Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

Abstract

In this study, we developed a transcriptomics based human in vitro model for predicting DILI in humans. The transcriptomics data (Affymetrix GeneChip Human Genome U133 Plus 2.0) from primary human hepatocytes were provided by the Japanese Toxicogenomics Project (TGP). The selected compounds were divided into two groups, i.e., most-DILI and no-DILI, based on FDA-approved drug labels. The compounds were further grouped in a training and validation set. The training set, containing the most extreme most-DILI and no-DILI compounds based on the in vivo rat clinical chemistry measurements from TGP, was used to develop the prediction model. The validation set showed high accuracy (> 90%) and performed better than splitting the compounds into training and validation set randomly.