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Development of QSP model of COVID-19 based on Immune Response Template
EP38771
Poster Title: Development of QSP model of COVID-19 based on Immune Response Template
Submitted on 11 May 2022
Author(s): Veronika Musatova, Oleg Demin, Maria Kupriyanova, Anna Viktorenko
Affiliations: InSysBio
This poster was presented at ACoP12
Poster Views: 257
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Poster Information
Abstract: Objectives: COVID-19 is associated with several respiratory symptoms that can progress to acute respiratory distress syndrome (ARDS). QSP modeling approach can potentially help to increase our understanding of virus interaction with host cell leading to immune response and associated inflammation. Immune response template (IRT) is ODE-based simulation platform focusing on interactions of multiple immune cell types, soluble mediators (cytokines, chemokines), cell-cell contact
effects. We have applied IRT to develop a prototype of QSP model of COVID-19.

Methods: The submodel of the SARS-CoV2 life cycle with empirical immune response description was developed as an ODE-based. Submodel describes infection of alveolar cell type II (ATII pneumocytes type II) with SARS-CoV-2 via binding to ACE2 located on the cell surface, virus-ACE2 complex internalization with subsequent virus penetration to cytoplasm, uncoating, replication, assembling of newly produced viral particles and their release. IRT [1] was applied to develop sub-model of immune response of COVID-19. IRT includes cell life cycles representing ODE-based sub-models describing dynamics of states of the cell caused by influx, proliferation, differentiation, activation, death, and migration between relevant tissues. Life cycles of macrophages, neutrophils and others were extracted from IRT, modified to consider specifics of COVID-19, and merged with sub-model of virus and host cell life cycle. Model was preliminary calibrated against available data describing lung physiology, SARS-CoV-2 structure, in vitro data describing ACE2 binding to Spike protein, in vivo data describing ATII life cycle in healthy subjects.
Cytocon DB [2] was used to extract baseline levels of appropriate immune cells and cytokines as well as their ranges of variation. These values were used for model calibration.

Results: The submodel of the SARS-CoV2 life cycle with empirical immune response was developed. It allows to reproduce average data on viral load taken from different sources and % of viral subgenomic mRNA in sputum. Submodel simulations enable us to conclude that there is a threshold in virus initial concentration. The values above the threshold lead to substantial steady state virus load. The values below the threshold do not allow detect virus in sputum at any time, i.e. viral load tends to steady state value equal to 0.Empirical immune response was substituted by detailed description using IRT templates. Prototype immune response QSP platform was calibrated to describe an in vivo data for healthy subjects.

Conclusions: Submodel of the virus replication cycle was developed with empirical immune response description. Empirical response was substituted by IRT templates stacked together to develop a prototype QSP model of innate immune response COVID-19.
Summary: Submodel of the virus replication cycle was developed with empirical immune response description. Empirical response was substituted by IRT templates stacked together to develop a prototype QSP model of innate immune response COVID-19. References: 1, Immune Response Template, Version 3.4.0 (release date:2021-03-30) https://irt.insysbio.com/
2. Cytocon DB, Version 1.3.6.14. http://cytocon.insysbio.com/
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