Computer Science  Hematology  Mathematics  Pathology  Pharmacology

An Acenocoumarol Dosing Algorithm Using Clinical and Pharmacogenetic Data in Spanish Patients with Thromboembolic Disease
Published:
Friday, July 20, 2012
Author:
Alberto M. Borobia et al.
by Alberto M. Borobia, Rubin Lubomirov, Elena Ramírez, Alicia Lorenzo, Armando Campos, Raul MuñozRomo, Carmen FernándezCapitán, Jesús Frías, Antonio J. Carcas
Appropriate dosing of coumarins is difficult to establish, due to significant interindividual variability in the dose required to obtain stable anticoagulation. Several genetic and other clinical factors have been associated with the coumarins dose, and some pharmacogeneticguided dosing algorithms for warfarin and acenocoumarol have been developed for mixed populations. We recruited 147 patients with thromboembolic disease who were on stable doses and with an international normalized ratio (INR) between 2 and 3. We ascertained the influence of clinical and genetic variables on the stable acenocoumarol dose by multiple linear regression analysis in a derivation cohort (DC; n?=?117) and developed an algorithm for dosing that included clinical factors (age, body mass index and concomitant drugs) and genetic variations of VKORC1, CYP2C9, CYP4F2 and APOE. For purposes of comparison, a model including only clinical data was created. The clinical factors explained 22% of the dose variability, which increased to 60.6% when pharmacogenetic information was included (p<0.001); CYP4F2 and APOE variants explained 4.9% of this variability. The mean absolute error of the predicted acenocoumarol dose (mg/week) obtained with the pharmacogenetic algorithm was 3.63 vs. 5.08 mg/week with the clinical algorithm (95% CI: 0.88 to 2.04). In the testing cohort (n?=?30), clinical factors explained a mere 7% of the dose variability, compared to 39% explained by the pharmacogenetic algorithm. Considering a more clinically relevant parameter, the pharmacogenetic algorithm correctly predicted the real stable dose in 59.8% of the cases (DC) vs. only 37.6% predicted by the clinical algorithm (95% CI: 10 to 35). Therefore the number of patients needed to genotype to avoid one over or underdosing was estimated to be 5.
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