A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMET TM Plus Platform

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 a...

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Bibliographic Details
Published in:International journal of molecular sciences 2020-04, Vol.21 (7)
Main Authors: Gim, Jeong-An, Kwon, Yonghan, Lee, Hyun A, Lee, Kyeong-Ryoon, Kim, Soohyun, Choi, Yoonjung, Kim, Yu Kyong, Lee, Howard
Format: Article
Language:English
Subjects:
3' Untranslated Regions
Adult
Cytochrome P-450 CYP2A6 - genetics
Cytochrome P-450 CYP3A - genetics
Decision Trees
Healthy Volunteers
Humans
Machine Learning
Male
Pharmacogenomic Variants
Polymorphism, Single Nucleotide
Precision Medicine
Republic of Korea
Tacrolimus - pharmacokinetics
Young Adult
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Summary: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. (CYP3A5) and (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.
ISSN:1422-0067