Fig 1.
Illustration of the canonical JAK/STAT pathway.
The key steps of the pathway in response to IFN-α are highlighted here. IFN binds to the receptor and activates JAK1 and TYK2, which subsequently activate the STATs in the cytoplasm, all by phosphorylation. The phosphorylated STATs and IRF9 form the transcription complex ISGF3, which translocates to the nucleus, where it activates hundreds of ISGs, including IRF9, Mx2 and SOCS1.
Fig 2.
Integrated multi-scale PBPK/PD model.
From left to right: Whole-body model (PBPK): The drug distribution is modelled using the PBPK approach at the level of the human whole-body. Integrated model (PBPK/PD): The integrated model encompasses the dose arriving from the whole-body PBPK model to the liver and the cellular model coupled to the PBPK model inside the liver. The target mediated drug disposition is modelled in the liver compartment of the PBPK model. It allows for organ level analysis after the whole-body drug distribution. Here, the biophase distribution is taken into account for large molecules. Cellular model (PD): The cellular model established by using data from hepatocytes consists of the mechanism based PD model comprising the receptor behaviour and signalling network. This model is an altered version of the model from [20] which is then coupled at the liver with the whole-body PBPK model.
Fig 3.
Physiologically based pharmacokinetic (PBPK) model fit and validation.
Physiologically based pharmacokinetic (PBPK) model simulations (lines) and experimental blood plasma profile (circles) of IFN-α in humans. A) Experimental results of Wills et al. [36] and the corresponding fit. B) Model validation using blood plasma profile experiments from literature [37, 38].
Fig 4.
Simulated tissue distribution of IFN-α for the dose reported in Wills et al. (1984) [36] for 24 hours.
The lines show time-course distribution of IFN-α in venous blood plasma vs. the concentration found in the tissue of the different organs.
Fig 5.
Fitting results of top 10 fits.
Comparison between experimental time series measurements of IFN-α induced intracellular responses and computational simulations after fitting. The time after IFN-α application is given on the x-axis of each subfigure. Experimentally determined levels of the measured proteins are presented as filled dots and dashed lines (A) pSTAT, C) pJAK (Act Rec Complex), D) IRF9 total in the nucleus, E) SOCS mRNAc and F) SOCS protein measured by quantitative-immunoblotting in Huh 7.5 cells after stimulation with 500 U IFN-α as in Maiwald et al. [20]. B) dynamic expression of IRF9 mRNA fold change in Huh 7 cells for 10 U IFN-α as shown in Bolen et al. [45]. Computational fit shown as solid lines. Fits to full 20 dataset are shown in the supplementary S2 Fig.
Fig 6.
The validation of the dose response for the top ten fits.
In A-D) the time course of the 4 experimental doses (circles) is plotted together with the calculated simulation of the top ten models (solid lines). The different doses taken in account are, in A) 500 U and B) 2500U of IFN-α as published by Bolen et al. [45]. C) 15U and D) 30U of IFN-α as in Jilg et al. [46]. In the panel E) and F) the 24 hour dose response (DR) curve is shown for all the data obtained from the literature where E) has DR simulation for 24th hours representing data from Bolen et al. [45] for 10U, 100U, 500U and 2500U.
Fig 7.
The model schema of the mechanistically based PBPK/PD model details at the liver.
The IFN-α is cleared via the binding to the respective receptors in the liver. The binding of IFN-α to the IFNAR2 and IFNAR1 takes place in the interstitium of the liver (M1-M5), hence activating the JAK/STAT pathway leading to the activation of mRNAc of the response protein IRF9 in the intracellular part of the liver (M6-M14). The reactions in the interstitim constitute the interaction between the PBPK and the intracellular model. Parameterisation is derived from the PBPK model.
Fig 8.
Relative difference in signalling dynamics in the in vivo (PBPK/PD) response (to 36U typically administered dose) vs. the in vitro (hepatocyte) response (to 500U as typically used in experimental setups).
PBPK/PD model (red lines), hepatocyte culture simulation (green lines). Simulation of concentration time profiles in human and in human cell lines of A) the non-linear IFN-α, B) activated receptor complex C) IRF9 mRNAc and D) SOCS1 activated downstream in the models.
Fig 9.
Comparison of cellular signalling responding to typical experimental vs in vivo stimulation with IFN-α.
Intracellular signalling was modelled in response to a constant dose of 500U (as typically used in vitro) in comparison to the calculated PBPK profile resulting from a typical dose of 36U. Fold difference is plotted of A) Activated receptor complex and B) IRF9 mRNAc simulation (solid lines) concentration time profile for the top ten models. C) The calculated fold difference Cmax achieved for activated receptor complex (Act Rec) (bars for each model respectively) and D) fold difference in the Cmax of IRF9 mRNAc.
Fig 10.
Parameter scan for the initial concentration of IFNA Receptor2.
PBPK/PD simulation (lines) of concentration time profile for A) IFN-α in venous blood plasma and the experimental blood plasma profile (solid circles) of IFN-α in humans. B) IRF9 mRNAc response to different initial concentrations of IFN-α Receptor2 that are 0.0005, 0.002, 0.003, 0.0015 and 0.001 μmol/l. The reference concentration in the initial simulation (dashed black line) for Receptor2 was 0.5 μmol/l.