Bioisosteres in medicinal chemistry

Edited by Nathan Brown Bioisosteres in Medicinal Chemistry Methods and Principles in Medicinal Chemistry Edited by R Mannhold, H Kubinyi, G Folkers Editorial Board H Buschmann, H Timmerman, H van de Waterbeemd, T Wieland Previous Volumes of this Series: Gohlke, Holger (Ed.) Sotriffer, Christoph (Ed.) Protein-Ligand Interactions Virtual Screening 2012 Principles, Challenges, and Practical Guidelines ISBN: 978-3-527-32966-3 Vol 53 2011 Kappe, C Oliver / Stadler, Alexander / Dallinger, Doris ISBN: 978-3-527-32636-5 Microwaves in Organic and Medicinal Chemistry Vol 48 Second, Completely Revised and Enlarged Edition Rautio, Jarkko (Ed.) Prodrugs and Targeted Delivery Towards Better ADME Properties 2012 ISBN: 978-3-527-33185-7 Vol 52 2011 ISBN: 978-3-527-32603-7 Vol 47 Smith, Dennis A / Allerton, Charlotte / Kalgutkar, Amit S / van de Waterbeemd, Han / Walker, Don K Smit, Martine J / Lira, Sergio A / Leurs, Rob (Eds.) Pharmacokinetics and Metabolism in Drug Design Chemokine Receptors as Drug Targets Third, Revised and Updated Edition ISBN: 978-3-527-32118-6 2012 ISBN: 978-3-527-32954-0 Vol 51 2011 Vol 46 Ghosh, Arun K (Ed.) De Clercq, Erik (Ed.) Aspartic Acid Proteases as Therapeutic Targets Antiviral Drug Strategies 2010 2011 ISBN: 978-3-527-31811-7 ISBN: 978-3-527-32696-9 Vol 45 Vol 50 Ecker, Gerhard F / Chiba, Peter (Eds.) Klebl, Bert / Müller, Gerhard / Hamacher, Michael (Eds.) Transporters as Drug Carriers Protein Kinases as Drug Targets Structure, Function, Substrates 2011 2009 ISBN: 978-3-527-31790-5 ISBN: 978-3-527-31661-8 Vol 49 Vol 44 Edited by Nathan Brown Bioisosteres in Medicinal Chemistry Series Editors Prof Dr Raimund Mannhold Molecular Drug Research Group Heinrich-Heine-Universität Universitätsstrasse 40225 Düsseldorf Germany mannhold@uni-duesseldorf.de Prof Dr Hugo Kubinyi Donnersbergstrasse 67256 Weisenheim am Sand Germany kubinyi@t-online.de Prof Dr Gerd Folkers Collegium Helveticum STW/ETH Zurich 8092 Zurich Switzerland folkers@collegium.ethz.ch Volume Editor Dr Nathan Brown The Institute of Cancer Research Cancer Research UK Cancer Therapeutics Unit 15 Cotswold Road Sutton SM2 5NG United Kingdom All books published by Wiley-VCH are carefully produced Nevertheless, authors, editors, and publisher not warrant the information contained in these books, including this book, to be free of errors Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate Library of Congress Card No.: applied for British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de # 2012 Wiley-VCH Verlag & Co KGaA, Boschstr 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages) No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers Registered names, trademarks, etc used in this book, even when not specifically marked as such, are not to be considered unprotected by law Composition Thomson Digital, Noida, India Printing and Binding Markono Print Media Pte Ltd, Singapore Cover Design Schulz Grafik-Design, Fgưnheim Print ISBN: ePDF ISBN: ePub ISBN: mobi ISBN: oBook ISBN: 978-3-527-33015-7 978-3-527-65433-8 978-3-527-65432-1 978-3-527-65431-4 978-3-527-65430-7 Printed in Singapore Printed on acid-free paper V Contents List of Contributors XI Preface XV A Personal Foreword XVII Part One Principles 1.1 1.2 1.3 1.4 1.4.1 1.4.2 1.4.3 1.5 2.1 2.2 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.4 2.4.1 2.4.2 2.4.3 2.4.4 Bioisosterism in Medicinal Chemistry Nathan Brown Introduction Isosterism Bioisosterism Bioisosterism in Lead Optimization Common Replacements in Medicinal Chemistry Structure-Based Drug Design Multiobjective Optimization 12 Conclusions 13 References 14 Classical Bioisosteres 15 Caterina Barillari and Nathan Brown Introduction 15 Historical Background 15 Classical Bioisosteres 17 Monovalent Atoms and Groups 17 Bivalent Atoms and Groups 17 Trivalent Atoms and Groups 18 Tetravalent Atoms 19 Ring Equivalents 19 Nonclassical Bioisosteres 20 Carbonyl Group 20 Carboxylic Acid 21 Hydroxyl Group 22 Catechol 22 VI Contents 2.4.5 2.4.6 2.4.7 2.4.8 2.4.9 2.5 Halogens 23 Amide and Esters 24 Thiourea 25 Pyridine 26 Cyclic Versus Noncyclic Systems Summary 27 References 27 Consequences of Bioisosteric Replacement 31 Dennis A Smith and David S Millan Introduction 31 Bioisosteric Groupings to Improve Permeability 32 Bioisosteric Groupings to Lower Intrinsic Clearance 40 Bioisosteric Groupings to Improve Target Potency 43 Conclusions and Future Perspectives 47 References 49 3.1 3.2 3.3 3.4 3.5 Part Two Data 4.1 4.2 4.2.1 4.2.2 4.2.3 4.3 4.3.1 4.3.2 4.3.3 4.3.3.1 4.3.3.2 4.3.3.3 4.3.3.4 4.3.3.5 4.3.3.6 4.4 4.4.1 4.4.2 4.4.3 4.5 4.6 4.7 27 53 BIOSTER: A Database of Bioisosteres and Bioanalogues 55 István Ujváry and Julian Hayward Introduction 55 Historical Overview and the Development of BIOSTER 56 Representation of Chemical Transformations for Reaction Databases 56 The Concept of ‘‘Biosteric Transformation’’ 57 Other Analogue and Bioisostere Databases 58 Description of BIOSTER Database 59 Coverage and Selection Criteria 59 Sources 59 Description of the Layout of Database Records 60 ID Code 60 Biosteric Transformation 60 Citation(s) 62 Activity 63 Fragments 63 Component Molecules and Fragments 64 Examples 64 Benzodioxole Bioisosteres 65 Phenol Bioisosteres 66 Ketoamides 66 Applications 69 Summary 70 Appendix 70 References 71 Contents 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 5.3.4 5.4 5.5 5.6 5.7 5.7.1 5.7.2 5.8 5.8.1 5.8.2 5.9 6.1 6.2 6.3 6.4 6.4.1 6.4.2 6.4.3 6.4.4 6.4.4.1 6.4.4.2 6.4.5 6.4.6 6.5 6.5.1 6.6 Mining the Cambridge Structural Database for Bioisosteres 75 Colin R Groom, Tjelvar S G Olsson, John W Liebeschuetz, David A Bardwell, Ian J Bruno, and Frank H Allen Introduction 75 The Cambridge Structural Database 76 The Cambridge Structural Database System 78 ConQuest 78 Mercury 78 WebCSD 79 Knowledge-Based Libraries Derived from the CSD 80 The Relevance of the CSD to Drug Discovery 83 Assessing Bioisosteres: Conformational Aspects 84 Assessing Bioisosteres: Nonbonded Interactions 86 Finding Bioisosteres in the CSD: Scaffold Hopping and Fragment Linking 91 Scaffold Hopping 91 Fragment Linking 92 A Case Study: Bioisosterism of 1H-Tetrazole and Carboxylic Acid Groups 94 Conformational Mimicry 94 Intermolecular Interactions 94 Conclusions 97 References 98 Mining for Context-Sensitive Bioisosteric Replacements in Large Chemical Databases 103 George Papadatos, Michael J Bodkin, Valerie J Gillet, and Peter Willett Introduction 103 Definitions 104 Background 105 Materials and Methods 109 Human Microsomal Metabolic Stability 109 Data Preprocessing 109 Generation of Matched Molecular Pairs 110 Context Descriptors 111 Whole Molecule Descriptors 111 Local Environment Descriptors 112 Binning of DP Values 112 Charts and Statistics 112 Results and Discussion 113 General Considerations 123 Conclusions 124 References 125 VII VIII Contents Part Three Methods 7.1 7.2 7.3 7.4 7.5 7.6 8.1 8.2 8.3 8.4 8.4.1 8.4.2 8.5 8.6 8.7 9.1 9.1.1 9.1.2 9.1.3 9.2 9.3 10 10.1 10.2 10.2.1 10.2.2 129 Physicochemical Properties 131 Peter Ertl Introduction 131 Methods to Identify Bioisosteric Analogues 132 Descriptors to Characterize Properties of Substituents and Spacers 132 Classical Methods for Navigation in the Substituent Space 135 Tools to Identify Bioisosteric Groups Based on Similarity in Their Properties 136 Conclusions 138 References 138 Molecular Topology 141 Nathan Brown Introduction 141 Controlled Fuzziness 141 Graph Theory 142 Data Mining 144 Graph Matching 144 Fragmentation Methods 145 Topological Pharmacophores 146 Reduced Graphs 149 Summary 151 References 152 Molecular Shape 155 Pedro J Ballester and Nathan Brown Methods 156 Superposition-Based Shape Similarity Methods 156 Superposition-Free Shape Similarity Methods 158 Choosing a Shape Similarity Technique for a Particular Project 160 Applications 161 Future Prospects 164 References 165 Protein Structure 167 James E J Mills Introduction 167 Database of Ligand–Protein Complexes 168 Extraction of Ligands 168 Assessment of Ligand and Protein Criteria 169 Contents 10.2.3 10.2.4 10.2.5 10.2.6 10.3 10.3.1 10.3.2 10.3.3 10.3.4 10.4 10.5 10.6 Cavity Generation 170 Generation and Validation of SMILES String 170 Generation of FASTA Sequence Files 171 Identification of Intermolecular Interactions 172 Generation of Ideas for Bioisosteres 173 Substructure Search 173 Sequence Search 175 Binding Pocket Superposition 175 Bioisostere Identification 176 Context-Specific Bioisostere Generation 177 Using Structure to Understand Common Bioisosteric Replacements 178 Conclusions 180 References 180 Part Four Applications 11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11 183 The Drug Guru Project 185 Kent D Stewart, Jason Shanley, Karam B Alsayyed Ahmed, and J Phillip Bowen Introduction 185 Implementation of Drug Guru 187 Bioisosteres 188 Application of Drug Guru 194 Quantitative Assessment of Drug Guru Transformations 195 Related Work 197 Summary: The Abbott Experience with the Drug Guru Project 197 References 198 Bioisosteres of an NPY-Y5 Antagonist 199 Nicholas P Barton and Benjamin R Bellenie Introduction 199 Background 199 Potential Bioisostere Approaches 201 Template Molecule Preparation 204 Database Molecule Preparation 206 Alignment and Scoring 206 Results and Monomer Selection 207 Synthesis and Screening 208 Discussion 209 SAR and Developability Optimization 211 Summary and Conclusion 214 References 214 IX X Contents 13 13.1 13.2 13.3 13.4 13.5 Perspectives from Medicinal Chemistry 217 Nicholas A Meanwell, Marcus Gastreich, Matthias Rarey, Mike Devereux, Paul L.A Popelier, Gisbert Schneider, and Peter Willett Introduction 217 Pragmatic Bioisostere Replacement in Medicinal Chemistry: A Software Maker’s Viewpoint 219 The Role of Quantum Chemistry in Bioisostere Prediction 221 Learn from ‘‘Naturally Drug-Like’’ Compounds 223 Bioisosterism at the University of Sheffield 224 References 227 Index 231 13.4 Learn from “Naturally Drug-Like” Compounds donor and acceptor strengths, multipole moments, and measures of surface polarity [19, 40] While similar quantities can be found in simpler approaches, the increased accuracy and level of detail offered by quantum chemistry lead to a more accurate measure of the similarity of two fragments in property space With a broad set of underlying descriptors such as those mentioned above, chosen to encapsulate the chemistry of a fragment, the final task is to create a tool that is sufficiently versatile to find bioisosteric replacements in a wide range of different situations The most general approach would be to compare all known properties of a query fragment with all properties of every other fragment stored in a database The most similar fragments might then be deemed the most robust bioisosteric replacements In practice, however, such a general approach seems unlikely to succeed For example, the size and conformation of a ligand fragment found in a cramped binding site are likely to be of much greater importance than the size or conformation of a fragment that is exposed to the surrounding solvent As such, it seems important to include as much context-specific information about a ligand’s biological activity as possible If ligand solubility is crucial, then relevant properties such as polar surface area should be prioritized in the search for a bioisosteric replacement One recent tool, “BROOD”,2) has taken this approach toward its logical conclusion, explicitly integrating algorithms that identify bioisosteres with docking software A single tool can then automatically identify the stabilizing interactions that contribute to the binding energy of a docked parent compound with its target Such a tool allows a tailor-made, highly specific search for a fragment that will maintain, for example, important hydrogen bonds and complementary electrostatics in a modified ligand with its intended target Subsequent redocking of a compound modified by a suggested bioisosteric replacement then provides validation of the method’s predictions Although to our knowledge no tools yet exist that exploit quantum chemical data in this way, there is clear potential for development In particular, the use of (atomic) multipole moments would provide a more accurate description of electrostatic interaction than point charges [41, 42] Steric properties of a fragment would be more rigorously defined by the extent of the underlying electron density, rather than by simple spherical atomic radii [19] Reliable 3D structure information could also be incorporated from ab initio geometry optimizations It has become increasingly apparent that more detailed descriptions of molecular interactions yield significantly improved descriptions of intermolecular energies and geometries in the related field of force field development [41, 43] In a similar vein, an injection of ab initio information into drug design may provide an important boost to the field of bioisostere prediction 13.4 Learn from “Naturally Drug-Like” Compounds Low approval rates of new chemical entities by national and international drug administrations and an increasing awareness and measurability of potential side effects of marketed drugs suggest that scaffold hopping and bioisosteric j223 224 j 13 Perspectives from Medicinal Chemistry replacements will remain one of the main tasks in medicinal chemistry Computerassisted techniques will undoubtedly play a central role in this critical endeavor as such methods have already demonstrated their ability to suggest functionally relevant, innovative molecular structures, and support hit and lead finding Exploring further potential of bioisosteric replacement strategies for drug repurposing and in silico polypharmacology appears well motivated Still, our understanding of the functional and structural equivalence of fragments and molecular building blocks is limited, resulting in equally limited prediction accuracy of software tools for scaffold hopping and bioisosteric replacement While many of the currently applied computational techniques work on rigid or single molecular conformations, we will have to devise methods that are able to better exploit functionally relevant information contained in conformer ensembles and develop appropriate molecular representations that account for the dynamic, time-dependent behavior and flexibility of bioactive compounds In particular, natural products as the main source of innovation in drug discovery deserve more attention We can learn much from “naturally drug-like” compounds and their scaffolds, and there is ample opportunity and demand for computer-assisted drug design Bioisostere extraction from pharmacologically relevant natural products and synthetic drugs, and their transfer to new chemically tractable compounds will continue to be among the most important responsibilities enabling innovation in medicinal chemistry 13.5 Bioisosterism at the University of Sheffield Our work in Sheffield on bioisosterism, and on scaffold hopping as a specific type of bioisosterism, has arisen as a result of a long-standing interest in the development of similarity methods for chemical databases This work started in the early 1980s and led to some of the pharmaceutical industry’s first operational systems for similarity searching and clustering These studies, and many of the subsequent ones, focused on the use of 2D fingerprints to represent the molecules that are to be processed; however, the bioisosterism research has additionally involved other types of representation, both 2D and 3D as discussed below In addition to the type of representation, it is also possible to categorize a bioisosterism study by the type of structural feature for which equivalences are being sort: whether an entire molecule can be substituted for another; whether a particular substituent (or more complex substructure) can be substituted for another; or whether a particular ring system can be substituted for another (i.e., scaffold hopping) Examples of all of these types of study have been carried out in Sheffield, as summarized in Table 13.1, which characterizes each of our principal studies in terms of the type of structural representation that is used, and the type of equivalence that is sought given the chosen representation Note that each study is listed only once here; however, some have involved more than one type of representation (e.g., 2D fingerprints were used for comparison with the field-based equivalence method of Schuffenhauer et al [44]) 13.5 Bioisosterism at the University of Sheffield Table 13.1 Sheffield studies of bioisosterism and scaffold hopping Representation Fingerprints Graphs Molecular fields Atomic properties Atomic coordinates Equivalenced structural feature Molecule Substituent Ring Birchall et al [51] Schuffenhauer et al [44] Papadatos et al [49] Gardiner et al [53] Barker et al [50] Bohl et al [45] Kennewell et al [48] Holliday et al [52] Watson et al [47] Bohl et al [46] Our first study to focus specifically on bioisosterism, rather than on similaritybased virtual screening in general, was reported by Schuffenhauer et al [44] This used FBSS (field-based similarity searching), a 3D similarity program based on a genetic algorithm that maximizes the overlap of molecules’ electrostatic and steric fields FBSS was employed to identify pairs of molecules that had been recorded in the BIOSTER database as being bioisosteric, and the results compared with analogous database searches using 2D fingerprints The two approaches were shown to be complementary, each finding some bioisosteric pairs not found by the other The study also defined a methodology, used subsequently both by ourselves and by other researchers, to quantify the extent to which a similarity measure could differentiate between pairs of molecules that were known to be bioisosteric (and listed as such in the BIOSTER database) and pairs of molecules that were not known to be so related A subsequent publication considered the effectiveness of FBSS, in combination with interatomic distance filters, for scaffold hopping [45] Specifically, given an existing combinatorial library based on a central ring system, the program sought to identify alternative scaffolds that could position functionality in the same geometric arrangement as the original ring system A further scaffold hopping study by Bohl et al [46] compared FBSS with a search method based on the centroid connecting path (CCP), a concise representation of the 3D structure of a ring system that encodes centroids, ring linker atoms, and other important points on the connection path between the centroids of rings comprising a ring system Both approaches enable effective scaffold hopping to be carried out, although CCP proved to be far more efficient in operation Watson et al used 3D coordinate data from the ISOSTAR knowledge base, which contains information on the geometries of nonbonded interactions between pairs of functional groups [47] The paper derived measures of similarity between pairs of substituents based on the extent to which they adopt similar crystallographic orientations with respect to a common central group These computed similarities then enabled the identification of pairs of substituents that could interact analogously with, for example, a key amino acid in a protein binding site and that could hence be regarded as functionally equivalent Kennewell et al also used X-ray crystallographic data, but differed in that it sought to identify equivalences that are specific to a particular biological target, rather than equivalences that are generally applicable j225 226 j 13 Perspectives from Medicinal Chemistry across a range of structural environments [48] Table 13.1 lists this approach as identifying molecular equivalences; more precisely, it identifies structural moieties (of whatever type) that exhibit a high degree of volume overlap when they are aligned in a specific binding site; the equivalenced features identified by the procedure were found to include both substituted and unsubstituted rings (or ring systems) and purely acyclic moieties The article is also notable in providing one of the first mentions in the literature of what is now commonly referred to as matched molecular pair analysis (MMPA), that is, the change in some molecular property that occurs when a specific substructural transformation is carried out on a lead compound Much of our work has focused on the use of graph representations, either complete molecular graphs (in which the nodes of a graph denote the non-hydrogen atoms in a molecule or substructure) or reduced graphs (in which the nodes denote larger ensembles of atoms, such as functional groups, individual rings, or ring systems) Complete graphs are exemplified by our recent MMPA study, the aim of which was to investigate the effect of variations in the structural environment on the types of bioisosteric equivalence that could be identified [49]: further work in this area is described in detail in Chapter of this book Barker et al [50] discuss the use of reduced graphs for scaffold hopping, with potential matches being identified using a maximum common subgraph (MCS) isomorphism algorithm and with the results obtained being compared with fingerprints derived from both the complete graphs and the reduced graphs This MCS work was further developed by Birchall et al [51], who reported extended similarity searches of the WOMBAT database They showed that inclusion of bioisosteric equivalences encoded by reduced graphs enabled the retrieval of active structures from the database that contained different, but equivalent, substructures from those in the reference structure for the similarity search However, such equivalences also contribute to the similarity between the reference structure and the vastly greater number of database inactives, meaning that inclusion of bioisosterism in the searches resulted in an overall decrease in screening effectiveness The two remaining studies listed in Table 13.1 use atomic property information and 2D fingerprints Holliday et al characterized substituents by the distribution of some atom-based property (such as elemental type, hydrophobicity, or atomic charge) at increasing numbers of bonds distant from the point of substitution on the parent scaffold [52] The representation hence takes account of both the physicochemical attributes of a substituent and its topology (and, indeed, of its geometry in the case of a rigid substituent) Finally, Gardiner et al have recently considered the use of 2D fingerprints, which are perhaps the most common type of representation in lead discovery studies, for scaffold hopping [53] While such a representation is clearly far from ideal, the study demonstrated that some types of fingerprint enabled at least some level of scaffold hopping to be achieved in simulated screening experiments using the WOMBAT and MDDR databases What conclusions can we draw from this body of work? Many of the published studies of bioisosterism, including the majority of those listed in Table 13.1, have involved the gross assumption that the equivalences that are identified are generally applicable, irrespective of the substructural environment or the biological target j References 227 We now believe that this assumption is of questionable value Thus, although the study by Birchall et al focused on the use of reduced graphs, structural equivalences with inactive, as against active, database structures are likely to be an equally significant problem with any type of structural representation [51] Again, the study of Papadatos et al demonstrated very clearly that conventional MMPA can be seriously misleading if no account is taken of the precise environment in which the substructural equivalences occur [49] We hence suggest that future work should focus on methods, such as that described by Kennewell et al [48], that can exploit the increasing volumes of structural and bioactivity 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C., and Willett, P (2011) Effectiveness of 2D fingerprints for scaffold hopping Future Medicinal Chemistry, 3, 405–414 j231 Index a ABT-719 186 acidic isostere 39 acidity 222 active pharmaceutical ingredient (API) 75 activity 63 – BIOSTER 62 ADMET (absorption, distribution, metabolism, excretion, and toxicity) property 93 a-adrenergic receptor agonist 23 b-adrenergic agonist 23 b-adrenoceptor antagonist – cardioselective 31 – nonselective 31 alignment 206 aminoheterocycle 32f amide/anilide bioisostere 32 analgesics 26 analogue database 58 (ỵ)-anatoxin-a 26 angiotensin antagonist 146 angiotensin II inhibitor – selective 37 antihistamine H1 146 antihypertensive drug 17, 70 antineoplastic drug 17 antiseptics 146 antiulcer drug 25 API, see active pharmaceutical ingredient area under the receiver operating characteristic curve (AUC) 161 aromatase (CYP19) inhibitor 23f aromatic atom 148 aromaticity 222 arylamine N-acetyltransferase family (hNAT1) 163 arylideno-imidazolinonoyl-glycine 74 AT1 receptor 39 atenolol 31ff atom – aromatic 148 – bivalent 17 – conjugated 148 – monovalent 17 – positively charged 148 – tetravalent 19 – trivalent 18 atom description 148 atom environment (AE) 98 atom environment descriptor 98, 124 atom pair descriptor 148 atom type 222 atomic property information 226 atorastatin 143 attachment point 90, 148 AUC, see area under the receiver operating characteristic curve b BB3 (G-protein-coupled receptor bombesin receptor subtype-3) inhibitor 21 benzazepine D1/D5 antagonist 22 benzodioxole 65 1,2,3-benzothiadiazole 65 binding pocket superposition 175 bioanalogue 55 – database 55 bioavailability 33 bioisostere 188, 221 – Cambridge Structural Database (CSD) 61 – classical 15ff – conformational aspect 70 – context-specific 177 – drug discovery 219 – finding in the CSD 77 – generation of ideas 173 – historical background 15 – identification 176 Bioisosteres in Medicinal Chemistry, First Edition Edited by Nathan Brown Ó 2012 Wiley-VCH Verlag GmbH & Co KGaA Published 2012 by Wiley-VCH Verlag GmbH & Co KGaA j Index 232 – nonbonded interaction 72 – nonclassical 20 – structure-based 174 bioisostere approach 201 bioisostere prediction 221 – quantum chemistry 221 bioisostere replacement 220 – medicinal chemistry 219 bioisosteric analogue – method for identification 132 bioisosteric fragment 64 bioisosteric fragment replacement 137 bioisosteric group – identification 136f bioisosteric groupings – lower intrinsic clearance 40 bioisosteric replacement 79, 131, 178, 223 – consequences 31ff – context-sensitive 89 bioisosteric substituent 136 bioisosterism 6ff., 16, 55 – classical 16 – Drug Guru project 188 – Friedman’s definition – lead optimization – medicinal chemistry 3ff – nonclassical 16 – Thornber’s parameter – University of Sheffield 224 BIOSTER 33ff – activity 63 – application 69 – citation 62 – component molecule 64 – coverage 59 – database record 60 – description 59 – development 56 – fragment 63 – historical overview 56 – ID code 61 – selection criteria 59 BIOSTER database 225 BIRB-796 19 bivalent atom 17 bivalent group 17 BLAST 171 blood platelet aggregation inhibitor 218 BMS-488043 19f bond cleavage rule 145 bond distance 148 bradykinin B receptor antagonist 42 bradykinin B1 receptor antagonist 219 BROOD program 79, 223 c Cambridge Crystallographic Data Centre (CCDC) 62 Cambridge Structural Database (CSD) 61ff – drug discovery 69 – knowledge-based library 66 cAMP phosphodiesterase III isozyme 217 captopril 70f Carbó index 156f carbonyl group 20 carboxylate 185 carboxylic acid 21, 36ff., 80 – replacement strategies to aid permeability 35 catechol 22 catechol transformation 189ff catecholamine 22f cathepsin K inhibitor 25 CATS (chemically advanced template search) 146 CATS descriptor 146f CAVEAT program 58, 77, 220 cavity generation 170 CCR5 antagonist 33f CDK2 174ff centroid connecting path (CCP) 225 chart 98 ChEMBL database 145 chemical database – mining for context-sensitive bioisosteric replacement 89 chemical transformation – reaction database 56 cholesterol 18 cholesterol biosynthesis inhibitor 18 CIF, see crystallographic information file cimetidine 25 ciprofloxacin 186 citation – BIOSTER 62 clearance – intrinsic 40 CNS disorder 26 color force field 202 component molecule – BIOSTER 64 computational approach – rational 221 – statistical method 221 conductor-like screening model (COSMO) 69 conformational aspect 70 – bioisostere 70 conformational mimicry 80 Index conjugated atom 148 ConQuest 64ff context descriptor 94ff corticotropin-releasing factor-1 (CRF1) receptor antagonist 42 Craig plot 135 CREDO 161 Cresset FieldPoint description 210 Cresset FieldPoint representation 205 Cresset FieldScreen technology 203 Cresset representation 204 crystallographic information file (CIF) 62 CRYSTALS package 67 CSD 168 cyclic system 27 cyclopentane 143 CYP19 inhibitor 23 cytochrome P450 inhibitor 63, 195 d DABE scheme 147 darunavir 168 data analysis module 65 data mining 144 data preprocessing 95 database – BIOSTER 45ff – ligand–protein complex 168 – mining for context-sensitive bioisosteric replacement 89ff database molecule preparation 206 database record 60 – BIOSTER 60 Daylight Chemical Information System 187 Daylight fingerprints 149, 171 Daylight Toolkit 92 descriptor 133f., 146ff., 223 – atom pair 148 – CATS 146f – context 94ff – molecule 97 – properties of substituents and spacers 132 – shape 143 – Similog 147 – whole molecule 97 developability optimization 211 20,25-diazacholesterol 18 trans-diethylstilbestrol 27 3,5-dihydroimidazo[2,1-b]quinazolin-2(1H)one 217f distributed multipole analysis (DMA) 222 dopamine 178, 183 drug design 137 – random strategy 185 – rational strategy 185 – structure-based drug discovery – bioisostere 219 – CSD 69 Drug Guru (drug generation using rules) computer programm 186ff – Abbott experience 197 – application 194 – bioisosterism 188 – implementation 187 – quantitative assessment of transformation 195 Drug Guru input 193 Drug Guru output 193 e eccentricity 143 edge 142 – incident 142 – weighted 142 edge density 142 EDULISS (Edinburgh University Ligand Selection System) 161 electrostatic interaction 222 EMIL (example-mediated innovation for lead evolution) 57 end point 94 Endcap group 149 EON 202 epoxide hydrolase inhibitor 33 ErG (extended reduced graph) representation 149f estradiol 27 EXP7711 38 extraction of ligand 168 f FASTA sequence file 171 FBSS (field-based similarity search) 146, 225 FieldPoint 203ff FieldPoint pattern 204f FieldScreen 207 2D fingerprints 226 flip-flop flag 149 5-fluoro-20 -deoxyuridylic acid (5-fluoro-dUMP) 17 5-fluorouracil 17 FPL 55712 39 fragment – BIOSTER 62 fragment linking 78 fragmentation method 145 j233 j Index 234 fungicide 146 fuzziness 141 g G-protein-coupled receptor (GPCR) 162 G-protein-coupled receptor bombesin receptor subtype-3 (BB3) inhibitor 21 Gaussian density 157 Gaussian description 157 Gaussian method 157 graph – reduced 149 graph matching 144 graph theory 142 Grimm’s hydride displacement law 5, 15 group – bivalent 17 – monovalent 17 – trivalent 18 h halogen 23 HCV NS3 protease inhibitor 218 hERG 212 hERG binding 195f Hert–Willett data set 149 high-throughput screening (HTS) 155 HIV protease 177f HIVprotease inhibitor 178 HIV-1 inhibitor 19 human microsomal metabolic stability 95 hydride displacement law 5, 15 hydrogen bond (HB) 133f hydrogen bond acceptor 148 hydrogen bond donor 148 hydrogen bonding 222 hydrophobicity 180 hydroxy-to-methoxy transformation 188 hydroxyl group 22 11b-hydroxysteroid dehydrogenase (h11b-HSD1) 163 i IBIS drug discovery software 69 ICI 204219 40 ID code 60 – BIOSTER 60 IDODEL 78 imatinib 143 indole 74 interaction hot spot 69 intermolecular interaction 80, 172 – identification 172 isopentane 143 isoproterenol 22 IsoStar 67ff IsoStar library 69 ISOSTAR knowledge base 225 isostere 16 – based on Grimm’s hydride displacement law 16 – defined by Erlenmeyer 16 – defined by Langmuir 5, 16 isosteric quality 193 isosterism 3ff., 55 – Erlenmeyer’s addition – Grimm’s hydride displacement law – Langmuir definition 4f j Jmol viewer 65 k ketoamide 66 KNIME workflow environment knowledge-based library – CSD 66 kynurenic acid 74 95 l L-006235 25 L-873724 25 lead optimization – bioisosterism LeadIT 220 LeadIT platform 79 leukotriene antagonist 146 liarozole 24 ligand – assessment 169 – extraction 168 ligand-based topological pharmacophore ligand–protein complex 172 – database 168 LINGO 69 lipophilicity – effective 37 – intrinsic 37 local environment descriptor 98 losartan 37, 80f 146 m MACCS 160 Maestro 205 Maestro conformation-derived library 207 matched molecular pair (MMP) 59, 90 – context-sensitive analysis 95 – generation 96 Index – method 89 matched molecular pair analysis (MMPA) 91ff., 226 materials module 65 maximum common subgraph (MCS) isomorphism algorithm 226 maximum common substructure (MCS) 144 maximum common substructure (MCS) extraction 91 MDL Drug Data Report (MDDR) database 91, 144, 226 medicinal chemistry – bioisostere replacement 219 – bioisosterism 3ff – perspectives 217ff melanin concentrating hormone receptor (MCHr1) antagonist 162 Mercury program 64 metabolic stability 118ff – beneficial transformation 119ff – detrimental transformation 119ff – neutral transformation 116f metabolic stability data set 114 metabolism N-methyl D-aspartate (NMDA) receptor inhibitor 74 metiamide 25 MK-05577 199 MMP, see matched molecular pair MMPA, see matched molecular pair analysis modafinil 20 MOE (molecular operating environment) 201, 220 MOE-derived library 207 Mogul 66ff molecular graph 142f – example 143 – undirected 142 molecular shape 155ff molecular shape complementarity 155 molecular shape similarity 156 – method 156 molecular similarity 222 molecular topology 141ff molecular transformation 90 molecule descriptor 97 monomer selection 207 monovalent atom 17 monovalent group 17 MSI 168 multiobjective optimization (MOOP) 12 multiparameter optimization (MPO) 12 Murcko framework 97, 124 n NAADP EON discovered (Ned) hit 163 NAADP ROCS discovered (Nrd) hit 163 naturally drug-like compound 223 neopentane 143 neuropeptide Y (NPY) 199f – bioisosteres of an NPY-Y5 antagonist 199ff nicotinic acetylcholine receptor (nAChR) 26 (a4)2(b2)3 nicotinic acetylcholine receptor inhibitor 26 nicotinic acid adenine dinucleotide phosphate (NAADP) 163 node 142 – adjacent 142 – colored 142 nonbonded interaction 72 – bioisostere 72 – intermolecular 75 – intramolecular 75 noncyclic system 27 nonhydrogen atom 148 nonsteroidal aromatase inhibitor (NSAI) drug 23f norepinephrine 22 NPY-Y5 receptor 199ff NPY-Y5 receptor antagonist 200 Nrd hit, see NAADP ROCS discovered hit nuclear receptor (NR) family 162 o odanacatib 25 olefin-to-amide transformation 195 olefin-to-o-phenyl transformation 195 Open Babel molecular toolkit module 98 OpenAstexViewer 65 optimization – multiobjective 12 oral absorption 38 oxazole 74 p DP distribution – global 99ff DP value 98ff p38 MAP kinase inhibitor 19 P450 inhibition 63, 195 PDB, see Protein Data Bank PDB ligand database 169 pentane 143 permeability 32ff – replacement strategies for carboxylic acids 36 j235 j Index 236 peroxisome proliferator-activated receptor (PPAR) 162 – PPAR-c 79 Pfizer metabolism index parameter, see PMI parameter pharmacokinetics 8, 34 pharmacophore 158 – ligand-based topological 146 3D pharmacophore searching 201 phenol bioisosteres 64 phenyl/pyridine bioisosteric replacement 20 PHT ((Ỉ)-pyrido[3,4-b]homotropane) 26 piperonyl butoxide 65 PMI (Pfizer metabolism index) parameter 94 polar surface area (PSA) 200f positively charged atom 148 PPAR (peroxisome proliferator-activated receptor) 162 PPAR-c (peroxisome proliferator-activated receptor gamma) 79 practolol 31f precedence rules 151 propranolol 31 protein criteria 169 Protein Data Bank (PDB) 71ff., 167ff – pocket 179 protein structure 167ff PSA, see polar surface area pseudoatom Pub3D 161 pyridine 26 (Ỉ)-pyrido[3,4-b]homotropane, see PHT q QSAR (quantitative structure–activity relationship) 91 quantum chemical topology (QCT) 222 quantum chemistry – bioisostere prediction 221 Quantum Isostere Database (QID) 137, 222 r R-group descriptor 69 rapid overlay of chemical structures, see ROCS RCSB web site 168 RDKit molecular toolkit 96 reaction database 56 – chemical transformation 56 RECAP, see retrosynthetic combinatorial analysis procedure receptor interaction ReCore 77, 220f reduced graph 149 reduced graph node 151 ReLiBase 168 remove–Cl transformation 195f replacement 10 – bioisosteric, see bioisosteric replacement – consequences of bioisosteric replacement 31ff retrosynthetic combinatorial analysis procedure (RECAP) 93, 145 RG generation algorithm 150 rilmenidine 17f ring equivalent 19 ring system 149 ROCS (rapid overlay of chemical structures) 148ff., 192, 200f ROCS shape searching 202 s SAR, see structure–activity relationship scaffold hopping 77, 224f SCH 23390 22 SCH 39166 22 SciFinder database 162 scoring 206 screening 208 3D SD format 168 c-secretase inhibitor 42 sequence search 175 SFK 38393 22 shape descriptor 143 shape similarity method – choosing 160 – superposition-based 156 – superposition-free 158 shape similarity tool 158 sigma profile 137 sildenafil 143, 186 silicon bioisosteric replacement 19 similarity – tools to identify bioisosteric group 136 Similog descriptor 147 simplified molecular input line entry system (SMILES) 95f., 168, 187, 206 SMARTS pattern 187 SMILES string – generation and validation 170 SMIRKS system 187ff spacer 132 spirocarbamate 209 SPLICE 77 structural moiety structure–activity relationship (SAR) 124, 158, 211 structure-based drug design Index subpocket hydrophobicity 180 substituent space – classical method for navigation 135 substructure search 173 sulfonamide groupings 40 SuMo 168 SuperStar 67ff surface property 137 SURFNET algorithm 170 trivalent atom 18 trivalent group 18 u UFSRAT (ultrafast shape recognition with atom types) 160ff ultrafast shape recognition (USR) 159ff UNITY 2D fingerprints 146 USR::OptIso 160 t v T-ANALYZE 91 T-MORPH 91 Tanimoto similarity coefficient 171 template molecule preparation 204 tetravalent atom 19 tetrazolate 82f tetrazole 36, 158, 169f., 175 1H-tetrazole 80ff thiourea 25 thrombin inhibitor 41 topological descriptor 142 topological pharmacophore 146 – ligand-based 146 topological representation 141 torsional barrier 222 transformation 90ff., 193 – beneficial 119ff – context-sensitive 122 – detrimental 119ff – metabolic stability 116ff – neutral 116 – quantitative assessment of Drug Guru transformation 195 Tripos SYBYL atom type 98 vardenafil 186 velneperit 199 vertices 142 VLA-4 antagonist 33 vorozole 24 w WebCSD 65 WENDI (web engine for non-obvious drug information) 161 WOMBAT database 69, 226 x XED 203 XED force field 206 XedView software 207 z zafirlukast 40 Zagreb index 144 – first 144 – second 144 zero-order Gaussians 158 ZINC 163 j237 ... Assessing Bioisosteres: Conformational Aspects 84 Assessing Bioisosteres: Nonbonded Interactions 86 Finding Bioisosteres in the CSD: Scaffold Hopping and Fragment Linking 91 Scaffold Hopping 91... 2.4.3 2.4.4 Bioisosterism in Medicinal Chemistry Nathan Brown Introduction Isosterism Bioisosterism Bioisosterism in Lead Optimization Common Replacements in Medicinal Chemistry Structure-Based... ends with a concluding chapter on perspectives from medicinal chemistry Whereas some reviews on bioisosteres are found in the literature, as well as chapters in medicinal chemistry books, no