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
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