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Pharmacogenomics. 2017 Jan;18(2):121-131. doi: 10.2217/pgs-2016-0072. Epub 2016 Dec 15.

Prediction of atorvastatin plasmatic concentrations in healthy volunteers using integrated pharmacogenetics sequencing.

Author information

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.

KEYWORDS:

Next-Gen sequencing; atorvastatin; pharmacokinetics

PMID:
27976987
DOI:
10.2217/pgs-2016-0072
[Indexed for MEDLINE]

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