Details

Autor(en) / Beteiligte

• Anand, Krishnan,
• Apostolopoulos, Vasso,
• Chavda, Vivek,

Titel

Bioinformatics tools for pharmaceutical drug product development

Ort / Verlag

Hoboken, NJ : Wiley,

Erscheinungsjahr

℗2023

Beschreibungen/Notizen

• Includes bibliographical references and index.
• Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Bioinformatics Tools -- Chapter 1 Introduction to Bioinformatics, AI, and ML for Pharmaceuticals -- 1.1 Introduction -- 1.2 Bioinformatics -- 1.2.1 Limitations of Bioinformatics -- 1.2.2 Artificial Intelligence (AI) -- 1.3 Machine Learning (ML) -- 1.3.1 Applications of ML -- 1.3.2 Limitations of ML -- 1.4 Conclusion and Future Prospects -- References -- Chapter 2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling -- 2.1 Introduction -- 2.2 Artificial Intelligence in Drug Discovery -- 2.2.1 Training Dataset Used in Medicinal Chemistry -- 2.2.2 Availability and Quality of Initial Data -- 2.3 AI in Virtual Screening -- 2.4 AI for De Novo Design -- 2.5 AI for Synthesis Planning -- 2.6 AI in Quality Control and Quality Assurance -- 2.7 AI-Based Advanced Applications -- 2.7.1 Micro/Nanorobot Targeted Drug Delivery System -- 2.7.2 AI in Nanomedicine -- 2.7.3 Role of AI in Market Prediction -- 2.8 Discussion and Future Perspectives -- 2.9 Conclusion -- References -- Chapter 3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability -- 3.1 Introduction -- 3.2 Points to be Considered for Peptide-Based Delivery -- 3.3 Overview of Peptide-Based Drug Delivery System -- 3.4 Tools for Screening of Peptide Drug Candidate -- 3.5 Various Strategies to Increase Serum Stability of Peptide -- 3.5.1 Cyclization of Peptide -- 3.5.2 Incorporation of D Form of Amino Acid -- 3.5.3 Terminal Modification -- 3.5.4 Substitution of Amino Acid Which is Not Natural -- 3.5.5 Stapled Peptides -- 3.5.6 Synthesis of Stapled Peptides -- 3.6 Method/Tools for Serum Stability Evaluation -- 3.7 Conclusion -- 3.8 Future Prospects -- References -- Chapter 4 Data Analytics and Data Visualization for the Pharmaceutical Industry -- 4.1 Introduction.
• 4.2 Data Analytics -- 4.3 Data Visualization -- 4.4 Data Analytics and Data Visualization for Formulation Development -- 4.5 Data Analytics and Data Visualization for Drug Product Development -- 4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management -- 4.7 Conclusion and Future Prospects -- References -- Chapter 5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics -- 5.1 Introduction -- 5.2 Mass Spectrometry - Protein Interaction -- 5.2.1 The Prerequisites -- 5.2.2 Finding Affinity Partner (The Bait) -- 5.2.3 Antibody-Based Affinity Tags -- 5.2.4 Small Molecule Ligands -- 5.2.5 Fusion Protein-Based Affinity Tags -- 5.3 MS Analysis -- 5.4 Validating Specific Interactions -- 5.5 Mass Spectrometry - Qualitative and Quantitative Analysis -- 5.6 Challenges Associated with Mass Analysis -- 5.7 Relative vs. Absolute Quantification -- 5.8 Mass Spectrometry - Lipidomics and Metabolomics -- 5.9 Mass Spectrometry - Drug Discovery -- 5.10 Conclusion and Future Scope -- 5.11 Resources and Software -- Acknowledgement -- References -- Chapter 6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology -- 6.1 Introduction -- 6.2 Bioinformatics Tools -- 6.3 The Genetic Basis of Diseases -- 6.4 Proteomics -- 6.5 Transcriptomic -- 6.6 Cancer -- 6.7 Diagnosis -- 6.8 Drug Discovery and Testing -- 6.9 Molecular Medicines -- 6.10 Personalized (Precision) Medicines -- 6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic -- 6.12 Prognosis of Ailments -- 6.13 Concluding Remarks and Future Prospects -- Acknowledgement -- References -- Chapter 7 Clinical Applications of "Omics" Technology as a Bioinformatic Tool -- Abbreviations -- 7.1 Introduction -- 7.2 Execution Method -- 7.3 Overview of Omics Technology -- 7.4 Genomics -- 7.5 Nutrigenomics -- 7.6 Transcriptomics.
• 7.7 Proteomics -- 7.8 Metabolomics -- 7.9 Lipomics or Lipidomics -- 7.10 Ayurgenomics -- 7.11 Pharmacogenomics -- 7.12 Toxicogenomic -- 7.13 Conclusion and Future Prospects -- Acknowledgement -- References -- Part II: Bioinformatics Tools for Pharmaceutical Sector -- Chapter 8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery -- Abbreviations -- 8.1 Introduction -- 8.2 Informatics and Drug Discovery -- 8.3 Computational Methods in Drug Discovery -- 8.3.1 Homology Modeling -- 8.3.2 Docking Studies -- 8.3.3 Molecular Dynamics Simulations -- 8.3.4 De Novo Drug Design -- 8.3.5 Quantitative Structure Activity Relationships -- 8.3.6 Pharmacophore Modeling -- 8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling -- 8.4 Conclusion -- References -- Chapter 9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products -- 9.1 Introduction -- 9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) -- 9.3 AI- and ML-Based Formulation Development -- 9.4 AI- and ML-Based Process Development and Process Characterization -- 9.5 Concluding Remarks and Future Prospects -- References -- Chapter 10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing -- Abbreviations -- 10.1 Introduction to Artificial Intelligence and Machine Learning -- 10.1.1 AI and ML in Pharmaceutical Manufacturing -- 10.1.2 AI and ML in Drug Product Marketing -- 10.2 Different Applications of AI and ML in the Pharma Field -- 10.2.1 Drug Discovery -- 10.2.2 Pharmaceutical Product Development -- 10.2.3 Clinical Trial Design -- 10.2.4 Manufacturing of Drugs -- 10.2.5 Quality Control and Quality Assurance -- 10.2.6 Product Management -- 10.2.7 Drug Prescription -- 10.2.8 Medical Diagnosis -- 10.2.9 Monitoring of Patients -- 10.2.10 Drug Synergism and Antagonism Prediction.
• 10.2.11 Precision Medicine -- 10.3 AI and ML-Based Manufacturing -- 10.3.1 Continuous Manufacturing -- 10.3.2 Process Improvement and Fault Detection -- 10.3.3 Predictive Maintenance (PdM) -- 10.3.4 Quality Control and Yield -- 10.3.5 Troubleshooting -- 10.3.6 Supply Chain Management -- 10.3.7 Warehouse Management -- 10.3.8 Predicting Remaining Useful Life -- 10.3.9 Challenges -- 10.4 AI and ML-Based Drug Product Marketing -- 10.4.1 Product Launch -- 10.4.2 Real-Time Personalization and Consumer Behavior -- 10.4.3 Better Customer Relationships -- 10.4.4 Enhanced Marketing Measurement -- 10.4.5 Predictive Marketing Analytics -- 10.4.6 Price Dynamics -- 10.4.7 Market Segmentation -- 10.4.8 Challenges -- 10.5 Future Prospects and Way Forward -- 10.6 Conclusion -- References -- Chapter 11 Artificial Intelligence and Machine Learning Applications in Vaccine Development -- 11.1 Introduction -- 11.2 Prioritizing Proteins as Vaccine Candidates -- 11.3 Predicting Binding Scores of Candidate Proteins -- 11.4 Predicting Potential Epitopes -- 11.5 Design of Multi-Epitope Vaccine -- 11.6 Tracking the RNA Mutations of a Virus -- Conclusion -- References -- Chapter 12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products -- Abbreviations -- 12.1 Introduction -- 12.2 AI and ML for Pandemic -- 12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development -- 12.3.1 Spectroscopic Techniques -- 12.3.2 Chromatographic Techniques -- 12.3.3 Electrochemical Techniques -- 12.3.4 Electrophoretic Techniques -- 12.3.5 Hyphenated Techniques -- 12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products -- 12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development -- 12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products.
• 12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research -- 12.5.2 Role of AI and ML in Clinical Study Protocol Optimization -- 12.5.3 Role of AI and ML in the Management of Clinical Trial Participants -- 12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management -- 12.6 Way Forward -- 12.7 Conclusion -- References -- Part III: Bioinformatics Tools for Healthcare Sector -- Chapter 13 Artificial Intelligence and Machine Learning in Healthcare Sector -- Abbreviations -- 13.1 Introduction -- 13.2 The Exponential Rise of AI/ML Solutions in Healthcare -- 13.3 AI/ML Healthcare Solutions for Doctors -- 13.4 AI/ML Solution for Patients -- 13.5 AI Solutions for Administrators -- 13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector -- 13.6.1 High Cost -- 13.6.2 Lack of Creativity -- 13.6.3 Errors Potentially Harming Patients -- 13.6.4 Privacy Issues -- 13.6.5 Increase in Unemployment -- 13.6.6 Lack of Ethics -- 13.6.7 Promotes a Less-Effort Culture Among Human Workers -- 13.7 AI/ML Based Healthcare Start-Ups -- 13.8 Opportunities and Risks for Future -- 13.8.1 Patient Mobility Monitoring -- 13.8.2 Clinical Trials for Drug Development -- 13.8.3 Quality of Electronic Health Records (EHR) -- 13.8.4 Robot-Assisted Surgery -- 13.9 Conclusion and Perspectives -- References -- Chapter 14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy -- Abbreviations -- 14.1 Introduction -- 14.2 Machine Learning Algorithm Models -- 14.2.1 Supervised Learning -- 14.2.2 Unsupervised Learning -- 14.2.3 Semi-Supervised Learning -- 14.2.4 Reinforcement Learning (RL) -- 14.3 Artificial Learning in Radiology -- 14.3.1 Types of Radiation Therapy -- 14.3.1.1 External Radiation Therapy -- 14.3.1.2 Internal Radiation Therapy -- 14.3.1.3 Systemic Radiation Therapy -- 14.3.2 Mechanism of Action.
• 14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy.
• Description based on print version record.