An in vitro coculture system of human peripheral blood mononuclear cells with hepatocellular carcinoma-derived cells for predicting drug-induced liver injury

Preventing clinical drug-induced liver injury (DILI) remains a major challenge, because DILI develops via multifactorial mechanisms. Immune and inflammatory reactions are considered important mechanisms of DILI; however, biomarkers from in vitro systems using immune cells have not been comprehensively studied. The aims of this study were (1) to identify promising biomarker genes for predicting DILI in an in vitro coculture model of peripheral blood mononuclear cells (PBMCs) with a human liver cell line, and (2) to evaluate these genes as predictors of DILI using a panel of drugs with different clinical DILI risk. Transcriptome-wide analysis of PBMCs cocultured with HepG2 or differentiated HepaRG cells that were treated with several drugs revealed an appropriate separation of DILI-positive and DILI-negative drugs, from which 12 putative biomarker genes were selected. To evaluate the predictive performance of these genes, PBMCs cocultured with HepG2 cells were exposed to 77 different drugs, and gene expression levels in PBMCs were determined. The MET proto-oncogene receptor tyrosine kinase (MET) showed the highest area under the receiver-operating characteristic curve (AUC) value of 0.81 among the 12 genes with a high sensitivity/specificity (85/66%). However, a stepwise logistic regression model using the 12 identified genes showed the highest AUC value of 0.94 with a high sensitivity/specificity (93/86%). Taken together, we established a coculture system using PBMCs and HepG2 cells and selected biomarkers that can predict DILI risk. The established model would be useful in detecting the DILI potential of compounds, in particular those that involve an immune mechanism.

Automated summary

This study established a coculture system using PBMCs and HepG2 cells and selected biomarkers that can predict DILI risk. A stepwise logistic regression model using the 12 identified genes showed the highest predictive performance with a high sensitivity/specificity.