Bioinformatics Tools for Pharmaceutical Drug Product Development

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