Data from: A systems toxicology approach for the prediction of kidney toxicity and its mechanisms in vitro

The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints towards their mechanism of toxicity. Gene expression and high content imaging phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning. Imaging features capturing changes in cell morphology and nucleus texture along with mRNA levels of HMOX1 and SQSTM1 were identified as the most powerful predictors of toxicity. These biomarkers were validated by their ability to accurately predict kidney toxicity of 4 out of 6 candidate therapeutics that exhibited toxicity only in in late stage preclinical/clinical studies. Network analysis of similarities in toxic phenotypes was performed based on live-cell high-content image analysis at seven time points. Using compounds with known mechanism as reference, we could infer potential mechanisms of toxicity of candidate therapeutics. In summary, we report an approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in vitro for new therapeutic candidates and chemical entities.systox.tarAll necessary code and data is housed within this archive. The archive should have an md5 checksum d56eabd45e46af9f28d7936bd7390899. The code is version controlled and any updates can be found at https://github.com/pvtodorov/systox . The data is also stored on synapse.org as syn11697964. The code and data within this repo are released under the CC Zero license