Letícia C Tavares1, Nubia E Duarte2, Leiliane R Marcatto1, Renata A G Soares1, Jose E Krieger1, Alexandre C Pereira1, Paulo Caleb Junior Lima Santos3,4. 1. Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, São Paulo, SP, Brazil. 2. Department of Mathematic and Statistics, Universidad Nacional de Colombia, Manizales, Caldas, Colombia. 3. Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), Faculdade de Medicina FMUSP, Universidade de Sao Paulo, São Paulo, SP, Brazil. paulo.caleb@unifesp.br. 4. Department of Pharmacology, Escola Paulista de Medicina, Universidade Federal de Sao Paulo UNIFESP, São Paulo, SP, Brazil. paulo.caleb@unifesp.br.
Abstract
PURPOSE: Interpatient variation of warfarin dose requirements may be explained by genetic variations and general and clinical factors. In this scenario, diverse population-calibrated dosing algorithms, which incorporate the main warfarin dosing influencers, have been widely proposed for predicting supposed warfarin maintenance dose, in order to prevent and reduce adverse events. The aim of the present study was to evaluate the impact of the inclusion of ABCB1 c.3435C>T and CYP4F2 c.1297G>A polymorphisms as additional covariates in a previously developed pharmacogenetic-based warfarin dosing algorithm calibrated for the Brazilian population. METHODS: Two independent cohorts of patients treated with warfarin (n = 832 and n = 133) were included for derivation and replication of the algorithm, respectively. Genotyping of ABCB1 c.3435C>T and CYP4F2 c.1297G>A polymorphisms was performed by polymerase chain reaction followed by melting curve analysis and TaqMan® assay, respectively. A multiple linear regression was performed for the warfarin stable doses as a dependent variable, considering clinical, general, and genetic data as covariates. RESULTS: The inclusion of ABCB1 and CYP4F2 polymorphisms was able to improve the algorithm's coefficient of determination (R2) by 2.6%. In addition, the partial determination coefficients of these variants revealed that they explained 3.6% of the warfarin dose variability. We also observed a marginal improvement of the linear correlation between observed and predicted doses (from 59.7 to 61.4%). CONCLUSION: Although our study indicates that the contribution of the combined ABCB1 and CYP4F2 genotypes in explaining the overall variability in warfarin dose is not very large, we demonstrated that these pharmacogenomic data are statistically significant. However, the clinical relevance and cost-effective impact of incorporating additional variants in warfarin dosing algorithms should be carefully evaluated.
PURPOSE: Interpatient variation of warfarin dose requirements may be explained by genetic variations and general and clinical factors. In this scenario, diverse population-calibrated dosing algorithms, which incorporate the main warfarin dosing influencers, have been widely proposed for predicting supposed warfarin maintenance dose, in order to prevent and reduce adverse events. The aim of the present study was to evaluate the impact of the inclusion of ABCB1 c.3435C>T and CYP4F2 c.1297G>A polymorphisms as additional covariates in a previously developed pharmacogenetic-based warfarin dosing algorithm calibrated for the Brazilian population. METHODS: Two independent cohorts of patients treated with warfarin (n = 832 and n = 133) were included for derivation and replication of the algorithm, respectively. Genotyping of ABCB1 c.3435C>T and CYP4F2 c.1297G>A polymorphisms was performed by polymerase chain reaction followed by melting curve analysis and TaqMan® assay, respectively. A multiple linear regression was performed for the warfarin stable doses as a dependent variable, considering clinical, general, and genetic data as covariates. RESULTS: The inclusion of ABCB1 and CYP4F2 polymorphisms was able to improve the algorithm's coefficient of determination (R2) by 2.6%. In addition, the partial determination coefficients of these variants revealed that they explained 3.6% of the warfarin dose variability. We also observed a marginal improvement of the linear correlation between observed and predicted doses (from 59.7 to 61.4%). CONCLUSION: Although our study indicates that the contribution of the combined ABCB1 and CYP4F2 genotypes in explaining the overall variability in warfarin dose is not very large, we demonstrated that these pharmacogenomic data are statistically significant. However, the clinical relevance and cost-effective impact of incorporating additional variants in warfarin dosing algorithms should be carefully evaluated.
Authors: P Lenzini; M Wadelius; S Kimmel; J L Anderson; A L Jorgensen; M Pirmohamed; M D Caldwell; N Limdi; J K Burmester; M B Dowd; P Angchaisuksiri; A R Bass; J Chen; N Eriksson; A Rane; J D Lindh; J F Carlquist; B D Horne; G Grice; P E Milligan; C Eby; J Shin; H Kim; D Kurnik; C M Stein; G McMillin; R C Pendleton; R L Berg; P Deloukas; B F Gage Journal: Clin Pharmacol Ther Date: 2010-04-07 Impact factor: 6.875
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