Bioinformatics Tools for Pharmaceutical Drug Product Development

440 Pages·2023·30.306 MB·English

by  Vivek Chavda, K. Anand, Vasso Apostolopoulos

Bioinformatics Tools
for Pharmaceutical Drug
Product Development
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected]) Bioinformatics Tools
Phillip Carmical ([email protected])
for Pharmaceutical Drug
Product Development
Edited by
Vivek Chavda
Department of Pharmaceutics and Pharmaceutical Technology,
L. M. College of Pharmacy, Ahmedabad, India
Krishnan Anand
Department of Chemical Pathology, School of Pathology, University of the Free
State, Bloemfontein, South Africa
and
Vasso Apostolopoulos
Institute for Health and Sport, Immunology and Translational Research Group,
Victoria University, Melbourne, Australia
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86511-7 
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xv
Part I: Bioinformatics Tools 1
1 Introduction to Bioinformatics, AI, and ML
for Pharmaceuticals 3
Vivek P. Chavda, Disha Vihol, Aayushi Patel,
Elrashdy M. Redwan and Vladimir N. Uversky
1.1 Introduction 4
1.2 Bioinformatics 4
1.2.1 Limitations of Bioinformatics 8
1.2.2 Artificial Intelligence (AI) 8
1.3 Machine Learning (ML) 11
1.3.1 Applications of ML 12
1.3.2 Limitations of ML 14
1.4 Conclusion and Future Prospects 14
References 15
2 Artificial Intelligence and Machine Learning-Based
New Drug Discovery Process with Molecular Modelling 19
Isha Rani, Kavita Munjal, Rajeev K. Singla
and Rupesh K. Gautam
2.1 Introduction 20
2.2 Artificial Intelligence in Drug Discovery 21
2.2.1 Training Dataset Used in Medicinal Chemistry 22
2.2.2 Availability and Quality of Initial Data 23
2.3 AI in Virtual Screening 24
2.4 AI for De Novo Design 25
2.5 AI for Synthesis Planning 26
2.6 AI in Quality Control and Quality Assurance 27
2.7 AI-Based Advanced Applications 28
2.7.1 Micro/Nanorobot Targeted Drug Delivery System 28
v
vi Contents
2.7.2 AI in Nanomedicine 29
2.7.3 Role of AI in Market Prediction 29
2.8 Discussion and Future Perspectives 30
2.9 Conclusion 31
References 31
3 Role of Bioinformatics in Peptide-Based Drug Design
and Its Serum Stability 37
Vivek Chavda, Prashant Kshirsagar and Nildip Chauhan
3.1 Introduction 37
3.2 Points to be Considered for Peptide-Based Delivery 38
3.3 Overview of Peptide-Based Drug Delivery System 40
3.4 Tools for Screening of Peptide Drug Candidate 41
3.5 Various Strategies to Increase Serum Stability of Peptide 42
3.5.1 Cyclization of Peptide 42
3.5.2 Incorporation of D Form of Amino Acid 44
3.5.3 Terminal Modification 44
3.5.4 Substitution of Amino Acid Which is Not Natural 46
3.5.5 Stapled Peptides 46
3.5.6 Synthesis of Stapled Peptides 47
3.6 Method/Tools for Serum Stability Evaluation 47
3.7 Conclusion 48
3.8 Future Prospects 49
References 49
4 Data Analytics and Data Visualization
for the Pharmaceutical Industry 55
Shalin Parikh, Ravi Patel, Dignesh Khunt, Vivek P. Chavda
and Lalitkumar Vora
4.1 Introduction 56
4.2 Data Analytics 57
4.3 Data Visualization 58
4.4 Data Analytics and Data Visualization for Formulation
Development 60
4.5 Data Analytics and Data Visualization for Drug
Product Development 65
4.6 Data Analytics and Data Visualization for Drug
Product Life Cycle Management 69
4.7 Conclusion and Future Prospects 71
References 72
Contents vii
5 Mass Spectrometry, Protein Interaction and Amalgamation
of Bioinformatics 77
Vivek Chavda, Kaustubh Dange and Madhav Joglekar
5.1 Introduction 77
5.2 Mass Spectrometry - Protein Interaction 79
5.2.1 The Prerequisites 80
5.2.2 Finding Affinity Partner (The Bait) 80
5.2.3 Antibody-Based Affinity Tags 80
5.2.4 Small Molecule Ligands 80
5.2.5 Fusion Protein-Based Affinity Tags 81
5.3 MS Analysis 81
5.4 Validating Specific Interactions 82
5.5 Mass Spectrometry – Qualitative and Quantitative Analysis 83
5.6 Challenges Associated with Mass Analysis 83
5.7 Relative vs. Absolute Quantification 85
5.8 Mass Spectrometry – Lipidomics
and Metabolomics 86
5.9 Mass Spectrometry – Drug Discovery 87
5.10 Conclusion and Future Scope 88
5.11 Resources and Software 89
Acknowledgement 89
References 89
6 Applications of Bioinformatics Tools in Medicinal
Biology and Biotechnology 95
Harshil Shah, Vivek Chavda and Moinuddin M. Soniwala
6.1 Introduction 96
6.2 Bioinformatics Tools 97
6.3 The Genetic Basis of Diseases 97
6.4 Proteomics 98
6.5 Transcriptomic 100
6.6 Cancer 101
6.7 Diagnosis 102
6.8 Drug Discovery and Testing 103
6.9 Molecular Medicines 105
6.10 Personalized (Precision) Medicines 106
6.11 Vaccine Development and Drug Discovery
in Infectious Diseases and COVID-19 Pandemic 108
6.12 Prognosis of Ailments 109
6.13 Concluding Remarks and Future Prospects 110
viii Contents
Acknowledgement 111
References 111
7 Clinical Applications of “Omics” Technology
as a Bioinformatic Tool 117
Vivek Chavda, Rajashri Bezbaruah, Disha Valu, Sanjay Desai,
Nildip Chauhan, Swati Marwadi, Gitima Deka
and Zhiyong Ding
Abbreviations 118
7.1 Introduction 118
7.2 Execution Method 119
7.3 Overview of Omics Technology 121
7.4 Genomics 124
7.5 Nutrigenomics 127
7.6 Transcriptomics 128
7.7 Proteomics 129
7.8 Metabolomics 130
7.9 Lipomics or Lipidomics 133
7.10 Ayurgenomics 134
7.11 Pharmacogenomics 134
7.12 Toxicogenomic 135
7.13 Conclusion and Future Prospects 139
Acknowledgement 139
References 139
Part II: Bioinformatics Tools for Pharmaceutical
Sector 147
8 Bioinformatics and Cheminformatics Tools
in Early Drug Discovery 149
Palak K. Parikh, Jignasa K. Savjani, Anuradha K. Gajjar
and Mahesh T. Chhabria
Abbreviations 150
8.1 Introduction 151
8.2 Informatics and Drug Discovery 152
8.3 Computational Methods in Drug Discovery 153
8.3.1 Homology Modeling 153
8.3.2 Docking Studies 155
8.3.3 Molecular Dynamics Simulations 158
8.3.4 De Novo Drug Design 159
8.3.5 Quantitative Structure Activity Relationships 160
8.3.6 Pharmacophore Modeling 161