A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform
Abstract
:1. Introduction
2. Results
2.1. Subjects
2.2. Genetic Associations with Tacrolimus Pharmacokinetics by Decision Tree, Random Forest, and LASSO Analyses
2.3. In silico Analysis of the SNPs in the 3′UTR
3. Discussion
4. Materials and Methods
4.1. Clinical Studies and Subjects
4.2. Determination of Tacrolimus Concentrations and Pharmacokinetic Analysis
4.3. DNA Extraction and Genotype Analysis
4.4. Statistical Analysis and Machine Learning Application
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Gene | SNP | Location | Reference Allele | Variant Allele | Reference Allele Frequency | Variant Allele Frequency | ||
---|---|---|---|---|---|---|---|---|
1000 Genomes * | Our Data ** | 1000 Genomes * | Our Data ** | |||||
CYP3A5 | rs776746 | Splice acceptor | T | C | 0.379 | 0.253 | 0.621 | 0.747 |
CYP2A6 | rs1137115 | Exon | T | C | 0.239 | 0.136 | 0.761 | 0.864 |
SLC7A5 *** | rs1060253 | 3′UTR | G | C | 0.698 | 0.370 | 0.302 | 0.630 |
Gene | SNP and Genotype | Location | Reference Allele | Variant Allele | Reference Allele Frequency | Variant Allele Frequency | Importance | ||
---|---|---|---|---|---|---|---|---|---|
1000 Genomes * | Our Data ** | 1000 Genomes * | Our data ** | ||||||
Cmax | |||||||||
CYP3A5 | rs776746 | Splice acceptor | T | C | 0.379 | 0.253 | 0.621 | 0.747 | 0.28524489 |
SLCO3A1 | rs2190748 | Intron | G | A | 0.517 | 0.525 | 0.483 | 0.475 | 0.14800742 |
ADC1 | rs1049793 | Exon | C | G | 0.627 | 0.358 | 0.373 | 0.642 | 0.13512953 |
SLC7A5 | rs1060253 | 3′UTR | G | C | 0.698 | 0.370 | 0.302 | 0.630 | 0.11857793 |
AUClast | |||||||||
CYP3A5 | rs776746 | Splice acceptor | T | C | 0.379 | 0.253 | 0.621 | 0.747 | 1.5377314 |
SLCO3A1 | rs2190748 | Intron | G | A | 0.517 | 0.525 | 0.483 | 0.475 | 0.3333521 |
CYP2A6 | rs1137115 | Exon | T | C | 0.239 | 0.136 | 0.761 | 0.864 | 0.1921316 |
NR1I2 | rs3814055 | Exon | C | T | 0.678 | 0.710 | 0.322 | 0.290 | 0.1419874 |
Gene | SNP | Location | Reference Allele | Variant Allele | Reference Allele Frequency | Variant Allele Frequency | Coefficient | ||
---|---|---|---|---|---|---|---|---|---|
1000 Genomes * | Our Data ** | 1000 Genomes * | Our Data ** | ||||||
Cmax | |||||||||
CYP3A5 | rs776746 | Splice acceptor | T | C | 0.379 | 0.253 | 0.621 | 0.747 | 0.13331 |
CBR1 | rs3787728 | Intron | T | C | 0.270 | 0.519 | 0.730 | 0.481 | 0.07863 |
NAT2 | rs1208 | Exon | G | A, T | 0.323 | 0.025 | 0.677 | 0.975 | 0.07224 |
AUClast | |||||||||
CYP3A5 | rs776746 | Splice acceptor | T | C | 0.379 | 0.253 | 0.621 | 0.747 | 0.36133 |
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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. https://doi.org/10.3390/ijms21072517
Gim J-A, Kwon Y, Lee HA, Lee K-R, Kim S, Choi Y, Kim YK, Lee H. A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform. International Journal of Molecular Sciences. 2020; 21(7):2517. https://doi.org/10.3390/ijms21072517
Chicago/Turabian StyleGim, Jeong-An, Yonghan Kwon, Hyun A Lee, Kyeong-Ryoon Lee, Soohyun Kim, Yoonjung Choi, Yu Kyong Kim, and Howard Lee. 2020. "A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform" International Journal of Molecular Sciences 21, no. 7: 2517. https://doi.org/10.3390/ijms21072517