Polypharmacology E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Practical guide to navigate problems involved with promiscuous ligands and multi-target drug discovery, supported by case studies and real examples
Polypharmacology covers the two-sided nature of polypharmacology: its relevance for adverse drug effects, as well as its benefit for certain therapeutic drug classes in effectively treating complex diseases like psychosis and cancer. The book provides practical guidelines and advice to help readers design drugs that have multiple targets while minimizing unwanted off-target effects, discusses important disease areas like viral infection, diabetes, and obesity that have advanced significantly in the last decade, and guides researchers in neighboring areas to polypharmacology.
The book is divided into four parts. Part A covers the link between off-targets and adverse drug reactions, how to screen for off-target activity, and how to recognize and optimize compounds with a potential for off-target activity. Part B discusses disease areas which benefit from polypharmacological approaches. Part C highlights important approaches, such as compound design, data mining with web-based tools, and multi-target peptides. Part D provides case study coverage on topics like CDK4/6 inhibitors for cancer treatment, the potential of multi-target ligands for COVID, and protein degraders and PROTACs.
Sample topics discussed in Polypharmacology include:
- Molecular properties and structural motifs in pharmacological promiscuity, covering lipophilicity, molecular weight, and other parameters
- Kinase liabilities in early drug discovery, covering core kinases driving the cell division cycle and consequences of interference, and cell cycle checkpoints controlling cell division
- Treatment of major depressive disorder, covering tricyclic antidepressants, monoamine oxidase inhibitors, and selective serotonin and norepinephrine reuptake inhibitors
- Trends in the field, such as novel antipsychotics, standardization of screening tools, and the SmartCube System®, as well as lessons from history
Delivering the latest research developments in the field, Polypharmacology is an essential reference on the subject for medicinal chemists, pharmacologists, biochemists, computational chemists, and biologists, as well as pharmaceutical professionals involved in drug discovery programs.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
References
1 Introduction
1.1 Origins
1.2 Pros and Cons
1.3 Discovery and Design
1.4 Structural Data
1.5 Activity Data
1.6 Drug Target Estimates
1.7 Explainable Machine Learning
1.8 Conclusion
References
Part A: Polypharmacology as a Safety Concern in Drug Discovery
2 The Safety Relevance and Interpretation of Compound Off-target Interactions
2.1 Introduction
2.2 Assessing Off-Target Interactions of Small Molecules
2.3 Interpretation of Data from Secondary Pharmacology Assays
2.4 Off-Target Interactions of Biologics: Polyreactivity and Polyspecificity
2.5 Case Study Examples
2.6 Physicochemical Properties
2.7
In Silico
Methods to Predict Off-Target Interactions
2.8 Predicting Antibody Specificity
References
3 Off-target Activity and Adverse Drug Reactions
3.1 Personal Perspective
3.2 Introduction
3.3 Secondary Pharmacology and Adverse Drug Reactions
3.4 A Practical Perspective
Acknowledgments
References
4 Off-Target Screening Strategies
4.1 Introduction
4.2 Small Molecules
4.3 Proteolysis-Targeting Chimeras (PROTACs)
4.4 Small Molecules Targeting RNA (smRNA)
4.5 Antisense Oligonucleotides
4.6 Large Molecules
4.7 Regulatory Aspects
4.8 Future Outlook
Acknowledgments
Addendum
References
5 Molecular Properties and Structural Motifs Related to Pharmacological Promiscuity
5.1 Introduction
5.2 Basicity and Protonation State
5.3 Lipophilicity
5.4 Molecular Weight
5.5 Other Parameters
5.6 Structural Motifs
5.7 Conclusion
References
6 Kinase Liabilities in Early Drug Discovery
6.1 Introduction
6.2 Protein Kinases and Inhibitor Binding Sites
6.3 Kinase-regulated Cardiac Functions and Potential Consequences of Inhibition
6.4 Core Kinases Driving the Cell Division Cycle and Consequences of Interference
6.5 Cell Cycle Checkpoints Controlling Cell Division
6.6 Selectivity Profiling of Kinase Inhibition
References
7 Activity at Cardiovascular Ion Channels
7.1 Introduction
7.2 Screening Methods
7.3 Structural Insights into the Interaction Between Drugs and CV Ion Channels
7.4 Medicinal Chemistry Approaches
7.5 Conclusion
References
Part B: Polypharmacology as an Opportunity in Different Disease Areas
8 Toward Mechanism-based Therapies and Network Pharmacology
8.1 A Crisis in the Pharmaceutical Industry
8.2 Disease Modules as Targets for Precision Medicine
8.3 Mechanism-based Therapies and Network Pharmacology
8.4 Implementing Mechanism-based Therapies
8.5 Summary and Conclusions
References
9 Advancements in Rational Multi-Targeted Drug Discovery
9.1 Introduction
9.2 Cancer and the Existing Treatment Strategies
9.3 Safety and Efficacy: A Double-Edged Sword
9.4 Rational Design of MTDs
9.5 Perspective, Limitations, and Challenges
References
10 Polypharmacology
10.1 Introduction
10.2 The Failure of Single-target-based Discovery of Antibiotics
10.3 Attempts at Purposeful Multitargeting
10.4 Cell Surface Targets and Macrocyclic Peptides (MCPs)
10.5 Conclusions
References
11 Multi-Specific Binding Strategy
11.1 Proteolysis Targeting Chimera (PROTAC)
11.2 Antibody Recruiting Molecules
11.3 Antibody-Drug Conjugates (ADCs)
11.4 Antiviral Drug Delivery Systems
11.5 Ribonuclease Targeting Chimeras
11.6 Other Bifunctional Small Molecules
11.7 Summary and Outlook
References
12 Polypharmacology for the Treatment of Major Depressive Disorder
12.1 Introduction
12.2 Multitargeted Antidepressants
12.3 Conclusions
References
13 Multi-target Drugs to Treat Metabolic Diseases
13.1 Introduction
13.2 Metabolic Diseases and Current Treatment Approaches
13.3 Strategies to Develop Multi-target Drugs for Metabolic Diseases
13.4 Approaches Involving Modulation of PPARs and Other Metabolically Relevant Nuclear Receptors
13.5 Approaches Involving Inhibition of DPP4
13.6 Diverse Target Combinations for Polypharmacological Treatment of Metabolic Disorders
13.7 Conclusion
References
14 Overcoming the Challenges of Multi-Target-Directed Ligands for Alzheimer’s Disease
14.1 Introduction
14.2 Target Identification: In the Search for New Target Pairs
14.3 PK Challenges in MTDL Optimization
14.4 Phenotypic Screening: In a Search for an Early Proof-of-Concept
14.5 Conclusions
References
15 The Role of Polypharmacology in the History of Drug Discovery
15.1 Introduction: Drug Discovery in the Twentieth Century
15.2 Natural Products
15.3 Historical Drugs with Multiple Actions
15.4 From Serendipity to Concept: Repurposing and Polypharmacology
References
Part C: How to Discover Polypharmacological Drugs
16 Strategies for Multi-target Drug Discovery
16.1 Introduction
16.2 Rational Design of Multitargeted Ligands
16.3 Discussion and Conclusion
References
17 Predicting Polypharmacology with Web-Based Tools
17.1 Introduction
17.2 PASS
17.3 SEA
17.4 Super-PRED
17.5 TargetHunter
17.6 SwissTargetPrediction
17.7 TargetNet
17.8 PPB
17.9 PPB2
17.10 Comparison of Different Web-Based Tools
17.11 Conclusion
Acknowledgement
References
18 Using Phenotypic Screening to Uncover the Full Potential of Polypharmacology
18.1 Introduction: Phenotypic Screening and Phenotypic Drug Discovery
18.2 Polypharmacology Discovered Using Phenotypic Screening
18.3 PDD Strategies to Discover Novel Polypharmacology
18.4 Optimizing Polypharmacology in Phenotypic Screening Hits
18.5 Understanding the MoA from a PDD and Polypharmacology Perspectives
18.6 The Path to Virtual PDD-Derived Polypharmacology
18.7 Conclusions and Future Directions
References
19 Phenotypic Polypharmacology Drug Discovery for CNS Applications
19.1 Introduction
19.2 BPDD Lessons from the History of Psychopharmacology
19.3 Current Trends in Psychopharmacology
19.4 A Machine Learning-Based System for Global Behavior Profiling for CNS Drug Discovery
19.5 Modeling Chemical and Phenotypic Relationships of Compounds Screened in SmartCube®
19.6 Privileged Scaffolds and BPDD with SmartCube®
19.7 Ulotaront (SEP-363856) a BPDD Case Study
19.8 Conclusions
References
Appendix
20 Multi-target Peptides for the Treatment of Metabolic Diseases
20.1 Introduction
20.2 Glucagon-like Peptide-1 (GLP-1) Receptor Agonists
20.3 Unimolecular Multiagonists Based on Glucagon-like Peptide-1 (GLP-1) Following the One-pharmacophore Approach
20.4 GLP-1 Receptor/Glucagon Receptor Dual Agonists
20.5 Clinical Advanced GLP-1/GCGR Dual Agonists
20.6 GLP-1 Receptor/Glucose-dependent Insulinotropic Polypeptide (GIP) Receptor Dual Agonists
20.7 GLP-1 Receptor/Glucagon Receptor/GIP Receptor Triple Agonists
20.8 Further Unimolecular Multiagonists Based on Glucagon-like Peptide-1 (GLP-1) Following the One-pharmacophore Approach
20.9 Unimolecular Multiagonists Based on Glucagon-like Peptide-1 (GLP-1) Following the Two-pharmacophore Approach
20.10 Conclusion
References
21 The SOSA Approach to Drug Discovery
21.1 Introduction
21.2 Definition, Rational, and Concept of the SOSA Approach
21.3 Drugs in Other Drugs: Drug as Fragments
21.4 Old Drugs
21.5 The SOSA Approach and Analog Design
21.6 Patentability and Interference Risk of the SOSA Approach
21.7 Case Studies and Examples
21.8 Conclusion
References
Part D: Polypharmacology, Classic Case Studies and Recent Research
22 Dual Inhibitors of CDK4/6 for Treating Cancer
22.1 Introduction
22.2 Selectivity Profile of Approved CDK4/6 Inhibitors
22.3 Clinical Experience with CDK4/6 Inhibitors
22.4 New Approaches and Agents for CDK4/6 Inhibition
22.5 Conclusion
Acknowledgment
References
23 Tapentadol, a Clinically Proven Analgesic with Two Mechanisms
23.1 Introduction
23.2 The Discovery of Tapentadol – From Morphine and Tramadol to the Discovery of Tapentadol
23.3 Pharmacokinetics of Tapentadol
23.4 The Polymorphic Forms of Tapentadol Hydrochloride
23.5 Pharmaceutical Salts of Tapentadol
23.6 Synthesis Routes to Tapentadol Hydrochloride
23.7 The Pharmacological Profile of Tapentadol as a Multiple Ligand for the Treatment of Several Types of Pain
23.8 Summary
References
24 Thalidomide – From a Banned Drug to Molecular Glues, PROTACs, and New Concepts in Drug Discovery
24.1 Introduction
24.2 Thalidomide History: From Tragedy to Therapeutic Revival
24.3 Polypharmacology of Thalidomide and its Derivatives
24.4 Structural Understanding of the Mechanisms of Action of CELMoDs
24.5 Challenges and Future Perspectives in the Development of CELMoDs
24.6 Conclusions
References
25 The Polypharmacology of Cariprazine and its Implications to Clinical Indications
25.1 Introduction
25.2 Structure and Binding
25.3 The Role of the Primary and Secondary Pharmacophore in Binding and Selectivity
25.4 Cariprazine–Functional Profile, Polypharmacology, and Functional Selectivity
25.5
In Vivo
Profile of Cariprazine
25.6 Cariprazine in Clinical Practice
25.7 Conclusions
References
26 Multi-Targeted Antivirals
26.1 Multi-Target Inhibitors Targeting Both SARS-CoV-2 and Host Proteins
26.2 Multi-Target Inhibitors Directly Targeting SARS-CoV-2
26.3 Summary and Prospect
Acknowledgments
References
27 Multi-target Antimalarials as a Strategy to Reduce Resistance Risk
27.1 Introduction
27.2 Next-generation Antimalarials
27.3 Resistance Risk as a Criterion for the Prioritization of New Molecules and Targets
27.4 Polypharmacology in Malaria Drug Discovery
27.5 Concluding Remarks and the Way Forward
References
28 Multi-target Compounds for Tuberculosis
28.1 Tuberculosis and the Problem of Antimicrobial Resistance
28.2 Polypharmacology to Fight
M. tuberculosis
Antimicrobial Resistance
28.3 Multitarget Compounds Against TB
28.4 Multitarget Compounds Against TB-HIV Co-infection
28.5 Conclusions
References
29 Dual-acting HIV Inhibitors
29.1 Introduction
29.2 HIV and Hepatitis Viruses Co-infections
29.3 Compounds with Dual Activity Against HIV and EV-A71
Acknowledgement
References
30 Multi-kinase Inhibitors for the Treatment of Pancreatic Cancer
Acknowledgements
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 In silico tools and references to perform off-target predictions a...
Chapter 3
Table 3.1 Selected novel
in vitro
off-target activities of marketed drugs.
Table 3.2 Novel target–ADR relationships with support in biomedical literatu...
Table 3.3 Newly explained ADR mechanisms based on drug off-target activities...
Chapter 4
Table 4.1 Overlap across the modalities discussed in this chapter in terms o...
Chapter 5
Table 5.1 Targets binding >20% of all basic compounds in the in the BioPrint...
Table 5.2 The tricyclic motif (red) in drugs such as promethazine, chlorprom...
Chapter 6
Table 6.1 Important kinases in the heart and vasculature based on geneticall...
Table 6.2 CDK family members involved in cell cycle regulation.
Table 6.3 Major M-phase-specific and checkpoint kinases.
Chapter 7
Table 7.1 Strategies for reducing affinity for the hERG channel.
Chapter 15
Table 15.1 Key events in the discovery and establishment of the concept of r...
Table 15.2 “Secondary indications” derived from clinical observation for dru...
Chapter 17
Table 17.1 Brief description of the molecular fingerprints mentioned in this...
Table 17.2 Summary of the datasets, methods, and final outputs of eight web-...
Chapter 19
Table 19.1 Different approaches to drug discovery and their reliance on prio...
Table 19.2 Activities of ulotaront and its enantiomer SEP-363855 at the TAAR...
Table 19.3 Affinity profile of 1,2 benzisoxazole compounds.
Table 19.4 Affinity profile of tryptamine-like compounds.
Chapter 21
Table 21.1 Pharmacokinetic profile for
6
(in rat).
Chapter 22
Table 22.1 Comparison of biochemical IC
50
values published for three approve...
Table 22.2 Comparison of PFS and OS from randomized, controlled clinical tri...
Chapter 23
Table 23.1 Pharmaceutical and crystalline salt forms of tapentadol as well a...
Chapter 25
Table 25.1 The binding affinity (K
i
/nM) of cariprazine, aripiprazole, and br...
Table 25.2 Functional activity of cariprazine, brexpiprazole and aripiprazol...
Table 25.3 Broader functional receptor profile of cariprazine, brexpiprazole...
Chapter 26
Table 26.1 Sequences and bioactivities of MN-1, MN-2, MN-3, and MN-4.
Table 26.2 Sequences and bioactivities of MR-1, MR-2, MR-3, and MR-4.
Chapter 28
Table 28.1 AI models used in drug discovery. Adapted from [68].
List of Illustrations
Chapter 1
Figure 1.1 X-ray structures of indomethacin in complex with three distinct t...
Figure 1.2 Scaffold of a multifamily ligand with kinase activity representin...
Figure 1.3 Target annotations of imatinib. Based on increasing volumes of ac...
Figure 1.4 Target-pair-based compound test system for diagnostic ML investig...
Chapter 2
Figure 2.1 Definitions of categories for the interpretation of secondary pha...
Figure 2.2 FDA-approved kinase inhibitors and their approved targets. There ...
Figure 2.3 Target bioactivity landscape. Bioactivity profiling for 47 target...
Chapter 3
Figure 3.1 Safety margin distributions of marketed drugs. Shown are boxplots...
Figure 3.2 Principle of multivariate statistical modeling. (a) Shown is a sc...
Chapter 4
Figure 4.1 Summary of the key aspects in established occupancy-driven second...
Figure 4.2 The mechanism of action and examples of (a) PROTACs.Ub: Ubiqu...
Chapter 5
Figure 5.1 Analysis of compounds with a basic center in the BioPrint® datase...
Figure 5.2 A small panel of representative, frequently hit targets may be us...
Figure 5.3 Matched molecular pairs leading to a reduction in general promisc...
Figure 5.4 The zwitterionic fexofenadine hits no off-targets in the BioPrint...
Figure 5.5 Dependence of off-target hits on the calculated lipophilicity of ...
Figure 5.6 Matched molecular pairs leading to a reduction in general promisc...
Figure 5.7 The “heteroaryl-NH-aryl” kinase hinge-binding motif [61] and exam...
Chapter 6
Figure 6.1 X-ray crystal structure of ATP binding to CDK2 (PDB identifier 1F...
Figure 6.2a Subregions of the conserved ATP-binding site.
Figure 6.2b Allosteric ATP-binding site. Liu and Gray [8].
Figure 6.3 Major hinge-binding motifs used to target the adenine region of t...
Figure 6.4 Major cell cycle kinases regulating mammalian cell cycle progress...
Figure 6.5 Most kinase inhibitors are reversible and ATP‐competitive; theref...
Chapter 7
Figure 7.1 (a) Diagram of cardiac ion channel activity indicators. The cardi...
Figure 7.2 Cryo-EM structure of rNa
v
1.5
c
.
(a) Side view of ribbon represe...
Figure 7.3 Cryo-EM structure of hERG
T
core region.(a) Side view of ribbo...
Figure 7.4 Selectivity filter of hERG
T
core region.Side view of ribbon r...
Scheme 7.1 A representative transformation analysis.
Scheme 7.2 Structural changes that reduce hERG affinity.
Scheme 7.3 hERG inhibition data for selected compounds.
Scheme 7.4 hERG inhibition data for selected compounds.
Chapter 8
Figure 8.1 Mechanism-based disease types. With the exception of rare monogen...
Figure 8.2 Mechanism-based network pharmacology. Network pharmacology treatm...
Chapter 9
Figure 9.1 Flowchart of the integrated approach to multi-targeted drug (MTD)...
Figure 9.2 A polypharmacological interaction map across kinase subfamilies i...
Figure 9.3 A broad classification of various medicinal chemistry strategies ...
Chapter 10
Figure 10.1 Structures of lincosamides of increasing potency and activity ag...
Figure 10.2 Structures of novel DNA gyrase/topoisomerase IV inhibitors. (a) ...
Figure 10.3 Structures of macrocyclic peptides (MCPs) that target cell surfa...
Figure 10.4 Inhibitors of BAM and Lpt complexes of the Gram-negative outer m...
Chapter 11
Figure 11.1 Outline of multi-specific binding strategies in the antiviral fi...
Figure 11.2 A mechanistic overview of PROTAC-mediated protein degradation. P...
Figure 11.3 Amino acid sequence of the HBV X-protein-targeting PROTAC. CPP: ...
Figure 11.4 Chemical structures of
2
,
3
and co-crystal structure of
2
bound ...
Figure 11.5 Chemical structures of
4
–
6
. OA: oleanolic acid; VHL: von Hippel–...
Figure 11.6 Chemical structures of
7
–
9.
Figure 11.7 Chemical structure of
10.
Figure 11.8 Chemical structures of
11
and
12.
Figure 11.9 Chemical structures of
13
–
23.
Figure 11.10 Chemical structures of
24
–
26.
Figure 11.11 Chemical structures of
27
–
34.
Figure 11.12 Chemical structures of
35
–
37.
Figure 11.13 Chemical structures of
38
and
39.
Figure 11.14 Chemical structures of
40
and
41
. mAb 38C2: aldolase antibody 3...
Figure 11.15 Chemical structures of
35
and
42
–
46.
Figure 11.16 Chemical structures of
47
–
49.
Figure 11.17 Chemical structures of
50
(a),
51
(b),
52
and
53
(c),
54
and
55
Figure 11.18 A mechanistic overview of RIBOTAC-mediated RNA degradation. RNa...
Figure 11.19 Chemical structures of
56
and
57
(
a
),
58–61
(
b
).
Figure 11.20 Chemical structures of
62
(a);
63
,
64
(b);
54
,
65
,
66
(c).
Chapter 12
Figure 12.1 Description of tricyclic antidepressants including commonly used...
Figure 12.2 Description of monoamine oxidase inhibitors including commonly u...
Figure 12.3 Description of selective serotonin reuptake inhibitors including...
Figure 12.4 Description of selective serotonin and norepinephrine reuptake i...
Chapter 13
Figure 13.1 Metabolic diseases have characteristics that include obesity, hy...
Figure 13.2 Dual modulators.
Chapter 14
Figure 14.1 Design strategies to new anti-AD MTDLs donecopride (a) and
1
(b)...
Figure 14.2 Optimization strategy to anti-AD MTDLs
2
and
3
.
Figure 14.3 Design strategies to new anti-AD MTDLs
4
–
6
(a) and
7
(b) evaluat...
Chapter 15
Figure 15.1 The multimodal action of valerian.
Chapter 16
Figure 16.1 The design strategy of dual-target inhibitors. The blue graphics...
Figure 16.2 (a) Design of MTDL
3
targeting HDACs and HMGR. (b) Design of MTD...
Figure 16.3 (a) Design of MTDL
9
targeting AChE and GSK-3β. (b) Design of MT...
Chapter 17
Figure 17.1 Swisstargetprediction results window as shown for triazine
1
as ...
Figure 17.2 PPB2 results window as shown for triazine
1
as query molecules, ...
Figure 17.3 (a) Structure of the TRPV6 inhibitor
cis
-
22a
with the table outc...
Chapter 18
Figure 18.1 Polypharmacology in PDD and interconnectivity between PDD and TD...
Figure 18.2 Representative examples of PDD-derived polypharmacology. Structu...
Figure 18.3 Characterization of phenotypic hits and potential targets in phe...
Figure 18.4 Prototypical drug discovery optimization process in PDD. (a) Ref...
Chapter 19
Figure 19.1 Drug discovery phases implementing BPDD: (a) during the initial ...
Figure 19.2 (a) Structures of compounds with proven or putative antidepressa...
Figure 19.3 Unsupervised two-dimensional t-SNE analysis (MATLAB 2020b; Stati...
Figure 19.4 Structures and SmartCube signatures of selected 1,2-benzisoxazol...
Figure 19.5 Structures and SmartCube® signatures of selected 1,2-benzisoxazo...
Figure 19.6 Structures of ulotaront and its enantiomer SEP-363855. Screening...
Chapter 20
Figure 20.1 (a) Different strategies employed for metabolically active pepti...
Figure 20.2 (a) Receptor overlay for GLP-1R, GCGR, and GIPR and interaction ...
Figure 20.3 GLP-1R/GIPR dual agonists and GLP-1R/GCGR/GIPR triple agonists
Figure 20.4 Multiagonists combining GLP-1R with other pharmacology
Chapter 21
Figure 21.1 Examples of full analogs.
Figure 21.2 Structural analogs of classical isosteric (clozapine, lozapine s...
Figure 21.3 Structural analogs of nonclassical bioisosteric replacement.
Figure 21.4 Functional GABA-A agonist analogs with similar pharmacological p...
Figure 21.5 Structural analogs with different biological activity.
Figure 21.6 Diazepam as a lead structure leading to compounds with phosphodi...
Figure 21.7 Structures of tolbutamide, morphine, warfarin, tipranavir, and s...
Figure 21.8 More potent lenalidomide and pomalidomide are derived from thali...
Figure 21.9 From the antidepressant drug bupropion used as an antismoking ag...
Figure 21.10 Chlorpromazine is used as a neuroleptic agent in psychiatry but...
Figure 21.11 Development of sulfonamide diuretics such as chlorothiazide sta...
Figure 21.12 Propranolol was derived from the early β-adrenergic antagonist’...
Figure 21.13 Minaprine analogs.
Figure 21.14 Viloxazine analogs.
Figure 21.15 Endothelin receptor antagonists as a representative example of ...
Figure 21.16 Modification of minaprine leading to compounds with M1 receptor...
Figure 21.17 Tadalafil analogs leading to novel antiplasmodial compounds.
Figure 21.18 An illustration of the concept of using CNS drugs for non-CNS t...
Figure 21.19 Advanced inhibitors of urokinase-type plasminogen activator.
Figure 21.20 The 2-ethoxy derivative
6
(measured pKa of 8.75) as an analog o...
Figure 21.21 Amiloride analogs as inhibitors of the urokinase-type plasminog...
Figure 21.22 Sulfated polysaccharide fondaparinux and flavonoids with an oli...
Figure 21.23 Schematic representation of the strategies used herein for the ...
Figure 21.24 5-Amino-8-hydroxyquinoline was developed from clioquinol by rep...
Figure 21.25 Antimalarial drug mefloquine and approach to its modification t...
Figure 21.26 Modification of FK506 to semisynthetic derivative APX879 with r...
Figure 21.27 Recently developed derivatives of antipsychotic drugs with impr...
Figure 21.28 Structure of diclofenac and its analogs
11
and
12
that exhibite...
Figure 21.29 Pranlukast and its derivative
13
developed as FXR modulator.
Figure 21.30 Structure of loxapine and derivative
14
and their Slack-activat...
Figure 21.31 Cinalukast and its derivative
15
optimized for enhanced PPARa a...
Figure 21.32 Talinolol and its structural analog with increased cholesterol-...
Chapter 22
Figure 22.1 Cyclin-dependent kinases and the cell cycle.
Figure 22.2 The four CDK4/6 inhibitors currently approved by the US Food and...
Figure 22.3 Investigational CDK4/6 inhibitors currently in clinical trials....
Chapter 23
Figure 23.1 Scientific publications and patents registered in the SciFinder ...
Figure 23.2 The four stereoisomers of 3-[1-(dimethylamino)-2-methylpentan-3-...
Figure 23.3 Chemical structure of tapentadol, morphine, and tramadol (the ab...
Figure 23.4 Chemical innovations introduced in the development of tapentadol...
Figure 23.5 Major metabolic pathways of tapentadol.
Figure 23.6 The synthesis of tapentadol hydrochloride as described in the fi...
Figure 23.7 The development of different synthetic approaches to tapentadol ...
Figure 23.8 Rodent potency ratios of tapentadol/morphine for
in vitro
(rat M...
Figure 23.9 Selective anti-hyperalgesic efficacy of tapentadol in a mouse mo...
Chapter 24
Figure 24.1 The structures of thalidomide and its derivatives. Thalidomide i...
Figure 24.2 Polypharmacological properties of CELMoDs. CELMoDs exert their d...
Figure 24.3 Structure of the complex containing CRBN and CELMoDs. (a) Three-...
Chapter 25
Figure 25.1 (a) The general concept of bitopic ligands that are built from a...
Chapter 26
Figure 26.1 (a) Domains of M
pro
dimer. Domain I (green), domain II (red), an...
Figure 26.2 Chemical structure of calpain inhibitors II (
1
) and XII (
2
).
Figure 26.3 (a) Olgotrelvir and its active form AC1115; (b) Co-crystal of AC...
Figure 26.4 The structure of MG-132 (
5
) and its binding mode with SARS-CoV-2...
Figure 26.5 The structure of MPI8 (
6
).
Figure 26.6 (a) Chemical structure of SM141 (
7
) and SM142 (
8
). (b) Co-crysta...
Figure 26.7 Chemical structures of compounds
9
,
10
,
11
, RI173 (
12
) and Celas...
Figure 26.8 Chemical structures of compounds
14
and
15
.
Figure 26.9 Chemical structure of compound GK241 (
20
).
Figure 26.10 Chemical structure of compound licorice-saponin A3 (
21
) and 7-O...
Figure 26.11 Chemical structures of camostat (
23
), K777 (
24
) and the bifunct...
Figure 26.12 The structures of 14a (
26
) and 14b (
27
).
Figure 26.13 Chemical structure of obatoclax (
28
).
Figure 26.14 (a) Structure of AT-527 (
29
) and its active metabolite AT-9010 ...
Figure 26.15 The structures of favipiravir (
31
), favipiravir nucleoside (
32
)...
Figure 26.16 (a) The structure of suramin (
33
). (b) Binding mode of a part o...
Figure 26.17 The structures of SS148 (
34
) and WZ16 (
35
).
Figure 26.18 The structures of lycorine (
36
), emetine (
37
), and cephaeline (
Figure 26.19 The structures of ebselen (
39
) and its derivatives
40
–
43
.
Figure 26.20 (a) The structure of LY1 (
44
) and LY1-O (
45
). (b) Covalent bind...
Figure 26.21 The structures of compounds
46
–
54
[66–73].
Figure 26.22 (a) The structures of baicalein (
55
) and baicalin (
56
); (b) Bin...
Figure 26.23 The structures of Tadalafil (
57
) and Lonafarnib (
58
).
Figure 26.24 The structures of clofazimine (
59
) and 15f (
60
).
Chapter 27
Figure 27.1
Plasmodium
plasmepsin X and IX inhibitors.
K
i
values were determ...
Figure 27.2 Inhibitors of the
Plasmodium
mitochondrial respiratory transport...
Figure 27.3 (a) Compounds targeting validated
Plasmodium
kinase targets PI4K...
Chapter 28
Figure 28.1 Possible strategy for the rational design of multitargeting comp...
Figure 28.2 Chemical structure of the multitargeting inhibitors of MmpL3: (a...
Figure 28.3 Chemical structure of the molecular hybrids of ZDV with the anti...
Chapter 29
Figure 29.1 Structures of reverse transcriptase inhibitors.
Figure 29.2 Metabolism of TDF to release TFV by esterases.
Figure 29.3 Structure of vesatolimod and of cyclophilin inhibitor
CRV431.
Figure 29.4 Structures of calix[4]arenes and ring-expanded nucleosides.
Figure 29.5 Structures of substituted fullerenes and cyclophilin inhibitors....
Figure 29.6 Structures of HIV/EV-A71 dual inhibitors.
Chapter 30
Figure 30.1 Mechanisms by which the multi-kinase inhibitor sorafenib and HDA...
Figure 30.2 Mechanisms by which the multi-kinase inhibitor neratinib enhance...
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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Polypharmacology
Strategies for Multi-Target Drug Discovery
Edited byJens-Uwe Peters
Copyright © 2025 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
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List of Contributors
Tero Aittokallio
University of Helsinki
Finland/University of Oslo
Norway
Alberto Ambesi-Impiombato
PsychoGenics
USA
Jürgen Bajorath
Department of Life Science Informatics
B-IT LIMES Unit Chemical Biology and Medicinal Chemistry
University of Bonn
Bonn
Germany
Ian M. Bell
Merck & Co.
Discovery Chemistry
USA
Eric A.G. Blomme
AbbVie Inc.
USA
Maria Laura Bolognesi
University of Bologna
Department of Pharmacy and Biotechnology
Italy
Martin Bossart
Sanofi Germany
Integrated Drug Discovery Frankfurt
Germany
Daniela Brunner
PsychoGenics
Icahn School of Medicine at Mount Sinai
USA
Helmut Buschmann
Pharma Consulting Aachen
Germany
María-José Camarasa
Instituto de Química Médica
Spain
Laurent R. Chiarelli
University of Pavia
Department of Biology and Biotechnology “Lazzaro Spallanzani”
Italy
Kelly Chibale
University of Cape Town
South Africa
Thomas Christoph
Translational R&D, Aachen
Germany
Anna Cichońska
Harmonic Discovery
Finland
Mario Cocorullo
University of Pavia
Department of Biology and Biotechnology “Lazzaro Spallanzani”
Italy
Lauren B. Coulson
University of Cape Town
South Africa
Sonia de Castro
Instituto de Química Médica
Spain
Maedeh Darsaraee
University of Berne
Department of Chemistry
Biochemistry and Pharmacy
Switzerland
Paul Dent
Virginia Commonwealth University
Department of Biochemistry and Molecular Biology
USA
Eleonora Diamanti
University of Bologna
Department of Pharmacy and Biotechnology
Italy
Dang Ding
Shandong University
P.R. China
Attila Egyed
Research Centre for Natural Sciences
Medicinal Chemistry
Drug Innovation Centre
Hungary
Shenghua Gao
Shandong University
School of Pharmaceutical Sciences
Department of Medicinal Chemistry
Key Laboratory of Chemical Biology
P.R. China
Samaneh Goorani
University of Arkansas for Medical Sciences
USA
Jonathon R. Green
AbbVie Inc.
USA
Hiroshi Handa
Tokyo Medical University
Department of Molecular Pharmacology
Japan
Norbert Handler
RD&C Research
Development & Consulting GmbH
Austria
Axel Helmstaedter
University of Marburg
Germany
Gerhard Hessler
Sanofi Germany
Integrated Drug Discovery Frankfurt
Germany
John D. Imig
University of Arkansas for Medical Sciences
USA
Sacha Javor
University of Berne
Department of Chemistry
Biochemistry and Pharmacy
Switzerland
György M. Keserű
Research Centre for Natural Sciences
Medicinal Chemistry
Drug Innovation Centre
Hungary
Meehyein Kim
Infectious Diseases Therapeutic Research Center
Korea Research Institute of Chemical Technology (KRICT)
Daejeon
Republic of Korea
[email protected]; [email protected]
Stephan Kirchner
F. Hoffmann-La Roche Ltd.
Roche Pharma Research and Early Development
Roche Innovation Center Basel
Switzerland
Dóra J. Kiss
Research Centre for Natural Sciences
Medicinal Chemistry
Drug Innovation Centre
Hungary
Armando A. Lagrutta
Merck & Co.
Nonclinical Drug Safety
USA
Xiangqian Li
State Key Laboratory of Microbial Technology
Shandong University
P.R. China
Helen L. Lightfoot
F. Hoffmann-La Roche Ltd.
Roche Pharma Research and Early Development
Roche Innovation Center Basel
Switzerland
Felix F. Lillich
University of Frankfurt
Germany
Xinyong Liu
Shandong University
School of Pharmaceutical Sciences
Department of Medicinal Chemistry
Key Laboratory of Chemical Biology
P.R. China
Prathap Kumar S. Mahalingaiah
AbbVie Inc.
USA
Lee McDermott
PsychoGenics
USA
Zeinab Mamdouh
Zagazig University
Department of Pharmacology and Toxicology
Egypt
Cristian Nogales
Maastricht University
Department of Pharmacology and Personalised Medicine
The Netherlands
and
Max Perutz Labs
Vienna Biocenter Campus (VBC)
Austria
and
University of Vienna
Center for Molecular Biology
Department of Structural and Computational Biology
Austria
Arsenio Nueda
Almirall, S.A.
Spain
Mayra Pacheco Pachado
Maastricht University
Pharmacology and Personalised Medicine
The Netherlands
Anna M. Pasieka
University of Bologna
Department of Pharmacy and Biotechnology
Italy
Alan Lars Pehrson
PsychoGenics
USA
Jens-Uwe Peters
Skyhawk Therapeutics
Switzerland
Zina Piper
Maastricht University
Pharmacology and Personalised Medicine
The Netherlands
Andrew Poklepovic
Virginia Commonwealth University
Department of Medicine
USA
Michal Poznik
RD&C Research
Development & Consulting GmbH
Austria
Ewgenij Proschak
University of Frankfurt
Germany
Rayees Rahman
Harmonic Discovery
USA
Balaguru Ravikumar
Harmonic Discovery
Finland
Jean-Louis Reymond
University of Berne
Department of Chemistry
Biochemistry and Pharmacy
Switzerland
Sonia Roberts
F. Hoffmann-La Roche Ltd.
Roche Pharma Research and Early Development
Roche Innovation Center Basel
Switzerland
Navriti Sahni
Harmonic Discovery
USA
Ana-Rosa San-Félix
Instituto de Química Médica
Spain
Tiffany Schwasinger-Schmidt
University of Kansas School of Medicine-Wichita
Department of Internal Medicine
USA
Dayong Shi
State Key Laboratory of Microbial Technology
Shandong University
P.R. China
Lynn L. Silver
LL Silver Consulting
USA
Letian Song
Shandong University
School of Pharmaceutical Sciences
Department of Medicinal Chemistry
Key Laboratory of Chemical Biology
P.R. China
Giovanni Stelitano
University of Pavia
Department of Biology and Biotechnology “Lazzaro Spallanzani”
Italy
Kai Tang
Shandong University
P.R. China
Peter L. Toogood
University of Michigan
USA
Elisa Uliassi
University of Bologna
Department of Pharmacy and Biotechnology
Italy
Terry R. Van Vleet
AbbVie Inc.
USA
Andy Vo
AbbVie Inc.
USA
Shujing Xu
Shandong University
P.R. China
Yuki Yamaguchi
Tokyo Institute of Technology
School of Life Science and Technology
Japan
Junichi Yamamoto
Tokyo Institute of Technology
School of Life Science and Technology
Japan
Bing Ye
Shandong University
School of Pharmaceutical Sciences
Department of Medicinal Chemistry
Key Laboratory of Chemical Biology
P.R. China
Dimitar Yonchev
F. Hoffmann-La Roche Ltd.
Roche Pharma Research and Early Development
Data & Analytics
Roche Innovation Center Basel
Switzerland
Peng Zhan
Shandong University
School of Pharmaceutical Sciences
Department of Medicinal Chemistry
Key Laboratory of Chemical Biology
P.R. China
Yang Zhou
Shandong University
P.R. China
Preface
Many drugs act on more than one target [1]. This can be necessary for efficacy, but can also lead to adverse effects [2]. For instance, it was discovered in the 1980s that dual D2/3 and 5-HT2a receptor antagonism is needed for efficacy in antipsychotic drugs [3]. Today we know that antipsychotics bind to more than 20 targets, some of which contribute to efficacy, but also cause adverse effects [4].
In the early 2000s, the term polypharmacology was introduced to describe this concept of drugs binding to several targets. During this time, it became increasingly recognized that multi-target activity is often needed for efficacy. For instance, the antibiotic research of the 1990s focused on single targets derived from bacterial genomes. These single-targeted approaches were generally fruitless and did not lead to new drugs. Instead, nearly all systemically efficacious antibiotics bind to multiple targets or to targets encoded by multiple genes, so that single mutations do not lead to drug resistance (further discussed in Chapter 10) [5]. It was also recognized that unintended “anti-target” activity leads to adverse effects. Here, the most prominent example is an unusual high number of drugs withdrawn from the market in 1996–2001. These drugs were withdrawn due to adverse effects, which could be traced back to anti-target activity. For instance, half a dozen of drugs was withdrawn due to their potential to cause cardiac arrhythmias caused by unintended blockade of the hERG channel (see Chapter 7) [6]. Thus, polypharmacology can be beneficial or detrimental – these two sides of the polypharmacology coin are further detailed in the introduction.
Following some widely read papers on concepts such as “Network Pharmacology” [7] or “Magic Shotguns” [8], polypharmacology became an increasingly popular research topic. From 2010 onward, Scifinder searches retrieve an ever-increasing number of publications on polypharmacology and related topics, such as multi-target, off-target, and secondary or network pharmacology. A first book on polypharmacology was published in 2012 and became a popular read [9]. This current book is a follow-up with an updated and expanded content.
The book is divided into four parts A–D. Part A discusses undesired polypharmacology, which is often a safety concern. For instance, many drugs bind to “anti-targets” or “off-targets”, e.g. to cardiac ion channels. This causes adverse effects such as cardiac arrhythmia. The relevance of such anti-targets for adverse effects will be discussed in a first chapter, followed by chapters on the link between off-targets and adverse drug reactions, on how to screen for off-target activity and how to recognize and optimize compounds with a potential for off-target activity. This is followed by a discussion of kinases and cardiac ion channels, two of the most important classes of anti-targets.
The remainder of the book is dedicated to intended polypharmacology. Part B discusses disease areas, which benefit from polypharmacological approaches. A first chapter outlines the general concept of network pharmacology and multi-target drugs. The following chapters focus on oncology, bacterial and viral infections, CNS diseases, and metabolic diseases, followed by a discussion of the role of polypharmacology in the history of drug discovery.
But how can we discover such multi-target drugs? Part C of the book highlights important approaches, such as compound design, data mining with web-based tools, multi-target peptides, as well as phenotypic screening in cells, tissues, and animal models. A related topic is the Selective Optimization of Side Effects (SOSA) approach to drug discovery, which will be discussed as well.
The final Part D collects case studies on polypharmacological drugs and current research. PROTACs and molecular glues are hot topics in drug discovery, and the first chapter outlines how these originate from the polypharmacology of thalidomide. Next is a story on achieving “selective dual activity” for cyclin-dependent kinase inhibitors. This is followed by a bouquet of topics, from the discovery of cariprazine and tapentadol, to current research on antivirals, malaria, tuberculosis, HIV, and pancreatic cancer.
This book on polypharmacology is intended as a comprehensive resource for industrial drug hunters and academic researchers. It illuminates all facets of polypharmacology, from anti-target screening, to the design of multi-target ligands. A comparison of the current book with the first book from 2012 [9] shows that polypharmacology has certainly come of age. Polypharmacology research has improved the drug discovery process, has delivered ideas for Biotech Startups, and has garnered the attention of the media [10]. Hopefully, this book will inspire readers for new drug discovery projects and will help to mitigate attrition due to safety issues.
I am very grateful to all contributing authors, who invested their time and their expertise into this book. Also, I thank the team at Wiley for proposing this book and for their advice throughout this project: Katherine Wong, Jonathan Rose, Sabeen Aziz, Shwathi Srinivasan, and Keerthana Baskaran.
Enjoy reading!
Jens-Uwe Peters
Basel, Switzerland, December 2024
References
1
Hu, Y. and Bajorath, J. (2013). Compound promiscuity: what can we learn from current data?
Drug Discov. Today
18 (13/14): 644–650.
2
Peters, J.-U. (2013). Polypharmacology – Foe or Friend?
J. Med. Chem.
56 (22): 8955–8971.
3
Meltzer, H.Y., Matsubara, S., and Lee, J.C. (1989). Classification of typical and atypical antipsychotic drugs on the basis of dopamine D-1, D-2 and serotonin2 pKi values.
J. Pharmacol. Exp. Ther.
251 (1): 238–246.
4
Riemer, C. (2012). Antipsychotics. In:
Polypharmacology in Drug Discovery
(ed. J.-U. Peters), 343–362. Hoboken: Wiley.
5
Silver, L.L. (2012). Polypharmacology as an Emerging Trend in Antibacterial Discovery. In:
Polypharmacology in Drug Discovery
(ed. J.-U. Peters), 167–202. Hoboken: Wiley.
6
Bell, I.M., Bilodeau, M.T., and Lagrutta, A.A. (2012, 2012). Activity at Cardiovascular Ion Channels: A Key Issue for Drug Discovery. In:
Polypharmacology in Drug Discovery
(ed. J.-U. Peters), 83–109. Hoboken: Wiley.
7
Hopkins, A.L. (2008). Network pharmacology: the next paradigm in drug discovery.
Nat. Chem. Biol.
4 (11): 682–690.
8
Roth, B.L., Sheffler, D.J., and Kroeze, W.K. (2004). Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia.
Nat. Rev. Drug Discovery
3 (4): 353–359.
9
Peters, J.-U. (ed.) (2012).
Polypharmacology in Drug Discovery
. Wiley: Hoboken.
10
Kurji, N. (2019). The master key to drug design: multi-target drugs.
https://www.forbes.com/sites/forbestechcouncil/2019/09/16/the-master-key-to-drug-design-multi-target-drugs/?sh=54ba77576cfe
(accessed 27 September 2024).
1IntroductionFacets of Polypharmacology – a Janus-Headed Concept for Drug Discovery
Jürgen Bajorath
1.1 Origins
Since the 1980s, target-centric approaches have dominated drug discovery efforts, triggered by the molecular-biology-driven reductionist approach [1] and leading to the “one drug, one target,” or “drug specificity” paradigm [2]. Molecular reductionism aimed at “dissecting biological systems into their constituent parts” [1]. Different from the preceding more holistic and pharmacology-oriented era in drug discovery, molecular sciences and the single-target (ST) focus took the centerstage and shaped drug discovery efforts for many years to come [1, 2]. These developments were paralleled by advances in X-ray crystallography and molecular graphics catalyzing a wave of structure-based (“rational”) drug design efforts [3, 4], which further emphasized the focus on target-specific compounds in drug discovery.
In the early 2000s, systems biology emerged [5] and also entered the drug discovery arena [6] introducing, for example, network representations of biological systems, pathway modeling, and computational disease models. These developments originating from bioinformatics also altered the view of traditional disciplines such as pharmacology, giving rise to a network perception of physiological processes and increasing the notion of their interdependence [7]. In pharmacological networks, multiple signaling and metabolic pathways establish functional links and dependencies between different target proteins [7, 8]. If pathways in such networks are perturbed or regulatory and control mechanisms compromised, different types of multifactorial diseases might arise, including various forms of cancer, complex diseases of the central nervous system, or metabolic diseases [9–12]. Such diseases could most likely not be effectively treated by therapeutic intervention of individual targets, but required multi-target (MT) engagement instead, thus departing from the target specificity paradigm in drug discovery. MT activity of drugs was not unknown and probably first observed for anti-psychotics and antiepileptics beginning in the late 1980s [12, 13].
In 2006, as a consequence of the increasing notion of pharmacological networks, the concept of polypharmacology was introduced [14], focusing on MT agents for the treatment of multifactorial diseases: “Contrary to the dogma that the ‘rational’ way to discover drugs is to design exquisitely selective ligands for single molecular targets, a rival hypothesis proposes polypharmacology or the promiscuous modulation of several molecular targets”[14]. In 2014, a formal definition of polypharmacology appeared in the US National Library of Medicine (NLM) as “the design or use of pharmaceutical agents that act on multiple targets or disease pathways.” Polypharmacology also encompasses the pharmacological effects resulting from the use of MT compounds (MT-CPDs), consistent with the principles of network pharmacology. MT activity of drugs and other bioactive compounds is often also referred to as “promiscuity” (not to be confused with nonspecific compound–protein interactions).
1.2 Pros and Cons
Following its inception, polypharmacology emerged as an alternative to reductionist approaches and rational drug design and further evolved into a multifaceted drug discovery strategy [15–17], albeit “Janus-headedly.” In Roman mythology, Janus, the god of the beginnings, passages, and endings, had two opposing faces. Accordingly, the “Janus head” became a symbol of duality and ambivalence – which exactly applied to the polypharmacology concept: on the one hand, MT activity of drugs is a prerequisite for therapeutic efficacy in the treatment of multifactorial diseases; on the other, it is responsible for unwanted (adverse) side effects [15, 18, 19]. While adverse side effects can be elicited by the engagement of a primary target, they are more frequently caused by inhibiting so-called anti-targets such as cardiac ion channels (hERG), drug-metabolizing cytochrome P450 isoforms, or G-protein-coupled neurotransmitter receptors [15, 16]. Furthermore, side effects of MT-CPDs might also be caused by interacting with other targets not implicated in immediate toxicity, due to pathway modulations. In the pharmaceutical industry, potential liabilities as a consequence of interactions with anti-targets are a major concern, for example, leading to the assessment of newly identified candidate compounds in various safety screens for activity against such targets. However, not all unexpected side effects are undesired, taking into consideration that MT activity also provides the basis for drug repurposing [20]. Benefits of MT activity of drugs were often discovered post hoc. For example, adenosine triphosphate (ATP)-site-directed kinase inhibitors used in cancer therapy were originally thought to be kinase-selective, based on reductionist assessment, before it was discovered that their clinical efficacy depended on multi-kinase activity and simultaneous interference with multiple deregulated signaling pathways [21]. This also applied to imatinib, the first kinase inhibitor marketed as a drug [21].
Despite the Janus-headed nature of polypharmacology and the risks associated with potential adverse side effects resulting from the MT activity of drugs, the positive impact of polypharmacology on drug discovery and development is undeniable, as demonstrated by the continuous occurrence of MT agents among newly approved drugs. For example, 10 of 49 European Medicines Agency (EMA)-approved drugs marketed in Germany in 2022 were annotated with two or more targets [22]. Of course, despite the strong impact of polypharmacology, the development of compounds with target selectivity or specificity continues to be a pillar of drug discovery and development. For example, for long-term treatment of chronic and non-life-threatening diseases, drug side effects must inevitably be minimized, rendering target-selective compounds highly desirable.
1.3 Discovery and Design
Similar to coincidental findings that side effects of drugs originally thought to be specific were caused by previously unknown secondary targets, new MT-CPDs are often discovered serendipitously, for example, in screening campaigns or target deconvolution of active compounds from phenotypic assays. Given the high interest in compounds with defined MT activity in different therapeutic areas, prospective design of such compounds is also a topical issue in drug discovery [23, 24]. However, consistent with findings that characteristic structural features of MT-CPDs generally depend on target combinations, as further discussed below, the prospective design of MT-CPDs with desired activity is challenging, mostly carried out on a case-by-case basis in medicinal chemistry and far from being routine. For all practical purposes, prospective design of MT-CPDs for polypharmacology is limited to two or at most three targets. To this end, combining or merging target-dependent pharmacophores is a popular knowledge-based approach for MT-CPD design that is readily applicable in the practice of medicinal chemistry and does not require sophisticated computations [23–25]. Pharmacophore fusion attempts can be further extended by screening of test compounds using pharmacophore models for different targets and follow-up analysis of shared hits [26]. As an alternative to pharmacophore modeling, scaffolds isolated from compounds with known activity against different targets can also be used as templates for MT-CPD design, as further discussed below.
In addition to knowledge-based design strategies that are close to practical medicinal chemistry, other ligand- or target-structure-based computational approaches have been applied to identify compounds for polypharmacology [27, 28]. For example, various machine learning (ML) models have been reported to distinguish between compounds with MT activity and corresponding ST activity (typically achieving reasonable to high prediction accuracy). Furthermore, ML models have been used for computational target profiling. Here, test compounds are virtually screened using large numbers of individually derived target-based models to predict MT-CPDs. As a deep learning alternative, multitask models have also been developed to predict compounds with activity against related targets. At the structural level, similarities of binding sites in different targets have been quantified as an indicator of polypharmacology potential at the target level. In addition, parallel docking campaigns or cross-docking screens have been carried out for structure-based target profiling. Furthermore, ligands bound to different proteins have been systematically compared to identify compound pairs with the highest shape similarity to prioritize and evaluate putative cross-target activities [28].
1.4 Structural Data
In addition to its relevance for polypharmacology, the study of MT-CPDs is also of interest from a basic scientific perspective. For example, exploring the mechanisms by which small molecules “multi-specifically” or “pseudo-specifically” interact with different targets helps to better understand these special molecular recognition phenomena. To this end, currently available X-ray structures of complexes formed by MT-CPDs and different proteins provide substantial information. For example, in 2018, we identified 1418 crystallographic MT-CPDs (>300 Da) in X-ray structures of complexes with different targets available in the Protein Data Bank (PDB) [29, 30]. These MT-CPDs included 702 ligands forming complexes with targets from different protein families (termed multifamily ligands) [30]. Bound conformations of multifamily ligands available in complexes with unrelated targets were compared in detail, revealing a variety of ligand binding modes [31]. In some instances, these ligands conformationally adapted to binding sites having different architectures and chemical features and displayed different binding modes; in others, binding modes were surprisingly conserved in differently shaped active sites. If binding modes of multifamily ligands were conserved, characteristic interaction patterns emerged for targets from a given family that differed from others, hence providing a possible rationale for the conservation of binding modes [31].
As a representative example, Figure 1.1 shows conserved and variable binding modes in different active sites for indomethacin, a nonsteroidal anti-inflammatory drug (NSAID) with known polypharmacology used for the treatment of acute pain and symptoms of osteoarthritis and rheumatoid arthritis.
For 243 of the 702 multifamily ligands, 168 analogue series were detected in the ChEMBL database [32]. These series consisted of a total of 4829 compounds, covered 190 additional targets, and yielded 133 unique analogue series-based scaffolds [30]. Figure 1.2 shows an exemplary scaffold. All analogue series scaffolds were annotated with different target combinations, providing a knowledge base of MT template compounds.
1.5 Activity Data
Rapidly growing volumes of compound activity data provide another information-rich resource for the study of MT-CPDs and polypharmacology. Since the analysis of MT activity is particularly vulnerable to false-positive activity annotations, compound activity data should be carefully curated and potential assay interference compounds [33, 34] or colloidal aggregators [35] should be removed. Indeed, results of MT activity analysis strongly depend on applied data confidence criteria [36], as illustrated in Figure 1.3 for imatinib, suggesting to restrict the assessment of MT-CPDs to high-confidence activity data [36].
There are different facets of MT activity. For instance, it is not very surprising that some active compounds exhibit a tendency to interact with more than one closely related target, such as ATP-site-directed protein kinase inhibitors. By contrast, compounds binding to structurally and functionally unrelated proteins are rather unexpected, but of special interest, from both a basic scientific and a polypharmacology perspective. For example, such compounds might interfere with distinct physiological functions and elicit unexpected pharmacological effects. Systematic analysis of compound activity data helps to estimate the frequency with which MT-CPDs occur and the number of targets they are active against. Especially for candidate compounds and drugs, such estimates are relevant to balance often articulated expectation values that are largely unsubstantiated (e.g., “most drugs bind to 10 or 20 targets …”). In addition, careful analysis of available compounds and activity data also helps to gauge predictions of MT-CPDs and their target numbers, for example, from computational target profiling (vide supra). Notably, compound-data-driven analysis principally underestimates MT activity due to data incompleteness, given that not “all compounds have been tested against all targets” (the ultimate goal of chemogenomics). This must be taken into consideration. On the other hand, analysis of the large and rapidly growing volumes of activity data available in the public domain should reveal some statistically sound trends [36]. For instance, in 2019, we carried out a large-scale analysis of biological screening data from PubChem [37] in the search for compounds with activity against targets from different classes [38]. A total of 1063 compounds were identified that were tested in assays for at least 100 human target proteins and were active against at least 10 targets from more than one class [38]. These findings showed that MT-CPDs with activity against distantly or unrelated targets occurred rather frequently.
Figure 1.1 X-ray structures of indomethacin in complex with three distinct targets. On the left, and right, pairwise superpositions of bound ligand conformations are shown, revealing conserved (left) and variable binding modes (right) in different protein environments.
Figure 1.2 Scaffold of a multifamily ligand with kinase activity representing an analogue series. For the ligand, crystal structures of complexes with Aurora and TEC kinases were available (PDB) and structural analogues found in ChEMBL were active against additional kinase targets from other families.
Figure 1.3 Target annotations of imatinib. Based on increasing volumes of activity data from ChEMBL collected over time, the number of targets reported for imatinib is monitored at three different confidence levels: all activity data (no confidence criteria were applied) medium- and high-confidence data.
Adapted from Hu and Bajorath [36].
The number of target annotations based on all activity data and medium-confidence data (690 and 406, respectively) is unrealistic.
1.6 Drug Target Estimates
Systematic experimental determination of the targets that drugs are active against is far from being an easy task. Accordingly, insights into drug target numbers are typically confined to case-by-case proteomic analysis or statistics from target panel assays such as kinome screens [39]. However, based on compound data analysis, different estimates of target numbers for drugs and other active compounds have been reported.
Early attempts to predict drug targets used network representations of drug–target interactions [40]. From different databases, drugs, targets, and interaction data were collected and analyzed in drug–target networks. From such network representations, it was estimated that a drug on average interacted with six targets. Depending on the data used, targets per drug ranged from approximately 3 to 13 [40]. Comparable estimates were obtained when approved and experimental drugs taken from DrugBank [41] were mapped to ChEMBL and drug data and targets were monitored over a 15-year period [42]. For bioactive compounds from screening assays, different target numbers were determined. In an early analysis of PubChem [37], MT activities were analyzed on the basis 600+ assays [43]. It was found that approximately 58% of active screening compounds only displayed ST activity in combined primary and confirmatory assays. In addition, based on high-confidence activity data extracted from ChEMBL, it was determined that a bioactive compound on average interacted with only one to two targets, with no significant variations across different families [44, 45]. Different from PubChem data, ChEMBL does not contain test frequencies for compounds taken from the literature (where negative data is typically not reported). Hence, data incompleteness generally plays a greater role in target analysis based on ChEMBL. On the other hand, high-confidence activity data can best be obtained from ChEMBL. In 2016, approximately 430,000 extensively assayed compounds were extracted from PubChem (tested in both primary and confirmatory assays), with mean and median values of 411 and 437 assays per compound, respectively [46]. Most extensively assayed screening hits were on average active against 2.5 targets [46]
