Polygenic Pharmacotherapy beyond Single-Gene Rules: An Evidence Map of Scores, Interactions, Ancestry Transferability, and Clinical Utility

Pharmacogenomics has traditionally translated inherited variation into treatment guidance through individual genes or compact variant panels. This framework remains clinically important but incompletely represents drug response when many variants, treatment exposures, disease states, concomitant medicines, environmental influences, and population-specific genomic structures act together. This evidence-mapping review examines how polygenic information is being developed and evaluated for precision pharmacotherapy, with emphasis on score construction, pharmacokinetic and pharmacodynamic applications, interaction effects, ancestry transferability, incremental prediction, and clinical utility. Peer-reviewed literature was identified through structured searches of biomedical and multidisciplinary databases, verified against prespecified eligibility criteria, and charted across model class, therapeutic application, endpoint, population, validation level, evidence maturity, and translation domain. The mapped landscape shows comparatively developed methodological work on score construction, reporting, and general risk prediction, alongside emerging treatment-specific applications involving cardiovascular therapies, psychotropic-drug exposure, medication-use phenotypes, and genome-wide pharmacogenomic methods. Evidence is more fragmented for dynamic phenoconversion, drug–drug–gene interactions, multi-ancestry pharmacotherapy models, dose individualization, prospective decision support, and patient-outcome evaluation. Predictive performance, pharmacological mechanism, treatment interaction, and clinical utility are frequently treated as adjacent but are not interchangeable forms of evidence. The principal gaps concern drug-response phenotype quality, representative discovery populations, external calibration, individual-level agreement, integration with clinical predictors, and prospective evaluation of score-guided decisions. Polygenic pharmacotherapy may extend precision treatment beyond isolated gene rules, but translation requires sequential evidence connecting genomic models to exposure, response, treatment choice, and patient-relevant benefit without obscuring uncertainty or equity concerns.