Model-based identification of TNFα-induced IKKβ-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds

  • Drug-induced liver injury (DILI) has become a major problem for patients and for clinicians, academics and the pharmaceutical industry. To date, existing hepatotoxicity test systems are only poorly predictive and the underlying mechanisms are still unclear. One of the factors known to amplify hepatotoxicity is the tumor necrosis factor alpha (TNFα), especially due to its synergy with commonly used drugs such as diclofenac. However, the exact mechanism of how diclofenac in combination with TNFα induces liver injury remains elusive. Here, we combined time-resolved immunoblotting and live-cell imaging data of HepG2 cells and primary human hepatocytes (PHH) with dynamic pathway modeling using ordinary differential equations (ODEs) to describe the complex structure of TNFα-induced NFκB signal transduction and integrated the perturbations of the pathway caused by diclofenac. The resulting mathematical model was used to systematically identify parameters affected by diclofenac. These analyses showed that more than one regulatory module of TNFα-induced NFκB signal transduction is affected by diclofenac, suggesting that hepatotoxicity is the integrated consequence of multiple changes in hepatocytes and that multiple factors define toxicity thresholds. Applying our mathematical modeling approach to other DILI-causing compounds representing different putative DILI mechanism classes enabled us to quantify their impact on pathway activation, highlighting the potential of the dynamic pathway model as a quantitative tool for the analysis of DILI compounds.
Metadaten
Author:Angela Oppelt, Daniel Kaschek, Suzanna Huppelschoten, Rowena Sison-Young, Fang Zhang, Marie Buck-Wiese, Franziska Herrmann, Sebastian Malkusch, Carmen Krüger, Mara Meub, Benjamin Merkt, Lea Zimmermann, Amy Schofield, Robert P. Jones, Hassan Malik, Marcel Schilling, Mike HeilemannORCiDGND, Bob van de Water, Christopher E. Goldring, B. Kevin Park, Jens Timmer, Ursula Klingmüller
URN:urn:nbn:de:hebis:30:3-468238
DOI:https://doi.org/10.1038/s41540-018-0058-z
ISSN:2056-7189
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/29900006
Parent Title (English):npj Systems biology and applications
Publisher:Nature Publ. Group
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/06/11
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/07/03
Tag:Differential equations; Dynamic networks; Signal processing; Virtual drug screening
Volume:4
Issue:Art. 23
Page Number:16
First Page:1
Last Page:16
Note:
Rights and permissions: Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
HeBIS-PPN:435733443
Institutes:Biochemie, Chemie und Pharmazie / Biochemie und Chemie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0