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Verfasst von:Sobotta, Svantje [VerfasserIn]   i
 Lehmann, Wolf-Dieter [VerfasserIn]   i
 Gretz, Norbert [VerfasserIn]   i
 Schilling, Marcel [VerfasserIn]   i
 Klingmüller, Ursula [VerfasserIn]   i
Titel:Model based targeting of IL-6-induced inflammatory responses in cultured primary hepatocytes to improve application of the JAK inhibitor ruxolitinib
Verf.angabe:Svantje Sobotta, Andreas Raue, Xiaoyun Huang, Joep Vanlier, Anja Jünger, Sebastian Bohl, Ute Albrecht, Maximilian J. Hahnel, Stephanie Wolf, Nikola S. Mueller, Lorenza A. D'Alessandro, Stephanie Mueller-Bohl, Martin E. Boehm, Philippe Lucarelli, Sandra Bonefas, Georg Damm, Daniel Seehofer, Wolf D. Lehmann, Stefan Rose-John, Frank van der Hoeven, Norbert Gretz, Fabian J. Theis, Christian Ehlting, Johannes G. Bode, Jens Timmer, Marcel Schilling and Ursula Klingmüller
E-Jahr:2017
Jahr:09 October 2017
Umfang:25 S.
Fussnoten:Gesehen am 28.08.2018
Titel Quelle:Enthalten in: Frontiers in physiology
Ort Quelle:Lausanne : Frontiers Research Foundation, 2007
Jahr Quelle:2017
Band/Heft Quelle:8(2017) Artikel-Nummer 775, 25 Seiten
ISSN Quelle:1664-042X
Abstract:IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.
DOI:doi:10.3389/fphys.2017.00775
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: http://dx.doi.org/10.3389/fphys.2017.00775
 kostenfrei: Volltext: https://www.frontiersin.org/articles/10.3389/fphys.2017.00775/full
 DOI: https://doi.org/10.3389/fphys.2017.00775
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:acute phase response
 IL-6
 JAK/STAT signaling pathway
 mathematical modeling
 ODE models
 primary hepatocytes
 Ruxolitinib
K10plus-PPN:1580443923
Verknüpfungen:→ Zeitschrift

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