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

Omar Fernando Cruz-Correa, Rafael Baltazar Reyes León-Cachón, Hugo Alberto Barrera-Saldaña, Xavier Soberón

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

© 2017 Future Medicine Ltd. 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.
Original languageEnglish
Pages (from-to)121-131
Number of pages11
JournalPharmacogenomics
DOIs
Publication statusPublished - 1 Jan 2017

Fingerprint

Pharmacogenetics
Area Under Curve
Healthy Volunteers
Genes
Medicine
Atorvastatin Calcium

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Pharmacology

Cite this

@article{9123bdbde00947d1a4dc4471857f8770,
title = "Prediction of atorvastatin plasmatic concentrations in healthy volunteers using integrated pharmacogenetics sequencing",
abstract = "{\circledC} 2017 Future Medicine Ltd. 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.",
author = "Cruz-Correa, {Omar Fernando} and Le{\'o}n-Cach{\'o}n, {Rafael Baltazar Reyes} and Barrera-Salda{\~n}a, {Hugo Alberto} and Xavier Sober{\'o}n",
year = "2017",
month = "1",
day = "1",
doi = "10.2217/pgs-2016-0072",
language = "English",
pages = "121--131",
journal = "Pharmacogenomics",
issn = "1462-2416",
publisher = "Future Medicine Ltd.",

}

Prediction of atorvastatin plasmatic concentrations in healthy volunteers using integrated pharmacogenetics sequencing. / Cruz-Correa, Omar Fernando; León-Cachón, Rafael Baltazar Reyes; Barrera-Saldaña, Hugo Alberto; Soberón, Xavier.

In: Pharmacogenomics, 01.01.2017, p. 121-131.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of atorvastatin plasmatic concentrations in healthy volunteers using integrated pharmacogenetics sequencing

AU - Cruz-Correa, Omar Fernando

AU - León-Cachón, Rafael Baltazar Reyes

AU - Barrera-Saldaña, Hugo Alberto

AU - Soberón, Xavier

PY - 2017/1/1

Y1 - 2017/1/1

N2 - © 2017 Future Medicine Ltd. 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.

AB - © 2017 Future Medicine Ltd. 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.

U2 - 10.2217/pgs-2016-0072

DO - 10.2217/pgs-2016-0072

M3 - Article

SP - 121

EP - 131

JO - Pharmacogenomics

JF - Pharmacogenomics

SN - 1462-2416

ER -