RESUMO
AIM: To use variants found by next-generation sequencing to predict atorvastatin plasmatic concentration profiles (AUC) in healthy volunteers. SUBJECTS & METHODS: A total of 60 healthy Mexican volunteers were enrolled in this study. We used variants with a predicted functional effect across 20 genes involved in atorvastatin metabolism to construct a regression model using a support vector approach with a radial basis function kernel to predict AUC refining it afterwards in order to explain a greater extent of the variance. RESULTS: The final support vector regression model using 60 variants (including six novel variants) explained 94.52% of the variance in atorvastatin AUC. CONCLUSION: An integrated analysis of several genes known to intervene in the different steps of metabolism is required to predict atorvastatin's AUC.