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Front Physiol. 2017 Oct 9;8:775. doi: 10.3389/fphys.2017.00775. eCollection 2017.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

Author information

1
Division Systems Biology of Signal Transduction, German Cancer Research Center, Heidelberg, Germany.
2
Discovery Division, Merrimack Pharmaceuticals, Cambridge, MA, United States.
3
Institute of Physics, Albert Ludwigs University of Freiburg, Freiburg, Germany.
4
BIOSS Centre for Biological Signalling Studies, Albert Ludwigs University of Freiburg, Freiburg, Germany.
5
Clinic of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany.
6
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
7
Department of Hepatobiliary Surgery and Visceral Transplantation, Leipzig University, Leipzig, Germany.
8
Institute of Biochemistry, University of Kiel, Kiel, Germany.
9
Transgenic Service, Center for Preclinical Research, German Cancer Research Center, Heidelberg, Germany.
10
Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
11
Department of Mathematics, Technical University of Munich, Garching, Germany.

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.

KEYWORDS:

IL-6; acute phase response; mathematical modeling; primary hepatocytes; ruxolitinib

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