A multipathway phosphoproteomic signaling network model of idiosyncratic drug- and inflammatory cytokine-induced toxicity in human hepatocytes

Idiosyncratic drug hepatotoxicity is a hepatotoxicity subset that occurs in a very small fraction of human patients, is poorly predicted by standard preclinical models and in clinical trials, and frequently leads to post-approval drug failure. Animal models utilizing bacterial LPS co-administration to induce an inflammatory background and hepatocyte cell culture models utilizing cytokine mix co-treatment have successfully reproduced idiosyncratic hepatotoxicity signatures for certain drugs, but the hepatocyte signaling mechanisms governing these drug-cytokine toxicity synergizes are largely unclear. Here, we summarize our efforts to computationally model the signaling mechanisms regulating inflammatory cytokine-associated idiosyncratic drug hepatotoxicity. We collected a "cue-signal-response" (CSR) data compendium in cultured primary human hepatocytes treated with many combinations of idiosyncratic hepatotoxic drugs and inflammatory cytokine mixes ("cues") and subjected this compendium to orthogonal partial-least squares regression (OPLSR) to computationally relate the measured intracellular phosphoprotein signals and hepatocellular death responses. This OPLSR model suggested that hepatocytes specify their cell death responses to toxic drug/cytokine conditions by integrating signals from four key pathways - Akt, p70 S6K, ERIC, and p38. An OPLSR model focused on data from these four signaling pathways demonstrated accurate predictions of idiosyncratic drug- and cytokine-induced hepatotoxicities in a second human hepatocyte donor, suggesting that hepatocytes from different individuals have shared network control mechanisms governing toxicity responses to diverse combinations of idiosyncratic hepatotoxicants and inflammatory cytokines.