Omar Fernando Cruz-Correa1, Rafael Baltazar Reyes León-Cachón2,3, Hugo Alberto Barrera-Saldaña2,4, Xavier Soberón1,5. 1. Instituto Nacional de Medicina Genómica, Periférico Sur No. 4809, Col. Arenal Tepepan, Delegación Tlalpan, México, D.F. C.P. 14610, Mexico. 2. Departamento de Bioquímica y Medicina Molecular, Facultad de Medicina, Universidad Autónoma de Nuevo León, Ave. Madero, Col. Mitras Centro, Monterrey, Nuevo León, C.P. 64640, Mexico. 3. División Ciencias de la Salud, Departamento de Ciencias Básicas, Centro de Diagnóstico Molecular y Medicina Personalizada, Universidad de Monterrey, Ave. Ignacio Morones Prieto Pte. 4500, Col. Jesús M. Garza, San Pedro Garza García, Nuevo León, C.P. 66238, Mexico. 4. Vitagénesis, SA de CV., Col. Colinas de San Jerónimo. Monterrey, Nuevo León, C.P. 64630, Mexico. 5. Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, Cuernavaca, Morelos, C.P. 62210, Mexico.
Abstract
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.
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.
Authors: Rafael B R León-Cachón; Aileen-Diane Bamford; Irene Meester; Hugo Alberto Barrera-Saldaña; Magdalena Gómez-Silva; María F García Bustos Journal: Sci Rep Date: 2020-06-01 Impact factor: 4.379