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Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
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Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced
Roland Moore1, Kristin Ashby1, Tsung-Jen Liao1
1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR 72079, USA.
International Journal of Environmental Research and Public Health
|October 23, 2021
Summary
Identifying genetic risk factors for drug-induced liver injury (DILI) is crucial. Machine learning methods like MARS and MDR effectively identified gene-gene interactions impacting DILI susceptibility, outperforming individual genetic markers.
Area of Science:
- Pharmacogenomics
- Computational Biology
- Toxicology
Background:
- Drug-induced liver injury (DILI) is a significant challenge in drug development and post-market surveillance.
- Individual genetic variations (SNPs) often have limited predictive power for DILI susceptibility.
- Understanding complex gene-gene and gene-environment interactions is key to identifying DILI risk factors.
Purpose of the Study:
- To evaluate statistical methods for investigating gene-gene and gene-environment interactions in DILI.
- To identify robust machine learning approaches for predicting DILI chronicity.
- To explore the utility of combined genetic variants in assessing DILI risk.
Main Methods:
- Applied Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression.
- Conducted simulation studies to assess method robustness against genotype permutations.
- Utilized a real-life DILI chronicity dataset to compare method performance.
Main Results:
- MARS and MDR demonstrated robustness in identifying SNP-SNP interactions in simulations.
- MARS and MDR identified combined genetic variants associated with DILI chronicity, outperforming individual SNPs in the real-life dataset.
- A decision tree model incorporating SNPs identified by MARS/MDR showed fair performance in predicting DILI chronicity.
Conclusions:
- Machine learning approaches, specifically MARS and MDR, are effective in identifying gene-gene interactions relevant to DILI.
- These methods offer a promising avenue for assessing complex genetic risk factors for DILI chronicity.
- Integrating machine learning can enhance the prediction of complex diseases like DILI.
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