Bioinformatics Tools for Pharmaceutical Drug Product Development

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.

• Preface xvPart I: Bioinformatics Tools 11 Introduction to Bioinformatics, AI, and ml for Pharmaceuticals 3Vivek P. Chavda, Disha Vihol, Aayushi Patel, Elrashdy M. Redwan and Vladimir N. Uversky1.1 Introduction 41.2 Bioinformatics 41.2.1 Limitations of Bioinformatics 81.2.2 Artificial Intelligence (AI) 81.3 Machine Learning (ML) 111.3.1 Applications of ml 121.3.2 Limitations of ml 141.4 Conclusion and Future Prospects 14References 152 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling 19Isha Rani, Kavita Munjal, Rajeev K. Singla and Rupesh K. Gautam2.1 Introduction 202.2 Artificial Intelligence in Drug Discovery 212.2.1 Training Dataset Used in Medicinal Chemistry 222.2.2 Availability and Quality of Initial Data 232.3 AI in Virtual Screening 242.4 AI for De Novo Design 252.5 AI for Synthesis Planning 262.6 AI in Quality Control and Quality Assurance 272.7 AI-Based Advanced Applications 282.7.1 Micro/Nanorobot Targeted Drug Delivery System 282.7.2 AI in Nanomedicine 292.7.3 Role of AI in Market Prediction 292.8 Discussion and Future Perspectives 302.9 Conclusion 31References 313 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability 37Vivek Chavda, Prashant Kshirsagar and Nildip Chauhan3.1 Introduction 373.2 Points to be considered for Peptide-Based Delivery 383.3 Overview of Peptide-Based Drug Delivery System 403.4 Tools for Screening of Peptide Drug Candidate 413.5 Various Strategies to Increase Serum Stability of Peptide 423.5.1 Cyclization of Peptide 423.5.2 Incorporation of D Form of Amino Acid 443.5.3 Terminal Modification 443.5.4 Substitution of Amino Acid Which is Not Natural 463.5.5 Stapled Peptides 463.5.6 Synthesis of Stapled Peptides 473.6 Method/Tools for Serum Stability Evaluation 473.7 Conclusion 483.8 Future Prospects 49References 494 Data Analytics and Data Visualization for the Pharmaceutical Industry 55Shalin Parikh, Ravi Patel, Dignesh Khunt, Vivek P. Chavda and Lalitkumar Vora4.1 Introduction 564.2 Data Analytics 574.3 Data Visualization 584.4 Data Analytics and Data Visualization for Formulation Development 604.5 Data Analytics and Data Visualization for Drug Product Development 654.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management 694.7 Conclusion and Future Prospects 71References 725 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics 77Vivek Chavda, Kaustubh Dange and Madhav Joglekar5.1 Introduction 775.2 Mass Spectrometry - Protein Interaction 795.2.1 The Prerequisites 805.2.2 Finding Affinity Partner (The Bait) 805.2.3 Antibody-Based Affinity Tags 805.2.4 Small Molecule Ligands 805.2.5 Fusion Protein-Based Affinity Tags 815.3 MS Analysis 815.4 Validating Specific Interactions 825.5 Mass Spectrometry – Qualitative and Quantitative Analysis 835.6 Challenges Associated with Mass Analysis 835.7 Relative vs. Absolute Quantification 855.8 Mass Spectrometry – Lipidomics and Metabolomics 865.9 Mass Spectrometry – Drug Discovery 875.10 Conclusion and Future Scope 885.11 Resources and Software 89Acknowledgement 89References 896 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology 95Harshil Shah, Vivek Chavda and Moinuddin M. Soniwala6.1 Introduction 966.2 Bioinformatics Tools 976.3 The Genetic Basis of Diseases 976.4 Proteomics 986.5 Transcriptomic 1006.6 Cancer 1016.7 Diagnosis 1026.8 Drug Discovery and Testing 1036.9 Molecular Medicines 1056.10 Personalized (Precision) Medicines 1066.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic 1086.12 Prognosis of Ailments 1096.13 Concluding Remarks and Future Prospects 110Acknowledgement 111References 1117 Clinical Applications of “Omics” Technology as a Bioinformatic Tool 117Vivek Chavda, Rajashri Bezbaruah, Disha Valu, Sanjay Desai, Nildip Chauhan, Swati Marwadi, Gitima Deka and Zhiyong DingAbbreviations 1187.1 Introduction 1187.2 Execution Method 1197.3 Overview of Omics Technology 1217.4 Genomics 1247.5 Nutrigenomics 1277.6 Transcriptomics 1287.7 Proteomics 1297.8 Metabolomics 1307.9 Lipomics or Lipidomics 1337.10 Ayurgenomics 1347.11 Pharmacogenomics 1347.12 Toxicogenomic 1357.13 Conclusion and Future Prospects 139Acknowledgement 139References 139Part II: Bioinformatics Tools for Pharmaceutical Sector 1478 Bioinformatics and Cheminformatics Tools in Early Drug Discovery 149Palak K. Parikh, Jignasa K. Savjani, Anuradha K. Gajjar and Mahesh T. ChhabriaAbbreviations 1508.1 Introduction 1518.2 Informatics and Drug Discovery 1528.3 Computational Methods in Drug Discovery 1538.3.1 Homology Modeling 1538.3.2 Docking Studies 1558.3.3 Molecular Dynamics Simulations 1588.3.4 De Novo Drug Design 1598.3.5 Quantitative Structure Activity Relationships 1608.3.6 Pharmacophore Modeling 1618.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling 1658.4 Conclusion 168References 1699 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products 183Vivek P. Chavda9.1 Introduction 1849.2 Current Scenario in Pharma Industry and Quality by Design (QbD) 1859.3 AI- and ML-Based Formulation Development 1879.4 AI- and ML-Based Process Development and Process Characterization 1899.5 Concluding Remarks and Future Prospects 192References 19310 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing 197Kajal Baviskar, Anjali Bedse, Shilpa Raut and Narayana DarapaneniAbbreviations 19810.1 Introduction to Artificial Intelligence and Machine Learning 19910.1.1 AI and ML in Pharmaceutical Manufacturing 20010.1.2 AI and ML in Drug Product Marketing 20110.2 Different Applications of AI and ML in the Pharma Field 20210.2.1 Drug Discovery 20210.2.2 Pharmaceutical Product Development 20210.2.3 Clinical Trial Design 20310.2.4 Manufacturing of Drugs 20310.2.5 Quality Control and Quality Assurance 20310.2.6 Product Management 20310.2.7 Drug Prescription 20410.2.8 Medical Diagnosis 20410.2.9 Monitoring of Patients 20410.2.10 Drug Synergism and Antagonism Prediction 20410.2.11 Precision Medicine 20510.3 AI and ML-Based Manufacturing 20510.3.1 Continuous Manufacturing 20510.3.2 Process Improvement and Fault Detection 20910.3.3 Predictive Maintenance (PdM) 21010.3.4 Quality Control and Yield 21110.3.5 Troubleshooting 21110.3.6 Supply Chain Management 21210.3.7 Warehouse Management 21310.3.8 Predicting Remaining Useful Life 21410.3.9 Challenges 21510.4 AI and ML-Based Drug Product Marketing 21710.4.1 Product Launch 21710.4.2 Real-Time Personalization and Consumer Behavior 21810.4.3 Better Customer Relationships 21910.4.4 Enhanced Marketing Measurement 22010.4.5 Predictive Marketing Analytics 22010.4.6 Price Dynamics 22110.4.7 Market Segmentation 22210.4.8 Challenges 22310.5 Future Prospects and Way Forward 22310.6 Conclusion 224References 22511 Artificial Intelligence and Machine Learning Applications in Vaccine Development 233Ali Sarmadi, Majid Hassanzadeganroudsari and M. Soltani11.1 Introduction 23411.2 Prioritizing Proteins as Vaccine Candidates 23711.3 Predicting Binding Scores of Candidate Proteins 23811.4 Predicting Potential Epitopes 24311.5 Design of Multi-Epitope Vaccine 24411.6 Tracking the RNA Mutations of a Virus 245Conclusion 248References 24912 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products 255Avinash Khadela, Sagar Popat, Jinal Ajabiya, Disha Valu, Shrinivas Savale and Vivek P. ChavdaAbbreviations 25612.1 Introduction 25712.2 AI and ML for Pandemic 25812.3 Advanced Analytical Tools Used in Preclinical and Clinical Development 25912.3.1 Spectroscopic Techniques 26012.3.2 Chromatographic Techniques 26312.3.3 Electrochemical Techniques 26312.3.4 Electrophoretic Techniques 26412.3.5 Hyphenated Techniques 26412.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products 26512.4.1 Various Computational Tools Used in Pre-Clinical Drug Development 26612.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products 26812.5.1 Role of AI, ML, and Bioinformatics in Clinical Research 27012.5.2 Role of AI and ML in Clinical Study Protocol Optimization 27212.5.3 Role of AI and ML in the Management of Clinical Trial Participants 27212.5.4 Role of AI and ML in Clinical Trial Data Collection and Management 27212.6 Way Forward 27512.7 Conclusion 276References 277Part III: Bioinformatics Tools for Healthcare Sector 28513 Artificial Intelligence and Machine Learning in Healthcare Sector 287Vivek P. Chavda, Kaushika Patel, Sachin Patel and Vasso ApostolopoulosAbbreviations 28813.1 Introduction 28813.2 The Exponential Rise of AI/ML Solutions in Healthcare 28913.3 AI/ML Healthcare Solutions for Doctors 29113.4 AI/ML Solution for Patients 29313.5 AI Solutions for Administrators 29513.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector 29713.6.1 High Cost 29713.6.2 Lack of Creativity 29813.6.3 Errors Potentially Harming Patients 29813.6.4 Privacy Issues 29813.6.5 Increase in Unemployment 29913.6.6 Lack of Ethics 29913.6.7 Promotes a Less-Effort Culture Among Human Workers 29913.7 AI/ML Based Healthcare Start-Ups 29913.8 Opportunities and Risks for Future 30413.8.1 Patient Mobility Monitoring 30513.8.2 Clinical Trials for Drug Development 30513.8.3 Quality of Electronic Health Records (EHR) 30513.8.4 Robot-Assisted Surgery 30513.9 Conclusion and Perspectives 306References 30714 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy 315Sanket Chintawar, Vaishnavi Gattani, Shivanee Vyas and Shilpa DawreAbbreviations 31614.1 Introduction 31714.2 Machine Learning Algorithm Models 31814.2.1 Supervised Learning 31814.2.2 Unsupervised Learning 31914.2.3 Semi-Supervised Learning 31914.2.4 Reinforcement Learning (RL) 32014.3 Artificial Learning in Radiology 32114.3.1 Types of Radiation Therapy 32114.3.1.1 External Radiation Therapy 32214.3.1.2 Internal Radiation Therapy 32314.3.1.3 Systemic Radiation Therapy 32314.3.2 Mechanism of Action 32314.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy 32414.4.1 Delineation of the Target 32414.4.2 Radiotherapy Delivery 32514.4.3 Image Guided Radiotherapy 32714.5 Implementation of Machine Learning Algorithms in Radiotherapy 32814.5.1 Image Segmentation 32814.5.2 Medical Image Registration 32914.5.3 Computer-Aided Detection (CAD) and Diagnosis System 32914.6 Deep Learning Models 33114.6.1 Deep Neural Networks 33114.6.2 Convolutional Neural Networks 33214.7 Clinical Implementation of AI in Radiotherapy 33214.8 Current Challenges and Future Directions 339References 33915 Role of AI and ML in Epidemics and Pandemics 345Rajashri Bezbaruah, Mainak Ghosh, Shuby Kumari, Lawandashisha Nongrang, Sheikh Rezzak Ali, Monali Lahiri, Hasmi Waris and Bibhuti Bhushan Kakoti15.1 Introduction 34615.2 History of Artificial Intelligence (AI) in Medicine 34715.3 AI and MI Usage in Pandemic and Epidemic (COVID-19) 34815.3.1 SARS-CoV-2 Detection and Therapy Using Machine Learning and Artificial Intelligence 34915.3.2 SARS-Cov-2 Contact Tracing Using Machine Learning and Artificial Intelligence 35015.3.3 SARS-CoV-2 Prediction and Forecasting Using Machine Learning and Artificial Intelligence 35015.3.4 SARS-CoV-2 Medicines and Vaccine Using Machine Learning and Artificial Intelligence 35015.4 Cost Optimization for Research and Development Using Al and ml 35115.5 AI and ML in COVID 19 Vaccine Development 35215.6 Efficacy of AI and ML in Vaccine Development 35715.7 Artificial Intelligence and Machine Learning in Vaccine Development: Clinical Trials During an Epidemic and Pandemic 35815.8 Clinical Trials During an Epidemic 36015.8.1 Ebola Virus 36015.8.2 SARS-CoV- 2 36115.9 Conclusion 361References 36216 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment 371Susmit Mhatre, Somanshi Shukla, Vivek P. Chavda, Lakshmikanth Gandikota and Vandana Patravale16.1 Fundamentals of Cell Therapy 37216.1.1 Stem Cell Therapies 37416.1.1.1 Mesenchymal Stem Cells (MSCs) 37516.1.1.2 Hematopoietic Stem Cells (HSCs) 37516.1.1.3 Mononuclear Cells (MNCs) 37516.1.1.4 Endothelial Progenitor Cells (EPCs) 37516.1.1.5 Neural Stem Cells (NSCs) or Neural Progenitor Cells (NPCs) 37616.1.2 Adoptive Cell Therapy 37616.1.2.1 Tumor-Infiltrating Lymphocyte (TIL) Therapy 37616.1.2.2 Engineered T-Cell Receptor (TCR) Therapy 37716.1.2.3 Chimeric Antigen Receptor (CAR) T Cell Therapy 37716.1.2.4 Natural Killer (NK) Cell Therapy 37716.2 Fundamentals of Gene Therapy 37816.2.1 Identification 37816.2.2 Treatment 37916.3 Personalized Cell Therapy 38116.4 Manufacturing of Cell and Gene-Based Therapies 38216.5 Development of an Omics Profile 38516.6 ml in Stem Cell Identification, Differentiation, and Characterization 38716.7 Machine Learning in Gene Expression Imaging 38916.8 AI in Gene Therapy Target and Potency Prediction 39016.9 Conclusion and Future Prospective 391References 39217 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector 401Prashant Pokhriyal, Vivek P. Chavda and Mili Pathak17.1 Current Scenario 40217.2 Way Forward 406References 407Index 417