Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies

Mitra, Kishalay and Giri, Lopamudra (2023) Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies. Biotechnology and Bioengineering, 120 (6). pp. 1640-1656. ISSN 0006-3592

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Abstract

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Giri, Lopamudrahttp://orcid.org/0000-0002-2352-7919
Item Type: Article
Uncontrolled Keywords: clinical management; COVID-19; cytokine dynamics; disease staging; mathematical model and simulation; systems biology; COVID-19; Cytokines; Humans; Interleukin-6; Macrophages; Tumor Necrosis Factor-alpha; Cell death; Dynamics; Immune system; Macrophages; T-cells; Viruses; carvedilol; certolizumab pegol; cyclosporine; favipiravir; interleukin 6; remdesivir; sarilumab; tacrolimus; thalidomide; tocilizumab; tumor necrosis factor; cytokine; interleukin 6; tumor necrosis factor; Clinical management; Computational framework; Cytokine dynamic; Cytokines; Disease staging; Mathematical modeling and simulation; Proinflammatory cytokines; Systems biology; Time course; Tumor necrosis factors; Article; controlled study; coronavirus disease 2019; disease course; disease exacerbation; disease severity; drug efficacy; epithelium cell; immune response; lung epithelium; lymphocyte count; macrophage; mathematical model; nonlinear system; T lymphocyte; virus load; coronavirus disease 2019; human; COVID-19
Subjects: Chemical Engineering
Chemical Engineering > Technology of industrial chemicals
Divisions: Department of Chemical Engineering
Depositing User: Mr Nigam Prasad Bisoyi
Date Deposited: 12 Nov 2023 11:04
Last Modified: 12 Nov 2023 11:04
URI: http://raiith.iith.ac.in/id/eprint/11746
Publisher URL: https://doi.org/10.1002/bit.28358
OA policy: https://v2.sherpa.ac.uk/id/publication/14548
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