Applied Biophysics for Drug Discovery

Applied Biophysics for Drug Discovery

Edited by

Donald Huddler

Widener University Delaware Law SchoolWilmington, USA

Edward R. Zartler

Quantum Tessera ConsultingCollegeville, USA

This edition first published 2017© 2017 John Wiley & Sons Ltd

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Library of Congress Cataloging‐in‐Publication Data

Names: Huddler, Donald Preston, 1971– editor. | Zartler, Edward, editor.Title: Applied biophysics for drug discovery / edited by Donald Huddler, Edward R. Zartler.Description: Hoboken, NJ : Wiley, 2017. | Includes bibliographical references and index. | Identifiers: LCCN 2017013165 (print) | LCCN 2017014921 (ebook) | ISBN 9781119099499 (pdf) | ISBN 9781119099505 (epub) | ISBN 9781119099482 (hardback)Subjects: | MESH: Drug Discovery–methods | Biophysical PhenomenaClassification: LCC RS420 (ebook) | LCC RS420 (print) | NLM QV 745 | DDC 615.1/9–dc23LC record available at https://lccn.loc.gov/2017013165

Cover image: Provided by Edward Zartler; (Background) © hakkiarslan/Getty ImagesCover design: Wiley

Set in 10/12pt Warnock by SPi Global, Pondicherry, India

10 9 8 7 6 5 4 3 2 1

v

List of Contributors xiii

1 Introduction 1Donald Huddler

References 3

2 Thermodynamics in Drug Discovery 7Ronan O’Brien, Natalia Markova, and Geoffrey A. Holdgate

2.1 Introduction 72.2 Methods for Measuring Thermodynamics of Biomolecular Interactions 82.2.1 Direct Method: Isothermal Titration Calorimetry 82.2.2 Indirect Methods: van’t Hoff Analysis 82.2.2.1 Enthalpy Measurement Using van’t Hoff Analysis 82.3 Thermodynamic‐Driven Lead Optimization 92.3.1 The Thermodynamic Rules of Thumb 92.3.2 Enthalpy–Entropy Compensation 102.3.3 Enthalpy–Entropy Transduction 132.3.4 The Role of Water 142.4 Enthalpy as a Probe for Binding 152.4.1 Thermodynamics in Fragment‐Based Drug Design (FBDD) 152.4.2 Experimental Considerations and Limitations When

Working with Fragments 162.4.3 Enthalpic Screening 172.5 Enthalpy as a Tool for Studying Complex Interactions 172.5.1 Identifying and Handling Complexity 172.6 Current and Future Prospects for Thermodynamics

in Decision‐Making Processes 24 References 25

Contents

Contentsvi

3 Tailoring Hit Identification and Qualification Methods for Targeting Protein–Protein Interactions 29Björn Walse, Andrew P. Turnbull, and Susan M. Boyd

3.1 Introduction 293.2 Structural Characteristics of PPI Interfaces 293.3 Screening Library Properties 313.3.1 Standard/Targeted Libraries/DOS 313.3.2 Fragment Libraries 333.3.3 Macrocyclic and Constrained Peptides 333.3.4 DNA‐Encoded Libraries 343.4 Hit‐Finding Strategies 343.4.1 Small‐Molecule Approaches 363.4.2 Peptide‐Based Approaches 383.4.3 In Silico Approaches 393.5 Druggability Assessment 393.5.1 Small Molecule: Ligand‐Based Approaches 413.5.2 Small Molecule: Protein Structure‐Based Approaches 413.6 Allosteric Inhibition of PPIs 423.7 Stabilization of PPIs 433.8 Case Studies 433.8.1 Primary Peptide Epitopes 433.8.1.1 Bromodomains 443.8.2 Secondary Structure Epitopes 463.8.2.1 Bcl‐ 463.8.2.2 p53/MDM 473.8.3 Tertiary Structure Epitopes 473.8.3.1 CD80–CD 483.8.3.2 IL‐17A 483.9 Summary 49 References 50

4 Hydrogen–Deuterium Exchange Mass Spectrometry in Drug Discovery - Theory, Practice and Future 61Thorleif Lavold, Roman Zubarev, and Juan Astorga‐Wells

4.1 General Principles 614.2 Parameters Affecting Deuterium Incorporation 634.2.1 Primary Sequence 634.2.2 Intramolecular Hydrogen Bonding 634.2.3 Solvent Accessibility 634.2.4 pH Value 634.3 Utilization of HDX MS 644.3.1 Binding Site and Structural Changes Characterization upon Ligand Binding 644.3.1.1 Protein Stability - Biosimilar Characterization 644.4 Practical Aspects of HDX MS 654.4.1 Labeling 664.4.1.1 Deuterium Oxide and Protein Concentration 66

Contents vii

4.4.1.2 Ligand/Protein Ratio 664.4.1.3 Incubation–Labeling Time 664.4.1.4 Careful Preparation of the Control Sample 664.4.2 Sample Analysis 664.4.3 Data Analysis 674.5 Advantages of HDX MS 674.6 Perspectives and Future Application of HDX MS 68 References 69

5 Microscale Thermophoresis in Drug Discovery 73Tanja Bartoschik, Melanie Maschberger, Alessandra Feoli, Timon André, Philipp Baaske, Stefan Duhr, and Dennis Breitsprecher

5.1 Microscale Thermophoresis 735.1.1 Theoretical Background 745.1.2 Added Values for Small‐Molecule Interaction Studies 765.1.2.1 Size‐Change Independent Binding Signals 765.1.2.2 Difficult Targets and Assay Conditions 785.1.2.3 Detection of Aggregation and Other Secondary Effects 805.1.2.4 Quantification of Thermodynamic Parameters by MST 805.2 MST‐Based Lead Discovery 825.2.1 Single‐Point Screening 825.2.2 Secondary Affinity‐Based Fragment Screening by MST 855.2.3 Hit Identification and Affinity Determination

of Small‐Molecule Binders to p38 Alpha Kinase 87 References 87

6 SPR Screening: Applying the New Generation of SPR Hardware 93Kartik Narayan and Steven S. Carroll

6.1 Platforms for Screening 936.2 SensiQ Pioneer as a “OneStep” Solution

for Hit Identification 956.3 Deprioritization of False Positives Arising

from Compound Aggregation 996.4 Concluding Remarks 103 References 104

7 Weak Affinity Chromatography (WAC) 107Sten Ohlson and Minh‐Dao Duong‐Thi

7.1 Introduction 1077.2 Theory of WAC 1097.3 Virtual WAC 1107.4 Equipment and Procedure 1117.5 Validation of WAC 1137.6 Applications 114

Contentsviii

7.6.1 Inhibitors for Cholera Toxin 1157.6.2 Drug/Hormone: Protein Binding 1157.6.3 Analysis of Stereoisomers 1197.6.4 Carbohydrate Analysis with Antibodies and Lectins 1207.6.5 Fragment Screening 1217.6.6 Membrane Proteins 1227.7 Conclusions and Future Perspectives 124 Acknowledgments 125 References 125

8 1D NMR Methods for Hit Identification 131Mary J. Harner, Guille Metzler, Caroline A. Fanslau, Luciano Mueller, and William J. Metzler

8.1 Introduction 1318.2 NMR Methods for Quality Control 1318.2.1 Compound DMSO Stock Concentration Determination 1328.2.2 Compound Solubility Measurements in Aqueous Buffer 1348.2.3 Compound Structural Integrity 1368.2.4 Protein Reagent Characterization 1368.3 NMR Binding Assays 1368.3.1 Saturation Transfer Difference Assay 1388.3.2 T2 Relaxation Assay 1408.3.3 WaterLOGSY Assay 1418.3.4 19F Displacement Assay 1428.4 Multiplexing 1438.5 Specificity 1448.6 Automation 1468.7 Practical Considerations for NMR Binding Assays 1468.7.1 Compound Libraries 1468.7.2 Tube Selection and Filling 1478.7.3 Buffers 1488.7.4 Targets 1498.7.5 Experiment Selection 1508.8 Conclusions 151 References 151

9 Protein‐Based NMR Methods Applied to Drug Discovery 153Alessio Bortoluzzi and Alessio Ciulli

9.1 Introduction 1539.2 Chemical Shift Perturbation 1549.2.1 Using Chemical Shift Perturbation to Study a Binding Event

Between a Protein and a Ligand 1549.2.2 Tackling the High Molecular Weight Limit by Reducing

Transverse Relaxation and by Selective Labeling Patterns 156

Contents ix

9.2.3 CSP as Tool for Screening Campaigns 1579.2.4 Structure–Activity Relationship by NMR 1609.3 Methods for Obtaining Structural Information

on Protein–Ligand Complex 1609.3.1 SOS‐NMR 1619.3.2 NOE‐Matching 1629.3.3 Paramagnetic NMR Spectroscopy 1629.4 Recent and Innovative Examples of Protein‐Observed

NMR Techniques Applied Drug Discovery 1639.4.1 An NMR‐Based Conformational Assay to Aid the Drug

Discovery Process 1639.4.2 In‐Cell NMR Techniques Applied to Drug Discovery 1659.4.3 Time‐Resolved NMR Spectroscopy as a Tool for Studying

Inhibitors of Posttranslational Modification Enzymes 1669.4.4 Protein‐Observed 19F NMR Spectroscopy 1689.5 Conclusions and Future Perspectives 170 References 170

10 Applications of Ligand and Protein‐Observed NMR in Ligand Discovery 175Isabelle Krimm

10.1 Introduction 17510.2 Ligand‐Observed NMR Experiments Based

on the Overhauser Effect 17610.2.1 Transferred NOE, ILOE, and INPHARMA Experiments 17610.2.1.1 Principle of the Transferred 2D 1H‐1H NOESY Experiment 17610.2.1.2 Fragment‐Based Screening Using 2D Tr‐NOESY

Experiment 17810.2.1.3 Elucidation of the Active Conformation of the Ligand Using

2D 1H‐1H NOESY Experiment 17810.2.1.4 Design of Protein Inhibitors Using Interligand NOEs 17810.2.1.5 Identification of the Ligand Binding Site and Binding

Mode Using INPHARMA 17810.2.1.6 Design of Protein Inhibitors Using INPHARMA with

Protein–Peptide Complexes 17910.2.1.7 Experimental Conditions of the 2D 1H‐1H NOESY Experiment 17910.2.2 Saturation Transfer Difference Experiment 18010.2.2.1 Principle of the STD Experiment 18010.2.2.2 Detection of Interactions and Library Screening by STD 18010.2.2.3 Epitope Mapping by STD 18110.2.2.4 Affinity Measurement by STD 18110.2.2.5 Quantitative STD Using CORCEMA 18310.2.2.6 Experimental Conditions 18310.2.3 WaterLOGSY Experiment 18410.2.3.1 Principle of the WaterLOGSY Experiment 184

Contentsx

10.2.3.2 Screening and Affinity Measurement by WaterLOGSY 18410.2.3.3 Epitope Mapping and Water Accessibility in Protein–Ligand

Complexes by WaterLOGSY 18410.2.3.4 Experimental Conditions 18510.3 Protein‐Observed NMR Experiments: Chemical Shift

Perturbations 18510.3.1 Principle 18510.3.2 Affinity Measurement Using CSPs 18610.3.3 Localization of Binding Sites Using CSPs 18610.3.3.1 Chemical Shift Mapping 18610.3.3.2 J‐Surface Modeling 18710.3.4 Comparison of CSPs from Analogous Ligands 18710.3.5 Back‐Calculation of Ligand‐Induced CSPs for Ligand Docking 18710.3.5.1 CSP‐Based Post‐Docking Filter 18910.3.5.2 CSP‐Guided Docking 18910.4 Conclusion 189 Acknowledgments 191 References 191

11 Using Biophysical Methods to Optimize Compound Residence Time 197Geoffrey A. Holdgate, Philip Rawlins, Michal Bista, and Christopher J. Stubbs

11.1 Introduction 19711.2 Biophysical Methods for Measuring Ligand Binding Kinetics 19711.3 Measuring Structure–Kinetic Relationships: Some Example

Case Studies 20011.4 Effects of Conformational Dynamics on Binding Kinetics 20111.5 Kinetic Selectivity 20411.6 Mechanism of Binding and Kinetics 20711.7 Optimizing Residence Time 20711.8 Role of BK in Improving Efficacy 20911.9 Effect of Pharmacokinetics and Pharmacodynamics 21011.10 Summary 212 References 213

12 Applying Biophysical and Biochemical Methods to the Discovery of Allosteric Modulators of the AAA ATPase p 217Stacie L. Bulfer and Michelle R. Arkin

12.1 p97 and Proteostasis Regulation 21712.2 Structure and Dynamics of p 21812.3 Drug Discovery Efforts against p 22212.4 Uncompetitive Inhibitors of p Discovered by

High‐Throughput Screening 22312.4.1 Biochemical MOA Studies 22312.4.2 Surface Plasmon Resonance 22512.4.3 Nuclear Magnetic Resonance 226

Contents xi

12.4.4 Cryo‐EM Defines the Binding Site for an Uncompetitive Inhibitor of p 228

12.4.5 Effect of Inhibitors on p97 PPI and MSP1 Disease Mutations 23112.5 Fragment‐Based Ligand Screening 23112.5.1 Targeting the ND1 Domains 23212.5.2 Targeting the N‐Domain 23312.6 Conclusions 234 References 234

13 Driving Drug Discovery with Biophysical Information: Application to Staphylococcus aureus Dihydrofolate Reductase (DHFR) 241Parag Sahasrabudhe, Veerabahu Shanmugasundaram, Mark Flanagan, Kris A. Borzilleri, Holly Heaslet, Anil Rane, Alex McColl, Tim Subashi, George Karam, Ron Sarver, Melissa Harris, Boris A. Chrunyk, Chakrapani Subramanyam, Thomas V. Magee, Kelly Fahnoe, Brian Lacey, Henry Putz, J. Richard Miller, Jaehyun Cho, Arthur Palmer III, and Jane M. Withka

13.1 Introduction 24113.2 Results and Discussion 24513.2.1 Protein Dynamics of SA WT and S1 Mutant DHFR in Apo

and Bound States 24513.2.2 Protein Backbone 15N, 13C, and 1H NMR

Resonance Assignments 24613.2.3 Protein Residues Show Severe Line Broadening

due to Conformational Exchange 24613.2.4 R2 Relaxation Dispersion NMR Experiments 24813.2.5 Kinetic Profiling of DHFR Inhibitors 25113.2.6 Characterization of SA WT and S1 Mutant DHFR–TMP

Interactions in Solution 25313.2.7 Prospective Biophysics Library Design 25413.3 Conclusion 258 References 259

14 Assembly of Fragment Screening Libraries: Property and Diversity Analysis 263Bradley C. Doak, Craig J. Morton, Jamie S. Simpson, and Martin J. Scanlon

14.1 Introduction 26314.2 Physicochemical Properties of Fragments 26514.3 Molecular Diversity and Its Assessment 26814.4 Experimental Evaluation of Fragments 27414.5 Assembling Libraries for Screening 27514.6 Concluding Remarks 279 References 280

Index 285

xiii

Timon AndréNanoTemper Technologies GmbHMunichGermanyCurrent address: Heidelberg UniversityGermany

Michelle R. Arkin, Ph.D.Department of Pharmaceutical ChemistrySmall Molecule Discovery CenterUniversity of California, San FranciscoUSA

Juan Astorga‐Wells, Ph.D.Biomotif AB & HDXperts AB and Department of Medical Biochemistry and BiophysicsKarolinska InstitutetStockholmSweden

Philipp Baaske, Ph.D.NanoTemper Technologies GmbHMunichGermany

Tanja BartoschikNanoTemper Technologies GmbHMunichGermany

Michal Bista, Ph.D.Structure and BiophysicsDiscovery SciencesAstraZenecaCambridgeUK

Alessio Bortoluzzi, Ph.D.Division of Biological Chemistry and Drug Discovery, James Black CentreSchool of Life SciencesUniversity of DundeeUKCurrent address: Immunocore LtdMilton ParkAbingdonOxfordshireUK

Kris A. Borzilleri, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Susan M. Boyd, D.Phil.IOTA Pharmaceuticals Ltd.St. John’s Innovation CentreCambridgeUK

List of Contributors

List of Contributorsxiv

Dennis Breitsprecher, Ph.D.NanoTemper Technologies GmbHMunichGermany

Stacie L. Bulfer, Ph.D.Department of Pharmaceutical ChemistrySmall Molecule Discovery CenterUniversity of California, San FranciscoUSA

Current address: Deciphera PharmaceuticalsLawrence, KSUSA

Steven S. Carroll, Ph.D.Merck & Co.West Point, PAUSA

Jaehyun Cho, Ph.D.Department of Biochemistry and Molecular BiophysicsColumbia University, NYUSA

Boris A. Chrunyk, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Alessio Ciulli, Ph.D.Division of Biological Chemistry and Drug Discovery, James Black CentreSchool of Life SciencesUniversity of DundeeUK

Bradley C. Doak, Ph.D.Medicinal ChemistryMonash Institute of Pharmaceutical SciencesMonash UniversityVictoriaAustralia

Stefan Duhr, Ph.D.NanoTemper Technologies GmbHMunichGermany

Minh‐Dao Duong‐ThiSchool of Biological SciencesNanyang Technological UniversitySingapore

Kelly Fahnoe, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Caroline A. Fanslau, M.S.Bristol‐Myers SquibbPrinceton, NJUSA

Alessandra Fenoli, Ph.D.NanoTemper Technologies GmbHMunichGermanyCurrent address: University of SalernoItaly

Mark Flanagan, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Mary J. Harner, Ph.D.Bristol‐Myers SquibbPrinceton, NJUSA

Melissa Harris, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

List of Contributors xv

Holly Heaslet, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Geoffrey A. HoldgateStructure and BiophysicsDiscovery SciencesAstraZenecaCambridgeUK

Donald Huddler, Ph.D.Computational and Structural ChemistryGlaxoSmithKline plcCollegeville, PAUSACurrent address: Widener University Delaware Law School, WilmingtonUSA

George Karam, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Isabelle Krimm, Ph.D.Institut des Sciences Analytiques, UMR5280 CNRSUniversité Lyon 1, Ecole Nationale Supérieure de LyonFrance

Brian Lacey, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Thorleif LavoldBiomotif AB & HDXperts ABDanderydSweden

Thomas V. Magee, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Natalia Markova, Ph.D.Scientific Marketing BiosciencesMalvern InstrumentsStockholmSweden

Melanie MaschbergerNanoTemper Technologies GmbHMunichGermany

Alex McColl, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Guille Metzler, B.S.Eng.PharmaCadence Analytical Services, LLCHatfield, PAUSA

William J. Metzler, Ph.D.Bristol‐Myers SquibbPrinceton, NJUSACurrent address: PharmaCadence Analytical Services, LLCHatfield, PAUSA

J. Richard Miller, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

List of Contributorsxvi

Craig J. Morton, Ph.D.Australian Cancer Research Foundation Rational Drug Discovery CentreSt. Vincent’s Institute of Medical ResearchVictoriaAustralia

Luciano Mueller, Ph.D.Bristol‐Myers SquibbPrinceton, NJUSA

Kartik Narayan, Ph.D.Sanofi PasteurSwiftwater, PAUSA

Ronan O’Brien, Ph.D.Business Development‐MicroCalMalvern InstrumentsNorthampton, MAUSA

Sten OhlsonSchool of Biological SciencesNanyang Technological UniversitySingapore

Arthur Palmer III, Ph.D.Department of Biochemistry and Molecular BiophysicsColumbia University, NYUSA

Henry Putz, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Anil Rane, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Philip RawlinsStructure and BiophysicsDiscovery SciencesAstraZenecaCambridgeUK

Parag Sahasrabudhe, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Ron Sarver, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Martin J. Scanlon, Ph.D.Medicinal ChemistryMonash Institute of Pharmaceutical SciencesMonash UniversityVictoriaAustralia

Veerabahu Shanmugasundaram, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Jamie S. Simpson, Ph.D.Medicinal ChemistryMonash Institute of Pharmaceutical SciencesMonash UniversityVictoriaAustralia

Christopher J. Stubbs, Ph.D.Structure and BiophysicsDiscovery SciencesAstraZenecaCambridge, UK

List of Contributors xvii

Tim Subashi, B.S.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Chakrapani Subramanyam, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Andrew P. Turnbull, Ph.D.Cancer Research Technology Ltd.London Bioscience Innovation CentreLondonUK

Björn Walse, Ph.D.SARomics Biostructures ABLundSweden

Jane M. Withka, Ph.D.Pfizer Worldwide Research and DevelopmentGroton, CTUSA

Roman Zubarev, Ph.D.Department of Medical Biochemistry and BiophysicsKarolinska InstitutetStockholmSweden

1

Applied Biophysics for Drug Discovery, First Edition. Edited by Donald Huddler and Edward R. Zartler. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

1

Over the last two decades, biophysics has reemerged as a core discipline in drug discov-ery. Many may argue that biophysical methods never truly left discovery, but all will note the renewed present importance and central role of such methods. This reemer-gence is driven by three primary forces: the birth of fragment‐based drug discovery schemes, the recognition of and desire to mitigate artifacts in traditional biochemical screening, and a desire to accelerate the transition from first‐in‐class to best‐in‐class molecules by focusing on hit and lead kinetics. Each of these strategies or goals requires various information‐rich biophysical methods to experimentally execute. This text aims to summarize some of the key methods emerging from these three broad enterprises. First, though, it will map the contours of these three drivers of biophysics’ reemergence and link them to the chapters that follow.

Fragment‐based drug discovery and fragment‐based lead discovery are slightly differ-ent names for the same discovery approach: using a library of relatively small com-pounds to probe the surface of a target protein for binding sites. Fragment‐based discovery approaches are animated by the information theory‐based idea that relatively simple, small compounds sample chemical space more effectively than larger, more complex molecules [1, 2]. In practice, this approach drives one to develop low complex-ity screening libraries [3, 4]; consequently, the binding interactions with target proteins are generally very weak. Weak interactions require sensitive methods to unambiguously detect the binding event [5]. In simple bimolecular binding, the concentration of the complex is driven by the concentration of the ligand; this drives many scientists to screen their fragment libraries at relatively high concentrations. Effective screening methods must both be able to detect relatively weak interactions in the context of rela-tively high compound concentrations; several biophysical methods are well suited for this demanding screening campaign [6]. Various NMR approaches have been successfully applied to identify and characterize weak small molecule–protein interactions [7]. This text explores both traditional protein‐detected NMR [8] approaches in Chapters 9 and 10

IntroductionDonald Huddler*

Computational and Structural Chemistry, GlaxoSmithKline plc, Collegeville, PA, USA

*Current address: Widener University Delaware Law School, Wilmington, USA

1 Introduction2

and nontraditional NMR [9, 10] approaches in Chapter 8. Both approaches have merit and are usefully applicable in partially overlapping circumstances. Surface plasmon resonance (SPR) [11, 12] and microscale thermophoresis (MST) [13] have also been successfully deployed in fragment screening campaigns to detect weak interactions. Chapters 5 and 6 explore applications of MST and SPR beyond fragment‐based discov­ery, respectively.

A second force driving the reemergence of biophysical methods in drug discovery has been the desire to identify and eliminate high‐throughput screening hits that operate through uninteresting nuisance mechanisms. Brian Schoichet recognized and charac­terized some commonly observed nuisance phenomena; many of these nuisance mechanism enzymatic assay hits had weak micromolar activities and showed either a flat or highly irregular SAR [14]. Schoichet’s team determined that the aberrant behavior in biochemical screening assays was driven by poor solubility resulting in compound aggregate formation. These compound aggregates, present in extremely low concentration, serve as protein sinks, adsorbing most of the target protein, yielding what appeared to be detectable but weak inhibition [15]. His team demonstrated that many of these aggregation‐based inhibitors could be culled from screening hits by comparing activity in an assay with no or very low detergent to a high detergent assay condition. Compounds that lose activity in the high detergent assay were likely to be uninteresting nuisance hits.

Several biophysical methods complement the differential detergent biochemical assay [16]. In the biochemical assay approach, the presence of aggregates is inferred, whereas in the biophysical approaches, the aggregates are directly detected. SPR is uniquely suited such direct detection of nuisance behavior in a buffer matched to the original biochemical screening buffer [17]. Aggregated compounds generate complex binding responses that are not simple 1 : 1 interactions but rather reflect the partitioning of the aggregated compound between the free buffer and the protein captured on the sensor chip. Aggregated compounds also show complex binding to the sensor surface with no target protein captured, providing a simple, parallel means to detect nonideal interac­tions in real time during library screening. Hit validation workflows now commonly employ SPR, mass spectrometry, and other biophysical methods to remove nuisance mechanism hits [18].

A third trend driving the reemergence of biophysics in drug discovery is the desire to optimize kinetic or thermodynamic properties with an aim to rapidly progress from a first‐in‐class compound to a best‐in‐class compound. When comparing a first‐in‐class compound to a best‐in‐class compound, the best‐in‐class molecule generally has high selectivity for the pharmacologic target and consequently a lengthy residence time with that target [19]. Detailed understanding of compound binding kinetics [20] and inhibitory mechanism leads to better candidates with properties more like an ideal best‐in‐class compound [21]. SPR allows real‐time analysis of binding kinetics [22]; streamlined experimental approaches allow rapid compound sorting based on kinetic parameters [23]. Combining thermodynamic data with affinity and kinetic data further characterizes the intermolecular interactions, enabling detailed SAR and further compound optimization [24]. This idea is explored and different methods applied inform interaction quality in Chapters 2, 4, 7, and 11.

The text concludes with a case study in Chapter 14 that joins many of the methods and concepts discussed in earlier chapters. The Pfizer research team used a combination of traditional biochemical analysis, focused structural information derived from NMR,

­eeerences 3

SPR kinetics, and NMR dynamics to optimize a Staphylococcus aureus DHFR inhibitor. Data from no one method assured success; it was the conjunction of data from the sev­eral biophysical techniques that enabled their focused, hypothesis‐driven prospective library design that ultimately yielded novel, nonacid cell‐active inhibitors. Importantly, the dynamics and kinetic data incorporated common resistance mutations, informing the library design and ultimately the candidate compounds. This discovery case study exemplifies the fully integrated discovery approach where data‐rich biophysical tech­niques continually inform discovery. This approach enables research teams to target transient protein conformations, protein–protein interaction surfaces, or complex enzyme targets—all examples of targets that have met will have little success with tradi­tional high‐throughput enzymatic screening [25].

This text is a survey of contemporary biophysical methods in drug discovery. Biophysical methods report on intermolecular interactions directly with rich detail; these methods naturally complement traditional high‐throughput screening [26, 27], particularly when attacking irregular, nonenzymatic [28, 29], or membrane protein [30, 31] targets.

References

1. Leach, A. R. and Hann, M. M. Molecular complexity and fragment‐based drug discovery: ten years on. Curr. Opin. Chem. Biol. 15:489–496 (2011).

2. Hann, M. M., Leach, A. R., and Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41:856–864 (2001).

3. Boyd, S. M., Turnbull, A. P., and Walse, B. Fragment library design considerations. WIREs Comput. Mol. Sci. 2:868–885 (2012).

4. Lau, W. F., Withka, J. M., Hepworth, D., Magee, T. V., Du, Y. J., Bakken, G. A., et al. Design of a multi‐purpose fragment screening library using molecular complexity and orthogonal diversity metrics. J. Comput. Aided Mol. Des. 25:621 (2011).

5. Mashalidis, E. H., Sledz, P., Lang, S., and Abell, C. A three‐stage biophysical screening cascade for fragment‐based drug discovery. Nat. Protoc. 8:2309–2324 (2013).

6. Joseph‐McCarthy, D., Campbell, A. J., Kern, G., and Moustakas, D. Fragment‐based lead discovery and design. J. Chem. Inf. Model. 54:693–704 (2014).

7. Kim, H. Y. and Wyss, D. F. NMR screening in fragment‐based drug design: a practical guide. Methods Mol. Biol. 1263:197–208 (2015).

8. Dias, D. M. and Ciulli, A. NMR approaches in structure‐based lead discovery: recent developments and new frontiers for targeting multi‐protein complexes. Prog. Biophys. Mol. Biol. 116:101–112 (2014).

9. Pilger, J., Mazur, A., Monecke, P., Schreuder, H., Elshorst, B., Bartoschek, S., et al. A combination of spin diffusion methods for the determination of protein‐ligand complex structural ensembles. Angew. Chem. 54:6511–6515 (2015).

10. Cala, O. and Krimm, I. Ligand‐orientation based fragment selection in STD NMR screening. J. Med. Chem. 58:8739–8742 (2015).

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1 Introduction4

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­eeerences 5

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7

Applied Biophysics for Drug Discovery, First Edition. Edited by Donald Huddler and Edward R. Zartler. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

2

2.1 Introduction

For the drug discovery scientist, the term “thermodynamics” refers to the study of the heat change that occurs when biomolecules interact. It can be measured either directly by isothermal titration calorimetry (ITC) or indirectly by using any technique that can be used to determine an affinity over a range of temperatures such as surface plasmon resonance (SPR) or fluorescence.

The change in temperature that occurs when molecules interact is, for all practical purposes, a universal phenomenon and has led to the use of ITC to study a wide variety of biomolecular interactions; these include, but are not limited to, protein–small mol-ecule, protein–protein, protein–nucleic acid, protein–metal ion, protein–carbohydrate, nucleic acid–nucleic acid, and ion–ion interactions. The broad applicability of ITC and the exceptionally low errors in affinity determination typically observed using the technique have made it the gold standard for measuring KD [1].

In addition to being a convenient label‐free probe for studying interactions, the heat change is related to the binding enthalpy (ΔH) of the interaction and, taken together with the affinity KD, can be used to calculate the change in entropy of the process. This thermodynamic data gives insight into the non‐covalent forces responsible for driving binding and recognition. It can be used to direct SAR programs and help reveal the energetic “hot spots” that are key for molecular recognition and that need to be retained throughout lead optimization.

In this chapter we present an overview of the current use of thermodynamics in the drug discovery process. This includes a brief outline of the techniques employed to generate thermodynamic data as well as more detailed discussion of the complexities surrounding the data interpretation. In addition, the utility of enthalpy as a probe for binding in fragment‐based drug discovery programs and for understanding complex interactions will be highlighted.

Thermodynamics in Drug DiscoveryRonan O’Brien1, Natalia Markova2, and Geoffrey A. Holdgate3

1 Business Development‐MicroCal, Malvern Instruments, Northampton, MA, USA2 Scientific Marketing Biosciences, Malvern Instruments, Stockholm, Sweden3 Structure and Biophysics, Discovery Sciences, AstraZeneca, Cambridge, UK

2 Thermodynamics in Drug Discovery8

2.2 Methods for Measuring Thermodynamics of Biomolecular Interactions

Thermodynamic data can be obtained either directly by ITC or indirectly by any method that can be used to determine a KD as a function of temperature such as SPR or fluorescence.

2.2.1 Direct Method: Isothermal Titration Calorimetry

Isothermal titration calorimeters measure the heat change that occurs when two molecules interact. Heat is liberated or absorbed as a result of the redistribution of non‐covalent bonds when the interacting molecules go from the free to the bound state. ITC monitors these heat changes by measuring the differential power required to maintain zero temperature difference between a reference and a sample cell as the binding partners are mixed.

The reference cell usually contains water or buffer, while the sample cell contains one of the binding partners and a stirring syringe that holds the other binding partner (the ligand). The ligand is injected into the sample cell, typically in 0.5–2 µl aliquots, until the ligand concentration is two‐ to threefold greater than the sample. Each ligand injec-tion results in a heat pulse that is integrated with respect to time and normalized for concentration to generate a titration curve of kcal/mol versus molar ratio (ligand/sample). A binding model is fitted to the the resulting isotherm (data) to obtain the affinity (KD), stoichiometry (N), and enthalpy of interaction (ΔH). The Gibbs free energy (ΔG) and the change in the entropy (ΔS) upon binding can then be calculated using the relationship

G RT K H T Sln D (2.1)

where R is the gas constant and T is the absolute temperature in Kelvin. In addition to these parameters, it is possible to determine the change in heat capacity of an interac-tion (ΔCp) by determining the change in enthalpy at different temperatures (T) and using the relationship

C HTp (2.2)

2.2.2 Indirect Methods: van’t Hoff Analysis

2.2.2.1 Enthalpy Measurement Using van’t Hoff AnalysisIt is possible to access enthalpy and entropy values without the need for calorimetric experiments. These thermodynamic parameters may be estimated using indirect meth-ods, which make use of the temperature dependence of the binding affinity, by employ-ing the van’t Hoff equation. This allows estimates of entropy and enthalpy to be made using any technique that allows the determination of the binding affinity at a range of temperatures. Equation 2.3 is an integrated form of the van’t Hoff equation, and it is clear from inspection that the enthalpy can be derived from changes in binding affinity as long as the constant pressure heat capacity change upon ligand binding (∆Cp) is known or can be fitted. The binding entropy can then be determined from the Gibbs–Helmholtz

2.3 Thermodynamic‐Driven ead Optimiiation 9

equation in the usual way. Thus, a number of alternative methods to measure KD, includ-ing SPR, microscale thermophoresis (MST), fluorescence, and radioligand binding assays can be used to determine van’t Hoff enthalpies. The experimental design should be such that binding affinities are determined over a wide temperature range (within which the protein retains its native fold) so that the enthalpy change associated with binding can then be calculated using the van’t Hoff relationship shown in Equation 2.3.

ln lnKK

H T CR T T

CR

TT

1

2

1 1

1 2

2

1

1 1p p (2.3)

where the values for K1 and K2 are the dissociation constants at different temperatures, T1 and T2.

The use of the indirect van’t Hoff approach is not without potential difficulties. Firstly, binding enthalpy is itself temperature dependent, and so the inclusion of the ∆Cp term is required. Estimating ∆Cp in the absence of calorimetric data is often difficult, as deriving ∆Cp from Equation 2.3 requires true curvature in the van’t Hoff plot to be distinguishable from apparent curvature due to errors in the affinity measurement. Hence, this indirect approach requires accurate and precise KD values. Secondly, the temperature‐dependent change in ∆G often is relatively small, which makes deriving the two correlated parameters from this data quite challenging, which may result in relatively large uncertainty in the derived enthalpy compared with the direct calorimetric approach.

2.3 Thermodynamic‐Driven Lead Optimization

The observation by Ernesto Freire [2] that for two drug classes, the HIV protease inhibi-tors and the statins, the “best‐in‐class” drugs have the most favorable binding enthalpy has driven many drug discovery laboratories to include thermodynamic data in their decision‐making processes.

It has also been suggested that thermodynamic profiles could be used to identify inhibitors that were optimized for a number of properties including flexibility, to mini-mize drug resistance caused by rapid mutation of the target binding site [3]; specificity, to reduce side effects caused by nonspecific binding [4–6]; and solubility in water, to maximize the ligand efficiency of polar interactions [7, 8].

2.3.1 The Thermodynamic Rules of Thumb

In the last 10 years or so, a series of guidelines have emerged that have been broadly used to interpret thermodynamic data and have been proposed as key drivers for lead optimization programs [9, 10]. At the simplest level they can be summarized as:

● Hydrogen bonds have a favorable enthalpy. ● Hydrophobic interactions have a favorable entropy. ● Conformational changes are entropically unfavorable.

By applying these guidelines the medicinal chemist can, in theory, test the success or failure of their optimization strategies. For example, if an effective hydrogen bond was

2 Thermodynamics in Drug Discovery10

successfully introduced, then one would expect to see an increase in the affinity of the interaction and a more negative enthalpy. If so, further iterations could be tested, and if not, determination of the complex structure may reveal some interesting and unex-pected SAR. Equally, the success or failure of strategies to rigidify a ligand scaffold can be assessed by monitoring any reduction in unfavorable entropy of an interaction.

A good example of this type of approach, and the use of these rules of thumb, is the interpretation of the thermodynamic data for the interaction of a parent inhibitor (KNI‐10026) and two derivatives (KNI‐10007 and KNI‐10006) with plasmepsin II, an antimalarial target [11] (see Figure 2.1).

The introduction of a hydroxyl group to the parent compound resulted in an increase in the favorable enthalpy of binding from −1.2 to −6.0 kcal/mol that is consistent with the introduction of an additional hydrogen bond. However there was a concomitant reduction in the affinity from 16 to 76 nM due to the greater entropy loss. This enthalpy–entropy compensation (EEC) is common in lead optimization and will be described in more detail elsewhere in this chapter. By changing the stereochemistry of the hydroxyl group in the second inhibitor, the affinity of the interaction was increased to 0.5 nM. In this case the enthalpic advantage of the additional hydrogen bond was maintained while minimizing the entropy loss. The differences in the change in entropy of this interaction were attributed to the additional burial of hydrophobic groups in the binding pocket for the tighter binder KNI‐10006.

Either coincidentally or because of the emergence of ITC as a convenient assay to determine the quality of a hydrogen bond, there have been a number of articles promot-ing enthalpy‐driven lead optimization strategies [4, 7]. It is clearly an attractive prospect to be able to quickly develop a drug with high efficacy using a combination of ITC, X‐ray crystallography, molecular modeling, and medicinal chemistry. However, more recently, and perhaps not surprisingly, examples have emerged [1] suggesting that thermodynamic lead optimization is more complex than originally thought. Here we outline a number of additional factors that need to be considered when attempting thermodynamic lead optimization.

2.3.2 Enthalpy–Entropy Compensation

EEC is a phenomenon that has been discussed in the scientific literature over many years. EEC appears to be a real and demonstrable effect that many groups have experi-enced, but the cause may be due to more than one effect occurring across and within the experimental measurements [9]. The basic proposal is quite simple. Consider com-plex formation between a target protein and a ligand. This binding event is the result of the disruption of interactions of each free partner with the solvent, forming new inter-actions with each other in the complex. During optimization, the structure of the ligand is modified in order to produce increased bonding interactions with the protein binding site. This will tend (generally) to make ∆H° more negative. However, by introducing fur-ther points of interaction, there tends to be an increased order in the complex as a result of the modification, producing a more unfavourable contribution to ∆S°. Often, these two opposing contributions to ∆G° tend to be of similar magnitude in many studies on biological systems. Hence, the traditional medicinal chemistry approach of building new chemical functionality into a molecule to improve the interaction with the binding site (favorable enthalpy) tends to introduce constraints to movement of the molecule