Precision pharmaceutics: bioinformatics and pharmacogenomics as catalysts for personalized drug therapy

Objectives This review summarizes how advances in bioinformatics are transforming pharmacogenomics by enabling prediction of individual drug responses and integration of pharmacogenomic data into clinical practice across multiple therapeutic areas. It aims to highlight key databases, computational tools, and artificial intelligence (AI) approaches that support personalized medicine. Methods Evidence from pharmacogenomic studies, centralized resources (for example, PharmGKB), and in silico prediction tools (such as PolyPhen and SIFT) is synthesized to illustrate how genetic variants affecting drug metabolism, efficacy, and toxicity are systematically analysed and translated into clinical applications. Key findings Pharmacogenomic applications in psychiatry, cardiology, oncology, and autoimmune disorders show that genetically guided therapies can enhance efficacy and reduce adverse effects. AI and machine learning improve interpretation of high-dimensional pharmacogenomic data, accelerating progress in precision medicine and drug discovery, although challenges such as computational complexity, data privacy, and underrepresentation of diverse populations persist. Conclusions Bioinformatics technologies are central to operationalizing pharmacogenomics by enabling robust variant interpretation and data integration for individualized therapy. Addressing technical, ethical, and equity-related challenges will be critical to fully realizing the potential of pharmacogenomics and advancing precision health care.