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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

403
18.1 Introduction
Single-celled microbes, known as bacteria, are capable of surviving in various settings, including
the human body. A wide range of diseases known as “bacterial infections” are brought on by dif-
ferent bacterial species. Some bacteria can cause illnesses when they enter the body and multiply,
although many of them are benign or even helpful [1, 2]. These infections can range in severity
from moderate to severe and can affect various body areas. Bacterial infections are conditions,
diseases, or ill health caused by bacteria and their toxins, which stand in second place as endoge-
nous causes of death throughout the globe. According to current statistics, bacterial infections may
be the cause of one out of every eight fatalities globally [3]. Pathogenic bacteria, which can result
in disease, are the main source of bacterial infections. Through various routes, including ingestion,
inhalation, cuts, and wounds, these bacteria can enter the body. Person-to-person contact, con-
taminated food or drink, insect bites, and exposure to polluted surfaces are common ways for the
disease to spread. Some infections can be more difficult to treat because bacteria can develop drug
resistance [4].
Bacterial infections are of different types based on which part of the host is infected, the
transmitted route or medium, etc., for example, pneumonia caused by bacteria, food poisoning by
bacteria (gastroenteritis), topical infections, and sexually transmitted infections. These infections
can be prevalent through carriers like mosquitoes or ticks, contact with an infected person, either
direct or indirect, airborne particles or droplets, contaminated water or food, etc. [5]. Depending
on the type of bacteria involved and the area of the body affected, the symptoms of a bacterial
infection can change. The most typical signs of bacterialinfections include fever, pain or discomfort,
fatigue, cough, diarrhea, skin changes, urinary symptoms, and neurological symptoms. Depending
on factors like sanitation, access to healthcare, and immunization rates, the incidence of particular
bacterial illnesses might differ by region [6]. Some bacterial infections, like the common cold or
18
Rational Design of Antibacterial Agents for
Multidrug-Resistant Infections
Sathish Kumar Konidala
1
, Podila Naresh
1
, Risy Namratha Jamullamudi
2
,
Kamma Harsha Sri
3
, Richie Rashmin Bhandare
4,5
, and Afzal Basha Shaik
1
1
Department of Pharmaceutical Sciences, School of Biotechnology and Pharmaceutical Sciences, Vignan’s Foundation for Science,
Technology and Research, Guntur, AP, India
2
Department of Pharmacy, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
3
Department of Pharmaceutical Sciences, Vignan Pharmacy College, Guntur, AP, India
4
Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
5
Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections404
urinary tract infections, are rather typical and frequently not serious. Others can be more difficult
to manage and treat, such as tuberculosis or bacterial strains resistant to antibiotics. There are
many reasons why new bacterial illnesses can appear,including bacterial mutations, environmental
changes, increasing human interaction, and travel. Significant public health issues could be caused
by these newly developing infections. The rise of antibiotic-resistant bacteria, also known as
“superbugs,” has become a significant concern in recent years. Because these bacteria are resistant
to many widely used medicines, illnesses are more challenging to treat [7].
New bacterial strains or antibiotic-resistant strains may eventually appear, and bacterial
infections have long been a public health problem. Carbapenem-resistant Enterobacteriaceae
(CRE) is a well-known example of a recently discovered bacterial illness in recent years. Several
antibiotics, including carbapenems, which are thought to be the last choice for treating such
infections, are ineffective against CRE bacteria. Clostridium difficile, or C. difficile, is another
noteworthy example. It is frequently linked to healthcare environments and produces extremely
severe diarrhea. Reports of a rise in the frequency and severity of C. difficile infections have
surfaced over time [8].
Center for Disease Control and Prevention reported the outbreak of some bacterial infections
like Salmonella infections (outbreak in 2023) most often in children younger than five years due to
the handling of small turtles and backyard poultry birds. Various environmental samples obtained
from two of the ice cream house locations were discovered to contain an outbreak strain of Listeria
(outbreak in 2023) [9]. Consumption of raw milk or its products contaminated with Brucella may
result in brucellosis (outbreak in 2019). A strain of Brucella known as Brucella melitensis or Brucella
abortus is responsible for the majority of brucellosis infections linked to raw milk in people who
have gone to nations where these strains are prevalent and have consumed tainted cow, sheep, or
goat milk [10].
18.2 Treatment
Antibiotics, which are drugs intended to either kill or stop the growth of bacteria, are commonly
used in the treatment of bacterial illnesses. The precise type of bacteria causing the infection and
its susceptibility to medications determine which antibiotic is appropriate and how long to treat
the infections [11]. The classification of antibacterial agents, commonly referred to as antibiotics,
takes into account several factors, such as their chemical composition, mode of action, range of
activity, and mode of delivery [12]. The antibacterial agents are categorized here in general as
follows, based on chemical structure: β-lactam antibiotics including penicillins, cephalosporins,
carbapenems, and monobactams contain a β-lactam ring in their chemical structure. Macrolides
include erythromycin, clarithromycin, and azithromycin, which have a macrocyclic lactone ring.
Quinolones include ciprofloxacin and levofloxacin, which have a quinolone core structure.
Tetracyclines like doxycycline and tetracycline have four linearly fused rings. Aminoglycosides like
gentamicin and amikacin are composed of amino-modified sugars [13, 14].
Though a wide range of antibiotics are available and newer antibiotic agents were discovered
timely for treating bacterial infections, the outbreak and prevalence of bacterial infections that are
already faced or controlled by the health system might be due to the development of antibacterial
resistance (ABR) [15], incomplete treatment courses, hospital-acquired infections (HAIs), global
travel and trade, poor sanitation and hygiene, immunocompromised populations, emerging infec-
tious diseases, climate change, lack of access to healthcare, inadequate surveillance and reporting,
etc. [16, 17].

18.3 Antibacterial Resistance 405
18.3 Antibacterial Resistance
ABR is one of the leading problems in controlling bacterial infections. The development of the ability
of bacteria to withstand exposure to antibiotics intended to either kill them or slow their growth is
knownas antibacterial resistanceorantibiotic resistance, anditis a globalpublic health concern [18–20].
Due to this condition, normal treatments become ineffective, which increases the danger of spreading
the virus to others and results in persistent infections. The development of ABR is due to overuse and
misuse of antibiotics, incomplete treatment courses, self-medication, use in agriculture, lack of new
antibiotics, globalization and travel, poor infection control, environmental factors, and a lack of
surveillance and monitoring [21]. In healthcare settings like hospitals, where antimicrobial agent
usage is increasing, multidrug resistance (MDR) is frequently been found in recent days. Resistance
may arise as a result of the selection pressure that using several medications exerts. The scarcity of
appropriate medications makes treating infections brought on by multidrug-resistant organisms more
difficult. As a result, combined treatments, multitargeted drugs, or the use of antibiotics as a last resort
may be necessary. Resistant infections can be encountered by promoting antibiotic stewardship
programs, global cooperation, increasing public a w ar eness about the usage and regulation of
antibiotics, and conducting research and development of new antibiotic agents [22, 23].
Therefore, developing a multitargeted medication design is essential for treating infections with
MDR. Understanding the process of resistance development is necessary to design and discover
the right drug for treating infections that are resistant to treatment [24]. There are a few possible
mechanisms shown in Figure 18.1 for the developmentof resistance in bacteria ormicrobes against
antibiotics, which are classified as follows [25, 26].
Enzyme
A B
Enzyme
Enzyme modification
Decreased permeability
Gene mutation
Horizontal gene transfer
Efflux pumps activation
Multidrug-resistant
Target site modification
Metabolic path
modification
ADP
ATP
ATP
P
+
ADP
ATP
Resistant gene amplification
Plasmids
Figure 18.1 Multidrug resistance mechanisms.

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections406
I) The genetic basis of antimicrobial resistance
a) Mutational resistance
b) Horizontal gene transfer
II) Mechanistic basis of antimicrobial resistance
a) Modification of the antibiotic molecule
i) Chemical modification of antibiotics
ii) Destruction of the antibiotic
b) Decreased antibiotic penetration and efflux
i) Decreased permeability
ii) Efflux pumps
c) Changes in target sites
i) Target protection
ii) Modification of the target site
● Mutation of the target site
● Enzymatic alterations of the target site
● Complete replacement or bypass of the target site
d) Resistance due to global cell adaptations
The microbial genome serves as the primary source of the genetic basis of antimicrobial
resistance, which can develop through various pathways.
18.3.1 Mutation
Unintentional changes in a microbial genome can result in modifications to vital genes, changing
the sites at which antimicrobial agents target them. This may lessen the medications’ ability to
combat the mutant microbes. Mycobacterium tuberculosis is one the best examples showing
resistance to different types of antibiotics such as rifampicin, streptomycin, ethambutol, and
pyrazinamide, through mutation in the gene [27].
18.3.2 Horizontal Gene Transfer (HGT)
Several resistance genes are passed from one species or strain of bacteria to another horizontally.
This process spreads resistance genes throughout bacterial populations by involving the exchange
of genetic material (integrons, transposons, and plasmids) between microorganisms. For instance,
Enterobacter cloacae transfer OXA-48 plasmid for other Enterobacteriaceae family members to
develop resistance against carbapenem through a HGT mechanism. Similarly, Staphylococcus
aureus receives the methicillin resistance gene mecA by HGT mechanism from other species of
bacteria. The specific molecular and cellular strategies that microorganisms use to withstand the
effects of antimicrobial agents are the mechanistic basis of antimicrobial resistance. These mecha-
nisms can change based on the kind of microorganism (bacteria, viruses, fungi, and parasites) and
the type of antimicrobial (e.g., antibiotics, antivirals, antifungals, and antiparasitics) [28].
18.3.3 Enzymatic Modification or Degradation
Certain microbes generate enzymes that can change or degrade antimicrobial agents, making
them less effective. β-Lactamases, for instance, are produced by bacteria and are responsible for the
breakdown of β-lactam antibiotics like cephalosporins and penicillins. The interaction between
the serine hydroxyl group in the active site of penicillin-binding protein (PBP) and the β-lactams

18.3 Antibacterial Resistance 407
carbonyl group makes the PBP inactive for antibacterial activity. Carbapenemase enzymes pro-
duced by Klebsiella pneumoniae species can degrade the carbapenems and other β-lactam
drugs [29].
18.3.4 Target Site Modification
Microbes can alter the locations where antimicrobial agents bind, making the medications less
effective. This modification can be brought about by other mechanisms that change the structure
of the drug-binding site or by mutations in the genes that encode the target proteins. Escherichia
coli with the mcr-1 gene can change its cell wall structure by adding compounds so that the colistin
antibiotic cannot bind to the cell wall of Escherichia coli having other types of genes. Similarly, tria-
zoles cannot bind to the cyp1A gene of the Aspergillus fumigatus due to target site change in this
type of gene than the other types [30].
18.3.5 Decreased Permeability
Certain microbes may evolve defense mechanisms to lessen the absorption of antibiotics,
preventing the medications from reaching their intended targets. This may entail modifications to
the permeability of the cell wall or membrane of the microorganism. Most common species such
as Klebsiella, Enterobacter, Serratia, and Salmonella can show resistance to antibiotics through
reduction of permeability across the cell membrane [31].
18.3.6 Efflux Pumps
Antimicrobial agents can be actively pumped out of bacteria’s cells by these pumps, which lowers
the drug’s intracellular concentration. Increased efflux and resistance can result from upregulating
the genes that encode these pumps. S. aureus develops intrinsic resistance against most antibiotics
through norA, mepA, etc., types of chromosomally encoded efflux pumps [32].
18.3.7 Plasmids
Small, circular DNA molecules called plasmids are distinct from bacterial chromosomes. They are
easily transmitted between bacteria and frequently carry genes encoding resistance mechanisms.
The spread of resistance is greatly aided by this horizontal transfer of resistance plasmids.
18.3.8 Transposons
DNA sequences known as transposons can travel throughout the genome and between plasmids
and chromosomes. Resistance genes may be carried by transposons, which would facilitate the
transfer of these genes between bacteria and aid in the spread of resistance.
18.3.9 Gene Amplification
Certain microbes can multiply particular genes, such as those that confer resistance. Higher
expression levels of resistance mechanisms may arise from this amplification, increasing the
microorganism’s resistance to the antimicrobial agent. Resistance against the sulfonamides and
trimethoprim by bacterial species might be due to gene amplification [33].

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections408
18.3.10 Formation of Biofilms
Communities of microorganisms encased in a matrix known as biofilms can increase resistance to
antimicrobials. The genes responsible for the formation of biofilms can tangentially enhance
resistance by shielding microbes from the effects of medications.
18.3.11 Modified Metabolic Pathways
The metabolic pathways of certain resistant microbes may differ. By allowing the microbes to redi-
rect resources away from processes that antimicrobials target, these changes may aid in developing
resistance.
18.3.12 Adaptive Evolution
When exposed to antibiotics, microorganisms may go through an adaptive evolutionary process.
This entails the accumulation of genetic alterations that give rise to a survival benefit when the
antimicrobial is present. The ongoing evolution of resistance mechanisms facilitates the persis-
tence of resistant strains.
18.4 Medicinal Chemistry Strategies for the Design of
Antibacterials Combating Multidrug-Resistant Bacterial Infections
The design and development of antibacterials to fight bacterial infections resistant to multiple drugs
is a difficult task that calls for creative solutions to deal with the changing face of antibioticresistance.
18.4.1 Structure-Based Drug Design
Making use of a thorough understanding of the bacterial target’s structure to create medication that
binds more successfully and might get around current resistance mechanisms. To understand the
mechanism of resistance of mutated targets, the study of both normal and mutated target structures
is essential, which might also be essential and helpful in predicting the better target feature to overcome
the resistance through a modeling approach. The topoisomerases are the target for fluoroquinolones
to exert potential antibacterial action, but over time, resistance to the fluoroquinolones was developed
due to the accumulation of mutants in the gyrA subunit of TopoIV and gyrase. To overcome this
resistance problem, scientists developed the compound GSK299423, which will act differently than
fluoroquinolones by binding to the active site in between two gyrA subunits [34].
O
N
NH
F
O
N
O
HO
Moxioxacin
N
N
O
N NH
N
S
O
GSK299423

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug-Resistant Bacterial Infections 409
Other research groups have reported the different heterocyclic lead molecules [35–38] to combat
the resistant bacterial strains based on the structure-based drug design.
18.4.2 Modification of Existing Antibiotics
Modifying the structure of reported antibiotics improves their activity against resistant strains or
prevents the development of resistance mechanisms. This mode of drug design was used in the
early stages of antibacterial development like the development of semisynthetic penicillins from
penicillin; still, this mode of antibacterial drug development is in use. For instance, the develop -
ment of doripenem forms a structural modification of imipenem to overcome the resistance to
β-lactamase and is prone to elimination by dehydropeptidase-1 [39].
O
S
O
H
2
N
H
N
N
H
S
N
O
HO
O
HO
Doripenem
O
OH
S
N NH
2
N
O
HO
H
2
O
Imipenem
Ridinilazole, a novel medication with a special structure that targets the bacterial cell directly,
was created specially to treat infections caused by Clostridium difficile. Delafloxacin is a modified
chemical structure of fluoroquinolone that increases its potency at varying pH values, making it
adaptable to diverse bacterial strains.
N
N
N
H
N
N
H
N
Ridinilazole
F
F
N
N
O
OH
O
Cl
F
N
HO
H
2
N
Delaoxacin
18.4.3 Bioisosterism
Enhancing a drug’s potency, stability, or bioavailability by substituting structurally identical but
chemically distinct groups for certain portions of the molecule is known as bioisosterism. The
replacement of the hydroxyl group in phenol with the amine group (resulting in aniline) is a
classical isosteric modification that can alter the physicochemical properties, pharmacokinetic
profile, and nature and nullify the activity, whereas nonclassic isosteric modification like the
replacement of the hydroxyl group in phenol in (±)-Phenylephrine with sulfonamide leads to
formation of methanesulfonic acid; though they are different in electronic and steric properties
they can augment the biological activity [40].

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections410
OH
NH
2
H
N
HO
H
N
N
H
OH
S
O
O
OH
Phenol
Aniline
Methanesulfonic acid
(±)-Phenylephrine
18.4.4 Prodrug Strategies
Prodrugs are the dormant precursors of active medications that become active inside the biological
system through biotransformation shown in Figure 18.2, leading to improving selectivity and
minimizing negative effects. When designing a prodrug, specific parent drug components can be
added or removed to change the drug’s permeability, bioavailability, and absorption without
changing the drug’s pharmacological activity. Three categories exist for prodrugs: (i) prodrugs
connected to carriers, in which the active ingredient is connected to a promoiety that is eliminated
through a chemical or enzymatic process, thereby releasing the active ingredient; (ii) bioprecursor
prodrugs, in which the active drug is molecularly modified and can be released through oxidation
or reduction reactions; and (iii) double prodrugs, which are joined by two linkers and can be
broken down by distinct mechanisms, such as a codrug, which is a combination of two
physiologically active drugs in a single molecule [41].
The poor absorption problem of β-lactams was solved by the prodrug approach, and the bioavail-
ability of ampicillin was increased by ester prodrugs like pivampicillin (the first prodrug),
Active drug
Active drug
Carrier Carrier
Carrier
Intracellular
Extracellular
Barrier
Active
drug
Active
drug
Prodrug
Prodrug
Figure 18.2 Biotransformation of prodrug at the site of action.

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug-Resistant Bacterial Infections 411
talampicillin, hetacillin, and bacampicillin; these prodrugs can be biotransformed to ampicillin by
the esterases through enzymatic hydrolysis. A prodrug cephalosporin-fluoroquinolone of
ciprofloxacin was developed to overcome the effect of the β-lactamase enzyme on ciprofloxacin
and its selective delivery into the ciprofloxacin-resistant bacteria.
O
O O
O
N
O
HN
O
H
2
N
S
Pivampicillin
O
O
O
O
N
O
N
H
O
H
2
N
S
Talampicillin
N
S
O
N
O
N
H
O
HO
Hetacillin
O O O
OO
N
HNH
2
N
O
O
S
H
Bacampicillin
18.4.5 Similar Bacterial Components Target
Determining and targeting the components of bacteria that are different from human cells but
necessary for their survival is another strategy in novel antibacterial drug design. Targets could
include things like bacterial cell walls, proteins, or enzymes that are absent or different in human
cells. The drugs used to fight against bacteria by targeting the regular regulatory targets are not
responsive since most of the antibacterials are developed with resistance so novel alternatives or
similar targets need to be selected to develop the antibacterial drug development. Targets like
C55-PP and lipid II, enzymes in the peptidoglycan (PG) biosynthesis, Mur enzymes (GlmS, GlmM,
and GlmU), etc., for instance, cyclic lipopeptide Friulimicin B forms a Ca
2+
-dependent complex
with bactoprenol phosphate carrier C55-P, suppress the formation of cell wall precursors [42].
O
N
O
N
H
O
HN
O
NH
O
NH
O
N
H
O
H
N
O
NH
O
N
O
HN
NH
2
OHO
O
OH
O
OH
H
N
O
O
NH
2
N
H
O
Friulimicin B

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections412
18.4.6 Combine or Combination Therapy
Employ combination treatments simultaneously targeting several bacterial pathways. This
strategy can improve treatment efficacy and lessen the chance that resistance will develop.
Combination therapy of Ceftazidime + Avibactam and Meropenem+ Vaborbactam with
β-lactamase inhibitors overcomes resistance mechanisms. Imipenem + Cilastatin + Relebactam
combined therapy along with β-lactamase inhibitors is designed to increase the range of
β-lactam antibiotics [43].
O
N
S
O
N
N
H
HO
O
HO
Meropenem
B
O
O
OH
H
N
O
S
HO
Vaborbactam
O
N
S
H
N
O
N
O
O
HO
N
S
NH
2
N
+
O
–
O
Ceftazidime
C
N
H
2
N
O
O
N
O
S
OH
O
O
Avibactam
O
OH
S
N NH
2
N
O
HO
H
2
O
Imipenem
O
H
N
S
O
OH
H
2
N
OHO
Cilastatin
N
N
O
O
S
O
O
OH
O
N
H
HN
Relebactam
In the treatment against Gram-negative bacterial infections, the combination of outer
membrane permeabilises and antibacterial drugs can synergize the treatment efficiency. For
instance, pentamidine along with novobiocin, colistin along with rifampicin, and thanatin
along with meropenem improve the antibacterial activity by increasing the permeability of
antibacterial ag ents through the Gram-negative bacteria cell membrane by outer membrane
permeabilizes.
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