 10: Making FBDD Work in Academia

10.1 Introduction

10.2 How Academic and Industry Drug Discovery Efforts Differ

10.3 The Making of a Good Academic FBDD Project

10.4 FBDD Techniques Currently Used in Academia

10.5 Project Structures for Doing FBDD in Academia

10.6 Conclusions and Perspectives

References

Chapter 11: Site-Directed Fragment Discovery for Allostery

11.1 Introduction

11.2 Caspases

11.3 Tethering K-Ras(G12C)

11.4 The Master Transcriptional Coactivator CREB Binding Protein

11.5 Tethering Against the PIF Pocket of Phosphoinositide-Dependent Kinase 1 (PDK1)

11.6 Tethering Against GPCRs: Complement 5A Receptor

11.7 Conclusions and Future Directions

References

Chapter 12: Fragment Screening in Complex Systems

12.1 Introduction

12.2 Fragment Screening and Detection of Fragment Hits

12.3 Validating Fragment Hits

12.4 Fragment to Hit

12.5 Fragment to Lead Approaches

12.6 Perspective and Conclusions

Acknowledgments

References

Chapter 13: Protein-Templated Fragment Ligation Methods: Emerging Technologies in Fragment-Based Drug Discovery

13.1 Introduction: Challenges and Visions in Fragment-Based Drug Discovery

13.2 Target-Guided Fragment Ligation: Concepts and Definitions

13.3 Reversible Fragment Ligation

13.4 Irreversible Fragment Ligation

13.5 Fragment Ligations Involving Covalent Reactions with Proteins

13.6 Conclusions and Future Outlook: How Far did We Get and What will be Possible?

References

Part III: Successes from Fragment-based Drug Discovery

Chapter 14: BACE Inhibitors

14.1 Introduction

14.2 FBDD Efforts on BACE1

14.3 Conclusions

References

Chapter 15: Epigenetics and Fragment-Based Drug Discovery

15.1 Introduction

15.2 Epigenetic Families and Drug Targets

15.3 Epigenetics Drug Discovery Approaches and Challenges

15.4 FBDD Case Studies

15.5 Conclusions

Abbreviations

References

Chapter 16: Discovery of Inhibitors of Protein–Protein Interactions Using Fragment-Based Methods

16.1 Introduction

16.2 Fragment-Based Strategies for Targeting PPIs

16.3 Recent Examples from Our Laboratory

16.4 Summary and Conclusions

Acknowledgments

References

Chapter 17: Fragment-Based Discovery of Inhibitors of Lactate Dehydrogenase A

17.1 Aerobic Glycolysis, Lactate Metabolism, and Cancer

17.2 Lactate Dehydrogenase as a Cancer Target

17.3 “Ligandability” Characteristics of the Cofactor and Substrate Binding Sites in LDHA

17.4 Previously Reported LDH Inhibitors

17.5 Fragment-Based Approach to LDHA Inhibition at AstraZeneca

17.6 Fragment-Based LDHA Inhibitors from Other Groups

17.7 Conclusions and Future Perspectives

References

Chapter 18: FBDD Applications to Kinase Drug Hunting

18.1 Introduction

18.2 Virtual Screening and X-Ray for PI3K

18.3 High-Concentration Screening and X-Ray for Rock1/2

18.4 Surface Plasmon Resonance for MAP4K4

18.5 Weak Affinity Chromatography for GAK

18.6 X-Ray for CDK 4/6

18.7 High-Concentration Screening, Thermal Shift, and X-Ray for CHK2

18.8 Virtual Screening and Computational Modeling for AMPK

18.9 High-Concentration Screening, NMR, and X-Ray FBDD for PDK1

18.10 Tethering Mass Spectometry and X-Ray for PDK1

18.11 NMR and X-Ray Case Study for Abl (Allosteric)

18.12 Review of Current Kinase IND's and Conclusions

References

Chapter 19: An Integrated Approach for Fragment-Based Lead Discovery: Virtual, NMR, and High-Throughput Screening Combined with Structure-Guided Design. Application to the Aspartyl Protease Renin.

19.1 Introduction

19.2 Renin as a Drug Target

19.3 The Catalytic Mechanism of Renin

19.4 Virtual Screening

19.5 Fragment-Based Lead Finding Applied to Renin and Other Aspartyl Proteases

19.6 Renin Fragment Library Design

19.7 Fragment Screening by NMR T1ρ Ligand Observation

19.8 X-Ray Crystallography

19.9 Renin Fragment Hit-to-Lead Evolution

19.10 Integration of Fragment Hits and HTS Hits

19.11 Conclusions

References

Index

End User License Agreement

List of Tables

Table 1.1

Table 1.2

Table 1.3

Table 1.4

Table 2.1

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Table 4.6

Table 5.1

Table 6.1

Table 7.1

Table 8.1

Table 9.1

Table 13.1

Table 13.2

Table 13.3

Table 14.1

Table 15.1

Table 18.1

List of Illustrations

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

Figure 1.5

Figure 1.6

Figure 1.7

Figure 1.8

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

Figure 3.5

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 4.7

Figure 4.8

Figure 4.9

Figure 4.10

Figure 4.11

Figure 4.12

Figure 4.13

Figure 4.14

Figure 4.15

Figure 4.16

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

Figure 5.8

Figure 5.9

Figure 5.10

Figure 5.11

Figure 6.1

Figure 6.2

Figure 6.3

Figure 6.4

Figure 6.5

Figure 6.6

Figure 6.7

Figure 6.8

Figure 6.9

Figure 6.10

Figure 6.11

Figure 6.12

Figure 7.1

Figure 7.2

Figure 7.3

Figure 7.4

Figure 7.5

Figure 7.6

Figure 7.7

Figure 7.8

Figure 7.9

Figure 7.10

Figure 7.11

Figure 7.12

Figure 7.13

Figure 7.14

Figure 7.15

Figure 8.1

Figure 8.2

Figure 8.3

Figure 8.4

Figure 8.5

Figure 8.6

Figure 8.7

Figure 8.8

Figure 10.1

Figure 10.2

Figure 10.3

Figure 10.4

Figure 10.5

Figure 11.1

Figure 11.2

Figure 11.3

Figure 11.4

Figure 11.5

Figure 11.6

Figure 11.7

Figure 11.8

Figure 11.9

Figure 11.10

Figure 11.11

Figure 12.1

Figure 12.2

Figure 12.3

Figure 12.4

Figure 12.5

Figure 12.6

Figure 12.7

Figure 12.8

Figure 12.9

Figure 12.10

Figure 12.11

Figure 13.1

Figure 13.2

Figure 13.3

Figure 13.4

Figure 13.5

Figure 13.6

Figure 13.7

Figure 13.8

Figure 13.9

Figure 13.10

Figure 13.11

Figure 13.12

Figure 13.13

Figure 13.14

Figure 13.15

Figure 13.16

Figure 13.17

Figure 14.1

Figure 14.2

Figure 14.3

Figure 14.4

Figure 15.1

Figure 15.2

Figure 15.3

Figure 15.4

Figure 15.5

Figure 16.1

Figure 16.2

Figure 16.3

Figure 16.4

Figure 16.5

Figure 17.1

Figure 17.2

Figure 17.3

Figure 17.4

Figure 17.5

Figure 17.6

Figure 17.7

Figure 17.8

Figure 17.9

Figure 17.10

Figure 17.11

Figure 17.12

Figure 17.13

Figure 17.14

Figure 17.15

Figure 18.1

Scheme 18.1

Scheme 18.2

Scheme 18.3

Scheme 18.4

Scheme 18.5

Scheme 18.6

Scheme 18.7

Scheme 18.8

Scheme 18.9

Scheme 18.10

Scheme 18.11

Scheme 18.12

Chart 18.1

Figure 19.1

Figure 19.2

Figure 19.3

Figure 19.4

Figure 19.5

Figure 19.6

Figure 19.7

Figure 19.8

Figure 19.9

Figure 19.10

Figure 19.11

Figure 19.12

Figure 19.13

Figure 19.14

Figure 19.15

Guide

Cover

Table of Contents

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Part 1

Chapter 1

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Methods and Principles in Medicinal Chemistry

Edited by R. Mannhold, H. Kubinyi, G. Folkers

Editorial Board

H. Buschmann, H. Timmerman, H. van de Waterbeemd

Previous Volumes of this Series:

Urbán, László/Patel, Vinod F./Vaz, Roy J. (Eds.)

Antitargets and Drug Safety

2015

ISBN: 978-3-527-33511-4

Vol. 66

Keserü, György M./Swinney, David C. (Eds.)

Kinetics and Thermodynamics of Drug Binding

2015

ISBN: 978-3-527-33582-4

Vol. 65

Pfannkuch, Friedlieb/Suter-Dick, Laura (Eds.)

Predictive Toxicology

From Vision to Reality

2014

ISBN: 978-3-527-33608-1

Vol. 64

Kirchmair, Johannes (Ed.)

Drug Metabolism Prediction

2014

ISBN: 978-3-527-33566-4

Vol. 63

Vela, José Miguel/Maldonado, Rafael/Hamon, Michel (Eds.)

In vivo Models for Drug Discovery

2014

ISBN: 978-3-527-33328-8

Vol. 62

Liras, Spiros/Bell, Andrew S. (Eds.)

Phosphodiesterases and Their Inhibitors

2014

ISBN: 978-3-527-33219-9

Vol. 61

Hanessian, Stephen (Ed.)

Natural Products in Medicinal Chemistry

2014

ISBN: 978-3-527-33218-2

Vol. 60

Lackey, Karen/Roth, Bruce (Eds.)

Medicinal Chemistry Approaches to Personalized Medicine

2013

ISBN: 978-3-527-33394-3

Vol. 59

Brown, Nathan (Ed.)

Scaffold Hopping in Medicinal Chemistry

2013

ISBN: 978-3-527-33364-6

Vol. 58

Hoffmann, Rémy/Gohier, Arnaud/Pospisil, Pavel (Eds.)

Data Mining in Drug Discovery

2013

ISBN: 978-3-527-32984-7

Vol. 57

Dömling, Alexander (Ed.)

Protein-Protein Interactions in Drug Discovery

2013

ISBN: 978-3-527-33107-9

Vol. 56

Edited by Daniel A. Erlanson and Wolfgang Jahnke

Fragment-based Drug Discovery

Lessons and Outlook

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Contributors

Chris Abell

University of Cambridge

Department of Chemistry

Lensfield Road

Cambridge

CB2 1EW

UK

Michelle R. Arkin

University of California, San Francisco

School of Pharmacy

Small Molecule Discovery Center

and

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158

USA

Christoph Arkona

Freie Universität Berlin

Institut für Pharmazie

Königin-Luise-Str. 2 +4

14195 Berlin

Germany

Mahendra Awale

University of Berne

Department of Chemistry and Biochemistry

Freiestrasse 3

3012 Berne

Switzerland

Justin Bower

Cancer Research UK Beatson Institute

Garscube Estate

Switchback Road

Bearsden

Glasgow

G61 1BD, UK

Alexander L. Breeze

AstraZeneca R&D

Discovery Sciences

Alderley Park

Macclesfield

SK10 4TG, UK

and

University of Leeds

Astbury Centre for Structural Molecular Biology

Faculty of Biological Sciences

Leeds

LS2 9JT, UK

Peter J. Brown

2 Structural Genomics Consortium

7th Floor, MaRS South Tower

101 College Street

Toronto

ON M5G 1L7

Canada

Stacie L. Bulfer

University of California, San Francisco

School of Pharmacy

Small Molecule Discovery Center

and

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158, USA

John A. Christopher

Heptares Therapeutics Ltd

BioPark, Broadwater Road

Welwyn Garden City AL73AX, UK

Miles Congreve

Heptares Therapeutics Ltd

BioPark, Broadwater Road

Welwyn Garden City

AL73AX, UK

Jared N. Cumming

Merck Research Laboratories

Structural Chemistry

2015 Galloping Hill Road

Kenilworth

NJ 07033

USA

Thomas G. Davies

Astex Pharmaceuticals

436 Cambridge Science Park

Milton Road

Cambridge

CB4 0QA, UK

Ben J. Davis

Vernalis Research

Granta Park

Cambridge

CB21 6 GB

UK

Martin Drysdale

Cancer Research UK Beatson Institute

Garscube Estate

Switchback Road

Bearsden

Glasgow

G61 1BD, UK

Minh-Dao Duong-Thi

Nanyang Technological University

School of Biological Sciences

60 Nanyang Drive

637551

Singapore

György G. Ferenczy

Hungarian Academy of Sciences

Research Center for Natural Sciences

Magyar tudósok krt. 2

H-1117 Budapest

Hungary

Stephen W. Fesik

Professor of Biochemistry

Pharmacology and Chemistry

Vanderbilt University School of Medicine

2215 Garland Ave.

607 Light Hall

Nashville

TN 37232-0146, USA

Anthony M. Giannetti

Google[x]

1600 Amphitheatre Parkway

Mountain View

CA 94043USA

Laurie E. Grove

Wentworth Institute of Technology

Department of Sciences

Boston

MA 02115

USA

Roderick E. Hubbard

Vernalis Research

Granta Park, Cambridge

CB21 6GB

UK

and

YSBL

University of York

Heslington, York

YO10 5DD

UK

Sean A. Hudson

University of California, San Francisco

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158

USA

Aman Iqbal

Proteorex Therapeutics Inc.

40 King Street West

Toronto, ON M5H 3Y4

Canada

Mike Jaegle

Freie Universität Berlin

Institut für Pharmazie

Königin-Luise-Str. 2 + 4

14195 Berlin

Germany

Frantz Jean-Francois

University of California, San Francisco

School of Pharmacy

Small Molecule Discovery Center

and

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158

USA

Harren Jhoti

Astex Pharmaceuticals

436 Cambridge Science Park

Milton Road

Cambridge

CB4 0QA

UK

György M. Keseru˝

Hungarian Academy of Sciences

Research Center for Natural Sciences

Magyar tudósok krt. 2

H-1117 Budapest

Hungary

Dima Kozakov

Boston University

Department of Biomedical Engineering

Boston

MA 02215

USA

Jürgen Maibaum

Novartis Pharma AG

Novartis Institutes for Biomedical Research

Novartis Campus

4002 Basel

Switzerland

Eric Nawrotzky

Freie Universität Berlin

Institut für Pharmazie

Königin-Luise-Str. 2 + 4

14195 Berlin

Germany

Sten Ohlson

Nanyang Technological University

School of Biological Sciences

60 Nanyang Drive

Singapore 637551

Singapore

Puja Pathuri

Astex Pharmaceuticals

436 Cambridge Science Park

Milton Road

Cambridge

CB4 0QA

UK

Angelo Pugliese

Cancer Research UK Beatson Institute

Garscube Estate

Switchback Road

Bearsden

Glasgow

G61 1BD

UK

Jörg Rademann

Freie Universität Berlin

Institut für Pharmazie

Königin-Luise-Str. 2 + 4

14195 Berlin

Germany

T. Justin Rettenmaier

University of California, San Francisco

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158

USA

Jean-Louis Reymond

University of Berne

Department of Chemistry and Biochemistry

Freiestrasse 3

3012 Berne

Switzerland

Simon Rüdisser

Novartis Pharma AG

Novartis Institutes for Biomedical Research

Novartis Campus

4002 Basel

Switzerland

Gordon Saxty

Fidelta Ltd.

Prilaz baruna Filipovića 29

10000 Zagreb

Croatia

Duncan E. Scott

University of Cambridge

Department of Chemistry

Lensfield Road

Cambridge

CB2 1EW

UK

Christina Spry

University of Cambridge

Department of Chemistry

Lensfield Road

Cambridge

CB2 1EW

UK

and

The Australian National University

Research School of Biology

Linnaeus Way

Canberra

ACT 2601

Australia

Andrew W. Stamford

Merck Research Laboratories

126 East Lincoln Avenue

Rahway NJ 07065

USA

Corey O. Strickland

Merck Research Laboratories

Structural Chemistry

2015 Galloping Hill Road

Kenilworth

NJ 07033

USA

Sandor Vajda

Boston University

Department of Biomedical Engineering and

Department of Chemistry

Boston

MA 02215

USA

Eric Vangrevelinghe

Novartis Pharma AG

Novartis Institutes for Biomedical Research

Novartis Campus

4002 Basel

Switzerland

Ricardo Visini

University of Berne

Department of Chemistry and Biochemistry

Freiestrasse 3

3012 Berne

Switzerland

Feng Wang

Vanderbilt University School of Medicine

Department of Biochemistry

2200 Pierce Ave.

802/804 RRB

Nashville

TN 37232-0146

USA

Richard A. Ward

AstraZeneca R&D

Oncology iMED

Alderley Park

Macclesfield

SK10 4TG

UK

James A. Wells

University of California, San Francisco

Department of Pharmaceutical Chemistry

UCSF Mission Bay Campus

1700 4th Street

San Francisco

CA 94158

USA

Glyn Williams

Astex Pharmaceuticals

436 Cambridge Science Park

Milton Road

Cambridge

CB4 0QA

UK

Jon Winter

AstraZeneca R&D

Oncology iMED

Alderley Park

Macclesfield

SK10 4TG

UK

Ee Lin Wong

Freie Universität Berlin

Institut für Pharmazie

Königin-Luise-Str. 2 + 4

14195 Berlin

Germany

Daniel F. Wyss

Merck Research Laboratories

Structural Chemistry

2015 Galloping Hill Road

Kenilworth

NJ 07033

USA

Preface

Just two decades ago, Stephen Fesik initiated fragment-based ligand design by developing an NMR-based method to search for small, low-affinity ligands in adjacent binding pockets of a protein and to link them to a high-affinity ligand [1]. A broader use of this approach was hindered both by its limitation to relatively small proteins and by a patent application. However, within short time alternative methods emerged, originally based on different NMR techniques, later using protein crystallography. Thus, structure-based design was not any longer restricted to “large” molecules: libraries of much smaller fragment-type compounds were tested experimentally or screened in silico, with the advantage that a small ligand has a much better chance to fit a certain binding site. In further steps, the ligand can grow into the environment of its pocket or can be linked to an adjacent fragment. The only critical step in fragment combination is the search for a linker that combines the fragments in a relaxed, bioactive conformation, optimally stabilizing this favorable conformation.

Ten years later, in 2006, time was already ripe to review the techniques and the accumulated experience in fragment-based ligand design: Wolfgang Jahnke and Daniel Erlanson edited the very first book on this topic [2]. Now, another 10 years later, the discipline has significantly developed and a major number of drug candidates resulted from its use. Thus, we are very grateful that both experts agreed to edit not only a new edition but also a completely new book on fragment-based design. In its introductory section, leading scientists of this area review the role of fragment-based approaches in lead finding and the selection of appropriate targets. Next, an overview on chemical space is provided. The second section discusses library design and various screening techniques, together with a major number of issues that are relevant in fragment-based ligand discovery. The last section presents a significant number of success stories, providing evidence for the broad applicability of fragment-based design in drug research.

As last time, we are very grateful to the editors Daniel Erlanson and Wolfgang Jahnke for assembling such a unique collection of important topics, as well as to all chapter authors for their excellent work. Last but not least we thank the publisher Wiley-VCH, in particular Waltraud Wüst and Frank Weinreich, for their valuable contributions to this project and the entire series.

DüsseldorfWeisenheim am SandZürich

Raimund Mannhold

Hugo Kubinyi

Gerd Folkers

October 2015

Reference

1.

Shuker, S.B., Hajduk, P.J., Meadows, R.P. and Fesik, S.W. (1996) Discovering high-affinity ligands for proteins: SAR by NMR.

Science

,

274

, 1531–1534.

2.

Jahnke, W. and Erlanson, D. eds., (2006)

Fragment-based Approaches in Drug Discovery (Volume 34 of Methods and Principles in Medicinal Chemistry

, eds.), R. Mannhold, H. Kubinyi, and G. Folkers Wiley-VCH Verlag GmbH, Weinheim.

A Personal Foreword

For the great things are not done by impulse, but by a series of small things brought together.

Vincent Van Gogh, 1888

When Wiley-VCH asked us whether we would be willing to edit a new book on fragment-based drug discovery, our first reaction was panic. Editing a book is a daunting task, and having done it once already we knew well what was in store.

Our second reaction was to ask whether a new book was really needed. Since the very first book on fragment-based drug discovery was published by Wiley-VCH in 2006, six more books have appeared, along with dedicated journal issues and dozens of reviews. Was there anything new to say?

Happily, as you will soon discover, the answer is an emphatic yes! This is clearly illustrated by a search for publications containing the phrase “fragment-based drug discovery” in SciFinder®, as seen in the figure.

The past few years have seen a bumper crop of papers on the topic, and given that this search was run in mid-June of 2015 this trend looks set to continue if not accelerate. From its origins as a niche technique, fragment-based approaches have spread throughout the world to organizations large and small and are embraced by biologists, biophysicists, chemists, modelers, and more. More than 30 drugs derived from fragments have entered the clinic (http://practicalfragments.blogspot.com/2015/01/fragments-in-clinic-2015-edition.html), and one (vemurafenib) has already been approved. This book is a comprehensive view of where the field stands – and where it is going.

We would like to thank Wiley-VCH, especially Frank Weinreich and Waltraud Wüst, for encouraging us to undertake this project and patiently working with us through the inevitable but nonetheless frustrating difficulties and delays. We would also like to thank our contributors, all of whom are extraordinarily busy and accomplished scientists. We are thrilled with the response we received to our invitations and with the depth and quality of the chapters. Finally, we would like to thank you for reading. We hope that you will find something useful to apply to your own research: each of our fragmentary efforts advances the great human enterprise of drug discovery.

San FranciscoBaselOctober 2015

Daniel A. ErlansonWolfgang Jahnke

Part IThe Concept of Fragment-based Drug Discovery

1The Role of Fragment-based Discovery in Lead Finding

Roderick E. Hubbard

1.1 Introduction

Fragment-based lead discovery (FBLD) is now firmly established as a mature collection of methods and approaches for the discovery of small molecules that bind to protein or nucleic acid targets. The approach is being successfully applied in the search for new drugs, with many compounds now in clinical trials (see summary in [1]) and with the first fragment-derived compound now treating patients [2]. The approach has also had a number of other impacts such as providing starting points for lead discovery for challenging, unconventional targets such as protein–protein interactions [3–5], increasing the use of biophysics to characterize compound binding and properties, and providing small groups, particularly in academia, with access to the tools to identify chemical probes of biological systems [6,7].

The other chapters in this book will discuss the details and new advances in the methods and provide examples of how fragments have been used in specific projects. In this chapter, I will draw on my own experiences and view of the literature to discuss three main areas. First, I will review current practice in FBLD, highlighting how and when fragments have an impact on the drug discovery process. Second, I will then review how the ideas have developed, with particular emphasis on the past 10 years. I will discuss how fragment methods and thinking have been extended and refined and how these developments have affected the lead discovery process in drug discovery. Finally, I will discuss some of the areas where we can see that improvements in fragment methods could have further impact on discovery.

The discussion will focus on fragment-based discovery against protein targets. Although there are a few examples of fragments being used against RNA [8–10] and DNA [11] targets, the majority of reported campaigns are against proteins. Two types of protein target will be considered. The first shall be called conventional targets. These are proteins such as kinases where although it is never straightforward to achieve the required selectivity and balance of physicochemical properties in the compound, the proteins usually behave in most of the experiments and assays. Crystal structures are usually readily obtained, large amounts of pure, homogeneous, and functional proteins can be generated for biophysical studies, and the activity assays are robust and well understood. The second class of target shall be called unconventional targets. There are two types here – the first are protein–protein interaction targets such as the proapoptotic Bcl-2 family or Ras, where experience over the years has eventually established reasonably robust assays and although crystal structures take some time to determine and the protein does not always behave in biophysical assays, it is possible to establish structure-based discovery. The main challenge here is the nature of the binding sites, with often large, hydrophobic, and sometimes flexible sites. The second type of unconventional targets are the results of recent advances in our understanding of mammalian disease biology and consist of new classes of enzymes (such as the ubiquitin processing machinery [12]), disrupting multiprotein complexes, and proteins that are intrinsically disordered in some way (such as the one described in [13]). Here, the primary challenges are often in producing sufficient, homogeneous, functional protein for study, knowing what the post-translational modification state or even which complex is the true target and establishing robust assays to report on activity or binding. This last issue is often not appreciated – it can take a long time to establish the assays on new classes of target, not only because there is intrinsic variability in the behavior of the system but also because there is often not a tool compound available with which to validate the assay.

1.2 What is FBLD?

There are two distinctive features of fragment-based discovery compared to other approaches to lead finding. The first is that the discovery process begins with screening a small (usually 1–2000 member) library of low molecular weight (typically less than 20 heavy atom) compounds for binding to a particular site on the target. Key is the molecular weight of the fragments – they are big enough to probe interactions in the protein but small enough to minimize chances of unfavorable interactions. The second distinctive feature lies in the approach to optimizing these hits to lead compounds, either through careful, usually structure-guided, growth of the fragment or through merging information from fragments and elsewhere to generate optimized hits.

In many ways, fragments can be viewed as a state of mind – an approach to use the fragments as chemical tools to dissect what the requirements are for the chemical matter that affects a particular target in the desired way (affinity, selectivity) and using a combination of rational, usually structure-guided, and often biophysics-based methods for generation of the optimized compounds. We can define a fragment approach as one of intent – and that intent affects the strategy, methods, and thinking that is applied during the early parts of a discovery project. Detection and characterization of such weakly binding compounds can be problematic for some classes of target, with concerns over false positive and false negative hits, changes in binding mode, and so on. So, fragment methods engender a questioning, problem-solving approach to research. This is carried through into the usually structure-guided evolution of the initial fragment hits, which allows careful assembly of compounds that bind with high efficiency combined with suitable compound properties.

1.3 FBLD: Current Practice

Figure 1.1 and its legend summarize the contemporary approach to fragment-based discovery followed by most practitioners. There are five main components to a fragment platform: a fragment library, a method for finding which fragments bind, characterizing how the fragments bind by determining structure and biophysical measurements, exploring fragment SAR to identify the best fragment(s) to progress, and using the fragment(s) to generate lead compounds. Figure 1.1 also emphasizes how information about binding motifs is combined with information from HTS hits, literature compounds, or virtual screening hits. Other chapters in this book will provide detail on each of these different areas. In this chapter, I am focusing on the impact fragments have had on the lead discovery process. This is best done with some examples.

Figure 1.1 the FBLD process. There are five main components to a fragment platform. (a) Fragment library: there is an extensive literature on the design of fragment libraries [26,31,32,41]. The choice of compounds is constrained both by the demands of the screening methods (solubility, detection) and by the need to evolve the compounds (elaboration vectors, synthetic tractability) as well as avoiding reactive or toxic substructures. Key is the number of heavy atoms in the compounds. Analyses by Reymond [38,39] suggest that the number of possible lead-like compounds (chemical space) increases by around eightfold for each heavy atom. There are many approximations but this means that a fragment library of 1000 compounds of average MW 190 is equivalent to 108 compounds of MW 280 and 1018 compounds of MW 450. (b) Fragment screening: Table 1.1 summarizes the experiences at Vernalis over the years that are variously described elsewhere [15]. For all techniques, the main limitations are whether the protein target can be prepared in a suitable format for screening and whether the fragments are sufficiently soluble. The most robust method of screening is ligand-observed NMR, which has the dynamic range (typically from 5 mM to 100 nM) seen for fragment binding and particularly important for unconventional targets, as the integrity of the ligand and protein is checked at each experiment. (c) Characterizing fragment binding: for conventional targets, it is often possible to rapidly determine crystal structures of the fragment binding to the protein and, if the biochemical or binding assay is not suitable, use a biophysical method to validate and if possible quantify potency. For unconventional targets, this step is particularly important as the targets can have challenging binding sites, where conformational flexibility or large hydrophobic surfaces can challenge reliable detection of fragment binding. NMR methods can be used for unconventional targets, ranging from binding site localization (HSQC) to NMR-guided models (measuring NOE distances from ligand atoms to protein residues) and full structure determination. These are constrained by the size of the protein and requirement for isotope labeling. (d) Fragment SAR and optimization: there are two well-established methods – (1) SAR by catalog where features of the fragment are used to identify commercially available compounds for purchase and assay and (2) detailed design of bespoke compounds to optimize the fragment itself and explore potential vectors for elaboration. More recently, there have been new methods such as off-rate screening [16] that allow rapid profiling of compounds where substituents have been added to particular positions on the fragment, prospecting for suitable vectors for fragment evolution. This can be particularly important when limited structural information is available. (e) Fragment to candidate: medicinal chemistry optimization, supported where possible by rapid crystal structure determination, to bring together information from the portfolio of fragments, hits, HTS, literature, and so on to design and optimize lead compounds.

1.3.1 Using Fragments: Conventional Targets

Conventional targets are ones with well-defined active sites (such as most enzymes) where structural information is readily available. It is usual to get a large number of fragment hits for such targets – at Vernalis our experience has been 50–150 validated hits from screening a library of about 1500 fragments [14,15]. A lower hit rate can indicate there may be issues with progressing compounds against the target as discussed later. Modeling of the binding of these fragments can be helpful, but the most effective fragment to hit to lead optimization campaigns uses the detailed information available from experimental structures determined by X-ray crystallography (preferred) or if necessary by NMR. The main issue with NMR is the time it takes to generate structures. A suitable crystal form can generate many hundreds of crystal structures during the early months of a project, whereas it takes at best a few days for NMR methods to generate models for binding. In addition, NMR models rarely have the resolution to give confidence in some of the subtleties of binding mode necessary for design of selective compounds (such as for kinases).

The three main ways of using fragments are growing, merging, and linking. Figure 1.2a–c shows some representative examples that we can use to describe the essential features of each approach.

Figure 1.2 (a) Evolving fragments – linking. The SAR by NMR approach was developed by the Abbott group in the 1990s [22] (see also the reviews [63,64]). Protein-observed NMR screening of a library identifies the first site binder (screen 1) that can then be optimized (optimize 1). The second screen (screen 2) is then performed in the presence of an excess of the optimized first site binder to identify the second site binder that can also be optimized (optimize 2). NMR structure determination identifies appropriate vectors for linking the two fragments (link) to give a compound that can then be optimized. The first disclosed example was for FKBP [22]; the first drug discovery project was on stromelysin [65] and arguably the most successful was for the Bcl-2 family of proteins [66–69]. For stromelysin, compound 1 was not from screening but is a known metalloprotease binding motif. Screening in the presence of 1 identified compounds such as 2 that after optimization gave 3. Combining these in 4 very neatly demonstrates the power of the method – a large increase in potency, clearly retaining the two weakly binding fragments. For the Bcl-2 family, the evolution from the two site binding fragments 5 and 6 is less obvious in compound 7, although the method did provide starting points for chemistry where conventional HTS failed. A considerable amount of medicinal chemistry optimization was needed to generate ABT-737 [66] that briefly entered clinical trials, followed by ABT-263 [70] with better drug-like properties though still with a dual Bcl-2/Bcl-xL profile that can give undesired pharmacology. This has recently been succeeded in the clinic by the more Bcl-2 selective ABT-199 [71]. With few exceptions [72], the continued champion of the linking approach is Fesik, now at Vanderbilt (see Figure 1.2d). Most other practitioners find it difficult to identify such multiple sites and commit such dedicated chemistry resources to a linking strategy. (b) Evolving fragments – growing. There are two widely used approaches for growing fragments. The first is SAR by catalog, where features of the bound fragment are used to search a database of accessible compounds that are then assayed. An example is the HSP90 program at Vernalis that led to 15, AUY922, currently in multiple phase II clinical trials for various cancers. A ligand-observed screen identified the resorcinol 12 and near-neighbor fragments such as 13. Search of available compounds for resorcinols that were subsequently triaged with pharmacophore-biased docking, identified compounds such as 14, which show good affinity. Structure-guided optimization led to 15. A summary of the HSP90 discovery project is available [73] as well as more details on AUY922 [74,75]. The second approach is growing by careful structure-guided ligand design. The Aurora example from Astex [76] is a particularly good example where fragment 16 was identified from a crystallographic screen of fragments against CDK2; exploration (17) identified good vectors for optimization, leading to the hit 18 that was subsequently optimized to the clinical candidate 19. A similar chemogenomic approach (i.e., transferring knowledge about chemotypes that bind to a particular family) can be seen in the B-Raf kinase example from Plexxikon [2], where 20 was initially characterized binding to Pim-1 kinase, with the related 21 studied in FGFR1 kinase leading to 22 as a potent hit against B-Raf that was optimized to Vemurafenib, 23, the first fragment-derived compound on the market. (c) Evolving fragments – merging. This is an approach where information from fragments, derived hits, literature, and HTS hits is all combined together to generate the new lead compound. The approach relies heavily on multiple crystal structures to identify the subtle opportunities provided by binding modes for novel scaffolds and achieving selectivity. The example shown is for PDPK1 [56]. Compound 24 is a known promiscuous kinase inhibitor from which the fragment 25 was derived and the crystal structure determined bound to PDPK1. A ligand-observed screen with staurosporine as competitor identified more than 80 fragment hits, for 50 of which crystal structures were determined. The crystal structures of fragments 26 and 27 showed distinctive binding modes. The crystal structure of an inhibitor from a published study on CDK2, bound to PDPK1, identified a hydrophobic region adjacent to the carboxylic acid of 27. A search of the available chemicals identified a compound that with small optimization gave 28 with 1 µM affinity for PDPK1. Superposition of the structures of the hits suggested a number of combinations of scaffolds and features. One of these is taking the highlighted features from 25, 26, and 28 to generate 29 that showed low nM affinity with good selectivity against other kinases, but importantly affected in vivo PD markers in mouse xenograft models. (d) Recent protein–protein interaction FBLD projects. A number of studies have been published recently on the use of fragments for therapeutically important protein–protein interaction targets that have been studied extensively but with little published success through conventional drug discovery efforts. The Bcl-2 family member Mcl-1 is such an example. The Fesik group at Vanderbilt used SAR by NMR to identify 30 and 31 binding in adjacent sites that when linked (32) gave a useful starting lead compound for further evolution [77]. The same group used a similar approach to identify 33 that when grown gave 34 as an inhibitor of Ras [4], a key oncogene. Other recently published Ras projects are tethering through the G12C mutant as in 35 from a group at UCSF [5] and identification of another binding pocket for fragment 36 at Genentech [3].

Table 1.1 A summary of the characteristics of the most widely used fragment screening methods.

Method

Sensitivity

Issues

Ligand-observed NMR – a number of NMR experiments (STD [82], Water-Logsy [83], and CPMG [84]) detect binding of a ligand to the protein

10 mM–100 nM

Requires large amounts of protein (many 10 s mgs) but the most robust method for detecting weak binding. Each experiment confirms that the ligand and protein maintain their integrity in solution; the use of a competitor ligand to displace the fragment can identify nonspecific binding. These features make the technique particularly suitable for weak binding to challenging targets. Requires careful design to identify allosteric or cryptic binding sites

Protein-observed NMR – HSQC experiment detects changes in the local environment of

15

N or

13

C nuclei as ligand added

5 mM–100 nM

Requires isotopic labeling of the protein; limited to proteins<35 kDa; can titrate ligand onto protein and determine

K

D

; pattern of changes in spectra can confirm the same binding site for different ligands and identify allosteric sites; assignment of spectrum allows localization of site

X-ray crystallography: either cocrystallization (crystals formed from the preformed protein–ligand complex) or soaking (high concentrations of ligand added to apo crystals)

All affinities

Cocrystallization can require different crystal conditions for each ligand. Soaking of apo crystals requires crystal form with accessible protein binding site; depending on crystal form can identify cryptic sites Crystal structure provides information-rich description of protein–ligand interactions ready for design

Surface plasmon resonance [47]; monitor molecular weight change as one component flows past the other attached to a surface

500 µM lower limit

Two modes – direct binding (protein attached, ligand flows) allows kinetics (

k

on

and

k

off

) to be measured; indirect, or affinity in solution, where tool compound attached and protein (in the presence of possible fragment) is flowed past. Main issue is immobilization and integrity of protein on surface

Enzyme/binding assays

100 µM lower limit usually

The high concentrations of ligand interfere with most formats of assay preventing detection of mM binding fragments; effective for some assay formats and for well-defined active sites – for example, kinases

Isothermal titration calorimetry (ITC) [85]

1 mM–10 nM

Requires too much protein and ligand to be useful for screening, but the most robust method for measuring

K

D

as long as the interaction involves a change in Δ

H

Mass spectrometry

100 µM

Requires protein/buffer system that “flies” in the mass spectrometer and an interaction that can survive in the gas phase. Effective for covalent interactions –too variable for weakly binding fragments

Weak-affinity chromatography [51] – immobilize the target on a silica column, then use LC–MS to identify retained ligands

1 µM upper limit

A cheap way of measuring weak interactions (using simple LC–MS equipment). As for SPR, the main limitation is attachment of protein to surface and behavior of the fragments on the surface

Thermal shift analysis (TSA) [86] – measure the melting temperature of the target by monitoring the increase in fluorescence as the target is heated up in the presence of a dye plus and minus the ligand

500 µM lower limit

A relatively reliable technique for detecting binding of ligands that bind better than 10 µM, but many false positives and negatives in detecting fragment binding – the change in melting temperature is too small to measure. Uses small amounts of material and inexpensive instrumentation

Fragment linking is a conceptually very attractive idea – find two fragments that bind in adjacent sites and achieve a high-affinity compound by linking them together.This was the basis of the initial SAR by NMR approach, but with a few exceptions, only the Abbott group (such as summarized in Figure 1.2a, see also Table 1.3), and the follow-on work by Fesik at Vanderbilt (see later), has succeeded in making this work. Most practitioners have found that either the binding site of the target does not contain appropriate features or that they have found it difficult to either design or resource the chemistry required to link the two fragments and preserve the binding mode of the initial hits.

The growing approach is summarized in Figure 1.2b. This has been the most widely used with a number of variants. The first is to use the idea of SAR by catalog – that is, where the fragment hit provides a central scaffold with which to search for near-neighbor compounds from available compounds (either the corporate collection or that can be purchased). The second is to generate limited libraries of compounds based on the fragment to explore vectors for affinity or selectivity. Here, a recent innovation is to look at changes in the off-rate of binding as a marker for improvements in binding (the so-called off-rate screening [16]). For both of these approaches, the key value is in characterizing possible vectors for elaboration and the types of functional groups that could be used – developing SAR around the hit fragment. It is important to characterize the binding as it is probable that the evolved compound will include additional atoms that are not optimal – and these should be removed before further optimization is attempted. The final and most widely used method for growing fragments is to use detailed structure-based design to iteratively grow the fragment a few atoms at a time to pick up specific interactions with the binding site.

In the merging approach, insights from the binding mode of the fragments are combined with information from literature, virtual screening, or HTS hits to design new scaffolds. The PDPK1 in Figure 1.2c is one example; others include some of the series developed at Pfizer against biotin carboxylase [17] and the oral compound BEP800 [18] against HSP90 designed at Vernalis. For this type of approach, the main requirements are multiple crystal structures and confidence from the medicinal chemists to embark on such radical compound redesign. This is one of the approaches that is being used more frequently as fragment-based approaches are being embraced by large pharmaceutical companies and integrated with HTS. It is fascinating to see how a medicinal chemist with experience of fragment-based discovery approaches HTS hits – their first instinct is to dissect the compound down to the core binding motifs, and once that has been identified at the fragment level and then optimized, the functionalization from the HTS hit can be reassembled.

1.3.2 Using Fragments: Unconventional Targets

One of the striking advantages of FBLD is that fragment hits can be found for nearly all targets (see Figure 1.5 later). This is particularly valuable for more challenging unconventional targets such as protein–protein interactions, multiprotein complexes, intrinsically disordered proteins, and new classes of targets (such as ubiquitin-specific proteases [12]) where HTS screening of conventional libraries fails to identify tractable hits. The early, high-profile example of this was the discovery of ABT-737 (and subsequently, ABT-263 and ABT-199) as seen in Figure 1.2a; additional examples are shown in Figure 1.2d such as the work on Mcl-1 by the Vanderbilt group and that at Genentech and UCSF in identifying hit compounds that affect the activity of K-Ras (referenced in the figure legends). These proteins are often difficult to work with in terms of solubility, homogeneity, and folding. In addition, the binding sites are often quite shallow or diffuse, which means that binding affinity (and thus ligand efficiency, see Table 1.3) is low. The main published successes have, therefore, not surprisingly, used NMR methods to detect and characterize binding, a technique that not only has the necessary sensitivity but also importantly has the in-built quality control to assess the protein state for each binding experiment.

1.4 What do Fragments Bring to Lead Discovery?

Later in this chapter, I will discuss some of the details of how our ideas and practice of FBLD has evolved over the past 10 years. Here, the major features will be summarized of how FBLD is used and has had an impact on drug discovery, with a somewhat arbitrary separation of comments against both conventional and unconventional classes of targets.

For conventional targets:

Fragments can sample the chemical space of what will bind to a binding site. There are still not many examples where this has been analyzed in detail (see [19] for an analysis on HSP90 compounds), but fragments usually recapitulate the key binding features seen in compounds derived by other methods (such as HTS or natural product derivatives).

Fragments can show selectivity even for closely related proteins and even when a fragment binds to many similar targets, it can adopt different binding modes (– see the example for kinases in

Table 1.4

and

Figure 1.7

discussed later).

Where crystal structures are available, the important first step in assimilating the set of fragment hits is to categorize the fragments on binding mode.

The selection of which fragments to take forward for evolution is as much about opportunity (such as IP, selectivity, and chemical tractability) as the current affinity of a specific fragment. Often, there will be regions of a fragment that are not optimal or required for binding. For this reason, it is important to explore the SAR of the initial fragment(s) before optimization, identifying which binding modes and potential vectors offer the opportunity to gain selectivity and affinity.

It is usually the case that the central core of the fragment does not change the binding mode as the fragment is evolved. If it does, then it can be a sign that the initial fragment was not optimal or that the additional atoms added to the fragment have challenged the binding efficiency – as seen in the evolution of the same fragment in three different kinases [20].

A concern voiced by some is how it is possible to achieve novelty when most are screening very similar fragment libraries against the same targets. As discussed later (and shown in

Figure 1.8

), even where the same fragments are found, the medicinal chemists will optimize differently.

For unconventional targets:

Fragments provide the opportunity to assess challenging targets for chemical matter that binds; the hit rate can be an indication of how difficult it is going to be to progress compounds against a target (see

Figure 1.5

later).

It is usually not an issue in identifying fragments that bind to such targets.

The major challenge is establishing robust, validated assays – both for establishing binding and for activity. An issue is that, often, there are not validated tool compounds available and so it can take some time (and iteration with evolving fragments) to establish an assay that can be relied on.

The use of multiple (sometimes called orthogonal) binding experiments can be crucial to success – helping to validate the binding and binding mode.

A major issue is the time it takes to generate lead compounds – it takes commitment to the long haul. A project can spend many years in the exploratory phase, establishing robust assays and validated starting points before a drug discovery program can begin. This time in fragment and early hit space does not necessarily require large resources, but it can be long. The key to continuation of the project is maintaining confidence in the target and hits and that the next steps for the project are clear.

For all targets:

A fragment screen is a rapid way of assessing how difficult it will be to find new chemical matter that will bind to a particular target – a low fragment hit rate does not necessarily mean the target is undruggable, but it can indicate that finding high affinity, selective compounds could be a challenge.

Start the fragment campaign early enough in the project cycle

– Many large companies have found they needed to establish dedicated teams that promoted the fragment approaches – this required top-down implementation by management to ensure the resources were applied (and staff objectives suitably adjusted).

– The main issues are cultural – most organizations have the different technologies/capabilities in place to perform fragment screening. Key is integration alongside more conventional HTS type of methods and building a culture of seeing the techniques as complementary and not a competition.

The focus (and required assays) is on binding rather than activity in the early part of lead identification.

An important feature of fragments is that the optimization process starts with a core that is small. Careful optimization can maximize the ligand efficiency of evolving compounds and ensure incorporation of the optimal properties. A drop in ligand efficiency on optimization should always be questioned. In addition, maintaining a high ligand efficiency during early to mid lead optimization allows that efficiency to be spent in fine-tuning the physicochemical properties and efficacy of the compound in the later stages. Overall, fragments provide the opportunities and scope for the medicinal chemist to develop compounds with optimal properties.

For most targets, fragments provide choice – this gives potential for many different chemical series as starting points, providing backups, ideas on key interactions and binding modes that can be exploited in optimization or the potential for scaffold hopping if issues appear during optimization (such as physicochemical properties, CYP inhibition, or hERG).

In addition to explicit FBLD campaigns, the fragment ideas have permeated conventional lead optimization. The most recent example I have come across is the work at Kaken Pharmaceuticals on PDE7 [21], where a novel HTS hit was dissected back to a fragment and then evolved – there are many other examples emerging in the literature that show how fragment thinking has infected many areas of medicinal chemistry.

1.5 How did We Get Here?

1.5.1 Evolution of the Early Ideas and History

It is now nearly 20 years since the first publication described a fragment-based approach to discover potent lead compounds [22] and almost 10 years since the publication of the first edition of this book [23]. Table 1.2 provides a summary of the ideas and methods that made an important contribution to the development of the first use of fragments in ligand discovery. Table 1.3 summarizes the early developments in the field that led to the publication of the first edition of this book in 2006.

Table 1.2 The ideas and concepts that underpinned the emergence of the first demonstration of fragment-based lead discovery in 1996.

Double the Δ

G

; square the

K

D

: papers by Jencks [87] from the early 1970s remind the community that Δ

G

= −

RT

ln

K

– so combining two weak interactions gives a strong association. Also that the first ligand binding overcomes rotational and translational entropy, so additional interactions are stronger.

High-throughput crystallography: Perutz and coworkers [88] demonstrated in the early 1980s the benefit of multiple crystal structures in analyzing protein–drug interactions

Functional group efficiency: Andrews, Craik, and Martin [89] developed the idea in the early 1980s that particular functional groups make a distinct average contribution to binding

Functional group binding – computational: Goodford [90] developed GRID in the early 1980s to map the predicted binding of single-point probes to an active site with impact on drug discovery such as Relenza [91]. The MCSS [92] approach extended this to larger functional groups and LUDI [93] derived interactions from crystal structures

Functional group binding – experimental: the first experimental mapping of a binding site was by crystallography, with the MCSC approach pioneered by Ringe and coworkers [94] in the early 1990s and developed by others [95,96]

Fragment linking – computational: Approaches such as HOOK [97], Caveat [98], and LUDI [99] were developed in the early 1990s to link functional groups; the main challenges were predicting binding affinity and design of synthetically tractable molecules

Table 1.3 The developments in FBLD between 1996 and 2005.

SAR by NMR: the phrase “SAR by NMR” was coined to describe the approach developed at Abbott that saw the first publication on fragment-based drug discovery [22]. Protein-observed NMR (

N

15

-

H

1