Pharmacogenomics in Personalized Medicine Approaches

Abstract

Pharmacogenomics--the study of how genetic variation influences individual drug response in terms of efficacy, toxicity,
and dosing requirements--has transitioned from a research discipline to a clinical standard of care across oncology,
psychiatry, cardiology, and infectious disease over the past two decades, driven by the declining cost of genotyping and
the accumulating body of clinically actionable pharmacogenomic (PGx) associations validated in prospective trials. This
review comprehensively surveys the current landscape of pharmacogenomics in personalised medicine, covering: the
molecular basis of PGx variation in drug-metabolising enzymes (CYP450 family, UGT1A1, TPMT, DPYD), drug
transporters (SLCO1B1, ABCB1), and drug targets (VKORC1, EGFR, KRAS, HER2); clinical implementation evidence
from landmark trials including CPIC guideline-supported drug-gene pairs; the expanding role of polygenic risk scores
(PRS) in drug response prediction beyond single-variant pharmacogenomics; current clinical decision support integration
challenges; and the regulatory and health economic framework for PGx-guided prescribing. Key clinically validated
examples reviewed include: warfarin dosing guided by CYP2C9/VKORC1/CYP4F2 genotype (reducing serious bleeding
events by 31-43%); clopidogrel response prediction by CYP2C19 loss-of-function alleles (associated with 3.58-fold
increased MACE risk in poor metabolisers); DPYD genotyping before fluoropyrimidine chemotherapy (reducing severe
toxicity from 35% to 4.7%); and HER2/KRAS/BRAF tumour genotyping directing targeted therapy selection in metastatic
colorectal and breast cancer. Remaining implementation barriers including incomplete population diversity in PGx
reference datasets, clinical workflow integration complexity, and reimbursement heterogeneity across European
healthcare systems are critically evaluated alongside emerging opportunities from direct-to-consumer genomics,
preemptive panel genotyping, and AI-guided phenotype prediction.