A multipathway phosphoproteomic signaling network model of idiosyncratic drug- and inflammatory cytokine-induced toxicity in human hepatocytes - PubMed (original) (raw)

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

Benjamin D Cosgrove et al. Annu Int Conf IEEE Eng Med Biol Soc. 2009.

Abstract

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 postapproval drug failure. Animal models utilizing bacterial LPS co-administration to induce an inflammatory background and hepatocyte cell culture models utilizing cytokine mix cotreatment 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, ERK, 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.

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Figures

Fig. 1

Fig. 1

OPLSR modeling demonstrates accurate predictions of drug- and cytokine-induced hepatotoxicity across human hepatocyte donors. Phosphoprotein signaling and response data from two human hepatocyte donors [10]. An OPLSR model was trained on the 66-condition (only a subset of 6 conditions shown here), 6-phosphoprotein CSR data compendium from donor #1. This OPLSR model generated quantitatively accurate predictions of cell death responses in donor #1 and donor #2, even though donor-specific signaling network activation profiles and cell death responses were observed under the same drug/cytokine treatments. Compare clarithromycin (CLA) ± cytokine mix across the two donors. The predictive accuracy of this OPLSR model suggests that a common-effector processing mechanism (f(x) = y) encompassing the integration of the survival and stress signaling network (x) yields quantitatively concerted cell death responses (y) to toxic drug/cytokine conditions exists and is shared between hepatocytes from different human donors. Data and schematic reproduced from [10].

Fig. 2

Fig. 2

Inflammatory cytokine-associated idiosyncratic drug hepatotoxicity as a “network toxicity”. The multipathway modeling approach presented here suggests that an integration of multiple intracellular signaling pathway -- namely the MEK–ERK, mTOR–p70 S6K, Akt, and p38–HSP27 pathways -- activities is necessary for hepatocytes to specify death responses to hepatotoxic drug/cytokine co-treatment conditions. This provides motivation of the network-level consideration of multiple survival, stress, and apoptosis signaling pathways in evaluating the hepatotoxicity mechanisms of context-dependent hepatotoxic drugs. Schematic reproduced from [10].

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