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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Computational Methods for Rational Drug Design
- •Contents
- •1.1.2.2 GROMACS
- •1.1.2.3 Amber
- •1.1.2.4 CHARMM
- •1.1.2.5 AutoDock
- •1.1.2.6 VMD
- •1.1.2.7 PyMOL
- •1.1.2.8 Open Babel
- •List of Contributors
- •Preface
- •1. Molecular Modeling and Drug Design
- •1.1 Introduction
- •1.1.1 What Is Molecular Modeling?
- •1.1.2 Software Used for Molecular Modeling
- •1.1.2.1 Schrodinger
- •1.1.2.9 Avogadro
- •1.1.2.10 Discovery Studio
- •1.1.3 Molecular Mechanics
- •1.1.3.1 Prediction of Binding Affinity
- •1.1.3.2 Conformational Analysis
- •1.1.3.3 Virtual Screening
- •1.1.3.4 Lead Discovery
- •1.1.3.5 Mechanism of Action
- •1.2 Types of Molecular Models
- •1.2.1 Ball-and-Spoke Model
- •1.2.1.1 Future Directions
- •1.2.2 Space-filling Models
- •1.2.2.1 Future Directions
- •1.2.3 Crystal Lattice Models
- •1.2.3.1 Future Directions
- •1.3 Computational Methods in Drug Discovery
- •1.3.1 What Is Drug Discovery?
- •1.3.2 Computational Platforms for Drug Discovery
- •1.3.2.1 NCBI
- •1.3.2.2 Chemical Databases
- •1.3.2.3 PDB
- •1.3.2.5 UniProt
- •1.3.2.6 QSAR
- •1.3.2.8 Desmond
- •1.3.2.9 OpenBabel
- •1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
- •1.3.2.11 SBML
- •1.3.2.12 Virtual Screening
- •1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
- •1.4 Potential Use and Application of AI in Drug Designing
- •1.4.1 Target Identification and Validation
- •1.4.2 Drug Screening and Lead Optimization
- •1.4.3 De Novo Drug Design
- •1.4.4 Predictive Toxicology and ADMET
- •1.4.5 Clinical Trial Optimization
- •1.4.6 Drug Repurposing
- •1.4.7 Concept of Personalized Medicine
- •1.4.8 Drug Combination Optimization
- •1.5 Limitations of Current Methods
- •1.5.1 Data Restrictions
- •1.5.2 Interpretability
- •1.5.3 Generalization
- •1.5.4 Resources and Computation
- •1.5.5 Ethical Considerations
- •1.5.6 Validation and Experimentation
- •1.5.7 Regulatory Obstacles
- •1.6 Case Studies
- •1.7 Molecular Docking
- •1.7.1 What Is Molecular Docking?
- •1.7.1.1 Procedure
- •1.7.1.2 Biophysical Laws
- •1.7.1.3 Rigid and Flexible Docking
- •1.7.1.4 Types of Docking
- •1.7.1.5 Challenges and Future Perspectives
- •1.7.2 Applications of Molecular Docking in Drug Designing
- •1.7.3 Success of Molecular Docking Cases in Drug Designing
- •1.8 Conclusion and Future Works
- •References
- •2. Bioactive Small Molecules and Drug Discovery
- •2.1 Introduction
- •2.1.1 Introduction to Drug Design and Discovery
- •2.1.2 Brief History of Small-Molecule Drug Discovery
- •2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
- •2.2.1 Structure-Based Methods
- •2.2.2 Ligand-Based Methods
- •2.2.3 Network-Based Methods
- •2.3 Natural Products in Bioactive Small-Molecule Discovery
- •2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
- •2.3.2 Anticancer Agents as Bioactive Molecules
- •2.3.3 Antiviral Agents as Bioactive Molecules
- •2.3.4 Antimalarial Agents as Bioactive Molecules
- •2.6.6 Toxicity and Side Effects
- •2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
- •2.6.8 Structural Diversity and Novelty
- •2.6.9 Patentability and Intellectual Property
- •2.3.5 Marine Bioactive Products
- •2.4.1 Importance of DFT in Small-Molecule Drug Discovery
- •2.5 Application of DFT to Bioactive Small Molecules
- •2.5.1 HOMO–LUMO Calculation
- •2.5.1.1 Molecular Electrostatic Potential (MEP) Map
- •2.5.1.3 Natural Bond Orbital (NBO) Analysis
- •2.5.1.4 Implementations and Tools
- •2.6.1 Target Identification and Validation
- •2.6.2 Target Specificity
- •2.6.3 Bioavailability and Pharmacokinetics
- •2.6.4 Chemical Structure and Drug-likeness
- •2.6.5 Safety and Toxicity
- •2.7 Conclusion
- •References
- •3. Novel Drug Targets for Small Molecule-based Drug Discovery
- •3.1 Introduction
- •3.2 Drug Target Identification
- •3.3 Classification of Novel Drug Targets
- •3.3.1 Transcription Factors
- •3.3.2 Cytokines
- •3.3.3 Chaperones
- •3.3.4 Viral Targets
- •3.3.5 G Protein-coupled Receptors
- •3.3.6 Transporters
- •3.3.7 Enzymes
- •3.3.8 RNA Targets
- •3.4 Small Molecules as Drugs
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Structure-Based Drug Discovery Concept
- •4.2.1 Structure Generation of the Target
- •4.2.1.1 The Detailed Description of Each Tool
- •4.2.2 Active Binding Site Within the Target
- •4.2.2.1 The Detailed Description of Each Tool
- •4.2.2.2 Molecular Docking Analysis
- •4.2.2.3 The Detailed Description of Each Tool
- •4.2.3 Molecular Dynamic Simulations
- •4.2.3.1 The Detailed Description of Each Tool
- •4.3 Ligand-Based Drug Discovery Concept
- •4.3.1.1 The Detailed Description of Each Tool
- •4.4 Structure- and Ligand-Based Assisted Studies
- •4.4.1 The Detailed Description of Each Tool
- •4.4.2 The Detailed Description of Each Tool
- •4.5 Advancement and Challenges in SBDD and LBDD
- •4.6 Conclusion
- •References
- •5. Virtual Screening and Lead Discovery
- •5.1 Introduction to Virtual Screening and Lead Discovery
- •5.1.1 Overview of Drug Discovery Process
- •5.1.2 Role of Virtual Screening
- •5.1.3 Importance of Lead Discovery
- •5.2 Molecular Targets and Biomolecular Structures
- •5.3 Virtual Screening Approaches
- •5.3.1 Structure-based Virtual Screening
- •5.3.2 Ligand-based Virtual Screening
- •5.3.3 Hybrid Approaches
- •5.4 Databases and Compound Collections
- •5.4.1 Overview of Chemical Databases
- •5.4.2 Compound Filtering and Preparation
- •5.4.3 Diversity and Size of Compound Collections
- •5.5 Molecular Docking
- •5.5.1 Principles of Molecular Docking
- •5.5.2 Docking Algorithms and Scoring Functions
- •5.5.3 Validation of Docking Results
- •5.6 Pharmacophore Modeling
- •5.6.1 Concept of Pharmacophores
- •5.6.2 Generating Pharmacophore Models
- •5.6.3 Applications in Lead Discovery
- •5.7 Quantitative Structure–Activity Relationship (QSAR)
- •5.7.1 Basics of QSAR
- •5.7.2 Model Development and Validation
- •5.7.3 QSAR in Virtual Screening
- •5.8 Machine Learning and AI in Virtual Screening
- •5.8.1 Introduction to Machine Learning and AI
- •5.8.2 Feature Selection and Model Training
- •5.8.3 Applications in Virtual Screening
- •5.9 Hit-to-Lead Optimization
- •5.9.1 Prioritizing Hits from Virtual Screening
- •5.9.2 SAR Analysis and Iterative Design
- •5.9.2.1 SAR Analysis (Structure–Activity Relationship)
- •5.9.2.2 Iterative Design
- •5.9.3 ADME/Tox Considerations
- •5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
- •5.9.3.2 Toxicity Considerations
- •5.10 Case Studies and Examples
- •5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
- •5.11 Challenges and Future Directions
- •5.11.1 Limitations of Virtual Screening
- •5.11.2 Emerging Technologies and Trends
- •5.11.3 Integration with High-throughput Experimentation
- •5.12 Ethical and Regulatory Considerations
- •5.12.1 Intellectual Property and Patents
- •5.12.2 Ethical Use of Computational Tools
- •5.12.3 Regulatory Approval Process
- •5.13 Conclusion
- •5.13.1 Future Prospects in Virtual Screening and Lead Discovery
- •5.13.2 Summary of Key Points
- •References
- •6. ADMET and Physicochemical Assessments in Drug Design
- •6.1 ADMET
- •6.1.1 Absorption
- •6.1.1.1 Solubility and Dissolution
- •6.1.1.2 Lipophilicity
- •6.1.1.3 Permeability
- •6.1.2 Distribution
- •6.1.3 Metabolism
- •6.1.4 Excretion
- •6.1.5 Toxicity
- •6.2 Physicochemical Assessments
- •6.2.1 Partition Coefficient
- •6.2.2 Log D: Ionizable Compound Lipophilicity
- •6.2.2.1 Methods for Calculating Lipophilicity
- •6.2.2.2 Direct Experimental Determination of Lipophilicity
- •6.2.2.3 Indirect Experimental Determination of Lipophilicity
- •6.2.3 Acid–Base Properties and Ionization
- •6.2.4 Solubility
- •6.2.5 Polymorphism
- •6.2.6 Molecular Weight
- •6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA)
- •References
- •7. In Silico Modeling and Drug Design
- •7.1 Introduction
- •7.2 Target Identification
- •7.2.1 Experimental Approaches
- •7.2.2 Computational Target Identification
- •7.2.3 Target Validation
- •7.3 Computer-Aided Drug Design
- •7.3.1 Ligand-based CADD
- •7.3.2 Structure-Based CADD
- •7.4 ADMET Assessment
- •7.5 Conclusion
- •References
- •8. Pharmacophore Modeling in Drug Design
- •8.1 Introduction
- •8.1.1 The Role of Pharmacophore Modeling in Drug Design
- •8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
- •8.2 Essential Concepts in Pharmacophore Hypothesis Generation
- •8.2.1.1 Partitioning Initial Data into Distinctive Datasets
- •8.3 Diverse Approaches to Pharmacophore Modeling
- •8.3.1 Ligand-Based Pharmacophore Modeling
- •8.3.2 Structure-Based Pharmacophore Modeling
- •8.4 Application of Pharmacophore Modeling
- •8.4.1 Applications of Pharmacophore-Based Virtual Screening
- •8.4.1.1 Drug Discovery
- •8.4.2 Applications in Drug Target Fishing
- •8.4.3 Applications in Ligand Profiling
- •8.4.4 Applications in Docking
- •8.4.5 Applications in ADMET
- •8.4.6 Modulation of the Immune System
- •8.5 Emerging Trends in Pharmacophore Model Development
- •8.5.1 Involvement of Machine Learning
- •8.5.2 Prediction of Pharmacokinetic Properties
- •8.5.3 Structural Biology and Protein Functionality Studies
- •8.5.4 Integration with MDs Simulations
- •8.6 Case Studies
- •8.6.1 Case 1
- •8.6.2 Case 2
- •8.7 Challenges in Pharmacophore Modeling
- •8.8 Conclusion
- •Acknowledgments
- •References
- •9. Scaffold Hopping and De Novo Drug Design
- •9.1 Introduction
- •9.2 Scaffold Hopping
- •9.2.1 Classification of Scaffold Hopping
- •9.2.1.1 1° Hop: Heterocycle Replacement
- •9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
- •9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
- •9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
- •9.2.2 Advantages of Scaffold Hopping
- •9.2.3 Disadvantages of Scaffold Hopping
- •9.2.4 Reasons for Scaffold Hopping
- •9.2.5 Properties and Key Methods of Scaffold Hopping
- •9.3 De Novo Drug Design
- •9.3.1 Classification of De Novo Drug Design
- •9.3.1.1 Structure-based Drug Design
- •9.3.1.2 Ligand-based Drug Design
- •9.3.1.3 De Novo Design Strategies
- •9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
- •9.3.1.5 Hybrid Approaches
- •9.3.2 Basic Principle of De Novo Drug Design
- •9.3.3 Application of De Novo Drug Design
- •9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
- •9.3.5 Methodological Approaches in De Novo Drug Design
- •9.3.5.1 Structure-based De Novo Drug Design
- •9.3.5.2 Ligand-based De Novo Drug Design
- •9.3.5.3 Generation of Drug-Like Molecular Fragments
- •9.3.5.4 Similarity Searching
- •9.3.5.5 Selection of Target Reference Structure
- •9.3.5.6 Similarity Analysis of De Novo-generated Compounds
- •9.3.5.7 Evaluation of Scaffold Diversity
- •9.4 Results and Discussion
- •9.4.1 Generation of Drug-Like Molecular Fragments
- •9.4.2 De Novo Design with a Single Reference Structure
- •9.4.3 De Novo Design with a Focused Set of Five Similar Templates
- •9.4.4 De Novo Design with a Diverse Set of Five Templates
- •9.6 Case Study
- •9.6.1 De Novo Drug Design
- •9.6.2 Scaffold Hopping
- •9.7 Conclusion
- •References
- •10. Fragment-based Drug Design and Drug Discovery
- •10.1 Introduction
- •10.2 The Process of Finding Fragments
- •10.3 FBDD Strategies
- •10.4 Case Studies
- •10.5 Conclusion and Future Perspectives
- •References
- •11. AI/ML Approaches in Drug Design
- •11.1 Introduction
- •11.2 Traditional Drug Design Methods
- •11.2.1 The Rise of Computational Methods
- •11.2.2 The Importance of AI/ML in Modern Drug Design
- •11.3 AI/ML Landscape in Drug Design
- •11.3.1 AI/ML Algorithms and Methods
- •11.3.1.1 Machine Learning Models
- •11.3.1.2 Neural Networks
- •11.3.2 Applications in Drug Design
- •11.3.2.1 Peptide Synthesis
- •11.3.2.2 Molecular Design
- •11.3.2.3 Virtual Screening (VS)
- •11.3.2.4 Quantitative Structure–Activity Relationship Models
- •11.3.2.5 Drug Repurposing
- •11.3.3 Challenges and Failures
- •11.4 Ethics, Reliability, and Regulatory Issues
- •11.5 Future Directions
- •11.6 Conclusion
- •References
- •12. Network-based Methods in Drug Discovery
- •12.1 Introduction
- •12.1.1 Background of Drug Discovery Future Challenges
- •12.1.2 Single Target Approach Limitations
- •12.1.3 Emergence of Network Biology and Polypharmacology
- •12.2 Network Pharmacology: Practical Guide
- •12.2.1 Common Network Pharmacology Databases
- •12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
- •12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
- •12.2.1.3 Target Genes of Phytoconstituents
- •12.2.2 Network Analysis and Visualization
- •12.2.3 Applications of Network Pharmacology in Drug Discovery
- •12.3 Ayurveda and Traditional Indian Medicine
- •12.3.1 Overview of Ayurveda and Its Complex Formulations
- •12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
- •12.4 Network Pharmacology in Herbal Remedies
- •12.4.1 Application of Network Pharmacology in Herbal Drug Discovery
- •12.4.1.1 Cancer
- •12.4.1.2 Cardiovascular Diseases (CVDs)
- •12.4.1.3 Diabetes Mellitus (DM)
- •12.4.2 Screening Pharmacological Efficacy of Herbal Remedies
- •12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases
- •12.5 Conclusion and Future Prospects
- •References
- •13. Rational Design of Natural Products for Drug Discovery
- •13.1 Introduction
- •13.2 Natural Products for the Development of New Drugs
- •13.3 Criteria for Selecting Natural Products for Drug Design
- •13.4 Importance of Biodiversity in Sourcing Natural Products
- •13.5 Structural Elucidation of Natural Products
- •13.6.3 High-Throughput Screening Methods for Efficient Compound Selection
- •13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability
- •13.6.5 ADMET Attributes Predicted In Silico
- •13.7 Formulation Challenges with Natural Products
- •13.8 Quality by Design (QbD) Approaches
- •13.8.1 Use of Computational Models for Formulation Optimization
- •13.9 Conclusion
- •References
- •14. Design of Enzyme Inhibitors in Drug Discovery
- •14.1 Introduction
- •14.3 Classification of Enzyme Inhibitors
- •14.3.1 Reversible Inhibitors
- •14.3.2 Irreversible Inhibitors
- •14.3.3 Competitive Inhibitors
- •14.3.4 Noncompetitive Inhibitors
- •14.3.5 Allosteric Modulators
- •14.4.1 Structure-Based Design
- •14.4.2 Computer-Aided Design
- •14.4.3 Fragment-Based Design
- •14.4.4 Virtual Screening Method
- •14.4.4.1 Ligand Based
- •14.4.4.2 Receptor Based
- •14.4.5 Natural Product-Based Discovery
- •14.4.6 Using Iterative Protein Crystallographic Analysis
- •14.4.7 Utilization of Covalent Inhibitors
- •14.4.8 Encapsulation Techniques
- •14.4.9 Based on Active-Site Specificity
- •14.4.10 Machine Learning Inhibitor Design
- •14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry
- •14.5 Limitations and Challenges
- •14.6 Future Directions
- •14.7 Conclusion
- •References
- •15.1 Introduction
- •15.2 Peptides as Therapeutics
- •15.2.1 Peptide Antibiotics
- •15.2.1.1 Peptides in Bone Diseases
- •15.2.1.2 Peptides in Cancer
- •15.2.1.3 Peptides in Metabolic Diseases
- •15.2.1.4 Peptides in Gastrointestinal Diseases
- •15.2.2 Advantages and Limitations of Peptide Therapeutics
- •15.2.3 FDA-Approved Peptide Therapeutics
- •15.2.4 Peptide-Based Entities in Clinical Trials
- •15.2.5 Peptide Synthesis and Diversification
- •15.2.5.1 Chemical Synthesis of Peptides
- •15.2.5.2 Chemical Modification of Peptide and Peptidomimetics
- •15.2.5.3 Backbone Modification of Peptides
- •15.2.5.4 Side-Chain Modification of Peptides
- •15.2.5.5 Peptide Cyclization
- •15.2.5.6 Peptide Mimicking of α-Helices and Stabilization
- •15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets
- •15.2.5.8 Peptide Production by Recombinant Technology
- •15.2.5.9 Peptides Modification by Genetic Code Expansion
- •15.2.5.10 PEGylation of Peptides and Proteins
- •15.3 New Technologies for Peptide-Based Drug Discovery
- •15.3.1 Phage Display
- •15.3.2 mRNA Display
- •15.3.3 DNA-Encoded Libraries
- •15.3.4 Cell-Penetrating Peptides
- •15.3.5 Macrocyclic Peptides
- •15.4 Computational Approaches in Peptide Drug Discovery
- •15.5 Conclusion
- •References
- •16. Rational Design of Drugs for Neurodegenerative Disorders
- •16.1 Introduction
- •16.2 Common Mechanism of Neurodegeneration
- •16.3 Brief Overview of Computational Methods in Drug Design
- •16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder
- •16.4.1 Epidemiology of Parkinson’s Disease
- •16.4.2 Pathogenesis of PD
- •1) Accumulation of Lewy bodies in substantia nigra
- •2) Mitochondrial dysfunction
- •3) Genetic factors
- •4) Neuroinflammation
- •5) Impaired protein handling
- •6) Oxidative stress
- •7) Environmental toxins
- •16.4.3 Signaling Pathway of Parkinson’s Disease
- •1) DA signaling
- •2) MAPK/ERK pathway
- •3) PI3K/Akt/mTOR pathway
- •4) Wnt/β-catenin pathway
- •5) NF-κB (nuclear factor-κB) pathway
- •6) Autophagy-lysosomal pathway
- •7) JNK (c-Jun N-terminal kinase) pathway
- •8) AMPK (AMP-activated protein kinase) pathway
- •9) Nrf2 (nuclear factor erythroid 2-related factor 2) pathway
- •16.4.4 Enzymatic Targets in Parkinson’s Disease
- •1) MAO-B (monoamine oxidase B)
- •2) COMT (catechol-O-methyltransferase)
- •3) LRRK2
- •4) GCase (glucocerebrosidase)
- •5) PARP-1 [poly(ADP-ribose) polymerase-1]
- •6) PINK1
- •7) DJ-1 (Parkinson protein 7)
- •8) Nrf2
- •16.4.5 Current Therapeutic Approaches to Treat PD
- •1) Drugs to treat motor symptoms of PD
- •2) Drugs to treat non-motor symptoms of PD
- •3) Disease-modifying therapies to treat PD
- •16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
- •1) Symptomatic relief only
- •2) Motor fluctuations and dyskinesias
- •3) Limited efficacy in nonmotor symptoms
- •4) Disease progression
- •5) Side effects
- •6) Limited treatment options for advanced PD
- •7) Individual variability
- •16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
- •16.4.8 Significance of Computational Approaches in Parkinson’s Disease
- •16.4.9 Use of Computational Tools in Identifying Biomarkers
- •16.4.10 Neuroprotective Strategies Through Computational Insights
- •16.4.10.1 Computational Models for Neuroprotection
- •1) Target identification and validation
- •2) Drug repurposing
- •3) Alpha-synuclein aggregation inhibitors
- •4) Deep learning in biomarker discovery
- •5) Personalized medicine
- •6) Drug-induced neuroprotection
- •7) Optimizing clinical trials
- •1) ML and AI-based diagnostics
- •2) Wearable technology integration
- •3) Multimodal data fusion
- •4) Predictive modeling of disease progression
- •5) Network analysis of brain connectivity
- •6) Personalized treatment optimization
- •7) Data sharing and collaboration platforms
- •16.5 Conclusion
- •References
- •17. Rational Design of Anti-inflammatory Therapeutics
- •17.1 Introduction
- •17.2 Navigating Inflammation and its Microenvironment
- •17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
- •17.2.2 Acidosis
- •17.2.3 Increased Oxidative Stress in Tissues
- •17.3 The Demand for Advanced Anti-inflammatory Medications
- •17.5 Rational Design of Anti-inflammatory Agents
- •17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids
- •17.5.3 Rational Design of Novel Aminopiperidinyl Amide
- •17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents
- •17.6 Conclusion and Future Perspectives
- •Authors’ Contribution
- •References
- •18.1 Introduction
- •18.2 Treatment
- •18.3 Antibacterial Resistance
- •18.3.1 Mutation
- •18.3.2 Horizontal Gene Transfer (HGT)
- •18.3.3 Enzymatic Modification or Degradation
- •18.3.4 Target Site Modification
- •18.3.5 Decreased Permeability
- •18.3.6 Efflux Pumps
- •18.3.7 Plasmids
- •18.3.8 Transposons
- •18.3.9 Gene Amplification
- •18.3.10 Formation of Biofilms
- •18.3.11 Modified Metabolic Pathways
- •18.3.12 Adaptive Evolution
- •18.4.1 Structure- Based Drug Design
- •18.4.2 Modification of Existing Antibiotics
- •18.4.3 Bioisosterism
- •18.4.4 Prodrug Strategies
- •18.4.5 Similar Bacterial Components Target
- •18.4.6 Combine or Combination Therapy
- •18.4.7 Drug Repurposing
- •18.4.8 Resistant Mechanism Blocking
- •18.4.9 Improving Drug Delivery by Nanotechnology
- •18.4.10 Phage Intervention
- •18.4.11 Host Targeting
- •18.4.12 CRISPR-Cas Technique
- •18.4.13 Peptides as Antibacterials
- •18.4.14 Immunizations and Immunotherapy
- •18.4.15 Natural Product Derivatives
- •18.4.16 Fragment- Based Drug Discovery (FBDD)
- •18.4.17 Metabolomics and Genetics
- •18.4.18 Cheminformatics
- •18.5 Summary and Conclusion
- •References
- •19. Rational Design of Antiviral Therapeutics
- •19.1 Introduction to Antiviral Therapeutics
- •19.1.1 Overview
- •19.1.2 Blueprints for Antiviral Drug Interventions
- •19.1.2.1 Protein Folding and Binding Sites
- •19.1.2.2 Conformational Changes
- •19.1.2.3 Protein–Protein Interactions (PPIs)
- •19.1.2.4 Capsid and Envelope Structures
- •19.1.2.5 Structural Vulnerabilities
- •19.1.2.6 Enzymatic Activities
- •19.1.2.7 Viral Attachment
- •19.1.2.8 Viral Assembly and Replication Machinery
- •19.1.2.9 The Host’s Immune Response
- •19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
- •19.2.1 Enzyme Inhibitors
- •19.2.2 Antiviral Peptides
- •19.2.3 Antiviral Antibodies
- •19.2.4 Lipid-Mimicking Compounds
- •19.2.5 Vaccines
- •19.2.6 Immunomodulation
- •19.3 Rational Strategies for Antiviral Therapeutics
- •19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
- •19.3.2 AI and ML
- •19.3.3 Systems Biology and Network Pharmacology
- •19.3.4 CRISPR Systems
- •19.3.5 Nanotechnology-Based Design and Delivery Systems
- •19.3.6 Reverse Vaccinology
- •19.4 Conclusion
- •References
- •20. Rational Design of Anticancer Therapeutics
- •20.1 Introduction
- •20.2 Rational Design of Nanomedicine for Cancer Treatment
- •20.4.1 Particle Size
- •20.4.2 Shape
- •20.4.3 Surface Modification
- •20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
- •20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
- •20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
- •20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
- •20.7 Conclusion
- •References
- •21. PROTAC and ProTide Strategies in Drug Design
- •21.1 Introduction
- •21.2 Drug Design: Past to Present
- •21.3 PROTAC Strategy in Drug Design
- •21.3.1 Ubiquitin Proteasome System and PROTACs
- •21.3.2 Chemical Formulations of PROTACs
- •21.3.3 Advent of PROTACs as Antiviral
- •21.3.4 NS3/4A-Targeting PROTACs Against HCV
- •21.3.4.1 Neuraminidase-Targeting PROTACs
- •21.4 Emergence of ProTide Technology in Drug Design
- •21.5 Approaches of ProTides in Drug Development
- •21.6 Implementation of ProTides as Nucleoside Analogs
- •21.6.1 Antiviral Applications of ProTides
- •21.7 Conclusion
- •References

eferences 323
6 Nikolic, K., Mavridis, L., Bautista-Aguilera, O.M. et al. (2015). Predicting targets of compounds
against neurological diseases using cheminformatic methodology. Journal of Computer-Aided
Molecular Design 29: 183–198. https://doi.org/10.1007/s10822-014-9816-1.
7 Saura, J., Bleuel, Z., Ulrich, J. et al. (1996). Molecular neuroanatomy of human monoamine
oxidases A and B revealed by quantitative enzyme radioautography and in situ hybridization
histochemistry. Neuroscience 70: 755–774. https://doi.org/10.1016/S0306-4522(96)83013-2.
8 Rojas, R.J., Edmondson, D.E., Almos, T. et al. (2015). Reversible and irreversible small molecule
inhibitors of monoamine oxidase B (MAO-B) investigated by biophysical techniques. Bioorganic &
Medicinal Chemistry 23: 770–778. https://doi.org/10.1016/j.bmc.2014.12.063.
9 Lawrence, C.M., Menon, S., Eilers, B.J. et al. (2009). Structural and functional studies of archaeal
viruses. The Journal of Biological Chemistry 284: 12599–12603.
10 Jugder, B.E. and Watnick, P.I. (2020). Vibrio cholera sheds its coat to make itself comfortable in the
gut. Cell Host & Microbe 27: 161–163.
11 Yang, L.W. and Bahar, I. (2005). Coupling between catalytic site and collective dynamics: a
requirement for mechanochemical activity of enzymes. Structure 13: 893–904. https://doi. org/
10.1016/j.str.2005.03.015.
12 Herschlag, D. and Natarajan, A. (2013). Fundamental challenges in mechanistic enzymology:
progress toward understanding the rate enhancement of enzyme. Biochemistry 52: 2050–2067.
https://doi.org/10.1021/bi4000113.
13 Cleland, W.W. (1963). The kinetics of enzyme-catalyzed reactions with two or more substrates or
products. II. Inhibition: nomenclature and theory. Biochimica et Biophysica Acta 67: 173–187.
https://doi.org/10.1016/0926-6569(63)90226-8.
14 Powers, J.C., Asgian, J.L., Ekici, Ö.D., and James, K.E. (2002). Irreversible inhibitors of serine,
cysteine, and threonine proteases. Chemical Reviews 102 (12): 4639–4750. https://doi.org/10.1021/
cr010182v.
15 Molla, G. (2017). Competitive inhibitors unveil structure/function relationships in human D-
amino acid oxidase. Frontiers in Molecular Biosciences 4: 80. https://doi.org/10.3389/ fmolb.
2017.00080.
16 Abdel-Magid, A.F. (2015). Allosteric modulators: an emerging concept in drug discovery. ACS
Medicinal Chemistry Letters 6 (2): 104–107. https://doi.org/10.1021/ml5005365.
17 Rosse, G. (2013). Negative allosteric modulators of metabotropic glutamate receptor subtype. ACS
Medicinal Chemistry Letters 4 (6): 500–501. https://doi.org/10.1021/ml400138p.
18 Salum, L., Polikarpov, I., and Andricopulo, A.D. (2008). Structure-based approach for the study of
estrogen receptor binding affinity and subtype selectivity. Journal of Chemical Information and
Modeling 48: 2243–2253.
19 Kalyaanamoorthy, S. and Chen, Y.P. (2011). Structure-based drug design to augment hit discovery.
Drug Discovery Today 16: 831–839.
20 Drwal, M.N. and Griffith, R. (2013). Combination of ligand- and structure-based methods in
virtual screening. Drug Discovery Today: Technologies 10: e395–e401.
21 Valasani, K.R., Vangavaragu, J.R., Day, V.W., and Yan, S.S. (2014). Structure-based design,
synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for
identification of novel cyclophilin D inhibitors. Journal of Chemical Information and Modeling 54:
902–912.
22 Blaney, J. (2012). A very short history of structure-based design: how did we get here and where do
we need to go? Journal of Computer-Aided Molecular Design 26: 13–14.
23 Wilson, G.L. and Lill, M.A. (2011). Integrating structure-based and ligand-based approaches for
computational drug design. Future Medicinal Chemistry 3: 735–750.

324
24 Shoichet, B.K. and Kobilka, B.K. (2012). Structure-based drug screening for G-protein-coupled
receptors. Trends in Pharmacological Sciences 33: 268–272.
25 Song, C.M., Lim, S.J., and Tong, J.C. (2009). Recent advances in computer-aided drug design.
Briefings in Bioinformatics 10 (5): 579–591. https://doi.org/10.1093/bib/bbp023.
26 Veselovsky, A.V., Zharkova, M.S., Poroikov, V.V., and Nicklaus, M.C. (2014). Computer-aided design
and discovery of protein–protein interaction inhibitors as agents for anti-HIV therapy. SAR and
QSAR in Environmental Research 25 (6): 457–471. https://doi.org/10.1080/1062936X.2014.898689.
27 Rees, D.C., Congreve, M.S., Murray, C.W., and Carr, R. (2004). Fragment-based lead discovery.
Nature Reviews. Drug Discovery 3: 660–672.
28 Erlanson, D.A., McDowell, R.S., and O’Brien, T. (2004). Fragment-based drug discovery. Journal of
Medicinal Chemistry 47: 3463–3482.
29 Sousa, S.F., Cerqueira, N.M., Fernandes, P.A., and Ramos, M.J. (2010). Virtual screening in drug
design and development. Combinatorial Chemistry & High Throughput Screening 13 (5): 442–453.
https://doi.org/10.2174/138620710791293001.
30 Stahura, F.L. and Bajorath, M. (2005). New methodologies for ligand-based virtual screening.
Current Pharmaceutical Design 11: 1189–1202.
31 Lengauer, T., Lemmen, C., Rarey, M., and Zimmermann, M. (2004). Novel technologies for virtual
screening. Drug Discovery Today 9: 27–34.
32 Atanasov, A.G., Waltenberger, B., Pferschy-Wenzig, E.-M. et al. (2015). Discovery and resupply of
pharmacologically active plant-derived natural products: a review. Biotechnology Advances 33:
1582–1614.
33 Harvey, A.L., Edrada-Ebel, R., and Quinn, R.J. (2015). The re-emergence of natural products for
drug discovery in the genomics era. Nature Reviews. Drug Discovery 14: 111–129.
34 Appelt, K., Bacquet, R.J., Bartlett, C.A. etal. (1991). Design of enzyme inhibitors using iterative
protein crystallographic analysis. Journal of Medicinal Chemistry 34 (7): 1925–1934. https://doi.
org/10.1021/jm00111a001.
35 Mehlman, T.S., Biel, J.T., Azeem, S.M. et al. (2023). Room-temperature crystallography reveals
altered binding of small-molecule fragments to PTP1B. eLife 12: https://doi.org/10.7554/ eLife.
84632.
36 Potashman, M.H. and Duggan, M.E. (2009). Covalent modifiers: an orthogonal approach to drug
design. Journal of Medicinal Chemistry 52: 1231–1246.
37 Robertson, J.G. (2005). Mechanistic basis of enzyme-targeted drugs. Biochemistry 44: 5561–5571.
38 Zhang, Y., Wu, H., Li, J. et al. (2008). Protamine-templated biomimetic hybrid capsules: efficient
and stable carrier for enzyme encapsulation. Chemistry of Materials 20: 1041–1048.
39 Glasgow, J.E., Asensio, M.A., Jakobson, C.M. etal. (2015). Influence of electrostatics on small
molecule flux through a protein nanoreactor. ACS Synthetic Biology 4: 1011–1019. https://doi. org/
10.1021/acssynbio.5b00037.
40 Betancor, L. and Luckarift, H.R. (2008). Luckarift bioinspired enzyme encapsulation for
biocatalysis. Trends in Biotechnology 26: 566–572. https://doi.org/10.1016/j.tibtech.2008.06.009.
41 Zdarta, J., Meyer, A.S., Jesionowski, T., and Pinelo, M. (2018). Pinelo A general overview of support
materials for enzyme immobilization: characteristics, properties, practical utility. Catalysts 8: 92.
42 Zhang, Z., Zhang, R., and McClements, D.J. (2017). Lactase (β-galactosidase) encapsulation in
hydrogel beads with controlled internal pH microenvironments: impact of bead characteristics on
enzyme activity. Food Hydrocolloids 67: 85–93. https://doi.org/10.1016/j.foodhyd.2017.01.005.
43 de la Lastra, P., Manuel, J., Baca-González, V. et al. (2021). Antibodies targeting enzyme inhibition
as potential tools for research and drug development. Biomolecular Concepts 12 (1): 215–232.
https://doi.org/10.1515/bmc-2021-0021.

eferences 325
44 Chattopadhyay, K., Lazar-Molnar, E., Yan, Q. etal. (2009). Sequence, structure, function, immunity:
structural genomics of costimulation. Immunological Reviews 229(1): 356–86. https://doi.org/
10.1111/j.1600-065X.2009.00778.x
45 Mäntsälä, P. and Niemi, J. (2009). Enzymes: the biological catalysts of life. Physiology and
Maintenance 2: 1–22.
46 Mazurenko, S., Prokop, Z., and Damborsky, J. (2020). Machine learning in enzyme engineering.
ACS Catalysis 10 (2): 1210–1223. https://doi.org/10.1021/acscatal.9b04321.
47 Libbrecht, M.W. and Noble, W.S. (2015). Machine learning applications in genetics and genomics.
Nature Reviews. Genetics 16: 321–332.
48 Huang, R. and Leung, I.K.H. (2016). Protein-directed dynamic combinatorial chemistry: a guide to
protein–ligand and inhibitor discovery. Molecules 21 (7): 910. https://doi.org/10.3390/molecules
21070910.
49 Shi, B., Stevenson, R., Campopiano, D.J., and Greaney, M.F. (2006). Discovery of glutathione S-
transferase inhibitors using dynamic combinatorial chemistry. Journal of the American Chemical
Society 128 (26): 8459–8467. https://doi.org/10.1021/ja058049y.
50 de la Fuente, M., Lombardero, L., Gómez-González, A. et al. (2021). Enzyme therapy: current
challenges and future perspectives. International Journal of Molecular Sciences 22 (17): 9181.
https://doi.org/10.3390/ijms22179181.
51 Tjhung, K.F., Shokhirev, M.N., Horning, D.P., and Joyce, G.F. (2020). An RNA polymerase
ribozyme that synthesizes its ancestor. Proceedings of the National Academy of Sciences of the
United States of America 117: 2906–2913. https://doi.org/10.1073/pnas.1914282117.
52 Kaiser, E.T., Lawrence, D.S., and Rokita, S.E. (1985). The chemical modification of enzymatic
specificity. Annual Review of Biochemistry 54: 565–595. https://doi.org/10.1146/annurev.bi.54.
070185.003025.
53 Robertson, J.G. (2007). Enzymes as a special class of therapeutic target: clinical drugs and modes
of action. Current Opinion in Structural Biology 17: 674–679. https://doi.org/10.1016/j.sbi.
2007.08.008.
54 Kuldeep, S., Jeetendra, G.K., Devendra, P., and Shivendra, K. (2023). The use of enzyme inhibitors
in drug discovery: current strategies and future prospects. Current Enzyme Inhibition 19 (3):
https://doi.org/10.2174/1573408019666230731113105.


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15.1 Introduction
The side effects associated with small molecule drugs have always created a need for an alternative
therapeutic preposition. In this context, the peptides represent a unique class of therapeutic agents
having features of small molecule drugs, large proteins, and other endogenous biologics [1]. All
the signaling molecules regulating the metabolic pathways are enzymes, hormones, and other
endogenous proteins. Consequently, the peptides emerge as an appropriate class of pharmaco-
therapeutic agents emulating natural entities when there is an imbalance or insufficiency in
endogenous secretion levels [2].
The discovery of peptides as therapeutic entities can be dated back to 1921–1922 when insulin
was first isolated from bovine and porcine pancreas, proving a boon for human civilization in the
management of type 1 diabetes [3]. In the 1950s, with the development of solution phase peptide
synthetic methods, several other peptides were chemically developed like synthetic oxytocin [4]
and synthetic vasopressin [5]. Later, in the mid-1960s, the peptide drug discovery was fueled by the
inception of solid-phase synthetic methods [6]. Implementation of more advanced techniques
such as microwave irradiation in both solid and solution phase peptide syntheses, chemical liga-
tions, and late-stage functionalizations further led to the clinical development of synthetic thera-
peutic peptides like recombinant insulin, gonadotropic-releasing hormones, leuprolide, and
goserelin [7]. Since 2000, about 40 peptides have attained FDA approval as drugs. Peptide drugs
have a vast therapeutic repertoire [8–12]. Currently, more than 200 peptides are under active clini-
cal trials, along with several others undergoing preclinical investigations [13].
The peptide molecules can potentially and selectively bind to larger protein interfaces, thereby
advocating low off-target side effects and toxicity [14]. With the advances in computational
approaches, the rational design and development of therapeutic peptides have become more effec-
tive [15]. In silico molecular docking methods enable accurate prediction of protein structure, sur-
face topology, and interaction affinities. Peptide docking has been divided into template-based and
template-free docking depending on the requirement size of input data [16]. The physics-based
15
Rational Design of Peptides and Protein Molecules in
Drug Discovery
Ipsa Padhy
1
, Abanish Biswas
2
, Chandan Nayak
3
, and Tripti Sharma
1,4
1
Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University),
Bhubaneswar, Odisha, India
2
Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Ranchi, Jharkhand, India
3
School of Pharmaceutical Education and Research, Berhampur University, Berhampur, Odisha, India
4
School of Pharmaceutical Sciences and Research, Chhatrapati Shivaji Maharaj University, Navi Mumbai, Maharashtra, India

328
molecular simulations are currently well integrated with emerging artificial (AI)- and alfafold
(AF)-based tools for reshaping the peptide discovery landscape [17].
In this chapter, we discuss various methods utilized in the design and development of therapeutic
peptides with a special focus on current technological advancements. We emphasize the therapeutic
applications of peptides. A comparative analysis between the peptide and small molecule drugs
respective to their pharmacodynamic and a pharmacokinetic property has also been conversed.
We outline various state-of-the-art peptide docking methods. An overview of available specific
peptide docking software tools is provided.
15.2 Peptides as Therapeutics
The management of diseases by small molecule drugs is impeccable. However, the side effects
associated with therapeutic small molecules have also generated a need for alternative therapeutic
entities. In this scenario, peptides present themselves as a distinct class of therapeutic candidates.
Metabolic disorders and cancers have been the thrust disease areas driving peptide drug discover-
ies [18]. The emergence of antimicrobial-resistant pathogens has also irked the development of
novel peptide antibiotics [19]. Gastrointestinal (GI) disorders have also provided a research area
eliciting a lot of interest in the development of peptides as potential therapeutic treatment
options [20].
15.2.1 Peptide Antibiotics
Antimicrobial resistance has become a global health issue basically arising due to overprescribing
antibiotics for nonbacterial infections or frequent misuse of antibiotics as prophylactics [21]. With
growing microbial resistance, the last-line antimicrobials have entered the pre-antibiotic era as well
a very slow pace in discovery of newer broad-spectrum antimicrobials for multidrug-resistant path-
ogens, the therapeutic utilization of peptide antibiotics becomes the foremost important [22, 23].
Moreover, peptide antibiotics stand high as a source of new antimicrobials [24].
The peptide antibiotics or host defense peptides are front-line defense entities in both prokary-
otes and eukaryotes. The antimicrobial peptides have broad-spectrum inhibitory activity against
bacteria, protozoan parasites, pathogenic fungi, and viruses. The peptide antibiotics are diverse
with respect to their structure, physicochemical properties, and mechanism of action. The peptide
antibiotics are isolated from bacteriophages, bacteria, fungi, mollusks, amphibians, arthropods,
mammals, and plants as well [25]. The first reported peptide antibiotic was gramicidin, which
showed inhibitory activity against many Gram-positive bacteria. It was isolated from the soil bac-
terium Bacillus brevis [26]. The antimicrobial peptide database (APD3) has cataloged more than
3000 peptide antibiotics [27].
In general, the peptide antibiotics comprise 50 amino acid residues. The majority of the AMPs
are cationic, with more than 45% hydrophobic residues [27]. The cationic nature, as well as the
hydrophobic residues of these peptides, generate an amphipathic backbone facilitating the bacte-
rial membrane infiltration [28]. Different models have been demonstrated for targeting microbial
cell membranes by the antimicrobial peptides (AMPs) (Figure 15.1).
Cathelicidin LL-37 peptides induce anionic lipid clustering to permeate bacterial membranes
and rupture the phospholipid bilayer by carpet/toroidal models. The proline-rich peptides target
bacterial ribosomes, while lantibiotics and cyclotides can both bind to phosphatidylethanola-
mines (PEs) [29–32]. Rather, these peptides may work synergistically for optimal outcomes.

329
Finally, AMPs can boost the immune response to further clear invading pathogens. All of these
mechanisms make it difficult for pathogens to develop resistance.
15.2.1.1 Peptides in Bone Diseases
Bone diseases are a wide group of musculoskeletal disorders that often require high drug doses
for effective therapy. However, such high doses of small-molecule drugs evoke adverse effects.
In such cases, bone tissue-targeted pharmacotherapy is a prerequisite peptide that has been
developed in the last three decades for the treatment of bone disorders, especially degenerative
bone resorption triggered by osteoporosis and bone metastasis [33–35]. Numerous peptide ther-
apeutic candidates can be classified into four categories like bone resorption inhibitors (W9,
OP3-4, RANKL inhibitor peptide), bone formation stimulators (B2A, P1, P2, P3, P24, P15,
TP508, OGP, PTH), dual bone resorption inhibitors, and bone formation stimulators and bone
targeting peptides [36]. The designed peptides inhibited bone resorption by antagonizing
actions of TNF-α and RANKL, modulating RANK–RANKL signaling pathways, mimic Loop3
of RANKL, and blocked the RANKL-binding activity and RANKL-induced differentiation of
osteoclast, blocking NEMO/IKK-β interactions and suppressing of the activity of the IKK com-
plex inhibiting TNF-α-induced NF–κB signaling pathway for bone resorption preventing
inflammatory bone resorption.
15.2.1.2 Peptides in Cancer
Immunotherapy majorly attacks the tumor cells by targeting immune checkpoints. FDA has
approved monoclonal antibodies (MABs) for cancer therapy by targeting specific immune check-
points. Although very effective, MABs have some disadvantages associated with high immuno-
genicity, poor solubility, and very high cost [37]. Being smaller in size, good stability, and less
immunogenicity, the peptides have been seeking serious attention in the arena of cancer therapy
and diagnosis. The peptides are applied for cancer theranostics in many different ways (Figure 15.2).
Antimicrobial peptide
Bacterial cell
membrane
Toroidal pore model
Carpet model
Conformational change in
peptide and attachment to
bacterial cell membrane
Barrel stave model
Bacterial cell rupture Peptide translocation to intracellular
targets
Figure 15.1 Models displaying the mechanism of membrane disruption by peptide antibiotics.

330
The peptides can be labeled with dyes, radioisotopes, or other specific molecules for cancer
diagnosis or imaging. The peptides are conjugated with nanocarriers for targeted therapy. The
peptides have been developed as vaccines. Peptides are directly used as targeted pharmacothera-
peutics [38, 39].
The peptide-based imaging probes bind to receptors expressed on the cell surface (α-integrins,
somatostatin receptor, transferrin receptor, neurotensin receptor), within the intracellular matrix
(cyclin A, cyclin kinase) or extracellular matrix (fibronectin, matrix metalloproteinases, prostate-
specific antigen) [38]. The tumor distribution can be visualized by single photon emitted computed
tomography imaging/scanning techniques. Octreoscan and depreotide are radiolabeled conjugates
of somatostatin peptide approved by the FDA for diagnosis of neuroendocrine and lung can-
cers [40]. Although depreotide was later withdrawn. Tc-3PRGD2 is an iodine labeled α-integrin-
based peptide probe developed for the detection of thyroid cancer [41]. On a similar note, cancer
radiotherapy with isotope-labeled peptides was developed. A good example is Lutetium 177 dota-
tate, somatostatin labeled with radioactive lutetium 177. It is FDA-approved for the treatment of
gastroenteric neuroendocrine cancers [42–44]. The major side effect of targeted radiotherapy is
inevitable damage to normal tissues surrounding the metastatic tissues [45].
Peptides are also conjugated with anticancer drugs, genes, and RNAs for targeting tumor
cells [45]. Adriamycin-conjugated AN-150 and AN-207 (luteinizing releasing hormone analogs)
were found to be effective against endometrial and ovarian cancers in phases I and II clinical trials.
The PEGylated NGR peptide conjugated to liposome polycation DNA effectively delivered the
small interfering RNA to tumor cells in vivo and, decreased expressions of c-Myc and induced
apoptosis [46]. The cell-penetrating peptides act as carriers for other peptides, nucleic acids, and
drugs to target tumor sites [47]. Antigenic peptides like TERT572Y have been developed as anti-
cancer vaccines (lung cancer), which trigger immune activities of killer T cells or helper T cells by
Tumor cells
Linker
Nanocarrier
Anti-cancer
drug
Radiolabeled
nucleotide
Peptide
A
B
C
D
E
F
Imaging agent
Antibody
Immunity booster
Targeted delivery
Immunotherapy
Targeted therapy
Targeted radiotherapy
Tumor diagnosis
Figure 15.2 Peptide-based cancer theranostics. (A) Peptide vaccine; (B) nanocarrier-loaded peptides;
(C) peptide conjugated to the antibody; (D) peptide conjugated to anticancer drug; (E) peptide conjugated
to radionucleotide; and (F) peptide conjugated to the imaging agent.

331
coupling to MHC I and II complexes [48]. Peptides conjugation to specific antigens induce vaccine
immunogenicity [49, 50]. Itself peptides exert anticancer effects. PD-1/PD-L1 is one of the major
signaling pathways being targeted for anticancer peptide drug discovery. Notably, PD-LI peptide
mimics like PL120131, DS-I & II, FITC-YT-16, and NY-12 were developed that demonstrated prom-
ising antitumor potency against preclinical cancer models [51–54].
Interestingly, natural spider venom toxins like Hanatoxin-1 from the Chilean spider [55] and
Lafr26, a porogenic peptide isolated from the venom of Lachesana species spiders [56] have shown
effective anticancer potency against colon and lung cancers in vitro. The natural venom-derived
peptides exert anticancer activity by blocking potassium ion channels and specific transmembrane
receptors Many other naturally derived and semisynthetic peptides have shown promising anti-
cancer activities, like aurein 1.2, BMAP-27, BMAP-28, brevinine, cecropin A and B, citropin 1.1,
gaegurins, HMGB 1, HNP 1, 2 and 3, hBD3, and PR-39 against leukemia, bladder cancer, breast
cancer, and colon cancers in vitro. The potency of natural antimicrobial peptides such as LL-37,
magainin 2, LfcinB, melittin, and tachyplesin 1 against cancers has also been revealed [57]. A
chemically synthesized θ-defensin peptide analog exerted anti-breast cancer activity, probably by
membrane rupturing activity [58].
15.2.1.3 Peptides in Metabolic Diseases
T2DM has been successfully treated with peptide drugs, including GLP-1 receptor agonists [59].
Exenatide, liraglutide, lixisenatide, dulaglutide, and semaglutide are some FDA-approved peptide
drugs for clinical use in type 2 diabetes [60]. Clinical trial reports have proven that GLP-1 receptor
agonists like lixisenatide are instrumental in improving diabetic nephropathy [61]. Moreover, it
was reported that liraglutide and semaglutide antiatherosclerotic potency [62, 63].
The renin–angiotensin–aldosterone system (RAAS) is an exclusive target for the pharmacother-
apy of cardiac disorders by peptides. Synthetic angiotensin II was approved by the FDA in 2017 for
increasing blood pressure via intravenous infusion in adults with septicemia or other distributed
shock [64]. Four peptides (WPRGYFL, GPDRPKFLGPF, WYGPDRPKFL, and SDWDRF) isolated
from Tetradesmus obliquus microalgae inhibited the angiotensin-converting enzymes (ACEs) [65].
IRW, an egg white-derived tripeptide exhibited antihypertensive activity in vivo by upregulating
angiotensin-converting enzyme (ACE2) [66]. The natriuretic peptide receptors pose as excellent
targets for cardiovascular drugs [67, 68]. Nesiritide is a recombinant human BNP that was approved
by the FDA in 2001 for the treatment of acutely decompensated heart failure in patients [69, 70].
NPs act mainly through NPR-A and/or NPR-B receptors, while NPR-C is mainly used for scav-
enging NPs [71]. Cenderitide (from green mamba snake) is a dual NPR-A/NPR-B agonist in an
active clinical trial and is having a safe therapeutic index [16, 72]. Infusing vasoactive intestinal
peptides increased the concentration of myocardial vasoactive intestinal peptides and reversed
existing myocardial fibrosis in rats [73, 74]. Cyclopeptide RD808 attenuated β1-adrenergic induced
myocardial injury in vivo [75]. The central adrenocorticotropin-releasing factor (CRF)-related
peptide system is currently attracting increasing attention as a target for the prevention of cardio-
vascular disease [76, 77].
15.2.1.4 Peptides in Gastrointestinal Diseases
The gut microbiome of human secretes a plethora of antimicrobial peptides. GI diseases and excessive
use of antibiotics degrade the symbiotic gut microflora that usually acts as a gut defense system. GI
diseases are being now targeted by peptides. Proline-arginine-39, isolated from porcine bone mar-
row and lymphoid tissue, exhibited antibacterial, immunomodulatory, and intestinal epithelial
repair functions and may provide a safe alternative therapy for IBD [78]. Subcutaneous injection

332
of teduglutide a GLP-2 analogue developed by recombinant DNA technique, increased intestinal
absorption in patients with short bowel syndrome (SBS) [79, 80]. EGF, erythropoietin, and hepato-
cyte growth factor have also shown therapeutic potential in SBS [81].
Periplanetasin-2 isolated from the American cockroach blocked the mucosal damage and inflam-
mation induced by Clostridium difficile toxin A [82]. Preincubating or co-incubating (Clostridium
perfringens endotoxin) CPE with the claudin-4 extracellular loop ECL-2 peptide significantly inhib-
ited CPE-induced luminal fluid accumulation and histological lesions in rabbit intestinal loop [83],
indicating synthetic peptide ECL-2 can be used to negate food poisoning. Cathelicidin produced from
the human colon defended Salmonella typhimurium infection by obstructing bacteria infiltration
into the colon epithelium by upregulating Toll-like receptor-4 and releasing pro-cytokines [84].
15.2.2 Advantages and Limitations of Peptide Therapeutics
The peptide drugs basically mimic endogenous hormones, growth factors, neurotransmitters, ion
channel ligands, or anti-infective agents. Typically, they attach to cellular receptors and induce
specific effects intracellularly. The peptide drugs, in comparison to other therapeutic biologicals,
incur less production cost as well as display less immunogenetic [2]. Therapeutic peptides, in com-
parison to small molecule drugs, are target-specific and have minimal side effects owing to shorter
half-life. The tissue accumulation by therapeutic peptides is low [85]. Therapeutic peptides have
two intrinsic drawbacks, membrane impermeability and poor in vivo stability, which represent
major stumbling blocks for peptide drug development [86, 87].
15.2.3 FDA-Approved Peptide Therapeutics
Peptide drug discovery is targeted at various diseases, such as autoimmune, endocrinological, skin,
bone disorders, reproductive and cardiovascular diseases, and diseases related to metabolic dys-
function. However, the prime focus has remained on anticancer and antimicrobial therapeutics,
justified by the high number of drug approvals for these indications (Table 15.1).
15.2.4 Peptide-Based Entities in Clinical Trials
Peptides currently in clinical trials (Table 15.2) are being investigated for a wide range of applications,
including microbial infections, diabetes, hormonal disorders, and cancer [7]. Interestingly, some
trials are being carried out simultaneously in two phases, such as phase I/II or II/III, to hasten the
approval process (https://www.clinicaltrials.gov). The data highlight that FDA approvals of peptide
drugs had been predominantly via parenteral rather than oral routes of administration. However,
advances in chemical modifications have led to an almost equivalent presence of oral peptides in
clinical trials to that of the intravenous route. This is a promising trend, given that oral administra-
tion is expected to widen the applicability of peptide drugs.
15.2.5 Peptide Synthesis and Diversification
With rapid technological innovations in the fields of synthetic biology, biotechnology, and chemical
sciences, peptide drug discovery and development has achieved enormous success. In this chapter,
we recapitulate the existing state of art techniques (Figure 15.3) employed for peptide synthesis
and diversification.
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