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In Silico Prediction of DILI - Extraction of Histopathology Data from Preclinical Toxicity Data Studies of the eTOX Database for new In SIlico Models of Hepatotoxicity

pdf fileAmberg A; Anger LT; Stolte M; Hemmerich J; Matter H; Fisk L; Tluczkiewicz I; Pinto-Gil K; Lopez-Massaguer O; Pastor M;

Presented by Alexander Amberg, Sanofi, at the 57th SOT Annual Meeting.

The eTOX consortium extracted in vivo data from unpublished preclinical toxicity studies of 13 EFPIA partners. Various training datasets were compiled based on these data and depending on the species, treatment durations and administrations routes. Then, different modeling approaches were applied on these datasets, including structural alerts, fragment-based and molecular descriptor-based machine learning approaches. Models were validated and optimized, first by internal validation then using Sanofi’s confidential data. These validation results show that by reasonable clustering histopathology data from eTOX, it is possible to develop highly predictive in silico models for drug-induced liver injury (DILI).

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