Table of Contents
Cover
Title Page
Copyright
Preface
References
A Personal Foreword
Part I: Introduction
Chapter 1: Polypharmacology in Drug Discovery
1.1 Polypharmacology
1.2 Multitarget versus Target-Specific Drugs
1.3 Polypharmacology and Related Concepts in Drug Discovery
1.4 Polypharmacology (and Polypharmacy): Case Studies
1.5 Computational Strategies to Explore Polypharmacology
1.6 Summary Conclusions
Acknowledgments
References
Part II: Selectivity of Marketed Drugs
Chapter 2: Kinase Inhibitors
2.1 Overview
2.2 Kinase Profiling
2.3 Definition and Quantification of Selectivity Levels
2.4 Selectivity of Approved Kinase Inhibitors
2.5 Conclusion and Perspective
Acknowledgment
References
Chapter 3: Repositioning of Drug – New Indications for Marketed Drugs
3.1 Introduction
3.2 New Uses from Adverse Effects
3.3 New Uses Based on Known Mechanism of Action
3.4 New Uses from Genome, Network, and Signal Pathway Analysis
3.5 New Uses Based on New Target Identification (Off-Target Effects)
3.6 Computational and Systematic Drug Repositioning
3.7 Perspective
3.8 Acknowledgment
References
Chapter 4: Discovery Technologies for Drug Repurposing
4.1 Introduction
4.2 Biological Drug Screening Methods
4.3
In silico
Tools for Drug Repurposing
4.4 Conclusion
References
Part III: Unselective Drugs in Drug Discovery
Chapter 5: Personalized Medicine
5.1 Roots of Personalized Medicine
5.2 The Return of the Active Pharmaceutical Ingredients (APIs)
5.3 Systems Pharmacology
5.4 The Patient in the Focus of Research
5.5 Personalized Therapy
5.6 Gene Therapy
5.7 Regenerative Medicine
5.8 Individualized Medicines
5.9 Stratified Medicines
5.10 Drug Selectivity
5.11 Smart Innovation
5.12 Electronic Health
5.13 Doctor and Patient
5.14 The Competent Patient
5.15 Conclusion
References
Chapter 6: Drug Discovery Strategies for the Generation of Multitarget Ligands against Neglected Tropical Diseases
6.1 Introduction
6.2 Drug Discovery for NTDs: The Past, the Present, and the Future
6.3 Search for New Anti-Trypanosomatid MTDL Hits: A Phenotypic Approach
6.4 Search for New Anti-Trypanosomatid MTDL Hits: A Target-Based Approach
6.5 Search for New Anti-Trypanosomatid MTDL Hits: A Drug Targeting Approach
6.6 Search for New Anti-Trypanosomatid MTDL Hits: A Combined Target/Targeting Approach
6.7 Conclusions
References
Chapter 7: Designing Approaches to Multitarget Drugs
7.1 Introduction
7.2 Target-Based Approaches for Multitarget Drug Design
7.3 Ligand-Based Approaches for Multitarget Drug Design
7.4 Designing Approaches Based on Phenotypic Assays
7.5 Conclusions
References
Chapter 8: The Linker Approach: Drug Conjugates
8.1 Introduction
8.2 Drug Conjugates
8.3 Linker Chemistry
8.4 Conclusion and Future Perspective
References
Chapter 9: Merged Multiple Ligands
9.1 Introduction
9.2 Computational Methods Utilized in Designing MMLs
9.3 Examples of Medicinal Chemistry Efforts of Designing MMLs in Drug Discovery Projects
9.4 Conclusions and Future Outlook
References
Chapter 10: Pharmacophore Generation for Multiple Ligands
10.1 Introduction
10.2 Ligand-Based Pharmacophore Modeling
10.3 Structure-Based Pharmacophore Modeling
10.4 Pharmacophore-Based Virtual Screening
10.5 Pharmacophore-Based
De Novo
Design
10.6 Limitations for Pharmacophore Modeling
10.7 Practical Strategy for Pharmacophore-Based Discovery of Multiple Ligands
10.8 Linked Fluoroquinolone–Flavonoid Hybrids as Potent Antibiotics against Drug-Resistant Microorganisms
10.9
N
-Phenylquinazolin-4-Amine Hybrids as Dual Inhibitors of VEGFR-2 and HDAC
10.10 Dual Inhibitors of Phospholipase A2 and Human Leukotriene A4 Hydrolase as Anti-Inflammatory Drugs
10.11 Dual Antagonists of the Bradykinin B
1
and B
2
Receptors Based on a Postulated Common Pharmacophore from Existing Non-Peptide Antagonists
10.12 Dual-Acting Peptidomimetics with Opioid Agonist–Neurokinin-1 Antagonist Effect
10.13 Novel Dual-Acting Compounds Targeting the Adenosine A
2A
Receptor and Adenosine Transporter for Neuroprotection
10.14 Aminobenzimidazoles as Dual-Acting Butyrylcholinesterase Inhibitors and
h
CB
2
R Ligands to Combat Neurodegenerative Disorders
10.15 Dual Acetylcholinesterase Inhibitors–Histamine H3 Receptor Antagonists for Treating Alzheimer's Disease
10.16 Identification of Potential Dual Agonists of FXR and TGR5 Using E-Pharmacophore-Based Virtual Screening
10.17 Arylboronic Acids as Dual-Acting FAAH and TRPV1 Ligands
10.18 Dual Type II Inhibitors of TGFβ-Activated Kinase 1 (TAK1) and Mitogen-Activated Protein Kinase 2 (MAP4K2)
10.19 Conclusion and Outlook
References
Chapter 11: Cellular Assays
11.1 Introduction
11.2 Cell-Based Molecular Assays
11.3 Cell Phenotypic Assays
11.4 Summary
11.5 Current and Future Perspectives
References
Part IV: Therapeutic Areas for Designed Multiple Ligands
Chapter 12: Developing Serotonergic Antidepressants Acting on More Than the Serotonin Transporter
12.1 5-HT Transporter-Based Multiple Ligands for Depression
12.2 Beyond SSRIs: Strategies to Improve upon SSRI Antidepressant Activity
12.3 Roster of Serotonergic Targets for Drug Developed Outside of the Serotonin Transporter (SERT)
12.4 Previously Approved Antidepressants with Multiple Serotonergic Molecular Targets
12.5 Tested and Failed/Technically Difficult Dual-Acting Serotonergic Compounds
12.6 Technical Challenges to Developing New Chemical Entities with Multiple Mechanisms of Action
12.7 Clinical Experiments with SSRIs and 5-HT
1A
Agonists/Antagonists
12.8 Clinical Experiments with SSRIs and Drugs Possessing 5-HT
2A
Receptor Blockade
12.9 Non-SERT Serotonergic Targets Mired in Phase 2/3
12.10 Conclusions and Outlook
References
Chapter 13: Multiple Ligands Targeting the Angiotensin System for Hypertension
13.1 Recent Advances in the Structural Basis for AT
1
Receptor Ligand Binding
13.2 Design of Dual AT
1
and Endothelin A Receptor Antagonists
13.3 Design of Dual AT
1
Receptor Antagonist/PPARγ Partial Agonists
13.4 Design of Dual AT
1
Receptor Blocker/NO-Releasing Agents
13.5 Design of Dual AT
1
Receptor Blocker/Antioxidant Activity Agents
13.6 Design of AT
1
Receptor Antagonists with Additional Activity in Other Pathways
13.7 Summary
References
Chapter 14: Multiple Peroxisome Proliferator-Activated Receptor-Based Ligands
14.1 Introduction
14.2 Dual and Pan PPAR Agonists
14.3 Other Multiple Ligands that Act through PPARs
14.4 Conclusions
Acknowledgments
References
Chapter 15: Antibiotics
15.1 Design of Single-Pharmacophore Molecules Acting on Multiple Targets
15.2 Design of Hybrid Molecules: Dual Pharmacophores Acting on Multiple Targets
15.3 Emerging Antibacterial Drugs Allowing Multitarget-Directed Ligand Design
15.4 Conclusion
References
Chapter 16: Multiple Ligands in Neurodegenerative Diseases
16.1 Introduction
16.2 Molecular Bases of Alzheimer's Disease
16.3 MTDLs Developed for the Treatment of Alzheimer's Disease
16.4 Parkinson's Disease
16.5 Conclusion
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Part I: Introduction
Begin Reading
List of Illustrations
Chapter 1: Polypharmacology in Drug Discovery
Figure 1.1 The “dual face” of multitarget compounds and relationship with “master key drugs.”
Figure 1.2 Examples of poor selectivity of kinase inhibitors. This Figure shows the cross-reactivity of dasatinib (a) and sunitinib (b) across the kinome.
Figure 1.3 Examples of the use of polypharmacology against kinases. Compounds
AZD6244
and
MK-2206
have been used in combination to inhibit the MAPK and PI3K pathways to obtain an enhanced phenotypic effect. Compound
PP121
inhibits both PI3K and mammalian target of rapamycin (mTOR) simultaneously. This dual inhibition has been proposed to be more potent than inhibiting either target individually. The rationale behind this idea is that mTOR activates a negative feedback loop that inhibits PI3K. The inhibition of mTOR alone results in the blockage of the negative feedback loop and in a hyperactivation of PI3K [18].
Figure 1.4 Visual representation of the chemical space of inhibitors of histone deacetylases (HDACs), bromodomains (BRDs), DNA methyltransferases (DNMTs), and generally recognized as safe (GRAS) compounds. The principal component analysis was done with six pharmaceutically relevant physicochemical properties. The first two principal components (PCs) are represented in the figure.
Figure 1.5 Schematic representation of a chemogenomics matrix; the rows represent all possible compounds and the columns represent all possible molecular targets.
Figure 1.6 Schematic representation of some computational strategies used to explore chemogenomics. (a) An example of proteochemometric model, which combines target and structural similarities to predict activity of new compounds against adenosine receptors [62]. (b) Target fishing, which is commonly used to identify targets of known compounds such as in the case of acepromazine [63]. (c) An example of systems pharmacology that was previously used to identify the anticancer activity of genistein [64].
Chapter 2: Kinase Inhibitors
Figure 2.1 FDA-approved non-covalent type I and type II small molecule protein kinase inhibitors. Inhibitors are listed based on reverse chronological order of their first FDA approval date, that is, alectinib, which was recently approved in December 2015, is in the top left position, and imatinib, which was the first FDA-approved SMKI, is in the bottom right position. Commercial names are provided in capsules beneath generic names.
Figure 2.2 Other types of FDA-approved kinase inhibitors. Commercial names are provided in capsules beneath generic names. (a) Light blue capsules indicate approved covalent SMKIs, (b) brown capsules indicate approved type III SMKIs, (c) thistle capsule indicates the only approved lipid kinase inhibitor, and (d) pale turquoise capsules indicate approved macrocyclic kinase inhibitors.
Figure 2.3 Examples of commercially available kinase panels and profiling services. The order is based on the size of kinase panels of each supplier, data collected from the corresponding website as of January 2016. The authors have no conflicts of interests with any of the previously listed suppliers.
Figure 2.4 Representation of selectivity by dotted kinome tree illustration exemplified by three types of SMKIs: (a) selumetinib is a highly selective inhibitor of MEK; (b) PI-103 is a dual PI3K/mTOR inhibitor that also interacts with multiple other kinases scattered in seven subfamilies of protein kinases; and (c) staurosporine is a widely studied promiscuous inhibitor with high binding potency against numerous kinases. High binding affinity is indicated by red circles with large diameters, and low binding affinity is indicated by red circles with small diameters. Affinities weaker than 1 μM (
K
d
) are not indicated. The eight main subfamilies of protein kinases are shown as differently colored branches (clockwise order from TK): TK, tyrosine kinase; TKL, tyrosine kinase-like; STE, homologs of yeast Sterile 7, Sterile 11, and Sterile 20 kinases; CK1, Casein kinase 1; AGC, containing PKA, PKG, PKC families; CAMK, calcium/calmodulin-dependent protein kinase; CMKC, containing CDK, MAPK, GSK3, CLK families, and other protein kinases.
Figure 2.5 Circular kinome tree illustration of selectivity for approved SMKIs, based on
K
d
profiling results of 442 protein kinases. High binding affinity is indicated by red circles with large diameters, and low binding affinity is indicated by red circles with small diameters. Affinities weaker than 1 μM (
K
d
) are not indicated. Selective SMKIs are positioned in the top left corner, and promiscuous SMKIs are positioned in the bottom right corner, that is, lapatinib is a representative selective inhibitor, while sunitinib is a representative promiscuous inhibitor.
Figure 2.6 FDA-approved inhibitors and their targeting kinases and therapeutic indications (Part I). The targets for which the approved SMKIs have shown inhibitory activities include but are not limited to the ones listed here. CML, chronic myeloid leukemia; GIST, gastrointestinal stromal tumor; NSCLC, non-small cell lung cancer; RCC, renal cell carcinoma.
Figure 2.7 FDA-approved inhibitors and their targeting kinases and therapeutic indications (Part II). The targets for which the approved SMKIs have shown inhibitory activities include but are not limited to the ones listed here. CLL, chronic lymphocytic leukemia; NSCLC, non-small cell lung cancer; RCC, renal cell carcinoma.
Figure 2.8 Representative examples of different types of approved SMKIs. Type I inhibitor: (a) gefitinib and its depicted binding mode with EGFR, (b) gefitinib co-crystal structure with EGFR (PDB ID: 2ITY), and (c) gefitinib co-crystal structure with mutant L858R + T790M EGFR (PDB ID: 4I22). Type II inhibitors: (d) imatinib and its depicted binding mode with BCR-Abl, (e, f) imatinib co-crystal structure with the kinase domain of Abl (PDB ID: 1IEP), (g) lapatinib and its depicted binding mode with EGFR, and (h, i) lapatinib co-crystal structure with EGFR (PDB ID: 1XKK). Type III inhibitor: (j) chemical structure of trametinib, (k) trametinib analog TAK-733 and its depicted binding mode with MEK, and (l) TAK-733 co-crystal structure with MEK1 (PDB ID: 3PP1). SMKIs are shown in magenta backbones, ATP is shown in cyan backbone, hydrogen bonds are indicated by red broken lines, residues interacting with SMKIs and ATP through hydrogen bonds are shown in green backbones, residues of the Asp–Phe–Gly motif of the activation loop are shown in white backbones, and residues 858 and 790 in the gefitinib EGFR co-crystal structure are shown in yellow backbones.
Chapter 3: Repositioning of Drug – New Indications for Marketed Drugs
Figure 3.1 Increase of publications in NCBI PubMed with the phrase “drug repositioning,” “drug redirecting,” “drug repurposing,” or “drug reprofiling” in the text.
Figure 3.2 An overview of the technical pipeline for cancer drug repositioning. (Reprint from Ref. [23].) The pipeline comprises six modules for the computation biology analyses (differential analysis, enrichment analysis, CSB analysis, survival analysis, signaling network analysis, and repositioning analysis) and two modules for experimental biology analyses (target validation and drug efficacy validation). Signaling network analysis, which is the core of the computational component, is to refine the general signaling networks to the core signaling network that is specific to the cancer of interest. Differential analysis and enrichment analysis modules provide the differential genes and enriched signaling pathways for the mathematical model, whereas CSB analysis supplies the essential cancer signaling network for the mathematical model. Survival analysis enables further narrowing of the signaling networks down to the core signaling network based on the metastasis-free survival times of patients. Repositioning analysis identifies the repositioned drug candidates from the available drug information, integrating with the two experimental biology modules, target validation, and drug efficacy validation.
Figure 3.3 Binding modes of (a) amoxapine (yellow), (b) 7-hydroxyamoxapine (lime) and 8-hydroxyamoxapine (dark green), and (c) loxapine (orange), (d) inhibitor 2 (pink) from 3LPF, and (e) amoxapine and inhibitor 2 in the same active site. (f) Pharmacophore features for amoxapine and inhibitor 2 (Reprint from Ref. [43].) The compounds and close contacting residues are shown in sticks. Residues in 5 Å around the ligands depicted: the active site are shown in lines. The primary monomer is colored in cyan and bacterial loop from the adjacent monomer is colored in green. The hydrogen bonds are drawn in lime dash lines. In the mode of loxapine, the critical hydrogen bond with E413 is missing. The pharmacophore based on amoxapine and inhibitor 2 is composed of two aromatic ring features (shown in orange spheres) and hydrogen bond donors pointed to E413 (purple and cyan spheres).
Chapter 4: Discovery Technologies for Drug Repurposing
Figure 4.1 Workflows of phenotypic and target-based screening approaches in drug discovery and repurposing. (a) Phenotypic screening requires organismal or cell-based models that are subjected to high-throughput drug screening (HTDS). Once the requisite change in phenotype is achieved, the lead drug can be optimized or, in the case of drug repurposing, taken directly to phase II clinical trials. If desired, identification of the drug's target can be pursued (dashed box), but target knowledge is not necessary. (b) Target-based screening is reliant on the identification and validation of a target associated with a disease of interest. An assay specific for that target needs to be developed and amendable to HTDS. However, though the assay may indicate an association between the target and a drug, the desired organismal effect may not be achieved as the target may only be one of many contributors to the disease state. Thus, target-based screening approaches tend to be less successful for discovering first-in-class drugs compared with phenotypic screening approaches, but are more useful for follower drugs that are optimized for reducing off-target effects and maximizing efficacy.
Figure 4.2 Schematic depicting drug repurposing through similarity between protein binding sites. For a given drug known to be associated with protein #1, it is hypothesized to also associate with protein #2 if there is sufficient binding site similarity between the two proteins determined by a variety of methods, such as structural alignment.
Figure 4.3 Hypothetical network polypharmacology. Hypothetical multitiered network connecting drugs (blue nodes) to protein targets (green nodes) and further to cellular pathways (red nodes) and clinical diseases (purple nodes). Protein–protein interactions are designated using red edges. Nodes found toward the center tend to have many relations, whereas those found toward the periphery exhibit fewer associations. Network analysis highlights drug promiscuity and off-target interactions, as well as protein similarity through the drug chemical space.
Chapter 6: Drug Discovery Strategies for the Generation of Multitarget Ligands against Neglected Tropical Diseases
Figure 6.1 Chemical structures of anti-trypanosomatid drugs melarsoprol (
1
), nifurtimox (
2
), and miltefosine (
3
).
Figure 6.2 Design strategy toward the naphthoquinone- and anthraquinone-derived library.
Figure 6.3 Chemical structure and multitarget profile of
4
.
Figure 6.4 Framework combination approach toward hybrid compounds for NTDs.
Figure 6.5 Design strategy toward quinone–coumarin hybrids
6
and
7
.
Figure 6.6 Design strategy toward targeted conjugates
10
and
11
.
Figure 6.7 Design strategy toward quinone–polyamine conjugate
12
.
Chapter 7: Designing Approaches to Multitarget Drugs
Figure 7.1 Fragment (scaffold)-based drug discovery at Plexxikon.
Figure 7.2 Development of dual PI3K/Tyr kinase inhibitors.
Figure 7.3 Design of type II hybrid inhibitors for c-Src kinase.
Figure 7.4 Multitarget anti-inflammatory drugs. LTA4H possesses both intrinsic aminopeptidase activity and epoxide hydrolase activity; the inhibition values for the two activities were reported, respectively.
Figure 7.5 Development of the multikinase inhibitor sorafenib.
Figure 7.6 Two different approaches led to the discovery of the same class of compounds that are potentially useful for psychiatric disorders: The first strategy involved an automated design (a machine learning approach) for the obtainment of compounds with the desired pharmacological profile, based on a training set of active compounds. The second was based on a medicinal chemistry approach (privileged structures, isosteric substitutions, etc.). The pharmacological activity of the compounds thus obtained was determined as the last step.
Figure 7.7 Development of LY294002 starting from the structure of the natural compound quercetin.
Figure 7.8 Discovery of PI103.
Figure 7.9 Evolution of PI3K/mTOR dual inhibitors containing a morpholine moiety.
Figure 7.10 Pharmacophore-based design of triple reuptake inhibitors developed at GSK, together with compounds triple reuptake inhibitors with similar structures present in the previous literature.
Figure 7.11 (Upper part):
In silico
design of dual TS/DHFR inhibitors. (Lower part): Design of experimentally validated dual TS/DHFR inhibitors, starting point for the generation of a pharmacophore model used in the virtual approach described above.
Figure 7.12 Generation of a multitarget MCH-1 antagonist/DPP-IV inhibitors.
Figure 7.13 Generation of a multitarget GPR119 agonist/DPP-IV inhibitors.
Figure 7.14 Development of multitarget 5-HT
1A
/SERT inhibitors as antidepressants, following a pharmacophore strategy (merging the two pharmacophores) and an SAR optimization process.
Figure 7.15 Discovery of dual AChE/BACE inhibitors following two strategies: SAR around a lead and pharmacophore linking (BACE-1 inhibitor with the AChEi donepezil).
Figure 7.16 Workflow of the discovery of dual-acting modulators based on literature data.
Figure 7.17 Strategies followed for multikinase antitumoral drug discovery based on RET targeting.
Figure 7.18 Development, by means of phenotypic assays, of multitarget antinociceptive drugs.
Figure 7.19 Multitarget drugs in advanced clinical trials and their design process.
Chapter 8: The Linker Approach: Drug Conjugates
Figure 8.1 Drug conjugates have gained considerable interest over the last decades, and with the approval of the first antibody–drug conjugates in 2011 and 2013, this intensive research started to yield clinical benefit. With several more agents in all stages of preclinical and clinical development, a growing significance of drug conjugates is growing.
Figure 8.2 The small molecule drug conjugate EC0225 is composed of one unit folate for targeting (green), a vinca alkaloid (blue), and the cytotoxic antibiotic mitomycin (red). Release of the cytotoxic agents inside the targeted folate receptor-positive cells is enabled by the use of disulfide bonds and a hydrazone moiety in the linkers.
Figure 8.3 The drug conjugate EC0746 is composed of one folate unit (blue) for targeting and one unit aminopterin (orange) as delivered agent. Both are connected by an amino sugar linker (red) that is cleavable in two positions (circles) – at a disulfide bond and at a hydrazone moiety – to release the delivered drug in its free form.
Figure 8.4 Drug conjugates glufosfamide and SW IV-134. Glufosfamide exploits glucose transporters for the targeted delivery of the alkylating agent ifosfamide (red). In SW IV-134, the sigma-2 ligand SW43 (blue) facilitates delivery of the Smac mimetic SWIV-52 (red).
Figure 8.5 Conjugation of the two antitumor agent irinotecan (blue) and chlorambucil (red) led to an amphiphilic drug conjugate that showed enhanced plasma half-life and additionally self-assembled to nano-sized micelles, which can exploit the EPR effect for tumor targeting.
Figure 8.6 Conjugates of photosensitizers (blue) and norfloxacin (red) showed increased antibacterial activity compared with the single agents and lower lipophilicity.
Figure 8.7 Schematic structure of antibody–drug conjugates.
Figure 8.8 Cytotoxic agents that are frequently used as small molecule component of antibody–drug conjugates.
Figure 8.9 In paclitaxel poliglumex, the tubulin inhibitor paclitaxel (red) is attached to poly-l-glutamic acid (blue) via an ester group. The conjugate has an average weight of 38.5 kDa and carries one paclitaxel unit per 11 units of glutamic acid.
Figure 8.10 EZN-2208 is a PEG conjugate of SN38 (red) that is the active moiety of irinotecan. The approximately 40 kDa polymeric carrier is branched and holds 4 units of the active agent. With the help of the hydrophilic carrier system, the conjugate is highly soluble (180 mg/mL).
Figure 8.11 Frequently used linkers.
Chapter 9: Merged Multiple Ligands
Figure 9.1 Design of a dual aromatase–STS inhibitor.
Figure 9.2 Design of a dual HDAC and TopoI inhibitor.
Figure 9.3 Design of dual HDAC and IMPDH inhibitors.
Figure 9.4 Design of dual MMP-1/CatL inhibitors.
Figure 9.5 Design of dual MMP-2/CA IX inhibitors.
Figure 9.6 Design of dual AChEI and AChEI-induced β-amyloid aggregation.
Figure 9.7 Design of dual AChE and MAO-B inhibitors.
Figure 9.8 Design of dual AChE and SERT inhibitors.
Figure 9.9 MML of an MAO-B inhibitor with an ion chelator.
Figure 9.10 Design of a dual A
2A
and MAO-B inhibitor.
Figure 9.11 Design of a dual SERT and 5-HT
1A
modulator.
Figure 9.12 Dual NK
1
receptor antagonist and SERT inhibitor identified by HTS.
Figure 9.13 Design of a dual H
3
and SERT modulator.
Figure 9.14 Design of omapatrilat, a dual ACE and NEP modulator.
Figure 9.15 Design of a dual AT
1
and ET
A
receptor antagonist .
Figure 9.16 MML example for MCH-1 and DPP-IV.
Figure 9.17 Design of a dual GPR119 and DPP-IV MML.
Figure 9.18 MML design of PSN-602.
Figure 9.19 Design of a dual COX-2/LTA
4
H inhibitor.
Figure 9.20 Design of a dual COX-2 and sEH inhibitor.
Chapter 10: Pharmacophore Generation for Multiple Ligands
Figure 10.1 The basic framework of pharmacophore architecture.
Figure 10.2 Basic steps for pharmacophore model generation, refinement, and application.
Figure 10.3 The degree of pharmacophore overlap varies significantly among designed multiple ligands. There is a continuum from conjugates where the pharmacophores are well separated by a linker group to ligand where the pharmacophores are highly intermingled.
Figure 10.4 Proposed strategy for developing multitarget compounds.
Figure 10.5 Molecular structure of floxacines. Tetramethylscutellarein and a dual-acting hybrid molecule.
Figure 10.6 Molecular structure of compound
1
.
Figure 10.7 Design of dual VEGFR-2/HDAC inhibitors.
Figure 10.8 Chemical structure of most effective dual VEGFR-2/HDAC inhibitor.
Figure 10.9 Chemical structures of selective hnps-PLA2 inhibitor (LY311727) and LTA4H-h inhibitor (bestatin).
Figure 10.10 Chemical structures of dual LTA4H-h/hnps-PLA2 ligands.
Figure 10.11 Pharmacophores and inhibitors of LTA4H-h and hnps-PLA2. Solid spheres represent the pharmacophore of LRA4H-h and dotted spheres the pharmacophores of hnps-PLA2. Cyan spheres stand for hydrophobic centers; red spheres represent the H-bond acceptor center; yellow spheres stand for the feature that coordinates with a metal. To clearly show the interactions between the metals and other atoms, the radii of metals are not shown in realistic ratios. (a) Pharmacophore model of LTA4H-h; the inhibitor bestatin is shown. (b) Pharmacophore model of hnps-PLA2; the inhibitor indole A is shown. (c) Alignment of common pharmacophores of LTA4H-h and hnps-PLA2; the inhibitor compound
3
is shown; (d) interaction of compound
3
with LTA4H-h; (e) interaction model of compound
4
with hnps-PLA2.
Figure 10.13 Chemical structures used as bradykinin B
1
/B
2
inhibitors.
Figure 10.13 Example for a designed dual ligand as opioid agonist-NK1 antagonist.
Figure 10.14 Chemical structures of selective A
2A
R agonist CGS21680, the selective ENT1 inhibitor NTB1, and developed dual A
2A
R/ENT1 ligands.
Figure 10.15 Simplified pharmacophore model (pharmacophores circled) of a prototypical competitive BChE inhibitor A [70, 71] and application of this model on a recently described dual-acting compound with an indazole ether scaffold (B) [72] and on a CB
2
R-slective ligand described by AstraZeneca (C = 18).
Figure 10.16 Chemical structures to investigate SARs at AChE, BChE, and
h
CB
1
R,
h
CB
2
R, respectively.
Figure 10.17 The proposed three-dimensional pharmacophore for histamine H
3
with 1. The main features are two positive ionic centers and an aromatic ring forming the center of the molecule.
Figure 10.18 The e-pharmacophore sites of FXR
INT-767
(a) and TCR5
INT-767
(b) along with the intersite distances.
Figure 10.19 Schematic representation of the overall work flow applied for lead identification with e-pharmacophore-based virtual screening.
Figure 10.20 Schematic representation of protein–ligand interaction between FXR for the top hit.
Figure 10.23 Effect of the selected dual agonist on the activities of glutathione peroxidase (GPx) in uric acid-induced MIN-6 pancreatic β cells.
Figure 10.24 Chemical structures of FAAH blockers.
Figure 10.26 Pharmacophore model for arylboronic acid derivatives as dual FAAH/TRPV1 inhibitors.
Figure 10.27 General pharmacophore model for the rational design of type II inhibitors: examples of known type II inhibitors, which can be divided in a “type I” head (black) attached to a “type II” tail (blue).
Figure 10.28 Schematic representation of the rational design of new type II kinase inhibitors: A, hydrogen bond acceptor; D, hydrogen bond donor; HRB, hinge-region binding; HM, hydrophobic motif.
Figure 10.29 Chemical structures of lead compounds with TAK1/MAP4K2 dual activity) [77].
Chapter 11: Cellular Assays
Figure 11.1 Three different cell-based ligand binding assays. (a) Fluorescence imaging after fluorescent-labeled ligands bind to a cell surface receptor. (b) Fluorescence resonance energy transfer (FRET) from a donor fluorescent protein tagged to the target receptor to an acceptor fluorescent ligand. Time-resolved FRET occurs when the donor is a lanthanide label. (c) Bioluminescence resonance energy transfer (BRET) from a donor bioluminescent protein (e.g., luciferase) tagged to the target intracellular protein to a fluorescent-labeled probe ligand acceptor.
Figure 11.2 Three mass spectrometry-based proteomic techniques for drug–target interaction analysis across the human proteome. The shotgun global approach profiles the entire proteome (left). The affinity purification-based proteomics investigates the proteins that bind to a capture agent (e.g., kinobeads) (middle). The activity-based proteomics investigates the proteins that bind to a chemoproteomics probe, or are associated with a specific organelle, or involve in a specific signaling complex (right).
Figure 11.3 Two popular label-free biosensor techniques used for cell phenotypic screening. (a) Resonant waveguide grating (RWG), wherein cells are cultured onto its waveguide surface and only the mass redistribution within the bottom portion of cells is monitored in real-time as the shift in resonance wavelength of the reflected light. (b) Electric biosensor, wherein the cells are cultured on the surface of the biosensor having arrayed gold microelectrodes and both flows of extracellular and transcellular currents are measured in real-time when a low alternating current voltage at variable frequencies is applied to the cell layer.
Chapter 12: Developing Serotonergic Antidepressants Acting on More Than the Serotonin Transporter
Figure 12.1 Chemical structure of SSRIs. The chemical structure of SSRIs approved for the treatment of MDD is shown: citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, and zimelidine.
Figure 12.2 Chemical structure of TCAs. The chemical structure of TCAs approved for the treatment of MDD is shown including especially amitriptyline, clomipramine, desipramine, imipramine, nortriptyline, and protriptyline.
Figure 12.3 Chemical structure of antidepressant and/or antipsychotic drugs acting on multiple serotonergic molecular targets. Included are tetracyclic antidepressants (mianserin and mirtazapine), serotonin antagonist–reuptake inhibitors (SARIs: trazodone, nefazodone, etoperidone), the multimodal serotonergic antidepressant vortioxetine, the SERT inhibitor/5-HT1A receptor agonist vilazodone, and atypical antipsychotic drugs (risperidone, olanzapine, and clozapine).
Figure 12.4 Heat map for affinity of representative tricyclic antidepressant drugs at monoamine transporters and receptors. The data plotted for this Figure comes from the PDSP
K
i
database and an additional reference [25, 26]). The tertiary amine TCA amitriptyline (AMITRIP) is metabolized into the secondary amine TCA nortriptyline (NORTRIP). Clomipramine (CIMIP) is the TCA that inspired Arvid Carlsson to champion the development of the first approved SSRI zimelidine. Imipramine (IMIP) is also a tertiary amine TCA that is metabolized into the secondary desmethylimipramine or desipramine (DMI). The color code represents different ranges of potency from <1 nm (dark red) to >10 μM (purple). Each row shows the affinity of these different tricyclic antidepressants for a range of transporters (SERT, NET, and DAT) as well as serotonergic (5-HT), histaminergic (H), muscarinic cholinergic (M), α-adrenergic (α
1
and α
2
), and dopaminergic (D) receptor subtypes as well as the sigma binding site.
Figure 12.5 Heat map for
in vitro
potency of representative atypical antidepressant drugs and a metabolite at monoamine transporters and receptors. The data plotted for this Figure comes from the National Institute of Mental Health (NIMH)-sponsored PDSP
K
i
database (trazodone, m-CPP, mianserin, mirtazapine) and additional references [28, 49–52] for the nefazodone and etoperidone data. Trazodone (TRAZ), nefazodone (NEFAZ), and etoperidone (ETOPER) are metabolized into
meta
-chlorophenylpiperazine (m-CPP). These serotonin antagonist–reuptake inhibitors (SARIs) bind most potently to 5-HT
2A
receptors and then α
1
adrenergic receptors. These drugs range from 8-fold (nefazodone) to 25-fold (etoperidone) less potency at SERT than 5-HT
2A
receptors. Mianserin (MIAN) and mirtazapine (MIRTAZ) are tetracyclic structural analogs. The color code represents different ranges of potency from <1 nm (dark red) to >10 μM (purple). Each row shows the affinity of these different tricyclic antidepressants for a range of transporters (SERT, NET, and DAT) as well as serotonergic (5-HT), histaminergic (H), muscarinic cholinergic (M), adrenergic (α
1
, α
2
β
1
, β
2
), and dopaminergic (D) receptor subtypes as well as the sigma binding sites.
Figure 12.6 Chemical structure of the SNRI duloxetine, the NET inhibitor reboxetine, and three other drugs blocking 5-HT
2A
receptors. These 5-HT
2A
receptor antagonists include ketanserin, pimavanserin, and the failed antidepressant flibanserin (also a 5-HT
1A
receptor agonist).
Figure 12.7 Heat map for
in vitro
potency of representative atypical and typical drugs at a range of monoamine receptors. The risperidone, olanzapine, clozapine, chlorpromazine, and thioridazine data plotted for this Figure comes from the PDSP
K
i
database. The remaining data are derived from the NIMH PDSP
K
i
database and additional references [107–112]. The atypical antipsychotics or second-generation antipsychotics include risperidone (RISP), brexpiprazole (BREX), olanzapine (OLZ), clozapine (CLZ), aripiprazole (ARIP), quetiapine (QUET), and the quetiapine metabolite nor-quetiapine (NQT). The typical antipsychotic drugs exemplified in this Figure are chlorpromazine (CPZ) and thioridazine (THIOR). The color code represents different ranges of potency from <1 nm (pink) to >10 μM (purple). Each row shows the affinity of these different tricyclic antidepressants serotonergic (5-HT), dopaminergic (D), histaminergic, α-adrenergic (α
1
and α
2
), muscarinic cholinergic (M), and histaminergic (H) receptor subtypes.
Chapter 13: Multiple Ligands Targeting the Angiotensin System for Hypertension
Figure 13.1 Crystal structure of the human AT
1
receptor in complex with
1
(magenta). Key residues from the AT
1
receptor involved in the interaction with ARBs are shown in green. The structure was retrieved from the Protein Data Bank using accession code 4YAY.
Figure 13.2 Common binding mode of ARBs to the AT
1
receptor reported by Zhang
et al
. includes interactions with three key residues: Arg167
ECL2
(red), Tyr35
1.39
(magenta), and Trp84
2.60
(blue) [7]. In addition, hydrophobic contacts are formed between the biphenyl motif and the alkyl tail with a set of lipophilic residues from the receptor (green). Interactions depicted for
1
or
2
are taken from their corresponding crystal structures (PDB accession codes 4YAY and 4ZUD, respectively). Docking simulations were used to propose the binding mode of other compounds.
Scheme 13.1 Design of dual AT
1
receptor antagonist/ET receptor blocker
9
.
Scheme 13.2 Design of dual AT
1
/ET
A
receptor blockers
11–13
.
Scheme 13.3 Design of dual AT
1
/ET
A
receptor blockers
15
and
16
.
Scheme 13.4 Design of dual AT
1
/ET
A
receptor blockers
18
and
19
.
Scheme 13.5 Design of dual AT
1
/ET
A
receptor blocker
21
.
Scheme 13.6 Design of dual AT
1
antagonist/partial PPARγ agonist
24
.
Scheme 13.7 Dual AT
1
antagonist/partial PPARγ agonists
25
and
26
.
Scheme 13.8 Design of dual AT
1
antagonist/partial PPARγ agonists
30
and
31
.
Scheme 13.9 Design of dual AT
1
antagonist/partial PPARγ agonists
33–35
.
Scheme 13.10 Dual AT
1
receptor blocker/partial PPARγ agonists
36
and
37
.
Scheme 13.11 Design of dual AT
1
receptor blocker/NO-releasing agents
38
and
39
.
Scheme 13.12 Dual AT
1
receptor blocker/NO-releasing agents
40–44
.
Scheme 13.13 Dual AT
1
receptor blocker/antioxidant agents
45–47
.
Scheme 13.14 Design of dual AT
1
receptor blocker/antioxidant agent
49
.
Scheme 13.15 Design of dual AT
1
receptor antagonist/calcium channel blockers
50
and
51
.
Scheme 13.16 Dual AT
1
receptor blocker/NEP inhibitors
52–54
.
Chapter 14: Multiple Peroxisome Proliferator-Activated Receptor-Based Ligands
Figure 14.1 Multiple sequence alignment of the primary structures of the LBDs of PPARα, PPARδ, and PPARγ. Numbers above and below the alignment represent the numeration of the residues in PPARα and PPARγ isoform 1, respectively.
Figure 14.2 Structural alignment of the protein backbones of the LBDs of PPARα (from PDB file 3VI8, shown in purple), PPARδ (from PDB file 2GWX, shown in blue), and PPARγ (from PDB file 2ZK0, shown in red).
Figure 14.3 The arms of the LBDs of PPARγ are represented as different colored surfaces (arm I in green, arm II in red, and arm III in blue). The full agonist rosiglitazone (green), from PDB file 1FM6, and the partial agonist SR145 (orange), from PDB file 2Q61 are shown.
Figure 14.4 Structures of aleglitazar complexed with the LBDs of (a) PPARα (from PDB file 3G8I) and (b) PPARγ (from PDB file 3G9E). (c) and (d) show the ligand interaction diagrams of aleglitazar with PPARα and PPARγ, respectively. Hydrogen bonds and electrostatic interactions are shown as dashed black lines.
Figure 14.5 Structures of GL479 complexed with the LBDs of (a) PPARα (from PDB file 4CI4) and (b) PPARγ (from PDB file 4CI5). (c) and (d) show the ligand interaction diagrams of GL479 with PPARα and PPARγ, respectively. Hydrogen bonds and electrostatic interactions are shown as dashed black lines.
Figure 14.6 Structures of indeglitazar complexed with the LBDs of (a) PPARα (from PDB file 3ET1), (b) PPARδ (from PDB file 3ET2), and (c) PPARγ (from PDB file 3ET3). (d–f) show the ligand interaction diagrams of indeglitazar with PPARα, PPARδ, and PPARγ, respectively. Hydrogen bonds and electrostatic interactions are shown as dashed black lines, and π–π stacking interactions are shown as dashed light blue lines.
Figure 14.7 Structures of indomethacin complexed with (a) the LBDs of PPARγ (from PDB file 3ADX), (b) phospholipase A2 (from PDB file 3H1X), and (c) cyclooxygenase-2 (from PDB file 4COX). (d–f) show the ligand interaction diagrams of indomethacin with PPARγ, phospholipase A2, and cyclooxygenase-2, respectively. Hydrogen bonds and electrostatic interactions are shown as dashed black lines, π–π stacking interactions are shown as dashed light blue lines and cation–π interactions are shown as dashed green lines.
Chapter 15: Antibiotics
Figure 15.1 Representative bacterial topoisomerases inhibitors.
Figure 15.2 (a) Moxifloxacin
3
(yellow) bound to
A. baumannii
ParC and cleaved DNA (green). The complementary strand is omitted for clarity. (b) Close view of
3
stacked between DNA base pairs and bound to Mg
2+
(green). Hydrogen bonds as black dotted lines (PDB code: 2XKK).
Figure 15.3 Structures of two representative isothiazoloquinolones and ciprofloxacin.
Scheme 15.1 Design of the 3-hydroxyquinazoline-2,4-dione scaffold.
Figure 15.4 3-Aminoquinazolinediones
7
core scaffold.
Figure 15.5 Structure of
7e
(yellow), bound to
S. pneumoniae
ParC, stacked between cleaved DNA base pairs (green). Key hydrogen bonds as black dotted lines (PDB code 3LTN) (PDB code: 2Y1O).
Figure 15.6 Bacterial topoisomerase inhibitors featuring a quinolyl propyl piperidine scaffold.
Figure 15.7 Novel bacterial topoisomerase inhibitors designed at GSK.
Figure 15.10 NBTIs featuring a tricyclic left-hand side.
Figure 15.8 Novel bacterial topoisomerase inhibitors designed at Actelion.
Figure 15.9 (a) Structure of
11
(yellow) bound at the interface between uncleaved DNA (green) and
S. aureus
GyrA subunits. (b) Close view of
11
. Only key GyrA amino acids are indicated. Key hydrogen bond as a black dotted line (PDB code: 2XCS).
Figure 15.11 Representative NBTIs featuring a LHS N-linked to the central core.
Figure 15.12 NBTI featuring a cyclohexyl central core.
Figure 15.13 Novel GyrB/ParE inhibitors designed at Vertex.
Figure 15.14 (a) Model of compound
22
(yellow) docked into the ATP-binding site, highlighting the interactions of the benzimidazole carbamate with water and Asp73. Potential repulsion between the carbamate oxygen and Asp73 is shown as a double arrow, with key hydrogen bonds as black dotted lines. (b) Model of compound
23
(yellow) docked into the ATP-binding site, highlighting the designed extended hydrogen-bond network involving
23
, Asp73, and the water, as well as the interaction with Arg136 (PDB code: 3FV5).
Figure 15.15 New pyrrolopyrimidines designed at Trius.
Figure 15.16 Structure of
29
(yellow) bound to
E. faecalis
GyrB. (a) Interaction with the salt bridge and the conserved Lys, and location of the mobile proline. (b) Interaction of the cyclic diamine with polar residues and solvent network. Key hydrogen bonds as black dotted lines (PDB code: 4GFN).
Figure 15.17 New pyrimidoindoles designed at Trius.
Scheme 15.2 Reaction intermediates of β-lactams with PBPs and β-lactamases. Boronate enzyme intermediate.
Figure 15.18 Boronic acid-based penicillin-binding-protein inhibitors.
Figure 15.19 Chemical structures of Mur ligase inhibitors.
Figure 15.20 Structure of compound
37
(yellow) bound to
E. coli
MurD. Key direct hydrogen bonds as black dotted lines (PDB code: 2Y1O).
Scheme 15.3 Fatty acid biosynthesis and known sites of inhibition.
Figure 15.21 Chemical structures of fatty acid synthases inhibitors.
Scheme 15.4 Mechanism of transpeptidase-induced release of
46
.
Figure 15.22 β-Lactam–fluoroquinolone hybrid molecules and their cleavage sites.
Figure 15.23 Structure of heterodimer
49
(TD-1792) and its two components.
Figure 15.24 Structures of hybrid molecules based on linezolid and ciprofloxacin.
Figure 15.25 Hybrid molecules with
in vitro
and
in vivo
anti-
Clostridium difficile
activities.
Figure 15.26 Structure of rifamycin and its fluoroquinolizine hybrid molecule
57
.
Figure 15.27 Comparative properties of compound
58a
(MCB-3681), linezolid, and ciprofloxacin.
Figure 15.28 Novel oxaborole containing antibiotics.
Figure 15.29 Novel
Bacillus subtilis
DNA Pol IIIC–Pol IIIE inhibitor.
Chapter 16: Multiple Ligands in Neurodegenerative Diseases
Figure 16.1 (a) Expansion of Alzheimer's disease lesions: amyloid plaques (bottom left) and neurofibrillary tangles (top right) [17]. (b) Drawing by Alois Alzheimer of the first neurofibrillary clusters observed during the autopsy of the brain of Auguste Deter.
Figure 16.2 Cleavage of the amyloid precursor protein according to the non-amyloidogenic pathway and the amyloidogenic pathway.
Figure 16.3 Process of forming amyloid fibrils [23].
Figure 16.4 Hyperphosphorylation of tau and neurofilament formation [28].
Figure 16.5 Structures of cholinesterase inhibitors that have received marketing authorization.
Figure 16.6 General chemical structure of the series that include NP-61 [39].
Figure 16.7 Examples of MTDL dual-binding site of AChE containing a tacrine and with other activities.
Figure 16.8 Design strategy of a DBS AChE inhibitor/antioxidant [41].
Figure 16.9 Release of the active chelator HLA20 from its prochelator and chelation of metal ions [42].
Figure 16.10 Design strategy of the prochelator HLA20A [42].
Figure 16.11 Design strategy of a DBS AChE/MAOs inhibitor containing a coumarin unit [44].
Figure 16.12 Design strategy of donecopride.
Figure 16.13 Synergistic effect of 5-HT
4
R activation and AChE inhibition.
Figure 16.14 Evaluations
in vivo
: memory performances, antiamnesic effect, and release of sAPPα.
Figure 16.16 Strategy design of memoquin and transformation of quinone to hydroquinone.
Figure 16.15 Structures of benextramine, caproctamine, and a rigidified analog.
Figure 16.17 Strategy design of ladostigil by adjoining two active principles [50].
Figure 16.18 Pharmacomodulation of compounds of first generation to obtain more powerful MTDL γ-secretase/PPARγ modulators.
Figure 16.19 Strategy design of a MTDL BACE1 inhibitor/metal chelator.
Figure 16.20 Structures of MTDL mAChRs/σ1 receptors inhibitors.
Figure 16.21 Structures of D520, M30, VAR103303, and Fasudil.