Bioinformatics Tools for Pharmaceutical Drug Product Development

by Chavda, Vivek; Anand, Krishnan; Apostolopoulos, Vasso

• ISBN13: 9781119865117
• ISBN10: 1119865115
• Edition: 1st
• Format: Hardcover
• Copyright: 2023-03-14
• Publisher: Wiley-Scrivener

Summary

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.

Author Biography

The editors are well-known experts in their respective fields of bioinformatics, computational chemistry, drug discovery, vaccines, nanotechnology and chronic disease management. Editors Dr. Vivek Chavda, Dr. Krishnan Anand and Dr. Vasso Apostolopoulos have several publications in the field of chronic diseases, vaccines, pharmaceutical drug product development, bioinformatics, drug design, and drug delivery systems.

Table of 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

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 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

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

8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling 165

8.4 Conclusion 168

References 169

9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products 183
Vivek P. Chavda

9.1 Introduction 184

9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) 185

9.3 AI- and ML-Based Formulation Development 187

9.4 AI- and ML-Based Process Development and Process Characterization 189

9.5 Concluding Remarks and Future Prospects 192

References 193

10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing 197
Kajal Baviskar, Anjali Bedse, Shilpa Raut and Narayana Darapaneni

Abbreviations 198

10.1 Introduction to Artificial Intelligence and Machine Learning 199

10.1.1 AI and ML in Pharmaceutical Manufacturing 200

10.1.2 AI and ML in Drug Product Marketing 201

10.2 Different Applications of AI and ML in the Pharma Field 202

10.2.1 Drug Discovery 202

10.2.2 Pharmaceutical Product Development 202

10.2.3 Clinical Trial Design 203

10.2.4 Manufacturing of Drugs 203

10.2.5 Quality Control and Quality Assurance 203

10.2.6 Product Management 203

10.2.7 Drug Prescription 204

10.2.8 Medical Diagnosis 204

10.2.9 Monitoring of Patients 204

10.2.10 Drug Synergism and Antagonism Prediction 204

10.2.11 Precision Medicine 205

10.3 AI and ML-Based Manufacturing 205

10.3.1 Continuous Manufacturing 205

10.3.2 Process Improvement and Fault Detection 209

10.3.3 Predictive Maintenance (PdM) 210

10.3.4 Quality Control and Yield 211

10.3.5 Troubleshooting 211

10.3.6 Supply Chain Management 212

10.3.7 Warehouse Management 213

10.3.8 Predicting Remaining Useful Life 214

10.3.9 Challenges 215

10.4 AI and ML-Based Drug Product Marketing 217

10.4.1 Product Launch 217

10.4.2 Real-Time Personalization and Consumer Behavior 218

10.4.3 Better Customer Relationships 219

10.4.4 Enhanced Marketing Measurement 220

10.4.5 Predictive Marketing Analytics 220

10.4.6 Price Dynamics 221

10.4.7 Market Segmentation 222

10.4.8 Challenges 223

10.5 Future Prospects and Way Forward 223

10.6 Conclusion 224

References 225

11 Artificial Intelligence and Machine Learning Applications in Vaccine Development 233
Ali Sarmadi, Majid Hassanzadeganroudsari and M. Soltani

11.1 Introduction 234

11.2 Prioritizing Proteins as Vaccine Candidates 237

11.3 Predicting Binding Scores of Candidate Proteins 238

11.4 Predicting Potential Epitopes 243

11.5 Design of Multi-Epitope Vaccine 244

11.6 Tracking the RNA Mutations of a Virus 245

Conclusion 248

References 249

12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products 255
Avinash Khadela, Sagar Popat, Jinal Ajabiya, Disha Valu, Shrinivas Savale and Vivek P. Chavda

Abbreviations 256

12.1 Introduction 257

12.2 AI and ML for Pandemic 258

12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development 259

12.3.1 Spectroscopic Techniques 260

12.3.2 Chromatographic Techniques 263

12.3.3 Electrochemical Techniques 263

12.3.4 Electrophoretic Techniques 264

12.3.5 Hyphenated Techniques 264

12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products 265

12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development 266

12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products 268

12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research 270

12.5.2 Role of AI and ML in Clinical Study Protocol Optimization 272

12.5.3 Role of AI and ML in the Management of Clinical Trial Participants 272

12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management 272

12.6 Way Forward 275

12.7 Conclusion 276

References 277

Part III: Bioinformatics Tools for Healthcare Sector 285

13 Artificial Intelligence and Machine Learning in Healthcare Sector 287
Vivek P. Chavda, Kaushika Patel, Sachin Patel and Vasso Apostolopoulos

Abbreviations 288

13.1 Introduction 288

13.2 The Exponential Rise of AI/ML Solutions in Healthcare 289

13.3 AI/ML Healthcare Solutions for Doctors 291

13.4 AI/ML Solution for Patients 293

13.5 AI Solutions for Administrators 295

13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector 297

13.6.1 High Cost 297

13.6.2 Lack of Creativity 298

13.6.3 Errors Potentially Harming Patients 298

13.6.4 Privacy Issues 298

13.6.5 Increase in Unemployment 299

13.6.6 Lack of Ethics 299

13.6.7 Promotes a Less-Effort Culture Among Human Workers 299

13.7 AI/ML Based Healthcare Start-Ups 299

13.8 Opportunities and Risks for Future 304

13.8.1 Patient Mobility Monitoring 305

13.8.2 Clinical Trials for Drug Development 305

13.8.3 Quality of Electronic Health Records (EHR) 305

13.8.4 Robot-Assisted Surgery 305

13.9 Conclusion and Perspectives 306

References 307

14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy 315
Sanket Chintawar, Vaishnavi Gattani, Shivanee Vyas and Shilpa Dawre

Abbreviations 316

14.1 Introduction 317

14.2 Machine Learning Algorithm Models 318

14.2.1 Supervised Learning 318

14.2.2 Unsupervised Learning 319

14.2.3 Semi-Supervised Learning 319

14.2.4 Reinforcement Learning (RL) 320

14.3 Artificial Learning in Radiology 321

14.3.1 Types of Radiation Therapy 321

14.3.1.1 External Radiation Therapy 322

14.3.1.2 Internal Radiation Therapy 323

14.3.1.3 Systemic Radiation Therapy 323

14.3.2 Mechanism of Action 323

14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy 324

14.4.1 Delineation of the Target 324

14.4.2 Radiotherapy Delivery 325

14.4.3 Image Guided Radiotherapy 327

14.5 Implementation of Machine Learning Algorithms in Radiotherapy 328

14.5.1 Image Segmentation 328

14.5.2 Medical Image Registration 329

14.5.3 Computer-Aided Detection (CAD) and Diagnosis System 329

14.6 Deep Learning Models 331

14.6.1 Deep Neural Networks 331

14.6.2 Convolutional Neural Networks 332

14.7 Clinical Implementation of AI in Radiotherapy 332

14.8 Current Challenges and Future Directions 339

References 339

15 Role of AI and ML in Epidemics and Pandemics 345
Rajashri Bezbaruah, Mainak Ghosh, Shuby Kumari, Lawandashisha Nongrang, Sheikh Rezzak Ali, Monali Lahiri, Hasmi Waris and Bibhuti Bhushan Kakoti

15.1 Introduction 346

15.2 History of Artificial Intelligence (AI) in Medicine 347

15.3 AI and MI Usage in Pandemic and Epidemic (COVID-19) 348

15.3.1 SARS-CoV-2 Detection and Therapy Using Machine Learning and Artificial Intelligence 349

15.3.2 SARS-Cov-2 Contact Tracing Using Machine Learning and Artificial Intelligence 350

15.3.3 SARS-CoV-2 Prediction and Forecasting Using Machine Learning and Artificial Intelligence 350

15.3.4 SARS-CoV-2 Medicines and Vaccine Using Machine Learning and Artificial Intelligence 350

15.4 Cost Optimization for Research and Development Using Al and ml 351

15.5 AI and ML in COVID 19 Vaccine Development 352

15.6 Efficacy of AI and ML in Vaccine Development 357

15.7 Artificial Intelligence and Machine Learning in Vaccine Development: Clinical Trials During an Epidemic and Pandemic 358

15.8 Clinical Trials During an Epidemic 360

15.8.1 Ebola Virus 360

15.8.2 SARS-CoV- 2 361

15.9 Conclusion 361

References 362

16 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment 371
Susmit Mhatre, Somanshi Shukla, Vivek P. Chavda, Lakshmikanth Gandikota and Vandana Patravale

16.1 Fundamentals of Cell Therapy 372

16.1.1 Stem Cell Therapies 374

16.1.1.1 Mesenchymal Stem Cells (MSCs) 375

16.1.1.2 Hematopoietic Stem Cells (HSCs) 375

16.1.1.3 Mononuclear Cells (MNCs) 375

16.1.1.4 Endothelial Progenitor Cells (EPCs) 375

16.1.1.5 Neural Stem Cells (NSCs) or Neural Progenitor Cells (NPCs) 376

16.1.2 Adoptive Cell Therapy 376

16.1.2.1 Tumor-Infiltrating Lymphocyte (TIL) Therapy 376

16.1.2.2 Engineered T-Cell Receptor (TCR) Therapy 377

16.1.2.3 Chimeric Antigen Receptor (CAR) T Cell Therapy 377

16.1.2.4 Natural Killer (NK) Cell Therapy 377

16.2 Fundamentals of Gene Therapy 378

16.2.1 Identification 378

16.2.2 Treatment 379

16.3 Personalized Cell Therapy 381

16.4 Manufacturing of Cell and Gene-Based Therapies 382

16.5 Development of an Omics Profile 385

16.6 ml in Stem Cell Identification, Differentiation, and Characterization 387

16.7 Machine Learning in Gene Expression Imaging 389

16.8 AI in Gene Therapy Target and Potency Prediction 390

16.9 Conclusion and Future Prospective 391

References 392

17 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector 401
Prashant Pokhriyal, Vivek P. Chavda and Mili Pathak

17.1 Current Scenario 402

17.2 Way Forward 406

References 407

Index 417