Authors
Anantha Lakshmi Jakka, Ripsy Merrin Chacko, Mallikarjun Vasam, Shanmugarathinam Alagarsamy, Siva Sai Chandragiri, Sai Deepthi Gavini, Mano Joseph Mathew
Published in
Pharmacogenomics. Pages 1-14. Nov 23, 2025. Epub Nov 23, 2025.
Abstract
At the crossroads of genomics and pharmacology, pharmacogenomics is revolutionizing healthcare by tailoring drug therapies to individual genetic profiles thereby reducing the risk of adverse drug reactions and propelling the field of precision medicine forward. This review delves into the role of pharmacogenomics in uncovering genetic variations, including single nucleotide polymorphisms, that affects how drugs are metabolized and effective. Artificial intelligence (AI) has ameliorated the discovery of biomarkers and the drug development process; enabled real-time clinical decision-making, expanding the possibilities of personalized medicine. AI-powered models, especially in machine learning and deep learning have demonstrated potential in forecasting drug responses and enhancing the precision of genetic variant identification, exemplified by tools like DeepVariant and AlphaFold. However, the diversity of data, the clarity of model interpretations, and the ethical issues surrounding data privacy and genetic discrimination remain as major hurdles. Efforts are underway to address these challenges through multi-omics integration, federated learning, and explainable AI, all aimed at improving clinical translation and promoting fair access to personalized treatments. This review enunciates the existing applications, translational pathways, and prospects of AI in pharmacogenomics, its promise in achieving the goal of precision medicine ensuring the proper treatment of right patient at the right moment.
PMID:
41277106
Bibliographic data and abstract were imported from PubMed on 24 Nov 2025.
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