Development and analysis of an in vitro model of inflammatory cytokine-mediated idiosyncratic drug hepatotoxicity

Abstract

Idiosyncratic drug reactions are a subset of adverse reactions frequently targeting the liver, which become obvious only in large sample populations. Drug-induced hepatotoxicity, occurring in a very small fraction of patients, poses a major challenge to pharmaceutical companies due to its unknown mechanism(s) of action and deficient models for study. In vitro model systems may have the potential to predict this liver injury by generating conditions possibly representing key processes involved, both directly and indirectly, in drug effects on cellular physiology. Our ultimate goal is to develop an in vitro model effectively mimicking certain relevant aspects of the in vivo response of the human liver. In our initial effort described herein, we have designed a novel cell-based system using alternatively in both a human hepatoma cell line and primary rat hepatocytes to study toxic effects in a background reflecting in vivo inflammatory conditions. This background incorporates bacterial lipopolysaccharide (LPS) administration along with inflammatory cytokines (tumor necrosis factor, interferon y, interleukin-1 a, interleukin-113, and interleukin-6) previously shown to increase in LPS-administrated rats. Our study began with an investigation of toxicities that are induced by combinations of five cytokines and LPS in HepG2 and C3A human hepatoma cell lines and in primary rat hepatocytes. Informed by the results of these experiments, we selected representative cytokine/LPS treatments and cell systems to examine drug-cytokine synergies in vitro and were able to identify multiple idiosyncratic hepatotoxins that induced synergistic toxicity in either the HepG2 cell line or primary rat hepatocytes.

(cont.) Finally, we measured the sensitization of these cell systems to a panel of these drugs, given an inflammatory background induced by an abbreviated set of cytokine treatments including four cytokines and LPS. Analysis of this multivariate drug-cytokine toxicity data set yielded a subset of representative cytokine treatments for future drug-cytokine synergy investigations. This subset will be used to characterize the differences between cell systems, including cultured human hepatocytes, and to hopefully develop a data-driven partial least squares regression model that predicts idiosyncratic liver injury. The implications are two-fold. First, this model could provide direction to pharmaceutical companies in focusing their drug discovery and development. Second, it could help physicians design better treatment plans for their patients.