Machine Learning Approaches to Pharmacogenomic s and Personalized Drug Therapy
Srinivasa Reddy Bireddy, Venkata Kiran Kumar Ravi · 2025
The integration of machine learning (ML) approaches in pharmacogenomics holds transformative potential for the development of personalized drug therapies. By leveraging large scale genomic, clinical, and environmental data, machine learning models enable the prediction of individual responses to drugs, optimizing therapeutic efficacy while minimizing adverse reactions. This chapter explores the intersection of ML and pharmacogenomics, with a focus on the challenges and opportunities that arise from data-driven precision medicine. Emphasis was placed on the application of various ML algorithms in drug therapy personalization, with a specific examination of ensemble methods, data integration strategies, and ethical considerations in multi source data use. The chapter addresses the regulatory landscape surrounding AI-driven drug therapies and the complexities in validating predictive models for real-world clinical deployment. Key case studies from cardiovascular and oncology drug therapies illustrate the practical applications and impact of these innovative technologies on patient outcomes. Ultimately, this work aims to provide a comprehensive understanding of the role of ML in shaping the future of personalized drug therapy while highlighting the critical need for regulatory frameworks, data integrity, and ethical considerations in clinical practice.