Idiosyncratic, drug-induced liver injury (IDILI) reactions, although typically rare, cause morbidity and death in patients. They also prompt lawsuits from harmed patients and are a common reason for withdrawal of drugs from the market or limitations on their use, thereby causing loss of investment and of proﬁt for pharmaceutical companies. Current preclinical testing strategies fail to identify drugs with the potential to cause such adverse reactions. We have established a relatively simple, cell-based approach capable of classifying drugs according to their IDILI potential with remarkable selectivity and speciﬁcity. The resulting IDILI-ROC Assay shows promise as a predictor of the likelihood that a drug candidate will cause a hepatocellular pattern of IDILI. Predictions based on the assay results could inform decisions about which candidates to move forward in the drug development process.
There is mounting evidence that the immune system is involved in IDILI reactions. Of potential importance in the hepatocellular killing that occurs is cytotoxic interaction of drugs with immune mediators such as tumor necrosis factor-alpha (TNF). In cultured hepatocytes, otherwise nontoxic concentrations of drugs associated with a hepatocellular pattern of DILI interact with these cytokines to cause cell death, and this interaction forms the basis for the assay (Maiuri et al., 2017).
The IDILI-ROC Assay is particularly attractive because (1) it is based on an underlying rationale that is consonant with IDILI mode of action (ie, immune system involvement); (2) uses a cell type that is easily obtained and maintained in culture and yields consistent results; (3) requires minimal amounts of test compound; (4)employs a single, easily measured endpoint that is directly relevant to IDILI pathogenesis (ie, hepatocellular death). Also, the ROC approach to analysis allows the client to evaluate various levels of speciﬁcity and selectivity according to the client’s risk tolerance.
Reports to clients include data resulting from concentration-response determination, graphical representation of curve ﬁtting and covariate calculation (eg, see Figure 1), statement of the covariate(s) used in the model(s) that are employed, and predicted probability for each compound at various risk tolerances (ie, levels of sensitivity and speciﬁcity based on the model’s ROC curve).
Synergistic Cytotoxicity from Drugs and Cytokines In Vitro as an Approach to Classify Drugs According to Their Potential to Cause Idiosyncratic Hepatotoxicity: A Proof-of-Concept Study.
Maiuri AR, Wassink B, Turkus JD, Breier AB, Lansdell T, Kaur G, Hession SL, Ganey PE, Roth RA.
J Pharmacol Exp Ther. 2017 Sep;362(3):459-473. doi: 10.1124/jpet.117.242354. Epub 2017 Jul 7.
Idiosyncratic Drug-Induced Liver Injury: Is Drug-Cytokine Interaction the Linchpin?
Roth RA, Maiuri AR, Ganey PE.
J Pharmacol Exp Ther. 2017 Feb;360(2):461-470. doi: 10.1124/jpet.116.237578. Epub 2016 Nov 15. Review.