Quantitative Systems Pharmacology of Interferon Alpha Administration and its induced cellular response in mice
Priyata Kalra (1,4), Bastian Kister (1,2) Thomas Gaub (3), Mario Koester (5), Rebecca Fendt (2,3,6), Christoph Niederalt (3), Joerg Lippert (3), Sven Sahle (1); Lars Kuepfer (2), Ursula Kummer (1)
(1) Department of Modeling of biological processes, COS/BIOQUANT, University of Heidelberg, Germany. (2) Institute for Systems medicine with focus on Organ Interaction, Joint Research Center for Computational Biomedicine, Pauwelsstrasse 19, 52074 Aachen, Germany. (3) Clinical Sciences, Bayer Pharma, Kaiser-Wilhelm-Allee 1, 51368 Leverkusen, Germany. (4) Now at Simulations Plus, Lancaster, California. (5) RG Model Systems for Infection and Immunity, Helmholtz Centre for Infection Research, Braunschweig 38124, Germany. (6) Now at Sanofi, Paris, France.
Introduction:
Drug development requires a mechanistic understanding of drug action to ensure the safety and efficacy of new therapies. However, investigating drug effects in vivo can be challenging due to an incomplete understanding of physiological processes governing drug disposition as well as of the resulting drug response itself. Quantitative systems pharmacology (QSP) combines physiological knowledge with quantitative computational models of drug responses at network level to enable a mechanistic analysis of drug effects.
To illustrate the capabilities of QSP modelling, we here present a QSP-based analysis of IFN-alpha treatment in mice and humans.
The cytokine IFN-alpha is used for the treatment of various diseases, e.g. hepatitis C virus (HCV) and multiple sclerosis. It triggers the innate immune responses and helps liver cells clear viruses.
Objectives:
To exemplify the scope of QSP based applications, we here established a mouse QSP model investigating simultaneously whole-body pharmacokinetics (PK) of murine IFN-alpha in blood plasma as well as different tissues such as the liver as well as the cellular pharmacodynamic (PD) effect through the antiviral response biomarker Mx2.
To this end, an intracellular model of IFN-alpha signalling in the JAK/STAT pathway was combined with a mouse PBPK model describing the disposition of IFN-alpha in the body.
Methods:
Using the case of IFN-alpha treatment in mice, we here present a novel approach for the integration of molecular pathway models at the cellular level into physiology-based pharmacokinetic (PBPK) models at the organism scale. For this, model development included the following three steps:
- Establishment of an intracellular PD model of the JAK/STAT signalling pathway in mice at cellular scale on the basis of a previously established human model [1] using COPASI [2]
- Establishment of a PBPK model of interferon administration at the whole-body level in mice using the Open Systems Pharmacology Suite [3]
- Integration of both models into a multiscale QSP model. The pharmacodynamic model was integrated into the interstitial space of the liver. The interface between the PBPK and PD model was the binding of IFN-alpha to its receptor at the target site.
Assessing differences in in vitro and in vivo drug responses, the final mouse QSP model was compared to the cellular model of the JAK/STAT pathway. We also performed interspecies analysis between the here established murine and the previously published human QSP [1] models of IFN-alpha.
Results:
The mouse QSP model of IFN-alpha enabled a mechanistic analysis of drug effects and provided a more complete understanding of the drug's effects. The in vitro drug response in the cellular model overestimated the in vivo response in mice, and the time scales in the in vitro model were time-delayed. For the same dose per body weight, the mouse QSP model and the human QSP model of IFN-alpha showed similar dynamic behavior, but significantly higher response levels in Mx2 expression in mice than in humans.
Conclusion:
This study demonstrated the potential applications of QSP modeling in drug development and research. The mouse QSP model of IFN-alpha provided a preclinical model to investigate the drug's effects in humans. By providing a more complete understanding of drug effects, QSP modelling can help optimize treatment designs and facilitate in vitro to in vivo and inter-species extrapolation.
References:
[1] Kalra P, Brandl J, Gaub T, Niederalt C, Lippert J, Sahle S, et al. Quantitative systems
pharmacology of interferon alpha administration: A multi-scale approach. PLOS ONE.
2019;14(2):1–21. doi:10.1371/journal.pone.0209587.
[2] Hoops S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, et al. COPASI - A COmplex PAthway
SImulator. Bioinformatics. 2006;22(24):3067–3074. doi:10.1093/bioinformatics/btl485.
[3] Lippert J, Burghaus R, Edginton A, Frechen S, Karlsson M, Kovar A, Lehr T, Milligan P, Nock V, Ramusovic S, Riggs M, Schaller S, Schlender J, Schmidt S, Sevestre M, Sjögren E, Solodenko J, Staab A, Teutonico D. Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences. CPT Pharmacometrics Syst Pharmacol. 2019 Dec;8(12):878-882. doi: 10.1002/psp4.12473. Epub 2019 Nov 12. PMID: 31671256; PMCID: PMC6930856.