Transporters expressed in the liver play a major role in drug pharmacokinetics and are a key component of the physiological bile flow. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury. Therefore, predicting the interaction profile of small molecules with transporters expressed in the liver may help to prioritize compounds in an early phase of the drug development process. Based on a comprehensive analysis of the data available in the public domain, the authors developed a set of classification models which allow predicting—for a small molecule—the inhibition of and transport by a set of liver transporters considered to be relevant by FDA, EMA, and the Japanese regulatory agency. The in silico models were validated by cross-validation and external test sets. Finally, models were implemented as an easy to use web-service which is freely available at https://livertox.univie.ac.at.
Vienna LiverTox Workspace—a set of machine learning models for prediction of interactions profiles of small molecules with transporters relevant for regulatory agencies
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