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003			CITU
005			20241018084552.0
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020			_a97/81119865117 _q(print)
020			_a9781119865728 _q(electronic bk. : oBook)
020			_a1119865727 _q(electronic bk. : oBook)
020			_a9781119865711 _q(electronic bk.)
020			_a1119865719 _q(electronic bk.)
020			_z1119865115
020			_z9781119865117
024	7		_a10.1002/9781119865728 _2doi
035			_a(OCoLC)1370189662 _z(OCoLC)1368353766 _z(OCoLC)1368397891
040			_aDG1 _beng _erda _epn _cDG1 _dDG1 _dUKAHL _dYDX
041			_aeng
050		4	_aRM301.25
082	0	4	_a615.1/9 _223/eng/20230215
245	0	0	_aBioinformatics tools for pharmaceutical drug product development / _cedited by Vivek Chavda, Krishnan Anand and Vasso Apostolopoulos.
264		1	_aHoboken, NJ : _bWiley ; _aBeverly, MA : _bScrivener Publishing, _c2023.
300			_a1 online resource.
336			_atext _btxt _2rdacontent.
337			_acomputer _bc _2rdamedia.
338			_aonline resource _bcr _2rdacarrier.
340			_2rdacc _0http://rdaregistry.info/termList/RDAColourContent/1003.
504			_aIncludes bibliographical references and index.
505	0		_aTable 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
520			_aA 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.
545	0		_aAbout the Author 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.
650		0	_aDrug development _0https://id.loc.gov/authorities/subjects/sh97007921 _xData processing. _0https://id.loc.gov/authorities/subjects/sh99005487.
650		0	_aBioinformatics. _0https://id.loc.gov/authorities/subjects/sh00003585.
655		4	_aElectronic books.
700	1		_aChavda, Vivek, _eeditor.
700	1		_aAnand, Krishnan, _0https://id.loc.gov/authorities/names/no2021068009 _eeditor.
700	1		_aApostolopoulos, Vasso, _eeditor.
776	0	8	_cOriginal _z1119865115 _z9781119865117 _w(OCoLC)1340031202.
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119865728 _yFull text available at Wiley Online Library Click here to view
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