Predicting interactions of compounds and metabolites with toxicity-associated targets
Peter Hunt gave this presentation at the ACS Fall 2016 National Meeting & Exposition held in Philadelphia, USA.
We describe the development of quantitative structure activity relationship (QSAR) models based on activity data from the ChEMBL database, to predict the interaction of compounds with protein targets associated with adverse outcome pathways and toxicities. However, systemic exposure to a compound will also result in the formation of metabolites, which themselves may be the cause of a toxic response. Therefore, we have developed an integrated system linking models that predict the enzymes responsible for metabolism of a parent compound and the resulting metabolites with QSAR models of target interactions. The combination of these models can predict potential toxicities resulting directly or indirectly from exposure to the parent compound. The initial implementation is focused on metabolism by Cytochrome P450 enzymes, but forms a framework that may be extended to other metabolic pathways and additional QSAR models of toxicity
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