Bioinformatics Tools for Pharmaceutical Drug Product Development (eBook, ePUB)

Redaktion: Chavda, Vivek P.; Apostolopoulos, Vasso; Anand, Krishnan

• Format: ePub

Produktbeschreibung

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. Readers will find in this book: * Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; * The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; * The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 448
• Erscheinungstermin: 9. Februar 2023
• Englisch
• ISBN-13: 9781119865704
• Artikelnr.: 67514644

Autorenporträt

Vivek Chavda, M. Pharm, is an assistant professor in the Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, India. He has more than 40 research articles in international journals. Krishnan Anand, PhD, is a research scientist in the Department of Chemical Pathology, University of the Free State, Bloemfontein, South Africa. He has more than 40 research articles in international journals and his research interests are in organic chemistry, medicinal chemistry, chemical pathology, bioinformatics, and nanotechnology. Vasso Apostolopoulos, PhD, is at the Institute for Health and Sport, Immunology and Translational Research Group, Victoria University, Melbourne, Australia. She received her PhD in immunology in 1995 from the University of Melbourne, and the Advanced Certificate in Protein Crystallography from Birkbeck College, University of London. Professor Vasso Apostolopoulos is a world-renowned researcher who has been recognized with over 100 awards for the outstanding results of her research and she was named one of the most successful Greeks abroad by the prestigious Times magazine. Vasso was the first in the world to develop the concept of immunotherapy for cancer in the early 1990s, which today is used by hundreds of labs around the world. Immunotherapy aims to boost specific immune cells and program them to kill cancer cells; it was used by Vasso to develop the world's first breast cancer vaccine with phase I, II, and III clinical trials completed. Of note, one of the studies now has long-term follow-up data showing that 20 years later those injected with the vaccine remain cancer free.

Inhaltsangabe

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

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

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