World Journal of
Pharmaceutical and Life Sciences

PHARMACOGENOMICS MEETS AI: PERSONALIZED CLINICAL PHARMACY OF THE FUTURE

Harshali Rajendra Patil, Sanika Mahendra More, Parag Chandrakant Patil*, Sughosh Upasani

ABSTRACT

Pharmacogenomics, the study of genetic determinants of drug response, has emerged as a cornerstone of precision medicine. By integrating artificial intelligence (AI) into pharmacogenomic research and clinical decision-making, healthcare is moving toward a new paradigm of individualized pharmacotherapy. Pharmacogenomics focuses on genetic polymorphisms that influence drug metabolism, transport, and receptor sensitivity, allowing for optimized treatment selection and dosage adjustment. Artificial intelligence—particularly machine learning (ML) and deep learning (DL)—enables the efficient interpretation of vast and complex genomic and clinical datasets. The synergy of these two domains has revolutionized clinical pharmacy by improving drug response prediction, minimizing adverse drug reactions (ADRs), and expediting the drug discovery process. AI-driven pharmacogenomic models can identify previously unrecognized genetic markers associated with therapeutic outcomes, transforming the conventional trial-and-error method into a predictive, evidence-based approach. The combination of pharmacogenomic data and AI algorithms enables clinical pharmacists to deliver safer and more effective therapy tailored to each patient’s genomic profile. However, challenges such as data integration, ethical governance, regulatory frameworks, and the interpretability of AI systems persist. Addressing these issues is crucial for translating computational discoveries into clinical realities. This article provides a comprehensive examination of the evolution, mechanisms, and applications of pharmacogenomics augmented by AI, with an emphasis on case studies, ethical implications, and future perspectives in precision pharmacy practice.

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