The emergence of high throughput in vitro assays has the potential to significantly improve toxicological evaluations and lead to more efficient, accurate, and less animal-intensive testing. However, directly using all available in vitro assays in a model is usually impractical and inefficient. On the other hand, mechanistic knowledge has always been critical for toxicological evaluations and should not be ignored even with the increasing availability of data. In this paper, we illus­trate a

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... mising approach to integrating mechanistic knowledge with multiple data sources for in vitro assays, using drug-induced liver injury (DILI) as an example. The adverse outcome pathway (AOP) framework was used as a source for mechanistic knowledge and as a selection tool for high throughput predictors. Our results confirm the value of AOPs as a knowledge source for understanding adverse events and show that the majority of drugs classified as most-DILI-concern were mapped to AOPs related to liver toxicity found in AOPwiki. AOPs were also used effectively to select a subset of assays from the Tox21 and L1000 projects as the predictors in predictive modeling of DILI risk. Together with previously published drug properties for daily dose, lipophilicity, and reactive metabolite formation, these assay endpoints were used to build a penalized logistic regression model for assessing DILI risk. This model obtained an accuracy of 0.91, thus confirming the potential power of integrating mechanistic knowledge with high throughput assays for toxicological evalu­ations. The results also provide a roadmap for data integration that could be used for other adverse effects.