A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a “big data compacting and data fusion”- concept to capture diverse adverse outcomes on cellular and organismal levels.  The approach generates from transcriptomics data set a “predictive toxicogenomics space” (PTGS) tool composed of 1331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving approximately 2.5 x 10⁸ data points and 1300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analyzing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy. Custom R code and methods to calculate the component-based PTGS scores using gene expression data.