Bioinformatics Tools for Pharmaceutical Drug Product Development 1st Edition by Vivek Chavda 1119865704 9781119865704

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ISBN-10 :  1119865704 

ISBN-13 : 9781119865704

Author : Vivek Chavda 

BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT
A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries.

The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall.

Bioinformatics Tools for Pharmaceutical Drug Product Development 1st Table of contents:

Part I: BIOINFORMATICS TOOLS
1 Introduction to Bioinformatics, AI, and ML for Pharmaceuticals
1.1 Introduction
1.2 Bioinformatics
1.3 Machine Learning (ML)
1.4 Conclusion and Future Prospects
References
2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling
2.1 Introduction
2.2 Artificial Intelligence in Drug Discovery
2.3 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
2.8 Discussion and Future Perspectives
2.9 Conclusion
References
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
3.6 Method/Tools for Serum Stability Evaluation
3.7 Conclusion
3.8 Future Prospects
References
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
5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics
5.1 Introduction
5.2 Mass Spectrometry – Protein Interaction
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
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
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
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
8.4 Conclusion
References
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
10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing
Abbreviations
10.1 Introduction to Artificial Intelligence and Machine Learning
10.2 Different Applications of AI and ML in the Pharma Field
10.3 AI and ML-Based Manufacturing
10.4 AI and ML-Based Drug Product Marketing
10.5 Future Prospects and Way Forward
10.6 Conclusion
References
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
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
12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products
12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products
12.6 Way Forward
12.7 Conclusion
References
Part III: BIOINFORMATICS TOOLS FOR HEALTHCARE SECTOR
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
13.7 AI/ML Based Healthcare Start-Ups
13.8 Opportunities and Risks for Future
13.9 Conclusion and Perspectives
References
14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy
Abbreviations
14.1 Introduction
14.2 Machine Learning Algorithm Models
14.3 Artificial Learning in Radiology
14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy
14.5 Implementation of Machine Learning Algorithms in Radiotherapy
14.6 Deep Learning Models
14.7 Clinical Implementation of AI in Radiotherapy
14.8 Current Challenges and Future Directions
References
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)
15.4 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
15.9 Conclusion
References
16 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment
16.1 Fundamentals of Cell Therapy
16.2 Fundamentals of Gene Therapy
16.3 Personalized Cell Therapy
16.4 Manufacturing of Cell and Gene-Based Therapies
16.5 Development of an Omics Profile
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
17 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector
17.1 Current Scenario
17.2 Way Forward

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