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  • Title: 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.
    Author: Maiuri AR, Wassink B, Turkus JD, Breier AB, Lansdell T, Kaur G, Hession SL, Ganey PE, Roth RA.
    Journal: J Pharmacol Exp Ther; 2017 Sep; 362(3):459-473. PubMed ID: 28687704.
    Abstract:
    Idiosyncratic drug-induced liver injury (IDILI) typically occurs in a small fraction of patients and has resulted in removal of otherwise efficacious drugs from the market. Current preclinical testing methods are ineffective in predicting which drug candidates have IDILI liability. Recent results suggest that immune mediators such as tumor necrosis factor-α (TNF) and interferon-γ (IFN) interact with drugs that cause IDILI to kill hepatocytes. This proof-of-concept study was designed to test the hypothesis that drugs can be classified according to their ability to cause IDILI in humans using classification modeling with covariates derived from concentration-response relationships that describe cytotoxic interaction with cytokines. Human hepatoma (HepG2) cells were treated with drugs associated with IDILI or with drugs lacking IDILI liability and cotreated with TNF and/or IFN. Detailed concentration-response relationships were determined for calculation of parameters such as the maximal cytotoxic effect, slope, and EC50 for use as covariates for classification modeling using logistic regression. These parameters were incorporated into multiple classification models to identify combinations of covariates that most accurately classified the drugs according to their association with human IDILI. Of 14 drugs associated with IDILI, almost all synergized with TNF to kill HepG2 cells and were successfully classified by statistical modeling. IFN enhanced the toxicity mediated by some IDILI-associated drugs in the presence of TNF. In contrast, of 10 drugs with little or no IDILI liability, none synergized with inflammatory cytokines to kill HepG2 cells and were classified accordingly. The resulting optimal model classified the drugs with extraordinary selectivity and specificity.
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