A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics

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 interpopulation 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

A European spectrum of pharmacogenomic biomarkers: Implications for clinical pharmacogenomics

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 interpopulation 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

A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics.

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

Correction: A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics.

[This corrects the article DOI: 10.1371/journal.pone.0162866.]

A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics

Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicitywith the underlying genetic composition, particularly in genes encoding for protein factorsand enzymes involved in drug metabolism and transport. In several Europeanpopulations, particularly in countries with lower income, information related to the prevalenceof pharmacogenomic biomarkers is incomplete or lacking. Here, we have implementedthe microattribution approach to assess the pharmacogenomic biomarkers allelicspectrum in 18 European populations, mostly from developing European countries, by analyzing1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant interpopulationpharmacogenomic biomarker allele frequency differences, particularly in 7 clinicallyactionable pharmacogenomic biomarkers in 7 European populations, affecting drugefficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on thedifferences observed in the prevalence of high-risk genotypes in these populations, as faras common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMTpharmacogenes are concerned. Also, our data demonstrate notable differences in predictedgenotype-based warfarin dosing among these populations. Our findings can beexploited not only to develop guidelines for medical prioritization, but most importantly tofacilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomictesting. This may subsequently contribute towards significant cost-savings in the overallhealthcare expenditure in the participating countries, where pharmacogenomics implementationproves to be cost-effective.Introductio

Outline of the predicted average warfarin dosage calculation for all populations.

<p>This table suggests the weekly average dosage along with the standard deviation, confidence interval (95%) and the respective upper bound and lower bound for each population.</p

Frequency of the clinically actionable genotypes in the European patients analyzed using the Affymetrix DMET^™ Plus platform.

<p>Green depicts genotypes with no actionable pharmacogenomic biomarkers, yellow depicts genotypes with at least one actionable pharmacogenomic biomarker, and red depicts genotypes with at least one high-risk actionable pharmacogenomic biomarker. As stated in PharmGKB, the term “actionable” does not discuss genetic or other testing for gene/protein/chromosomal variants, but does contain information about changes in efficacy, dosage or toxicity due to such variants.</p

Comparison of the frequencies (vertical axis; %) of the 36 actionable PGx biomarkers (depicted at the horizontal axis) among European, Saudi Arabian and South African populations.

<p>Comparison of the frequencies (vertical axis; %) of the 36 actionable PGx biomarkers (depicted at the horizontal axis) among European, Saudi Arabian and South African populations.</p