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.