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Open AccessArticle

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

1
Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 16229, Korea
2
Medical Science Research Center, College of Medicine, Korea University, Seoul 02841, Korea
3
Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul 03722, Korea
4
Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea
5
Laboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Ochang, Chungbuk 28116, Korea
6
GC Pharma, Yongin 16924, Korea
7
Daewoong Pharmaceutical Co., Ltd., Seoul 06170, Korea
8
Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 03080, Korea
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(7), 2517; https://doi.org/10.3390/ijms21072517
Received: 2 March 2020 / Revised: 29 March 2020 / Accepted: 2 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue Pharmacogenomics)
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. View Full-Text
Keywords: decision tree; random forest; machine learning; tacrolimus; genotype decision tree; random forest; machine learning; tacrolimus; genotype
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Gim, J.-A.; Kwon, Y.; Lee, H.A.; Lee, K.-R.; Kim, S.; Choi, Y.; Kim, Y.K.; Lee, H. A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform. Int. J. Mol. Sci. 2020, 21, 2517.

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