Systems pharmacology studies that utilize large data sets, such as protein–protein interaction networks and the FDA adverse event reports, can enhance the understanding of drug adverse events and pinpoint off-targets [1]. In this context, drug-induced liver injury (DILI) is a major challenge for drug development, as it comprises one of the main causes of attrition [2]. There are several indications in literature associating hepatic transporter inhibition with manifestations of DILI, such as OATP1B1 and 1B3 with hyperbilirubinemia [3] and BSEP with cholestasis [4]. Towards this direction, we developed statistical classification models for predicting hepatotoxic endpoints, such as hyperbilirubinemia, cholestasis and DILI, by combining the physicochemical and structural properties of compounds with their hepatic transporter inhibition profiles. For the latter task, we used our in-house transporter inhibition models for BSEP, P-glycoprotein, BCRP and OATP1B1 and OATP1B3. Several meta- and base-classifiers were investigated and the classification models obtained were of reasonable performance for all three endpoints. For the case of hyperbilirubinemia, OATP1B1 and 1B3 inhibition profiles are evaluated as important descriptors, even though there is no significant improvement of the statistical performance of the model when using transporters’ information. In contrast, for cholestasis the use of transporter inhibition profiles significantly improves the model’s performance, although the individual transporters are not ranked high in comparison to other physicochemical descriptors. Finally for general DILI prediction, descriptors annotating transporter inhibition do not influence the model’s performance. In addition, their importance is low compared to other physicochemical descriptors, such as lipophilicity. This suggests that for the entire liver system, there is no clear association pattern with transporters - at least not for the particular ones investigated. Acknowledgements The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115002 (eTOX), as well as from the Austrian Science Fund, grant F3502. References 1. Berger, S.I. et al., Interdiscip Rev Syst Biol Med 2011, 3, (2), 129–135 2. O’ Brien, P.J. et al., Arch Toxicol 2006, (80), 580–604 3. Chang, J. H. et al., Mol Pharm 2013, 10, (8), 3067-75 4. Vinken M. et al., Toxicol Sci 2013, 136,(1), 97–106

Predicting drug-induced hepatic systems' toxicity by integrating transporter interaction profiles

251st National Meeting (2016)

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