Jia, Xuelian. Development of mechanism-driven virtual adverse outcome pathway (vAOP) for hepatotoxicity. Retrieved from https://doi.org/doi:10.7282/t3-1vxx-9f39
DescriptionThe liver is an important organ for transforming and eliminating chemicals and thus is vulnerableto toxicity from the toxicants. A broad class of chemicals can be potential liver toxicants, including environmental and industrial chemicals, herbal and dietary supplements, traditional medicines, and medications. Drug-induced liver injury (DILI) represents the acute and chronic liver injuries that are caused by medications. Drug attrition during clinical trials and postmarketing because of DILI can cause extremely high expenses. As a result, there is great interest by regulators to develop in vitro and computational modeling to help identify which chemicals have the propensity to cause liver injury in the early stage of safety evaluation. For the prediction of complex toxicity endpoints like hepatotoxicity, using traditional computational strategies (e.g., Quantitative Structure-Activity Relationship, QSAR) and structural, chemical properties is not sufficient and often error-prone. As an advanced framework of risk assessment, the Adverse Outcome Pathway (AOP) was introduced to describe the mode and mechanism of toxicant action. The mechanisms of DILI are complex and be explained by various AOPs. In this study, we specifically focus on oxidative stress-involved hepatotoxicity. Reactive metabolites formed during drug metabolism or inhibition of the bile salt export pump can cause oxidative stress, which triggers the transcription of antioxidative enzymes found in the antioxidant response element (ARE) signaling pathway. The quantitative HTS (qHTS) ARE activation assay screened more than 10,000 compounds of interest and is an indicator of chemical-induced oxidative stress and subsequent hepatotoxicity. This assay, along with other in vitro mechanism-related assays in public big data sources will be collected and combined with advanced machine learning and deep learning algorithms, for the development of the virtual AOP (vAOP). The resulting vAOP framework will reveal hepatotoxicity mechanisms within the available big data and resolve the limitations of traditional QSAR modeling by providing accurate mechanism-based predictions for new compounds.