A new pharmacogenetic algorithm to predict the most appropriate dosage of acenocoumarol for stable anticoagulation in a mixed Spanish population

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9∗2 (rs1799853), CYP2C9∗3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11-21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.This study was funded by a grant from the Spanish Ministry of Health and Social Policy (Instituto de Salud Carlos III, PI07/0710) and the Andalusian Regional Ministry of Health (Progress and Health Foundation, PI-0717-2013

Patients correctly classified (predicted dose within ± 20% of actual dosage) and MAE from the entire cohort (n = 682) by genetic and clinical algorithms according to dosage groups.

<p>Patients correctly classified (predicted dose within ± 20% of actual dosage) and MAE from the entire cohort (n = 682) by genetic and clinical algorithms according to dosage groups.</p

Patients characteristics in the generation (n = 556) and validation (n = 129) cohorts.

<p>Patients characteristics in the generation (n = 556) and validation (n = 129) cohorts.</p

Patients correctly classified (predicted dose within ± 20% of the actual dosage) by genetic and clinical algorithms in the generation, validation and entire cohorts (n = 682).

<p>Patients correctly classified (predicted dose within ± 20% of the actual dosage) by genetic and clinical algorithms in the generation, validation and entire cohorts (n = 682).</p

Predictive performance of the pharmacogenetic algorithm by disease in the entire cohort (n = 682).

<p>Predictive performance of the pharmacogenetic algorithm by disease in the entire cohort (n = 682).</p

Pharmacogenetic and clinical algorithms.

<p>Pharmacogenetic and clinical algorithms.</p

Predictive performance of the pharmacogenetic and clinical algorithms in the generation, validation and entire cohorts.

<p>Predictive performance of the pharmacogenetic and clinical algorithms in the generation, validation and entire cohorts.</p