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      Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection

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          Abstract

          It is highly desired to explore the interventions of COVID-19 for early treatment strategies. Such interventions are still under consideration. A model is benchmarked research and comprises target cells, virus infected cells, immune cells, pro-inflammatory cytokines, and, anti-inflammatory cytokine. The interaction of the drug with the inflammatory sub-system is analyzed with the aid of kinetic modeling. The impact of drug therapy on the immune cells is modelled and the computational framework is verified with the aid of numerical simulations. The work includes a significant hypothesis that quantifies the complex dynamics of the infection, by relating it to the effect of the inflammatory syndrome generated by IL-6. In this paper we use the cancer immunoediting process: a dynamic process initiated by cancer cells in response to immune surveillance of the immune system that it can be conceptualized by an alternating movement that balances immune protection with immune evasion. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this process the immune system can both constrain and promote tumour development, which proceeds through three phases termed: (i) Elimination, (ii) Equilibrium, and, (iii) Escape [ 1]. We can also apply these concepts to viral infection, which, although it is not exactly “immunoediting”, has many points in common and helps to understand how it expands into an “untreated” host and can help in understanding the SARS-CoV2 virus infection and treatment model.

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          • •

            Drug therapy on immune cells is modelled.

          • •

            Cytokines storm during SARS-CoV2 infection is examined.

          • •

            The interaction of drug with the inflammatory sub-system is taken into account.

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          COVID-19: consider cytokine storm syndromes and immunosuppression

          Puja Mehta, Daniel McAuley, Michael Brown … (2020)
          As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).
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            Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2

            Renhong Yan, Yuanyuan Zhang, Yaning Li … (2020)
            How SARS-CoV-2 binds to human cells Scientists are racing to learn the secrets of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), which is the cause of the pandemic disease COVID-19. The first step in viral entry is the binding of the viral trimeric spike protein to the human receptor angiotensin-converting enzyme 2 (ACE2). Yan et al. present the structure of human ACE2 in complex with a membrane protein that it chaperones, B0AT1. In the context of this complex, ACE2 is a dimer. A further structure shows how the receptor binding domain of SARS-CoV-2 interacts with ACE2 and suggests that it is possible that two trimeric spike proteins bind to an ACE2 dimer. The structures provide a basis for the development of therapeutics targeting this crucial interaction. Science, this issue p. 1444
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              Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China

              Qiurong Ruan, Kun Yang, Wenxia Wang … (2020)
              Dear Editor, The rapid emergence of COVID-19 in Wuhan city, Hubei Province, China, has resulted in thousands of deaths [1]. Many infected patients, however, presented mild flu-like symptoms and quickly recover [2]. To effectively prioritize resources for patients with the highest risk, we identified clinical predictors of mild and severe patient outcomes. Using the database of Jin Yin-tan Hospital and Tongji Hospital, we conducted a retrospective multicenter study of 68 death cases (68/150, 45%) and 82 discharged cases (82/150, 55%) with laboratory-confirmed infection of SARS-CoV-2. Patients met the discharge criteria if they had no fever for at least 3 days, significantly improved respiratory function, and had negative SARS-CoV-2 laboratory test results twice in succession. Case data included demographics, clinical characteristics, laboratory results, treatment options and outcomes. For statistical analysis, we represented continuous measurements as means (SDs) or as medians (IQRs) which compared with Student’s t test or the Mann–Whitney–Wilcoxon test. Categorical variables were expressed as numbers (%) and compared by the χ 2 test or Fisher’s exact test. The distribution of the enrolled patients’ age is shown in Fig. 1a. There was a significant difference in age between the death group and the discharge group (p < 0.001) but no difference in the sex ratio (p = 0.43). A total of 63% (43/68) of patients in the death group and 41% (34/82) in the discharge group had underlying diseases (p = 0.0069). It should be noted that patients with cardiovascular diseases have a significantly increased risk of death when they are infected with SARS-CoV-2 (p < 0.001). A total of 16% (11/68) of the patients in the death group had secondary infections, and 1% (1/82) of the patients in the discharge group had secondary infections (p = 0.0018). Laboratory results showed that there were significant differences in white blood cell counts, absolute values of lymphocytes, platelets, albumin, total bilirubin, blood urea nitrogen, blood creatinine, myoglobin, cardiac troponin, C-reactive protein (CRP) and interleukin-6 (IL-6) between the two groups (Fig. 1b and Supplementary Table 1). Fig. 1 a Age distribution of patients with confirmed COVID-19; b key laboratory parameters for the outcomes of patients with confirmed COVID-19; c interval from onset of symptom to death of patients with confirmed COVID-19; d summary of the cause of death of 68 died patients with confirmed COVID-19 The survival times of the enrolled patients in the death group were analyzed. The distribution of survival time from disease onset to death showed two peaks, with the first one at approximately 14 days (22 cases) and the second one at approximately 22 days (17 cases) (Fig. 1c). An analysis of the cause of death was performed. Among the 68 fatal cases, 36 patients (53%) died of respiratory failure, five patients (7%) with myocardial damage died of circulatory failure, 22 patients (33%) died of both, and five remaining died of an unknown cause (Fig. 1d). Based on the analysis of the clinical data, we confirmed that some patients died of fulminant myocarditis. In this study, we first reported that the infection of SARS-CoV-2 may cause fulminant myocarditis. Given that fulminant myocarditis is characterized by a rapid progress and a severe state of illness [3], our results should alert physicians to pay attention not only to the symptoms of respiratory dysfunction but also the symptoms of cardiac injury. Further, large-scale studies and the studies on autopsy are needed to confirm our analysis. In conclusion, predictors of a fatal outcome in COVID-19 cases included age, the presence of underlying diseases, the presence of secondary infection and elevated inflammatory indicators in the blood. The results obtained from this study also suggest that COVID-19 mortality might be due to virus-activated “cytokine storm syndrome” or fulminant myocarditis. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 38 kb)
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Mol Liq
                Journal ID (iso-abbrev): J Mol Liq
                Title: Journal of Molecular Liquids
                Publisher: Elsevier B.V.
                ISSN (Print): 0167-7322
                ISSN (Electronic): 1873-3166
                Publication date PMC-release: 28 November 2020
                Publication date (Electronic): 28 November 2020
                Electronic Location Identifier: 114863
                Affiliations
                [a ]Institute of Systems Security and Control, College of Computer Science and Technology, Xian University of Science and Technology, Xi'an 710054, China
                [b ]Department of Mathematics, International Islamic University, Islamabad 44000, Pakistan
                [c ]Fulbright Fellow, University of California Riverside, Riverside 92521, USA
                [d ]Center for Study in Motor Science, 94 via di Tiglio, loc. Arancio, 55100, Lucca, Italy
                [e ]Department of Immunology, University of Pisa, Italy
                [f ]Department of Mathematics, Comsats Institute of Information Technology, Lahore 54000, Pakistan
                [g ]Center for Communications and IT Research, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
                Author notes
                [* ]Corresponding author at: Department of Mathematics, International Islamic University, Islamabad 44000, Pakistan.
                Article
                Publisher Item ID: S0167-7322(20)37105-1 Publisher ID: 114863
                DOI: 10.1016/j.molliq.2020.114863
                PMC ID: 7698669
                PubMed ID: 33281252
                SO-VID: d065c6f3-6390-4c2a-bf98-c22a57b7911d
                Copyright © © 2020 Elsevier B.V. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 27 October 2020
                Date revision received : 19 November 2020
                Date accepted : 23 November 2020
                Categories
                Subject: Article

                Keywords: covid-19,cytokines,il-6,drug therapy,immune response,chemical kinetics
                Data availability:
                Keywords: covid-19, cytokines, il-6, drug therapy, immune response, chemical kinetics

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