A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform.
Int J Mol Sci
; 21(7)2020 Apr 04.
Article
em En
| MEDLINE
| ID: mdl-32260456
ABSTRACT
Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.
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Base de dados:
MEDLINE
Assunto principal:
Tacrolimo
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Citocromo P-450 CYP3A
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Citocromo P-450 CYP2A6
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Variantes Farmacogenômicos
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article