-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in Disease Diagnosis, Drug Delivery and Treatment
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Advances in Surface Engineering and Materials Processing
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Computational Intelligence in Industrial Mathematics
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Human Centric Sustainability and Next Generation Technology
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Leading-Edge Breakthroughs in Artificial Intelligence
- Lifeline Molecules: Exploring Antioxidants and Their Impact
- Machine Learning in Biomedical Science and Healthcare Informatics
- Marine Nanoscience and Nanotechnology
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Robotics and Automation for Modern Applications
- Rotating Machinery Fundamentals and Advances
- Semiconductor Devices and Their Applications
- Separation Science
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Biopolymer Technologies and Applications
- Sustainable Computing and Optimization
- Thermoplastic Bionanocomposites Series
- Transformative Innovations in Materials Science
Browse Subject Areas
For Authors
Submit a ProposalPharmacogenics Using Artificial Intelligence
One Line Description
Bridging the critical gap between complex genomic data and actual clinical practice, this essential volume delivers the cutting-edge AI methodologies, expert bioinformatics insights, and practical case studies needed to unlock truly personalized medicine.
Description
The intersection of artificial intelligence and pharmacogenomics represents a transformative change in the life sciences industry. Pharmacogenomics, the study of how genetic variations influence an individual’s response to drugs, has long held the promise of enabling personalized treatments that are tailored to the genetic profile of individual patients, improving therapeutic outcomes and minimizing adverse drug reactions. However, the complexity of genomic data, massive scale of information, and challenge of interpreting the intricate relationships between genetic variations and drug responses have impeded the widespread implementation of personalized treatments in clinical practice. This volume explores how AI technologies are transforming personalized medicine by optimizing drug responses based on individual genetic profiles. The book will provide a comprehensive look at the role of AI in advancing pharmacogenomic research and its application in clinical practice, enabling healthcare professionals to predict the most effective and safest drugs for individual patients.
The book will be structured around the application of cutting-edge AI techniques in analyzing genomic data. Each chapter will highlight different aspects of AI-driven pharmacogenomics, from drug development and genetic variant identification to clinical implementation and ethical considerations. Experts from diverse fields, including bioinformatics, pharmacology, and data science, will contribute insights into how AI can be harnessed to analyze large genomic datasets, predict patient-specific drug responses, and overcome existing challenges in precision medicine. This volume will not only provide theoretical knowledge but also offer practical examples, case studies, and methodologies that researchers, clinicians, and healthcare professionals can utilize to enhance pharmacogenomic research and personalize patient care.
Back to Top
The intersection of artificial intelligence and pharmacogenomics represents a transformative change in the life sciences industry. Pharmacogenomics, the study of how genetic variations influence an individual’s response to drugs, has long held the promise of enabling personalized treatments that are tailored to the genetic profile of individual patients, improving therapeutic outcomes and minimizing adverse drug reactions. However, the complexity of genomic data, massive scale of information, and challenge of interpreting the intricate relationships between genetic variations and drug responses have impeded the widespread implementation of personalized treatments in clinical practice. This volume explores how AI technologies are transforming personalized medicine by optimizing drug responses based on individual genetic profiles. The book will provide a comprehensive look at the role of AI in advancing pharmacogenomic research and its application in clinical practice, enabling healthcare professionals to predict the most effective and safest drugs for individual patients.
The book will be structured around the application of cutting-edge AI techniques in analyzing genomic data. Each chapter will highlight different aspects of AI-driven pharmacogenomics, from drug development and genetic variant identification to clinical implementation and ethical considerations. Experts from diverse fields, including bioinformatics, pharmacology, and data science, will contribute insights into how AI can be harnessed to analyze large genomic datasets, predict patient-specific drug responses, and overcome existing challenges in precision medicine. This volume will not only provide theoretical knowledge but also offer practical examples, case studies, and methodologies that researchers, clinicians, and healthcare professionals can utilize to enhance pharmacogenomic research and personalize patient care.
Back to Top
Author / Editor Details
Umesh Kumar Lilhore, PhD is a Professor at Galgotias University, Greater Noida, India eith more than 20 years of experience. He has authored ten books and more than 100 research articles in international journals, and filed 50 patents across India and the United Kingdom. His research focuses on network security, computer networking, and the Internet of Things.
Umesh Kumar Lilhore, PhD is a Professor at Galgotias University, Greater Noida, India eith more than 20 years of experience. He has authored ten books and more than 100 research articles in international journals, and filed 50 patents across India and the United Kingdom. His research focuses on network security, computer networking, and the Internet of Things.
Kaamran Raahemifar, PhD is a Professor in the College of Information Sciences and Technology at Penn State University. He has authored and co-authored numerous highly cited publications and books in reputed international journals and conferences. His research focuses on AI-driven healthcare systems, machine learning, optimization, medical image processing, and intelligent smart systems.
Sarita Simaiya, PhD is a Professor at Galgotias University, Greater Noida, India with more than 18 years of experience. She has co-authored several peer-reviewed publications in reputed international journals and conferences, with a focus on integrating emerging AI technologies into healthcare and biomedical research. Her research interests include AI-driven healthcare systems, deep learning models, and intelligent data analytics for biomedical applications.
R. Sunder, PhD is an academician and researcher with expertise in artificial intelligence, machine learning, data analytics, and intelligent healthcare systems. His research interests include AI-driven biomedical applications, computational intelligence, and advanced data processing techniques for healthcare and pharmaceutical domains. He has contributed to interdisciplinary research projects and scholarly publications focused on emerging technologies, and digital healthcare innovation.
R. Lotus, PhD is a researcher and academician specializing in artificial intelligence, computational biology, and healthcare technologies. She has contributed to multidisciplinary research initiatives and scholarly publications focused on advancing digital healthcare, precision medicine, and emerging computational technologies. She is actively engaged in promoting innovative AI solutions for healthcare and biomedical research.
Back to Top
Table of Contents
Preface
1. Foundations of Pharmacogenomics: Understanding the Genetic Basis of Drug Response
Dhanesh Kumar, Thangiah Sathishkumar, Sarangam Kodati, Venkata Praveen Kumar Vuppala, Rasmi A. and Rajakumar Perumal
1.1 Introduction
1.2 Genetics of Drug Response Mechanisms
1.2.1 Types of Genetic Variation
1.2.2 Pharmacokinetics vs. Pharmacodynamics
1.2.3 Phenotypes and Clinical Effects
1.3 Clinically Actionable Examples
1.3.1 CYP2C19 and Clopidogrel
1.3.2 CYP2D6 and Codeine/Opioid Metabolism
1.3.3 Warfarin: VKORC1 and CYP2C9
1.3.4 Cancer Therapy Markers: HER2 and DPYD/UGT1A1
1.4 Implementation Frameworks and Clinical Integration
1.4.1 CPIC, PharmGKB, DPWG
1.4.2 Real-World Programs and Economic Impact
1.4.3 Clinical Decision Support Systems (CDSS)
1.4.4 Ethical, Legal, and Social Considerations
1.5 New Technologies and Emerging Trends
1.6 Conclusion
References
2. From Data to Therapy: Artificial Intelligence Applications in Pharmacogenomics
Yuvaraj Velusamy, L. Gandhimathi, Shaziya Islam, P. Jyothi, Saranya P. and S. Suresh
2.1 Introduction
2.1.1 Evolution of Pharmacogenomics from Bench to Bedside
2.1.2 The Increasing Complexity of Genomic Datasets
2.1.3 Transformative Tool for PGx
2.1.4 Problem Statement: Present Bottlenecks in PGx-Driven Clinical Translation
2.2 Data Foundations in AI-Driven Pharmacogenomics
2.2.1 Genomic Data Sources
2.2.2 Multi-Omics Integration
2.2.3 Clinical and Real-World Data
2.2.4 Data Quality, Curation, and Standardization
2.3 AI Methodologies in PGx
2.3.1 Machine Learning Techniques
2.3.2 Deep Learning Architectures
2.3.3 Natural Language Processing
2.3.4 Graph-Based AI Models
2.4 Translating Data to Therapy: Key AI-Driven PGx Applications
2.4.1 Drug Response Prediction
2.4.2 Dose Optimization Models
2.4.3 Drug Repurposing and Discovery
2.4.4 Clinical Decision Support Systems (CDSS)
2.5 Challenges and Limitations
2.5.1 Data Privacy and Ethics
2.5.2 Model Interpretability and Explainability
2.5.3 Bias and Generalizability
2.5.4 Regulatory and Policy Barriers
2.6 Future Perspectives
2.6.1 Explainable AI (XAI) for PGx
2.6.2 Federated Learning Technology to Cross-Organization Genomic Models
2.6.3 Real-Time PGx via IoT and Wearables
2.6.4 AI-Augmented Clinical Trials
2.7 Conclusion
References
3. Machine Learning Approaches for Genomic Data Analysis in Pharmacogenomics
Ashwin M., Sreenivas Mekala, V. Arun, Ashish, S. Mathumohan and K. Kaliraj
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.3.1 Data Acquisition
3.3.2 Data Preprocessing
3.3.3 Feature Selection and Dimensionality Reduction
3.3.4 Model Development
3.3.5 Model Training and Optimization
3.3.6 Validation and Benchmarking
3.3.7 Clinical Translation
3.4 Results and Discussions
3.4.1 Dataset Description
3.4.2 Performance Analysis
3.4.3 Ethical, Legal, and Social Considerations
3.4.4 Future Extensions
3.5 Conclusion
References
4. Deep Learning and Neural Networks: Unlocking Complex Patterns in Genomic Medicine
M. Sudharsan, K. Maithili, T. Ravi, Margaret Mary T., M. Rajesh Khanna and P. Eswaran
4.1 Introduction
4.1.1 Research Questions and Problem Statement
4.2 Related Works
4.3 Methodology
4.3.1 Data Acquisition and Preparation
4.3.2 Exploratory Data Analysis (EDA)
4.3.3 Model Architecture Design
4.3.4 Multi-Scale Representation Learning
4.3.5 Model Training and Optimization
4.3.6 Validation and Benchmarking
4.3.7 Clinical Translation and Deployment
4.4 Results and Discussions
4.4.1 Dataset Description
4.4.2 Performance Evaluation
4.4.3 Discussions
4.4.4 Limitations and Practical Implications of the Study
4.5 Conclusion
4.6 Future Directions of the Study
References
5. AI-Driven Drug Discovery: Accelerating Therapeutic Innovation through Genomics
K. Prakash, Phani Kumar Solleti, Tarak Hussain, Chilukala Mahender Reddy, Margaret Mary T. and P. Arumugam
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.3.1 Multi-Omics Data Acquisition
5.3.2 Data Preprocessing and Normalization
5.3.3 Multi-Modal Data Integration
5.3.4 Target Identification Using AI
5.3.5 Virtual Drug Screening and De Novo Drug Design
5.3.6 In Silico Validation Pipeline
5.3.7 In Vitro Experimental Validation
5.3.8 In Vivo Preclinical Models
5.3.9 AI-Driven Iterative Optimization
5.3.10 Translational and Clinical Integration
5.3.11 Implementation and Continuous Learning
5.4 Results and Discussions
5.4.1 General Discussions of the Study
5.4.2 Limitations and Practical Implications
5.5 Conclusion
5.6 Future Directions
References
6. Personalized Medicine Through Pharmacogenomics and AI: A Precision Therapeutics Approach
Dafik, Anto Lourdu Xavier Raj Arockia Selvarathinam, Priya K. V., Sreeram Indraneel, C. Ambhika and Ruth Ramya Kalangi
6.1 Introduction
6.1.1 Research Question and Problem Statement
6.2 Related Works
6.3 Methodology
6.3.1 Multi-Source Pharmacogenomic Data Acquisition
6.3.2 Data Preprocessing and Data Quality Control
6.3.3 AI-Driven Multi-Omics Integration
6.3.4 Predicting Drug Response and Stratification of Patients
6.3.5 AI-Guided Precision Drug Optimization
6.3.6 In Silico Validation and Simulation
6.3.7 Experimental and Clinical Validation
6.3.8 Deployment and Continuous Learning
6.4 Results and Discussions
6.4.1 Dataset Description
6.5 Discussion
6.6 Conclusion
6.7 Future Directions
References
7. Real-World Use Cases of AI in Pharmacogenomic Decision Support Systems
Kayal Padmanandam, IsaiVani Mariyappan, Anitha D., Sachin Chandravadan Karad, Pooja P. Raj and Umesh Kumar Lihore
7.1 Introduction
7.2 Background and Rationale
7.2.1 Pharmacogenomics and Clinical Decision Support
7.2.2 Role of AI in PGx DSS
7.3 Methodology
7.3.1 Real-World Use Cases
7.3.1.1 Warfarin Dose Optimization
7.3.1.2 Clopidogrel Therapy in Cardiovascular Care
7.3.1.3 Oncology Chemotherapy Selection
7.3.1.4 Antidepressant and Antipsychotic Selection (GeneSight)
7.3.1.5 Opioid Pain Management
7.3.1.6 Pediatric Precision Dosing
7.4 Quantitative Analysis
7.4.1 Statistical Measures
7.5 Discussion
7.6 Challenges and Barriers to Implementation
7.7 Future Directions
7.8 Conclusion
References
8. AI Algorithms for Predicting Drug Response in Diverse Populations: Bridging Pharmacogenomics and Precision Medicine
Fathimathul Rajeena P.P., Rahoof P. P. and Sunder R.
8.1 Introduction
8.2 Background
8.2.1 The Importance of Population Diversity
8.2.2 Limitations of Traditional Methods
8.3 Methodology
8.3.1 Data Acquisition
8.3.2 Data Preprocessing and Harmonization
8.3.3 Multi-Omics Integration and Feature Engineering
8.3.4 AI Model Developments
8.3.5 Prevention and Fairness Analysis
8.3.6 Validation
8.4 Results and Findings
8.4.1 Discussion
8.4.2 Future Directions
8.5 Conclusion
Acknowledgment
References
9. Artificial Intelligence for Genetic Variant Detection and Interpretation
Lokendra Singh Songare, Narendra B. Mustare, Kamepalli Sujatha, Albin Kurian, Aparajita Mukherjee and Umesh Kumar Lilhore
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Results and Findings
9.4.1 Dataset Description
9.4.2 Performance Evaluation
9.4.3 General Discussion
9.4.4 Implications and Clinical Practice Limitations
9.5 Conclusion
9.6 Future Directions
References
10. Cardiovascular Pharmacogenomics: Genetic Predictors of Drug Response and Toxicity
Sunder R., Shanimol Shajan, S. Anupkant, Donamol Joseph, D. Vetrithangam and Rasmi A.
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.4 Results and Findings
10.5 Conclusion
10.6 Future Directions
References
11. Wearable Devices and Real-Time Pharmacogenomic Monitoring
P. Kavitha, Sruthy Sukumaran, Kavya Clare P. Shaji, S. Chinnapparaj, Veeraiyah Thangasamy and Sunder R.
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.4 Results and Findings
11.5 General Discussion
11.6 Conclusions
11.7 Future Directions
References
12. Challenges and Limitations of Applying Artificial Intelligence in Pharmacogenomic Pipelines: Technical, Clinical, and Operational Perspectives
Yagyesh Godiyal, Maharani Abu Bakar, S. Madhusudhanan, Kochumol Abraham, Aparajita Mukherjee and Sunder R.
12.1 Introduction
12.2 Thematic Analysis of Challenges
12.2.1 Technical Barriers
12.2.2 Clinical Challenges
12.2.3 Operational Challenges
12.3 Identification of Repeated Patterns, Bottlenecks
12.3.1 Recurring Patterns
12.3.2 Bottlenecks
12.4 Strategies to Minimize these Challenges
12.4.1 Technical Challenges
12.4.2 Clinical Challenges
12.4.3 Operational Challenges
12.5 Real-World AI Applications in Pharmacogenomics
12.5.1 Economic Implications
12.5.2 Policy Implications
12.6 Conclusion
12.7 Future Research Directions
References
13. Ethical Frameworks for Integrating AI in Pharmacogenomics: A Focus on Equity and Justice
Ika Hesti Agustin, R. Kannamma, Nallametti Nagarjuna, Sheela S., D. Vetrithangam and Thilagavathi K.
13.1 Introduction
13.2 Related Works
13.3 Research Design
13.4 Results and Findings
13.4.1 Discussions
13.4.2 Limitations and Implications for Practice
13.5 Conclusion and Future Work
References
14. The Future of AI in Pharmacogenomics: Trends, Innovations, and Global Perspectives
Sanaj M.S., Minnuja Shelly, Asha S., Nor Asilah Wati Abdul Hamid, S. Mathumohan
and Sudhir Ramadass
14.1 Introduction
14.1.1 History of Pharmacogenomics
14.1.2 Genomic Medicine and Artificial Intelligence
14.1.3 Significance of AI to Personalized Therapeutics
14.2 Foundations of AI in Pharmacogenomics
14.2.1 AI, ML, and DL for Drug Response Prediction
14.2.2 Data Sources: Transcriptomic, Genomic, Proteomic, and Clinical Data
14.2.3 Pharmacogenomic Information Computer Models
14.3 Present Developments in Pharmacogenomics Using AI
14.3.1 Artificial Intelligence in Prediction and Optimization of Drug Response
14.3.2 Deep Learning for Gene–Drug Interaction Discovery
14.3.3 AI-Based Biomarker Discovery
14.3.4 Integration of Multi-Omics Data Using AI
14.4 Innovations and Emerging Technologies
14.4.1 Deep Learning for Integrating Multi-Omics
14.4.2 Natural Language Processing (NLP) in Pharmacogenomics
14.4.3 Federated Learning and Privacy-Preserving AI
14.4.4 Integration of AI with CRISPR and Gene Editing
14.5 Global Perspectives and Trends
14.5.1 Regional Disparities in Genomic Data
14.5.2 Policy and Regulatory Landscape
14.5.3 Industry Adoption
14.6 Challenges and Limitations
14.7 Future Directions
14.8 Conclusion
References
Index
Back to Top
Preface
1. Foundations of Pharmacogenomics: Understanding the Genetic Basis of Drug Response
Dhanesh Kumar, Thangiah Sathishkumar, Sarangam Kodati, Venkata Praveen Kumar Vuppala, Rasmi A. and Rajakumar Perumal
1.1 Introduction
1.2 Genetics of Drug Response Mechanisms
1.2.1 Types of Genetic Variation
1.2.2 Pharmacokinetics vs. Pharmacodynamics
1.2.3 Phenotypes and Clinical Effects
1.3 Clinically Actionable Examples
1.3.1 CYP2C19 and Clopidogrel
1.3.2 CYP2D6 and Codeine/Opioid Metabolism
1.3.3 Warfarin: VKORC1 and CYP2C9
1.3.4 Cancer Therapy Markers: HER2 and DPYD/UGT1A1
1.4 Implementation Frameworks and Clinical Integration
1.4.1 CPIC, PharmGKB, DPWG
1.4.2 Real-World Programs and Economic Impact
1.4.3 Clinical Decision Support Systems (CDSS)
1.4.4 Ethical, Legal, and Social Considerations
1.5 New Technologies and Emerging Trends
1.6 Conclusion
References
2. From Data to Therapy: Artificial Intelligence Applications in Pharmacogenomics
Yuvaraj Velusamy, L. Gandhimathi, Shaziya Islam, P. Jyothi, Saranya P. and S. Suresh
2.1 Introduction
2.1.1 Evolution of Pharmacogenomics from Bench to Bedside
2.1.2 The Increasing Complexity of Genomic Datasets
2.1.3 Transformative Tool for PGx
2.1.4 Problem Statement: Present Bottlenecks in PGx-Driven Clinical Translation
2.2 Data Foundations in AI-Driven Pharmacogenomics
2.2.1 Genomic Data Sources
2.2.2 Multi-Omics Integration
2.2.3 Clinical and Real-World Data
2.2.4 Data Quality, Curation, and Standardization
2.3 AI Methodologies in PGx
2.3.1 Machine Learning Techniques
2.3.2 Deep Learning Architectures
2.3.3 Natural Language Processing
2.3.4 Graph-Based AI Models
2.4 Translating Data to Therapy: Key AI-Driven PGx Applications
2.4.1 Drug Response Prediction
2.4.2 Dose Optimization Models
2.4.3 Drug Repurposing and Discovery
2.4.4 Clinical Decision Support Systems (CDSS)
2.5 Challenges and Limitations
2.5.1 Data Privacy and Ethics
2.5.2 Model Interpretability and Explainability
2.5.3 Bias and Generalizability
2.5.4 Regulatory and Policy Barriers
2.6 Future Perspectives
2.6.1 Explainable AI (XAI) for PGx
2.6.2 Federated Learning Technology to Cross-Organization Genomic Models
2.6.3 Real-Time PGx via IoT and Wearables
2.6.4 AI-Augmented Clinical Trials
2.7 Conclusion
References
3. Machine Learning Approaches for Genomic Data Analysis in Pharmacogenomics
Ashwin M., Sreenivas Mekala, V. Arun, Ashish, S. Mathumohan and K. Kaliraj
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.3.1 Data Acquisition
3.3.2 Data Preprocessing
3.3.3 Feature Selection and Dimensionality Reduction
3.3.4 Model Development
3.3.5 Model Training and Optimization
3.3.6 Validation and Benchmarking
3.3.7 Clinical Translation
3.4 Results and Discussions
3.4.1 Dataset Description
3.4.2 Performance Analysis
3.4.3 Ethical, Legal, and Social Considerations
3.4.4 Future Extensions
3.5 Conclusion
References
4. Deep Learning and Neural Networks: Unlocking Complex Patterns in Genomic Medicine
M. Sudharsan, K. Maithili, T. Ravi, Margaret Mary T., M. Rajesh Khanna and P. Eswaran
4.1 Introduction
4.1.1 Research Questions and Problem Statement
4.2 Related Works
4.3 Methodology
4.3.1 Data Acquisition and Preparation
4.3.2 Exploratory Data Analysis (EDA)
4.3.3 Model Architecture Design
4.3.4 Multi-Scale Representation Learning
4.3.5 Model Training and Optimization
4.3.6 Validation and Benchmarking
4.3.7 Clinical Translation and Deployment
4.4 Results and Discussions
4.4.1 Dataset Description
4.4.2 Performance Evaluation
4.4.3 Discussions
4.4.4 Limitations and Practical Implications of the Study
4.5 Conclusion
4.6 Future Directions of the Study
References
5. AI-Driven Drug Discovery: Accelerating Therapeutic Innovation through Genomics
K. Prakash, Phani Kumar Solleti, Tarak Hussain, Chilukala Mahender Reddy, Margaret Mary T. and P. Arumugam
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.3.1 Multi-Omics Data Acquisition
5.3.2 Data Preprocessing and Normalization
5.3.3 Multi-Modal Data Integration
5.3.4 Target Identification Using AI
5.3.5 Virtual Drug Screening and De Novo Drug Design
5.3.6 In Silico Validation Pipeline
5.3.7 In Vitro Experimental Validation
5.3.8 In Vivo Preclinical Models
5.3.9 AI-Driven Iterative Optimization
5.3.10 Translational and Clinical Integration
5.3.11 Implementation and Continuous Learning
5.4 Results and Discussions
5.4.1 General Discussions of the Study
5.4.2 Limitations and Practical Implications
5.5 Conclusion
5.6 Future Directions
References
6. Personalized Medicine Through Pharmacogenomics and AI: A Precision Therapeutics Approach
Dafik, Anto Lourdu Xavier Raj Arockia Selvarathinam, Priya K. V., Sreeram Indraneel, C. Ambhika and Ruth Ramya Kalangi
6.1 Introduction
6.1.1 Research Question and Problem Statement
6.2 Related Works
6.3 Methodology
6.3.1 Multi-Source Pharmacogenomic Data Acquisition
6.3.2 Data Preprocessing and Data Quality Control
6.3.3 AI-Driven Multi-Omics Integration
6.3.4 Predicting Drug Response and Stratification of Patients
6.3.5 AI-Guided Precision Drug Optimization
6.3.6 In Silico Validation and Simulation
6.3.7 Experimental and Clinical Validation
6.3.8 Deployment and Continuous Learning
6.4 Results and Discussions
6.4.1 Dataset Description
6.5 Discussion
6.6 Conclusion
6.7 Future Directions
References
7. Real-World Use Cases of AI in Pharmacogenomic Decision Support Systems
Kayal Padmanandam, IsaiVani Mariyappan, Anitha D., Sachin Chandravadan Karad, Pooja P. Raj and Umesh Kumar Lihore
7.1 Introduction
7.2 Background and Rationale
7.2.1 Pharmacogenomics and Clinical Decision Support
7.2.2 Role of AI in PGx DSS
7.3 Methodology
7.3.1 Real-World Use Cases
7.3.1.1 Warfarin Dose Optimization
7.3.1.2 Clopidogrel Therapy in Cardiovascular Care
7.3.1.3 Oncology Chemotherapy Selection
7.3.1.4 Antidepressant and Antipsychotic Selection (GeneSight)
7.3.1.5 Opioid Pain Management
7.3.1.6 Pediatric Precision Dosing
7.4 Quantitative Analysis
7.4.1 Statistical Measures
7.5 Discussion
7.6 Challenges and Barriers to Implementation
7.7 Future Directions
7.8 Conclusion
References
8. AI Algorithms for Predicting Drug Response in Diverse Populations: Bridging Pharmacogenomics and Precision Medicine
Fathimathul Rajeena P.P., Rahoof P. P. and Sunder R.
8.1 Introduction
8.2 Background
8.2.1 The Importance of Population Diversity
8.2.2 Limitations of Traditional Methods
8.3 Methodology
8.3.1 Data Acquisition
8.3.2 Data Preprocessing and Harmonization
8.3.3 Multi-Omics Integration and Feature Engineering
8.3.4 AI Model Developments
8.3.5 Prevention and Fairness Analysis
8.3.6 Validation
8.4 Results and Findings
8.4.1 Discussion
8.4.2 Future Directions
8.5 Conclusion
Acknowledgment
References
9. Artificial Intelligence for Genetic Variant Detection and Interpretation
Lokendra Singh Songare, Narendra B. Mustare, Kamepalli Sujatha, Albin Kurian, Aparajita Mukherjee and Umesh Kumar Lilhore
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Results and Findings
9.4.1 Dataset Description
9.4.2 Performance Evaluation
9.4.3 General Discussion
9.4.4 Implications and Clinical Practice Limitations
9.5 Conclusion
9.6 Future Directions
References
10. Cardiovascular Pharmacogenomics: Genetic Predictors of Drug Response and Toxicity
Sunder R., Shanimol Shajan, S. Anupkant, Donamol Joseph, D. Vetrithangam and Rasmi A.
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.4 Results and Findings
10.5 Conclusion
10.6 Future Directions
References
11. Wearable Devices and Real-Time Pharmacogenomic Monitoring
P. Kavitha, Sruthy Sukumaran, Kavya Clare P. Shaji, S. Chinnapparaj, Veeraiyah Thangasamy and Sunder R.
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.4 Results and Findings
11.5 General Discussion
11.6 Conclusions
11.7 Future Directions
References
12. Challenges and Limitations of Applying Artificial Intelligence in Pharmacogenomic Pipelines: Technical, Clinical, and Operational Perspectives
Yagyesh Godiyal, Maharani Abu Bakar, S. Madhusudhanan, Kochumol Abraham, Aparajita Mukherjee and Sunder R.
12.1 Introduction
12.2 Thematic Analysis of Challenges
12.2.1 Technical Barriers
12.2.2 Clinical Challenges
12.2.3 Operational Challenges
12.3 Identification of Repeated Patterns, Bottlenecks
12.3.1 Recurring Patterns
12.3.2 Bottlenecks
12.4 Strategies to Minimize these Challenges
12.4.1 Technical Challenges
12.4.2 Clinical Challenges
12.4.3 Operational Challenges
12.5 Real-World AI Applications in Pharmacogenomics
12.5.1 Economic Implications
12.5.2 Policy Implications
12.6 Conclusion
12.7 Future Research Directions
References
13. Ethical Frameworks for Integrating AI in Pharmacogenomics: A Focus on Equity and Justice
Ika Hesti Agustin, R. Kannamma, Nallametti Nagarjuna, Sheela S., D. Vetrithangam and Thilagavathi K.
13.1 Introduction
13.2 Related Works
13.3 Research Design
13.4 Results and Findings
13.4.1 Discussions
13.4.2 Limitations and Implications for Practice
13.5 Conclusion and Future Work
References
14. The Future of AI in Pharmacogenomics: Trends, Innovations, and Global Perspectives
Sanaj M.S., Minnuja Shelly, Asha S., Nor Asilah Wati Abdul Hamid, S. Mathumohan
and Sudhir Ramadass
14.1 Introduction
14.1.1 History of Pharmacogenomics
14.1.2 Genomic Medicine and Artificial Intelligence
14.1.3 Significance of AI to Personalized Therapeutics
14.2 Foundations of AI in Pharmacogenomics
14.2.1 AI, ML, and DL for Drug Response Prediction
14.2.2 Data Sources: Transcriptomic, Genomic, Proteomic, and Clinical Data
14.2.3 Pharmacogenomic Information Computer Models
14.3 Present Developments in Pharmacogenomics Using AI
14.3.1 Artificial Intelligence in Prediction and Optimization of Drug Response
14.3.2 Deep Learning for Gene–Drug Interaction Discovery
14.3.3 AI-Based Biomarker Discovery
14.3.4 Integration of Multi-Omics Data Using AI
14.4 Innovations and Emerging Technologies
14.4.1 Deep Learning for Integrating Multi-Omics
14.4.2 Natural Language Processing (NLP) in Pharmacogenomics
14.4.3 Federated Learning and Privacy-Preserving AI
14.4.4 Integration of AI with CRISPR and Gene Editing
14.5 Global Perspectives and Trends
14.5.1 Regional Disparities in Genomic Data
14.5.2 Policy and Regulatory Landscape
14.5.3 Industry Adoption
14.6 Challenges and Limitations
14.7 Future Directions
14.8 Conclusion
References
Index
Back to Top