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Updated: Jun 23, 2025
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development
Fahad Mostafa1,2, Victoria Howle1, Minjun Chen2
1Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA.
Abstract:
Drug-induced liver injury (DILI) poses a significant challenge for the pharmaceutical industry and regulatory bodies. Despite extensive toxicological research aimed at mitigating DILI risk, the effectiveness of these techniques in predicting DILI in humans remains limited. Consequently, researchers have explored novel approaches and procedures to enhance the accuracy of DILI risk prediction for drug candidates under development. In this study, we leveraged a large human dataset to develop machine learning models for assessing DILI risk. The performance of these prediction models was rigorously evaluated using a 10-fold cross-validation approach and an external test set. Notably, the random forest (RF) and multilayer perceptron (MLP) models emerged as the most effective in predicting DILI. During cross-validation, RF achieved an average prediction accuracy of 0.631, while MLP achieved the highest Matthews Correlation Coefficient (MCC) of 0.245. To validate the models externally, we applied them to a set of drug candidates that had failed in clinical development due to hepatotoxicity. Both RF and MLP accurately predicted the toxic drug candidates in this external validation. Our findings suggest that in silico machine learning approaches hold promise for identifying DILI liabilities associated with drug candidates during development.
Insights
Machine learning models accurately predict drug-induced liver injury (DILI) risk. These computational approaches show promise for identifying potential liver toxicity in new drug candidates during development.
Area of Science:
- Pharmacology and Toxicology
- Computational Biology
- Drug Development
Background:
- Drug-induced liver injury (DILI) is a major hurdle in drug development, with current prediction methods showing limited human efficacy.
- Existing toxicological assessments often fail to accurately predict DILI risk for drug candidates.
- Novel approaches are needed to improve the prediction of DILI for pharmaceuticals under development.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting DILI risk using a large human dataset.
- To assess the performance of random forest (RF) and multilayer perceptron (MLP) models in DILI prediction.
- To validate the predictive capabilities of these models on drug candidates that failed clinical trials due to hepatotoxicity.
Main Methods:
- Leveraged a comprehensive human dataset to train machine learning models.
- Employed a 10-fold cross-validation strategy to evaluate model performance.
- Utilized an independent external test set, including drug candidates with known hepatotoxicity, for validation.
Main Results:
- Random forest (RF) achieved an average cross-validation accuracy of 0.631.
- Multilayer perceptron (MLP) demonstrated the highest Matthews Correlation Coefficient (MCC) of 0.245 during cross-validation.
- Both RF and MLP models successfully identified toxic drug candidates in the external validation set.
Conclusions:
- In silico machine learning models show significant potential for predicting DILI liabilities.
- These computational tools can aid in identifying hepatotoxic drug candidates early in the development pipeline.
- Machine learning offers a promising strategy to enhance the safety assessment of new drugs.
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