دانلود کتاب Bioinformatics Tools for Pharmaceutical Drug Product Development

فهرست مطالب :

Cover Title Page Copyright Page Contents Preface Part I: Bioinformatics Tools Chapter 1 Introduction to Bioinformatics, AI, and ML for Pharmaceuticals 1.1 Introduction 1.2 Bioinformatics 1.2.1 Limitations of Bioinformatics 1.2.2 Artificial Intelligence (AI) 1.3 Machine Learning (ML) 1.3.1 Applications of ML 1.3.2 Limitations of ML 1.4 Conclusion and Future Prospects References Chapter 2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling 2.1 Introduction 2.2 Artificial Intelligence in Drug Discovery 2.2.1 Training Dataset Used in Medicinal Chemistry 2.2.2 Availability and Quality of Initial Data 2.3 AI in Virtual Screening 2.4 AI for De Novo Design 2.5 AI for Synthesis Planning 2.6 AI in Quality Control and Quality Assurance 2.7 AI-Based Advanced Applications 2.7.1 Micro/Nanorobot Targeted Drug Delivery System 2.7.2 AI in Nanomedicine 2.7.3 Role of AI in Market Prediction 2.8 Discussion and Future Perspectives 2.9 Conclusion References Chapter 3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability 3.1 Introduction 3.2 Points to be Considered for Peptide-Based Delivery 3.3 Overview of Peptide-Based Drug Delivery System 3.4 Tools for Screening of Peptide Drug Candidate 3.5 Various Strategies to Increase Serum Stability of Peptide 3.5.1 Cyclization of Peptide 3.5.2 Incorporation of D Form of Amino Acid 3.5.3 Terminal Modification 3.5.4 Substitution of Amino Acid Which is Not Natural 3.5.5 Stapled Peptides 3.5.6 Synthesis of Stapled Peptides 3.6 Method/Tools for Serum Stability Evaluation 3.7 Conclusion 3.8 Future Prospects References Chapter 4 Data Analytics and Data Visualization for the Pharmaceutical Industry 4.1 Introduction 4.2 Data Analytics 4.3 Data Visualization 4.4 Data Analytics and Data Visualization for Formulation Development 4.5 Data Analytics and Data Visualization for Drug Product Development 4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management 4.7 Conclusion and Future Prospects References Chapter 5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics 5.1 Introduction 5.2 Mass Spectrometry - Protein Interaction 5.2.1 The Prerequisites 5.2.2 Finding Affinity Partner (The Bait) 5.2.3 Antibody-Based Affinity Tags 5.2.4 Small Molecule Ligands 5.2.5 Fusion Protein-Based Affinity Tags 5.3 MS Analysis 5.4 Validating Specific Interactions 5.5 Mass Spectrometry – Qualitative and Quantitative Analysis 5.6 Challenges Associated with Mass Analysis 5.7 Relative vs. Absolute Quantification 5.8 Mass Spectrometry – Lipidomics and Metabolomics 5.9 Mass Spectrometry – Drug Discovery 5.10 Conclusion and Future Scope 5.11 Resources and Software Acknowledgement References Chapter 6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology 6.1 Introduction 6.2 Bioinformatics Tools 6.3 The Genetic Basis of Diseases 6.4 Proteomics 6.5 Transcriptomic 6.6 Cancer 6.7 Diagnosis 6.8 Drug Discovery and Testing 6.9 Molecular Medicines 6.10 Personalized (Precision) Medicines 6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic 6.12 Prognosis of Ailments 6.13 Concluding Remarks and Future Prospects Acknowledgement References Chapter 7 Clinical Applications of “Omics” Technology as a Bioinformatic Tool Abbreviations 7.1 Introduction 7.2 Execution Method 7.3 Overview of Omics Technology 7.4 Genomics 7.5 Nutrigenomics 7.6 Transcriptomics 7.7 Proteomics 7.8 Metabolomics 7.9 Lipomics or Lipidomics 7.10 Ayurgenomics 7.11 Pharmacogenomics 7.12 Toxicogenomic 7.13 Conclusion and Future Prospects Acknowledgement References Part II: Bioinformatics Tools for Pharmaceutical Sector Chapter 8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery Abbreviations 8.1 Introduction 8.2 Informatics and Drug Discovery 8.3 Computational Methods in Drug Discovery 8.3.1 Homology Modeling 8.3.2 Docking Studies 8.3.3 Molecular Dynamics Simulations 8.3.4 De Novo Drug Design 8.3.5 Quantitative Structure Activity Relationships 8.3.6 Pharmacophore Modeling 8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling 8.4 Conclusion References Chapter 9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products 9.1 Introduction 9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) 9.3 AI- and ML-Based Formulation Development 9.4 AI- and ML-Based Process Development and Process Characterization 9.5 Concluding Remarks and Future Prospects References Chapter 10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing Abbreviations 10.1 Introduction to Artificial Intelligence and Machine Learning 10.1.1 AI and ML in Pharmaceutical Manufacturing 10.1.2 AI and ML in Drug Product Marketing 10.2 Different Applications of AI and ML in the Pharma Field 10.2.1 Drug Discovery 10.2.2 Pharmaceutical Product Development 10.2.3 Clinical Trial Design 10.2.4 Manufacturing of Drugs 10.2.5 Quality Control and Quality Assurance 10.2.6 Product Management 10.2.7 Drug Prescription 10.2.8 Medical Diagnosis 10.2.9 Monitoring of Patients 10.2.10 Drug Synergism and Antagonism Prediction 10.2.11 Precision Medicine 10.3 AI and ML-Based Manufacturing 10.3.1 Continuous Manufacturing 10.3.2 Process Improvement and Fault Detection 10.3.3 Predictive Maintenance (PdM) 10.3.4 Quality Control and Yield 10.3.5 Troubleshooting 10.3.6 Supply Chain Management 10.3.7 Warehouse Management 10.3.8 Predicting Remaining Useful Life 10.3.9 Challenges 10.4 AI and ML-Based Drug Product Marketing 10.4.1 Product Launch 10.4.2 Real-Time Personalization and Consumer Behavior 10.4.3 Better Customer Relationships 10.4.4 Enhanced Marketing Measurement 10.4.5 Predictive Marketing Analytics 10.4.6 Price Dynamics 10.4.7 Market Segmentation 10.4.8 Challenges 10.5 Future Prospects and Way Forward 10.6 Conclusion References Chapter 11 Artificial Intelligence and Machine Learning Applications in Vaccine Development 11.1 Introduction 11.2 Prioritizing Proteins as Vaccine Candidates 11.3 Predicting Binding Scores of Candidate Proteins 11.4 Predicting Potential Epitopes 11.5 Design of Multi-Epitope Vaccine 11.6 Tracking the RNA Mutations of a Virus Conclusion References Chapter 12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products Abbreviations 12.1 Introduction 12.2 AI and ML for Pandemic 12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development 12.3.1 Spectroscopic Techniques 12.3.2 Chromatographic Techniques 12.3.3 Electrochemical Techniques 12.3.4 Electrophoretic Techniques 12.3.5 Hyphenated Techniques 12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products 12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development 12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products 12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research 12.5.2 Role of AI and ML in Clinical Study Protocol Optimization 12.5.3 Role of AI and ML in the Management of Clinical Trial Participants 12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management 12.6 Way Forward 12.7 Conclusion References Part III: Bioinformatics Tools for Healthcare Sector Chapter 13 Artificial Intelligence and Machine Learning in Healthcare Sector Abbreviations 13.1 Introduction 13.2 The Exponential Rise of AI/ML Solutions in Healthcare 13.3 AI/ML Healthcare Solutions for Doctors 13.4 AI/ML Solution for Patients 13.5 AI Solutions for Administrators 13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector 13.6.1 High Cost 13.6.2 Lack of Creativity 13.6.3 Errors Potentially Harming Patients 13.6.4 Privacy Issues 13.6.5 Increase in Unemployment 13.6.6 Lack of Ethics 13.6.7 Promotes a Less-Effort Culture Among Human Workers 13.7 AI/ML Based Healthcare Start-Ups 13.8 Opportunities and Risks for Future 13.8.1 Patient Mobility Monitoring 13.8.2 Clinical Trials for Drug Development 13.8.3 Quality of Electronic Health Records (EHR) 13.8.4 Robot-Assisted Surgery 13.9 Conclusion and Perspectives References Chapter 14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy Abbreviations 14.1 Introduction 14.2 Machine Learning Algorithm Models 14.2.1 Supervised Learning 14.2.2 Unsupervised Learning 14.2.3 Semi-Supervised Learning 14.2.4 Reinforcement Learning (RL) 14.3 Artificial Learning in Radiology 14.3.1 Types of Radiation Therapy 14.3.1.1 External Radiation Therapy 14.3.1.2 Internal Radiation Therapy 14.3.1.3 Systemic Radiation Therapy 14.3.2 Mechanism of Action 14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy 14.4.1 Delineation of the Target 14.4.2 Radiotherapy Delivery 14.4.3 Image Guided Radiotherapy 14.5 Implementation of Machine Learning Algorithms in Radiotherapy 14.5.1 Image Segmentation 14.5.2 Medical Image Registration 14.5.3 Computer-Aided Detection (CAD) and Diagnosis System 14.6 Deep Learning Models 14.6.1 Deep Neural Networks 14.6.2 Convolutional Neural Networks 14.7 Clinical Implementation of AI in Radiotherapy 14.8 Current Challenges and Future Directions References Chapter 15 Role of AI and ML in Epidemics and Pandemics 15.1 Introduction 15.2 History of Artificial Intelligence (AI) in Medicine 15.3 AI and MI Usage in Pandemic and Epidemic (COVID-19) 15.3.1 SARS-CoV-2 Detection and Therapy Using Machine Learning and Artificial Intelligence 15.3.2 SARS-Cov-2 Contact Tracing Using Machine Learning and Artificial Intelligence 15.3.3 SARS-CoV-2 Prediction and Forecasting Using Machine Learning and Artificial Intelligence 15.3.4 SARS-CoV-2 Medicines and Vaccine Using Machine Learning and Artificial Intelligence 15.4 Cost Optimization for Research and Development Using Al and ML 15.5 AI and ML in COVID 19 Vaccine Development 15.6 Efficacy of AI and ML in Vaccine Development 15.7 Artificial Intelligence and Machine Learning in Vaccine Development: Clinical Trials During an Epidemic and Pandemic 15.8 Clinical Trials During an Epidemic 15.8.1 Ebola Virus 15.8.2 SARS-CoV-2 15.9 Conclusion References Chapter 16 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment 16.1 Fundamentals of Cell Therapy 16.1.1 Stem Cell Therapies 16.1.1.1 Mesenchymal Stem Cells (MSCs) 16.1.1.2 Hematopoietic Stem Cells (HSCs) 16.1.1.3 Mononuclear Cells (MNCs) 16.1.1.4 Endothelial Progenitor Cells (EPCs) 16.1.1.5 Neural Stem Cells (NSCs) or Neural Progenitor Cells (NPCs) 16.1.2 Adoptive Cell Therapy 16.1.2.1 Tumor-Infiltrating Lymphocyte (TIL) Therapy 16.1.2.2 Engineered T-Cell Receptor (TCR) Therapy 16.1.2.3 Chimeric Antigen Receptor (CAR) T Cell Therapy 16.1.2.4 Natural Killer (NK) Cell Therapy 16.2 Fundamentals of Gene Therapy 16.2.1 Identification 16.2.2 Treatment 16.3 Personalized Cell Therapy 16.4 Manufacturing of Cell and Gene-Based Therapies 16.5 Development of an Omics Profile 16.6 ML in Stem Cell Identification, Differentiation, and Characterization 16.7 Machine Learning in Gene Expression Imaging 16.8 AI in Gene Therapy Target and Potency Prediction 16.9 Conclusion and Future Prospective References Chapter 17 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector 17.1 Current Scenario 17.2 Way Forward References Index EULA