Development of a cell-based assay system considering drug metabolism and immune- and inflammatory-related factors for the risk assessment of drug-induced liver injury

• Drug-induced liver injury (DILI) is a major safety concern in drug development.
• Four kinds of in vivo mouse DILI models we recently established were used.
• Five in vivo biomarkers were found and applied to a cell-based assay system.
• Drug metabolism and immune- and inflammatory-related factors were considered.
• We proposed the total sum score of gene expression level for the risk assessment.

Drug-induced liver injury (DILI) is a major safety concern in drug development and clinical pharmacotherapy. However, prediction of DILI is difficult because the underlying mechanisms are not fully understood. To establish a novel cell-based screening system to suggest drugs with hepatotoxic potential in preclinical drug development, comprehensive gene expression analyses during in vivo DILI are necessary. Using in vivo mouse DILI models and 4 sets of hepatotoxic positive and non-hepatotoxic drugs, we found that the hepatic mRNA levels of S100A8; S100A9; “NATCH, LRR, and pyrin domain-containing protein 3” (NALP3); interleukin (IL)-1β; and the receptor for advanced glycation endproducts (RAGE) were commonly increased in hepatotoxic drug-administered mice compared to non-hepatotoxic drug-administered mice. To clarify whether these 5 in vivo biomarkers can be applied to a cell-based screening system, we adapted human liver microsomes (HLM) in the presence of NADPH to assess the metabolic activation reaction, and we also adapted human monocytic leukemia cells HL-60, K562, KG-1 and THP-1 to assess the effects on mRNA expression of immune- and inflammatory-related factors. We investigated 30 clinical drugs with different safety profiles with regard to DILI and found that the total sum score of gene expression levels of S100A8, S100A9, RAGE, NALP3 and IL-1β mRNA in HL-60 or K562 cells incubated with HLM, could identify drugs at high risk for hepatotoxicity. We proposed the use of the total sum score of gene expression level for assessing metabolic activation by drug-metabolizing enzymes and immune- and inflammatory-related factors for the risk assessment of DILI in preclinical drug development.