Abstract
The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform.
Highlights
Drug-induced liver injury (DILI) are detrimental adverse effects caused by marketed drugs toward patients’ liver (Przybylak and Cronin, 2012)
Out of 760 compounds that were present in the training set, 263 were not predicted by the model. 263 compounds were correctly predicted as hepatotoxic (TP) and 144 were correctly predicted as non-hepatotoxic (TN). 72 molecules experimentally non-hepatotoxic were identified by the model as hepatotoxic (FP) and only 18 compounds experimentally hepatotoxic were predicted as non-hepatotoxic (FN)
In the external validation set (101 compounds), 59 chemicals were not predicted by the model, the numbers of TP and TN was 35 and 5 respectively. 10 compounds were wrongly classified as hepatotoxic (FP) and 9 as non-hepatotoxic (FN)
Summary
Drug-induced liver injury (DILI) are detrimental adverse effects caused by marketed drugs toward patients’ liver (Przybylak and Cronin, 2012). Despite pre-clinical and clinical safety assessment of drug candidates, DILI is often the reason for drug failure and for post-approval withdrawal from the market (Egan et al, 2004). The late discovery of hepatotoxicity of drugs may SAR Model for the Prediction of DILI have serious health consequences for humans (Howell et al, 2012). DILI is a matter of concern since it is the main cause of acute liver injury (Vinken, 2015). It was calculated that DILI was responsible for half the cases of acute liver failure in the United States (Holt and Ju, 2005)
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