The evolving landscape of pharmacogenomics: Current achievements and future directions

Pharmacogenomics investigates how inherited and acquired genetic variation shapes drug efficacy, toxicity, and treatment failure. The major therapeutic areas for pharmacogenomics-assisted drug therapy include oncology, cardiology, psychiatry, neurology, infectious diseases, pain management, and metabolic disorders. Next-generation sequencing has revealed the extensive landscape of pharmacogenetic polymorphisms at the population scale. As a result, the field has evolved from early single-gene pharmacogenetics to genome-wide approaches that encompass the entirety of pharmacogenetic variability. However, much of the heritable variation in drug response remains unexplained, reflecting rare and structural variants, complex haplotypes, and the importance of polymorphisms in factors that regulate pharmacogenes in trans. A recently emphasized factor is the importance of considering differences in substrate specificities between enzymes and transporters that carry amino acid changes. Allele frequencies of actionable genetic variants are often low, requiring large, well powered studies that carefully account for key confounders, including patient adherence, placebo effects, comorbidities, hepatic and renal dysfunction, inflammation, and drug-drug and food-drug interactions. At the same time, emerging in silico variant-effect predictors, deep mutational scanning, population biobanks, and organotypic 3-dimensional human tissue models provide scalable platforms for functionally annotating variants and modeling human drug disposition and toxicity. A major effort moving forward is the continued identification and accurate classification of clinically important drug-gene pairs, along with improved implementation of pharmacogenomics in clinical practice. Artificial intelligence can accelerate this process by enabling rapid genome interpretation, prioritizing clinically relevant variants, and translating complex data into actionable recommendations. It can also integrate pharmacogenomic findings with other omics and help mitigate bias, thereby improving equity in treatment outcomes. In conclusion, the field will continue to expand, but its success will require large, rigorously designed ancestrally diverse trials, harmonized international regulatory standards, robust cost-effectiveness evidence, and the seamless integration of artificial intelligence-supported pharmacogenomic decision tools into global clinical practice. SIGNIFICANCE STATEMENT: Pharmacogenomics is a rapidly evolving field. Here, we review its foundational background, the most important clinical applications, and future perspectives with respect to methodological advances, the role of artificial intelligence, and its translation into clinical practice.