Computational Modeling of Drugs Against Alzheimer’s Disease

Dedication
About the Editor
Preface to the Series
Preface
Contents
Contributors
Part I: Recent Progress in Understanding Molecular Etiology of Alzheimer´s Disease
Chapter 1: Recent Progress in the Treatment Strategies for Alzheimer´s Disease
1 Introduction
2 Estimated Morbidity and Mortality Rate of AD Country-Wise
3 The Effect of the COVID-19 Epidemic on the Mortality from AD
4 Neurobiology of AD and Promising Drug Targets
4.1 Amyloid Hypothesis
4.2 Tau Hypothesis
4.3 Oxidative Stress and Mitochondrial Hypothesis
4.4 Inflammatory Hypothesis
4.5 Epigenetics Hypothesis
4.6 Genetic Causes of AD
5 Role of Neurotransmitters and Their Receptors in AD
5.1 Cholinergic Signaling in AD
5.2 Serotonergic Signaling in AD
5.3 Glutamatergic Signaling in AD
5.4 Dopaminergic Signaling in AD
5.5 Adrenergic Signaling in AD
6 Novel Therapeutic Strategies for the Treatment of AD
6.1 Antioxidants
6.2 Anti-inflammatory Agents
6.3 Atypical Antipsychotics
6.4 Antidepressants
6.5 HMG-CoA Reductase Inhibitors
6.6 Hormone Replacement Therapy
6.7 7-Methoxytacrine Derivative
7 Drugs in Clinical Trials for the Treatment of AD
7.1 Reason for Failed Clinical Trials of Disease-Modifying Agents (DMA) for AD and Their Contribution to Current Research
8 Nanomaterials for the Treatment of AD
9 Natural Compounds a Promising Treatment Strategy Against AD
10 Multitarget-Directed Ligand (MTDL): A Promising Strategy for AD
11 Conclusions and Prospects
References
Part II: Recent Advances in Computational Modeling of Anti-Alzheimer Drugs
Chapter 2: Understanding the Mechanisms of Amyloid Beta (Aβ) Aggregation by Computational Modeling
1 Introduction
1.1 Tau-Derived Hexapeptide VQIVYK as a Novel Tool to Study Aβ Aggregation
2 Materials
3 Methods
3.1 Preparation of the Capped VQIVYK Peptide
3.2 Preparation of Aβ42 Oligomer Model
3.3 Molecular Docking of the Capped VQIVYK in the Aβ42 Oligomer Model
3.4 Preparation of Aβ42 Fibril Model and Molecular Docking of the Capped VQIVYK Peptide in the Aβ42 Fibril Model
3.5 Preparation of the Docked Complex of Capped the VQIVYK Peptide and Aβ42 Oligomer Model for MD Simulation
3.6 MD Simulation of the Docked Complex of the Capped VQIVYK Peptide and Aβ42 Oligomer Model
3.7 Preparation of the Docked Complex of the Capped VQIVYK Peptides and Aβ42 Fibril Model for MD Simulation
3.8 MD Simulation of the Docked Complex of the Capped VQIVYK Peptides and Aβ42 Fibril Model
4 Conclusions
5 Notes
References
Chapter 3: Recent Advances in Computational Modeling of BACE1 Inhibitors as Anti-Alzheimer Agents
1 Introduction
2 BACE1: Structure and Active Site Characteristics
3 CADD of Anti-Alzheimer BACE1 Inhibitors: Techniques and Recent Developments
3.1 Virtual Screening-Based Approaches
3.1.1 Molecular Docking-Molecular Simulations
3.1.2 Ligand- and Structure-Based Design: Cheminformatics
3.1.3 Machine Learning
3.2 Other Technique-Based Methods
3.2.1 Quantum Mechanical Approaches
4 Future Outlook
5 Conclusions
References
Chapter 4: Modeling of BACE-1 Inhibitors as Anti-Alzheimer´s Agents
1 Introduction
2 Methods
2.1 Structure- and Ligand-Based Virtual Screening Tools
2.1.1 Pharmacophore Models
2.1.2 Molecular Docking
2.2 Structure- and Ligand-Based Ligand Optimization Tools
2.2.1 QSAR
2.2.2 Molecular Dynamics
2.2.3 Hot Spot and Druggability Analysis
3 Results
3.1 Contributions and Limitations of Virtual Screening Toward BACE-1 Inhibitor Identification
3.2 Improvement of BACE-1/BACE-2 Selectivity Profile
3.3 Designing BACE-1 Allosteric Inhibitors
3.4 Hot Spot-Guided Optimization of BACE-1 Inhibitors
3.5 QSAR-Guided Optimization of BACE-1 Inhibitors
4 Conclusion and Perspectives
References
Chapter 5: Computational Modeling of Kinase Inhibitors as Anti-Alzheimer Agents
1 Introduction
1.1 Tau Protein and Microtubule Destabilization
1.2 Proline-Directed Protein Kinases (PDPK)
1.2.1 Glycogen synthase kinase-3 (GSK-3)
1.2.2 Cyclin-Dependent Protein Kinases (CDK)
1.2.3 p38α Mitogen-Activated Protein Kinase (p38α MAPK)
1.2.4 c-Jun N-Terminal Kinases (JNK)
1.3 Non-proline-Directed Kinases (NPDKs)
1.3.1 Dual-Specificity Tyrosine Phosphorylation-Regulated Kinases (DYRKs)
1.3.2 Casein Kinases (CKs)
1.3.3 Ca2+/Calmodulin-Dependent Protein Kinase II (CaMKII)
1.3.4 Protein Kinase A (PKA)
2 Computational Studies on Glycogen Synthase Kinase (GSK) and Its Inhibitors as Potential Anti-Alzheimer´s Agents
3 Computational Studies on Cyclin-Dependent Kinase-5 (CDK-5) Inhibitors as Potential Anti-Alzheimer´s Agents
4 Computational Studies on p38α Mitogen-Activated Protein Kinase (MAPK) Inhibitors as Potential Anti-Alzheimer´s Agents
5 Computational Studies on c-Jun N-Terminal Kinase 3 (JNK3) Inhibitors as Potential Anti-Alzheimer´s Agents
6 Computational Studies on Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase-1A (DYRK1A) as Potential Anti-Alzheimer´...
7 Computational Modeling Studies on Miscellaneous Kinases and Their Inhibitors as Anti-Alzheimer´s Agents
7.1 Casein Kinase Inhibitors
7.2 Ca2+/ Calmodulin-Dependent Protein Kinase II (CaMKII) Inhibitors
7.3 Multitarget Kinase Inhibitors
8 Conclusions and Future Prospects
References
Chapter 6: Computer-Assisted Drug Design: A Toolbox for Novel Tau Kinase Inhibitors and Its Implications in Alzheimer´s Disease
1 Introduction
1.1 Outline About Alzheimer´s Disease
1.2 Structural Aspect of Tau
2 Design of Inhibitors for the Receptor DYRK1A/CLK1
3 Our Design Approach and Contribution to CLK1/DYRK1A
4 Design of Inhibitors for the Receptors GSK3β and CDK5
5 General Docking Protocol
6 Conclusion
References
Chapter 7: Computational Modeling Approaches in Search of Anti-Alzheimer's Disease Agents: Case Studies of Phosphodiesterase I...
1 Introduction
1.1 Therapeutic Strategies Targeting PDEs and Existing PDE Inhibitors Clinically Being Used Against AD
2 Materials and Methods
2.1 Computer-Aided Drug Design (CADD) Approaches
2.1.1 Structure-Based Drug Design Approach
Three-Dimensional (3D) Protein Structure Prediction
Homology Modeling
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Fold Recognition and Threading Approach
Ab-Initio Modeling Approach
Importance of Target-Template Modeling
Protein-Protein Interaction Network Approach
Structure-Based Pharmacophore Modeling
Fragment-Based De Novo Design (FBDND)
Molecular Docking
Molecular Dynamics (MD) Simulation
2.1.2 Ligand-Based Drug Design Approach
Quantitative Structure-Activity Relationship (QSAR)
Similarity-Based Chemical Read-Across
Ligand-Based Pharmacophore Modeling
Similarity Search
2.1.3 Virtual Screening
3 Case Studies
3.1 In Silico Modeling of PDE Inhibitors Against Alzheimer´s Disease: Case Studies
3.1.1 Repurposing of PDE2 Inhibitors Against AD
3.1.2 Repurposing of PDE4 Inhibitors Against AD
3.1.3 Repurposing of PDE5 Inhibitors Against AD
3.1.4 Repurposing of PDE7 Inhibitors Against AD
3.1.5 Repurposing of PDE9 Inhibitors Against AD
3.1.6 Repurposing of PDE10 Inhibitors Against AD
3.1.7 Activity Data Sources, Chemical Databases, and Freely Available CADD Software and Tools
4 Conclusions and Prospects
References
Chapter 8: Recent Advances in Computational Modeling of Multi-targeting Inhibitors as Anti-Alzheimer Agents
1 Introduction
2 Materials
2.1 Drug Targets in Alzheimer´s Disease
2.2 Computational Modeling Methods in Drug Discovery
2.2.1 Structure-Based Drug Design
Homology Modeling
Molecular Docking
Molecular Dynamics Simulation
2.2.2 Ligand-Based Drug Design
Pharmacophore Modeling
Quantitative Structure-Activity Relationship
2.2.3 De Novo Drug Design
3 Method
3.1 Studies on Multi-targeting Inhibitors for Alzheimer´s Disease
3.2 Computational Modeling Approaches to Design Multi-targeting Inhibitors
3.2.1 Docking-Based Methods
Preparation of Compounds and Proteins
Molecular Docking Evaluation
Searching Algorithm
Scoring and Ranking Function
Applications of Molecular Docking
3.2.2 Pharmacophore-Based Methods
3.2.3 QSAR-Based Methods
To Prepare the Data Set
Calculation of Molecular Descriptors
Splitting Data Set
Algorithms for Building QSAR Models
Evaluation of the QSAR Model
4 Notes
References
Chapter 9: Computational Modeling of PET and SPECT Imaging Agents as Diagnostics for Alzheimer´s Disease
1 Introduction
2 PET Imaging
3 SPECT Imaging
4 Imaging Agents for Alzheimer´s Disease
5 Computational Modeling of PET and SPECT Imaging Agents Against Alzheimer´s Disease
6 Conclusions
References
Part III: Computational Modeling of Anti-Alzheimer Drugs Against Newer Targets
Chapter 10: Computational Modeling of DYRK1A Inhibitors as Potential Anti-Alzheimer Agents
1 Introduction
2 Overview of in Silico Methods
2.1 Ligand-Based (LB) Approaches
2.2 Structure-Based (SB) Approaches
3 Case Study: DYRK1A Computational Models
3.1 Database Collection
3.2 LB Models
3.2.1 SAR
3.2.2 QSAR
3.3 SB Models
3.3.1 Docking
3.3.2 3D Structural Study
3.4 Chemical Profile Predictions
3.4.1 Synthetic Accessibility Prediction (SAP)
3.4.2 Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Profile
4 Application: Screening of the NuBBE Database
4.1 LB Screening
4.2 SB Screening
4.3 Profiling
4.4 ADMET Predictions
5 Conclusion/Discussion
References
Chapter 11: Computational Modeling of MAO Inhibitors as Anti-Alzheimer Agents
1 Introduction
1.1 General Physiology of MAO
1.2 Structures of MAO-A and MAO-B
1.3 Involvement of MAO in Increasing Oxidative Stress in AD
1.4 Involvement of MAO in Aberrant Amyloid Aggregation in AD
1.5 Targeting MAO as a Therapeutic Approach in AD Treatment
2 Insights into the Binding Mechanism of Natural Inhibitors with MAOs
3 Computational Insights into the Key Interactions of Synthetic Inhibitors with MAOs
4 Conclusions and Future Perspectives
References
Chapter 12: Computational Modeling of Phosphodiesterase Inhibitors as Anti-Alzheimer Agents
1 Introduction
1.1 Alzheimer´s Disease
1.2 Phosphodiesterases and Their Role in AD
2 Computational Drug Design
2.1 Computational Studies of PDE Inhibitors
2.1.1 PDE2 Inhibitors
2.1.2 PDE4 Inhibitors
2.1.3 PDE5 Inhibitors
2.1.4 PDE9 Inhibitors
2.1.5 PDE10 Inhibitors
3 Conclusion
References
Chapter 13: Computational Methods for the Design and Development of Glutaminyl Cyclase Inhibitors in Alzheimer´s Disease
Abbreviations
1 Introduction
1.1 Application of Computational Methods in Drug Design of Glutaminyl Cyclase Inhibitors
2 Homology Modelling Studies
3 Docking Studies with the Structure of Human Glutaminyl Cyclase Enzyme
4 High-Throughput Virtual Screening for Designing QC Inhibitors
5 QSAR-Based Drug Design for QC Inhibitors
6 Conclusions
References
Part IV: Special Topics
Chapter 14: Basic Information Science Methods for Insight into Neurodegenerative Pathogenesis
1 Introduction
2 Chronic Disease Pathology as a Mineable Source of Neurodegenerative Insight
3 Information Retrieval Strategies for Intuiting Neurodegenerative Mechanistic Hypotheses
4 Resources
5 Methods and Notes
6 Conclusions
References
Chapter 15: Network Pharmacology for Drug Repositioning in Anti-Alzheimer´s Drug Development
1 Background
2 Drug Repositioning in the AD Drug Discovery
3 Approaches to Drug Repositioning
4 Network Pharmacology in the AD Drug Discovery
5 Drug Repositioning Using Network Pharmacology
5.1 In Drug Target Identification
5.2 Understanding the MoA of a Drug
5.3 Understanding Disease Mechanisms
5.4 Understanding Drug Toxicity and Adverse Reactions
6 Recent Efforts in Network Pharmacology-Based AD Drug Repositioning
7 Conclusions
References
Chapter 16: Web Services for the Prediction of ADMET Parameters Relevant to the Design of Neuroprotective Drugs
1 Introduction
2 Materials and Methods
2.1 Data Collection
2.1.1 Prediction Services
2.1.2 Prediction Test Set and Evaluation Procedure
2.2 Featured Services
3 Results and Discussion
3.1 Data Peculiarities and the Properties Analyzed
3.2 Analysis of Predictions for Specific Properties: Implications and Patterns
3.2.1 Lipophilicity
3.2.2 hERG Inhibition
3.2.3 Blood-Brain Barrier (BBB) Permeability
3.2.4 Caco-2 Permeability
3.2.5 Human Intestinal Absorption (HIA)
3.2.6 Water Solubility
3.2.7 Ames Mutagenicity
3.2.8 CYP Interactions
4 Conclusions
References
Index