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A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

T.J. Sego, Josua O. Aponte-Serrano, Juliano Ferrari Gianlupi, Samuel R. Heaps, Kira Breithaupt, Lutz Brusch, James M. Osborne, Ellen M. Quardokus, Richard K. Plemper, James A. Glazier
doi: https://doi.org/10.1101/2020.04.27.064139
T.J. Sego
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
2Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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Josua O. Aponte-Serrano
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
2Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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Juliano Ferrari Gianlupi
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
2Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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Samuel R. Heaps
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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Kira Breithaupt
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
3Cognitive Science Program, Indiana University, Bloomington, IN, USA
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Lutz Brusch
4Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Germany
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James M. Osborne
5School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
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Ellen M. Quardokus
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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Richard K. Plemper
6Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
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James A. Glazier
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
2Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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  • For correspondence: glazier@indiana.edu
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Abstract

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding differences in disease outcomes and optimizing therapeutic interventions. We present a multiscale model and simulation of an epithelial tissue infected by a virus, a simplified cellular immune response and viral and immune-induced tissue damage. The model exhibits basic patterns of infection dynamics: widespread infection, slowed infection, recurrence, containment and clearance. Inhibition of viral internalization and faster immune-cell recruitment promote containment of infection. Fast viral internalization and slower immune response lead to uncontrolled spread of infection. Because antiviral drugs can have side effects at high doses and show reduced clinical effectiveness when given later during the course of infection, we studied the effects on infection progression of both treatment potency (which combines drug effectiveness and dosage) and time-of-first treatment after infection. Simulation of a drug which reduces the replication rate of viral RNA shows that even a low potency therapy greatly decreases the total tissue damage and virus burden when given near the beginning of infection. However, even a high potency therapy rapidly loses effectiveness when given later near the time of peak viral load in the untreated case. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control of the virus (treatment success), while others show rapid infection of all epithelial cells in the simulated tissue subregion (treatment failure). This switch between a regime of consistent treatment success and failure occurs as the time of treatment increases. However, stochastic variations in viral spread mean that high potency treatments at late times are occasionally effective. The model is open-source and modular, allowing rapid development and extension of its components by groups working in parallel.

Author summary This study presents an open source multiscale model of viral immune interactions in epithelial tissues. The model is used to investigate how potential treatments influence the simulation outcome. Simulation results suggest that drugs that interfere with virus replication (e.g., remdesivir) yield substantially improved infection outcomes when administered prophylactically even at very low doses than when used at high doses as treatment for an infection that has already begun.

Competing Interest Statement

JAG is the owner/operator of Virtual Tissues for Health, LLC, which develops applications of multiscale tissue models in medical applications and is a shareholder in Gilead Life Sciences.

Footnotes

  • https://github.com/covid-tissue-models/covid-tissue-response-models/tree/master

  • https://compucell3d.org/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness
T.J. Sego, Josua O. Aponte-Serrano, Juliano Ferrari Gianlupi, Samuel R. Heaps, Kira Breithaupt, Lutz Brusch, James M. Osborne, Ellen M. Quardokus, Richard K. Plemper, James A. Glazier
bioRxiv 2020.04.27.064139; doi: https://doi.org/10.1101/2020.04.27.064139
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A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness
T.J. Sego, Josua O. Aponte-Serrano, Juliano Ferrari Gianlupi, Samuel R. Heaps, Kira Breithaupt, Lutz Brusch, James M. Osborne, Ellen M. Quardokus, Richard K. Plemper, James A. Glazier
bioRxiv 2020.04.27.064139; doi: https://doi.org/10.1101/2020.04.27.064139

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