Abstract
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.
Highlights
A big number of drugs with reliable Drug-induced liver injury (DILI) classifications are critical for the development of robust and accurate in silico DILI prediction models[20]
We developed DILI prediction models using a pattern recognition algorithm Decision Forest (DF)[25,26] based on this largest set of drugs[24]
To develop reliable models for prediction of human DILI risk, we used a set of 721 drugs, from DILIrank[24], that were classified into three classes with different human DILI risk based on FDA drug labeling
Summary
A big number of drugs with reliable DILI classifications are critical for the development of robust and accurate in silico DILI prediction models[20]. Considers these factors to summarize drug safety information from clinical trials, post-marketing surveillance, and literature publications[21]. This set of drugs was recommended as the standard list for developing DILI predictive models[22,23]. We have further refined DILI classification for 1036 drugs by combining their FDA drug labeling information and causality assessment reports in the literature. This refined approach classified a large set of drugs into four classes: most-DILI, less-DILI, no-DILI, and ambiguous DILI24. Our models could be helpful in identification of potential DILI drugs during preclinical development and would be eventually beneficial in reducing hepatotoxicity related attrition
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