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

Content Provider	IEEE Xplore Digital Library
Author	Cosgrove, B.D. Alexopoulos, L.G. Saez-Rodriguez, J. Griffith, L.G. Lauffenburger, D.A.
Copyright Year	2009
Description	Author affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA. He is now with the Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305 USA (Cosgrove, B.D.) \|\| Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA. He is now with the Department of Mechanical Engineering, National Technical University of Athens, Athens, Greece (Alexopoulos, L.G.) \|\| Systems Biology, Harvard Medical School, Boston, MA 02115 USA (Saez-Rodriguez, J.) \|\| Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA (Griffith, L.G.; Lauffenburger, D.A.)
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.
Starting Page	5452
Ending Page	5455
File Size	745970
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5334019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Drugs Predictive models Biomedical engineering Engineering in medicine and biology Biomedical imaging Stress Clinical trials Animals Biotechnology
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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