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. 2016 Nov 22:7:442.
doi: 10.3389/fphar.2016.00442. eCollection 2016.

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

Fabiola Pizzo  1 , Anna Lombardo  1 , Alberto Manganaro  1 , Emilio Benfenati  1
Affiliations

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

Fabiola Pizzo et al. Front Pharmacol. .

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.

Keywords: chemical clustering; drugs; hepatotoxicity; structural alerts; structure-activity relationship.

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Figures

Figure 1
Figure 1
Identification and validation of SAs for hepatotoxicity.
Figure 2
Figure 2
Decision tree developed for the hepatotoxicity model. Hep stands for “hepatotoxic” and non-hep for “non-hepatotoxic.”
Figure 3
Figure 3
Percentages of correctly predicted, wrongly predicted and non-predicted (unknown) compounds in the training, test and external validation sets.
See this image and copyright information in PMC

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