A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics

TLDR

Pharmacogenomics links genetic variation to drug response, yet data on biomarker prevalence remain sparse in many European, especially lower‑income, populations. Using a microattribution strategy, the authors profiled 1,931 pharmacogenomic biomarkers in 231 genes across 18 European populations, mainly from developing countries. The study uncovered marked allele frequency disparities in 7 actionable biomarkers across 7 populations, influencing 51 drug regimens, altering high‑risk genotype prevalence for key pharmacogenes, and affecting warfarin dose predictions, thereby supporting guideline development, pre‑emptive testing, and potential cost savings.

Abstract

Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant inter-population pharmacogenomic biomarker allele frequency differences, particularly in 7 clinically actionable pharmacogenomic biomarkers in 7 European populations, affecting drug efficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on the differences observed in the prevalence of high-risk genotypes in these populations, as far as common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMT pharmacogenes are concerned. Also, our data demonstrate notable differences in predicted genotype-based warfarin dosing among these populations. Our findings can be exploited not only to develop guidelines for medical prioritization, but most importantly to facilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomic testing. This may subsequently contribute towards significant cost-savings in the overall healthcare expenditure in the participating countries, where pharmacogenomics implementation proves to be cost-effective.