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97/81119865117 |
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(print) |
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9781119865728 |
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(electronic bk. : oBook) |
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1119865727 |
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(electronic bk. : oBook) |
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9781119865711 |
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(electronic bk.) |
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1119865719 |
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(electronic bk.) |
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1119865115 |
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9781119865117 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1002/9781119865728 |
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doi |
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(OCoLC)1370189662 |
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(OCoLC)1368353766 |
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(OCoLC)1368397891 |
| 040 ## - CATALOGING SOURCE |
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DG1 |
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eng |
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pn |
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DG1 |
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DG1 |
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UKAHL |
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YDX |
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eng |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
RM301.25 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
615.1/9 |
| Edition number |
23/eng/20230215 |
| 245 00 - TITLE STATEMENT |
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| Title |
Bioinformatics tools for pharmaceutical drug product development / |
| Statement of responsibility, etc |
edited by Vivek Chavda, Krishnan Anand and Vasso Apostolopoulos. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Hoboken, NJ : |
| Name of publisher, distributor, etc |
Wiley ; |
| Place of publication, distribution, etc |
Beverly, MA : |
| Name of publisher, distributor, etc |
Scrivener Publishing, |
| Date of publication, distribution, etc |
2023. |
| 300 ## - PHYSICAL DESCRIPTION |
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1 online resource. |
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text |
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rdacontent. |
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computer |
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rdamedia. |
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online resource |
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cr |
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rdacarrier. |
| 340 ## - PHYSICAL MEDIUM |
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rdacc |
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http://rdaregistry.info/termList/RDAColourContent/1003. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
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| Formatted contents note |
Table of Contents<br/>Preface xv<br/><br/>Part I: Bioinformatics Tools 1<br/><br/>1 Introduction to Bioinformatics, AI, and ml for Pharmaceuticals 3<br/>Vivek P. Chavda, Disha Vihol, Aayushi Patel, Elrashdy M. Redwan and Vladimir N. Uversky<br/><br/>1.1 Introduction 4<br/><br/>1.2 Bioinformatics 4<br/><br/>1.2.1 Limitations of Bioinformatics 8<br/><br/>1.2.2 Artificial Intelligence (AI) 8<br/><br/>1.3 Machine Learning (ML) 11<br/><br/>1.3.1 Applications of ml 12<br/><br/>1.3.2 Limitations of ml 14<br/><br/>1.4 Conclusion and Future Prospects 14<br/><br/>References 15<br/><br/>2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling 19<br/>Isha Rani, Kavita Munjal, Rajeev K. Singla and Rupesh K. Gautam<br/><br/>2.1 Introduction 20<br/><br/>2.2 Artificial Intelligence in Drug Discovery 21<br/><br/>2.2.1 Training Dataset Used in Medicinal Chemistry 22<br/><br/>2.2.2 Availability and Quality of Initial Data 23<br/><br/>2.3 AI in Virtual Screening 24<br/><br/>2.4 AI for De Novo Design 25<br/><br/>2.5 AI for Synthesis Planning 26<br/><br/>2.6 AI in Quality Control and Quality Assurance 27<br/><br/>2.7 AI-Based Advanced Applications 28<br/><br/>2.7.1 Micro/Nanorobot Targeted Drug Delivery System 28<br/><br/>2.7.2 AI in Nanomedicine 29<br/><br/>2.7.3 Role of AI in Market Prediction 29<br/><br/>2.8 Discussion and Future Perspectives 30<br/><br/>2.9 Conclusion 31<br/><br/>References 31<br/><br/>3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability 37<br/>Vivek Chavda, Prashant Kshirsagar and Nildip Chauhan<br/><br/>3.1 Introduction 37<br/><br/>3.2 Points to be considered for Peptide-Based Delivery 38<br/><br/>3.3 Overview of Peptide-Based Drug Delivery System 40<br/><br/>3.4 Tools for Screening of Peptide Drug Candidate 41<br/><br/>3.5 Various Strategies to Increase Serum Stability of Peptide 42<br/><br/>3.5.1 Cyclization of Peptide 42<br/><br/>3.5.2 Incorporation of D Form of Amino Acid 44<br/><br/>3.5.3 Terminal Modification 44<br/><br/>3.5.4 Substitution of Amino Acid Which is Not Natural 46<br/><br/>3.5.5 Stapled Peptides 46<br/><br/>3.5.6 Synthesis of Stapled Peptides 47<br/><br/>3.6 Method/Tools for Serum Stability Evaluation 47<br/><br/>3.7 Conclusion 48<br/><br/>3.8 Future Prospects 49<br/><br/>References 49<br/><br/>4 Data Analytics and Data Visualization for the Pharmaceutical Industry 55<br/>Shalin Parikh, Ravi Patel, Dignesh Khunt, Vivek P. Chavda and Lalitkumar Vora<br/><br/>4.1 Introduction 56<br/><br/>4.2 Data Analytics 57<br/><br/>4.3 Data Visualization 58<br/><br/>4.4 Data Analytics and Data Visualization for Formulation Development 60<br/><br/>4.5 Data Analytics and Data Visualization for Drug Product Development 65<br/><br/>4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management 69<br/><br/>4.7 Conclusion and Future Prospects 71<br/><br/>References 72<br/><br/>5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics 77<br/>Vivek Chavda, Kaustubh Dange and Madhav Joglekar<br/><br/>5.1 Introduction 77<br/><br/>5.2 Mass Spectrometry - Protein Interaction 79<br/><br/>5.2.1 The Prerequisites 80<br/><br/>5.2.2 Finding Affinity Partner (The Bait) 80<br/><br/>5.2.3 Antibody-Based Affinity Tags 80<br/><br/>5.2.4 Small Molecule Ligands 80<br/><br/>5.2.5 Fusion Protein-Based Affinity Tags 81<br/><br/>5.3 MS Analysis 81<br/><br/>5.4 Validating Specific Interactions 82<br/><br/>5.5 Mass Spectrometry – Qualitative and Quantitative Analysis 83<br/><br/>5.6 Challenges Associated with Mass Analysis 83<br/><br/>5.7 Relative vs. Absolute Quantification 85<br/><br/>5.8 Mass Spectrometry – Lipidomics and Metabolomics 86<br/><br/>5.9 Mass Spectrometry – Drug Discovery 87<br/><br/>5.10 Conclusion and Future Scope 88<br/><br/>5.11 Resources and Software 89<br/><br/>Acknowledgement 89<br/><br/>References 89<br/><br/>6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology 95<br/>Harshil Shah, Vivek Chavda and Moinuddin M. Soniwala<br/><br/>6.1 Introduction 96<br/><br/>6.2 Bioinformatics Tools 97<br/><br/>6.3 The Genetic Basis of Diseases 97<br/><br/>6.4 Proteomics 98<br/><br/>6.5 Transcriptomic 100<br/><br/>6.6 Cancer 101<br/><br/>6.7 Diagnosis 102<br/><br/>6.8 Drug Discovery and Testing 103<br/><br/>6.9 Molecular Medicines 105<br/><br/>6.10 Personalized (Precision) Medicines 106<br/><br/>6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic 108<br/><br/>6.12 Prognosis of Ailments 109<br/><br/>6.13 Concluding Remarks and Future Prospects 110<br/><br/>Acknowledgement 111<br/><br/>References 111<br/><br/>7 Clinical Applications of “Omics” Technology as a Bioinformatic Tool 117<br/>Vivek Chavda, Rajashri Bezbaruah, Disha Valu, Sanjay Desai, Nildip Chauhan, Swati Marwadi, Gitima Deka and Zhiyong Ding<br/><br/>Abbreviations 118<br/><br/>7.1 Introduction 118<br/><br/>7.2 Execution Method 119<br/><br/>7.3 Overview of Omics Technology 121<br/><br/>7.4 Genomics 124<br/><br/>7.5 Nutrigenomics 127<br/><br/>7.6 Transcriptomics 128<br/><br/>7.7 Proteomics 129<br/><br/>7.8 Metabolomics 130<br/><br/>7.9 Lipomics or Lipidomics 133<br/><br/>7.10 Ayurgenomics 134<br/><br/>7.11 Pharmacogenomics 134<br/><br/>7.12 Toxicogenomic 135<br/><br/>7.13 Conclusion and Future Prospects 139<br/><br/>Acknowledgement 139<br/><br/>References 139<br/><br/>Part II: Bioinformatics Tools for Pharmaceutical Sector 147<br/><br/>8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery 149<br/>Palak K. Parikh, Jignasa K. Savjani, Anuradha K. Gajjar and Mahesh T. Chhabria<br/><br/>Abbreviations 150<br/><br/>8.1 Introduction 151<br/><br/>8.2 Informatics and Drug Discovery 152<br/><br/>8.3 Computational Methods in Drug Discovery 153<br/><br/>8.3.1 Homology Modeling 153<br/><br/>8.3.2 Docking Studies 155<br/><br/>8.3.3 Molecular Dynamics Simulations 158<br/><br/>8.3.4 De Novo Drug Design 159<br/><br/>8.3.5 Quantitative Structure Activity Relationships 160<br/><br/>8.3.6 Pharmacophore Modeling 161<br/><br/>8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling 165<br/><br/>8.4 Conclusion 168<br/><br/>References 169<br/><br/>9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products 183<br/>Vivek P. Chavda<br/><br/>9.1 Introduction 184<br/><br/>9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) 185<br/><br/>9.3 AI- and ML-Based Formulation Development 187<br/><br/>9.4 AI- and ML-Based Process Development and Process Characterization 189<br/><br/>9.5 Concluding Remarks and Future Prospects 192<br/><br/>References 193<br/><br/>10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing 197<br/>Kajal Baviskar, Anjali Bedse, Shilpa Raut and Narayana Darapaneni<br/><br/>Abbreviations 198<br/><br/>10.1 Introduction to Artificial Intelligence and Machine Learning 199<br/><br/>10.1.1 AI and ML in Pharmaceutical Manufacturing 200<br/><br/>10.1.2 AI and ML in Drug Product Marketing 201<br/><br/>10.2 Different Applications of AI and ML in the Pharma Field 202<br/><br/>10.2.1 Drug Discovery 202<br/><br/>10.2.2 Pharmaceutical Product Development 202<br/><br/>10.2.3 Clinical Trial Design 203<br/><br/>10.2.4 Manufacturing of Drugs 203<br/><br/>10.2.5 Quality Control and Quality Assurance 203<br/><br/>10.2.6 Product Management 203<br/><br/>10.2.7 Drug Prescription 204<br/><br/>10.2.8 Medical Diagnosis 204<br/><br/>10.2.9 Monitoring of Patients 204<br/><br/>10.2.10 Drug Synergism and Antagonism Prediction 204<br/><br/>10.2.11 Precision Medicine 205<br/><br/>10.3 AI and ML-Based Manufacturing 205<br/><br/>10.3.1 Continuous Manufacturing 205<br/><br/>10.3.2 Process Improvement and Fault Detection 209<br/><br/>10.3.3 Predictive Maintenance (PdM) 210<br/><br/>10.3.4 Quality Control and Yield 211<br/><br/>10.3.5 Troubleshooting 211<br/><br/>10.3.6 Supply Chain Management 212<br/><br/>10.3.7 Warehouse Management 213<br/><br/>10.3.8 Predicting Remaining Useful Life 214<br/><br/>10.3.9 Challenges 215<br/><br/>10.4 AI and ML-Based Drug Product Marketing 217<br/><br/>10.4.1 Product Launch 217<br/><br/>10.4.2 Real-Time Personalization and Consumer Behavior 218<br/><br/>10.4.3 Better Customer Relationships 219<br/><br/>10.4.4 Enhanced Marketing Measurement 220<br/><br/>10.4.5 Predictive Marketing Analytics 220<br/><br/>10.4.6 Price Dynamics 221<br/><br/>10.4.7 Market Segmentation 222<br/><br/>10.4.8 Challenges 223<br/><br/>10.5 Future Prospects and Way Forward 223<br/><br/>10.6 Conclusion 224<br/><br/>References 225<br/><br/>11 Artificial Intelligence and Machine Learning Applications in Vaccine Development 233<br/>Ali Sarmadi, Majid Hassanzadeganroudsari and M. Soltani<br/><br/>11.1 Introduction 234<br/><br/>11.2 Prioritizing Proteins as Vaccine Candidates 237<br/><br/>11.3 Predicting Binding Scores of Candidate Proteins 238<br/><br/>11.4 Predicting Potential Epitopes 243<br/><br/>11.5 Design of Multi-Epitope Vaccine 244<br/><br/>11.6 Tracking the RNA Mutations of a Virus 245<br/><br/>Conclusion 248<br/><br/>References 249<br/><br/>12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products 255<br/>Avinash Khadela, Sagar Popat, Jinal Ajabiya, Disha Valu, Shrinivas Savale and Vivek P. Chavda<br/><br/>Abbreviations 256<br/><br/>12.1 Introduction 257<br/><br/>12.2 AI and ML for Pandemic 258<br/><br/>12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development 259<br/><br/>12.3.1 Spectroscopic Techniques 260<br/><br/>12.3.2 Chromatographic Techniques 263<br/><br/>12.3.3 Electrochemical Techniques 263<br/><br/>12.3.4 Electrophoretic Techniques 264<br/><br/>12.3.5 Hyphenated Techniques 264<br/><br/>12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products 265<br/><br/>12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development 266<br/><br/>12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products 268<br/><br/>12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research 270<br/><br/>12.5.2 Role of AI and ML in Clinical Study Protocol Optimization 272<br/><br/>12.5.3 Role of AI and ML in the Management of Clinical Trial Participants 272<br/><br/>12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management 272<br/><br/>12.6 Way Forward 275<br/><br/>12.7 Conclusion 276<br/><br/>References 277<br/><br/>Part III: Bioinformatics Tools for Healthcare Sector 285<br/><br/>13 Artificial Intelligence and Machine Learning in Healthcare Sector 287<br/>Vivek P. Chavda, Kaushika Patel, Sachin Patel and Vasso Apostolopoulos<br/><br/>Abbreviations 288<br/><br/>13.1 Introduction 288<br/><br/>13.2 The Exponential Rise of AI/ML Solutions in Healthcare 289<br/><br/>13.3 AI/ML Healthcare Solutions for Doctors 291<br/><br/>13.4 AI/ML Solution for Patients 293<br/><br/>13.5 AI Solutions for Administrators 295<br/><br/>13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector 297<br/><br/>13.6.1 High Cost 297<br/><br/>13.6.2 Lack of Creativity 298<br/><br/>13.6.3 Errors Potentially Harming Patients 298<br/><br/>13.6.4 Privacy Issues 298<br/><br/>13.6.5 Increase in Unemployment 299<br/><br/>13.6.6 Lack of Ethics 299<br/><br/>13.6.7 Promotes a Less-Effort Culture Among Human Workers 299<br/><br/>13.7 AI/ML Based Healthcare Start-Ups 299<br/><br/>13.8 Opportunities and Risks for Future 304<br/><br/>13.8.1 Patient Mobility Monitoring 305<br/><br/>13.8.2 Clinical Trials for Drug Development 305<br/><br/>13.8.3 Quality of Electronic Health Records (EHR) 305<br/><br/>13.8.4 Robot-Assisted Surgery 305<br/><br/>13.9 Conclusion and Perspectives 306<br/><br/>References 307<br/><br/>14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy 315<br/>Sanket Chintawar, Vaishnavi Gattani, Shivanee Vyas and Shilpa Dawre<br/><br/>Abbreviations 316<br/><br/>14.1 Introduction 317<br/><br/>14.2 Machine Learning Algorithm Models 318<br/><br/>14.2.1 Supervised Learning 318<br/><br/>14.2.2 Unsupervised Learning 319<br/><br/>14.2.3 Semi-Supervised Learning 319<br/><br/>14.2.4 Reinforcement Learning (RL) 320<br/><br/>14.3 Artificial Learning in Radiology 321<br/><br/>14.3.1 Types of Radiation Therapy 321<br/><br/>14.3.1.1 External Radiation Therapy 322<br/><br/>14.3.1.2 Internal Radiation Therapy 323<br/><br/>14.3.1.3 Systemic Radiation Therapy 323<br/><br/>14.3.2 Mechanism of Action 323<br/><br/>14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy 324<br/><br/>14.4.1 Delineation of the Target 324<br/><br/>14.4.2 Radiotherapy Delivery 325<br/><br/>14.4.3 Image Guided Radiotherapy 327<br/><br/>14.5 Implementation of Machine Learning Algorithms in Radiotherapy 328<br/><br/>14.5.1 Image Segmentation 328<br/><br/>14.5.2 Medical Image Registration 329<br/><br/>14.5.3 Computer-Aided Detection (CAD) and Diagnosis System 329<br/><br/>14.6 Deep Learning Models 331<br/><br/>14.6.1 Deep Neural Networks 331<br/><br/>14.6.2 Convolutional Neural Networks 332<br/><br/>14.7 Clinical Implementation of AI in Radiotherapy 332<br/><br/>14.8 Current Challenges and Future Directions 339<br/><br/>References 339<br/><br/>15 Role of AI and ML in Epidemics and Pandemics 345<br/>Rajashri Bezbaruah, Mainak Ghosh, Shuby Kumari, Lawandashisha Nongrang, Sheikh Rezzak Ali, Monali Lahiri, Hasmi Waris and Bibhuti Bhushan Kakoti<br/><br/>15.1 Introduction 346<br/><br/>15.2 History of Artificial Intelligence (AI) in Medicine 347<br/><br/>15.3 AI and MI Usage in Pandemic and Epidemic (COVID-19) 348<br/><br/>15.3.1 SARS-CoV-2 Detection and Therapy Using Machine Learning and Artificial Intelligence 349<br/><br/>15.3.2 SARS-Cov-2 Contact Tracing Using Machine Learning and Artificial Intelligence 350<br/><br/>15.3.3 SARS-CoV-2 Prediction and Forecasting Using Machine Learning and Artificial Intelligence 350<br/><br/>15.3.4 SARS-CoV-2 Medicines and Vaccine Using Machine Learning and Artificial Intelligence 350<br/><br/>15.4 Cost Optimization for Research and Development Using Al and ml 351<br/><br/>15.5 AI and ML in COVID 19 Vaccine Development 352<br/><br/>15.6 Efficacy of AI and ML in Vaccine Development 357<br/><br/>15.7 Artificial Intelligence and Machine Learning in Vaccine Development: Clinical Trials During an Epidemic and Pandemic 358<br/><br/>15.8 Clinical Trials During an Epidemic 360<br/><br/>15.8.1 Ebola Virus 360<br/><br/>15.8.2 SARS-CoV- 2 361<br/><br/>15.9 Conclusion 361<br/><br/>References 362<br/><br/>16 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment 371<br/>Susmit Mhatre, Somanshi Shukla, Vivek P. Chavda, Lakshmikanth Gandikota and Vandana Patravale<br/><br/>16.1 Fundamentals of Cell Therapy 372<br/><br/>16.1.1 Stem Cell Therapies 374<br/><br/>16.1.1.1 Mesenchymal Stem Cells (MSCs) 375<br/><br/>16.1.1.2 Hematopoietic Stem Cells (HSCs) 375<br/><br/>16.1.1.3 Mononuclear Cells (MNCs) 375<br/><br/>16.1.1.4 Endothelial Progenitor Cells (EPCs) 375<br/><br/>16.1.1.5 Neural Stem Cells (NSCs) or Neural Progenitor Cells (NPCs) 376<br/><br/>16.1.2 Adoptive Cell Therapy 376<br/><br/>16.1.2.1 Tumor-Infiltrating Lymphocyte (TIL) Therapy 376<br/><br/>16.1.2.2 Engineered T-Cell Receptor (TCR) Therapy 377<br/><br/>16.1.2.3 Chimeric Antigen Receptor (CAR) T Cell Therapy 377<br/><br/>16.1.2.4 Natural Killer (NK) Cell Therapy 377<br/><br/>16.2 Fundamentals of Gene Therapy 378<br/><br/>16.2.1 Identification 378<br/><br/>16.2.2 Treatment 379<br/><br/>16.3 Personalized Cell Therapy 381<br/><br/>16.4 Manufacturing of Cell and Gene-Based Therapies 382<br/><br/>16.5 Development of an Omics Profile 385<br/><br/>16.6 ml in Stem Cell Identification, Differentiation, and Characterization 387<br/><br/>16.7 Machine Learning in Gene Expression Imaging 389<br/><br/>16.8 AI in Gene Therapy Target and Potency Prediction 390<br/><br/>16.9 Conclusion and Future Prospective 391<br/><br/>References 392<br/><br/>17 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector 401<br/>Prashant Pokhriyal, Vivek P. Chavda and Mili Pathak<br/><br/>17.1 Current Scenario 402<br/><br/>17.2 Way Forward 406<br/><br/>References 407<br/><br/>Index 417 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
A 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# - BIOGRAPHICAL OR HISTORICAL DATA |
|---|
| Biographical or historical note |
About the Author<br/>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.<br/><br/>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.<br/><br/>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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Drug development |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh97007921 |
| General subdivision |
Data processing. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh99005487. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Bioinformatics. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh00003585. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Chavda, Vivek, |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Anand, Krishnan, |
| Authority record control number |
https://id.loc.gov/authorities/names/no2021068009 |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Apostolopoulos, Vasso, |
| Relator term |
editor. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Qualifying information |
Original |
| International Standard Book Number |
1119865115 |
| -- |
9781119865117 |
| Record control number |
(OCoLC)1340031202. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119865728 |
| Link text |
Full text available at Wiley Online Library Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
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| Item type |
EBOOK |