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Voutouri C, Nikmaneshi MR, Hardin CC, Patel AB, Verma A, Khandekar MJ, Dutta S, Stylianopoulos T, Munn LL, Jain RK. In silico dynamics of COVID-19 phenotypes for optimizing clinical management. Proc Natl Acad Sci U S A 2021; 118:e2021642118. [PMID: 33402434 PMCID: PMC7826337 DOI: 10.1073/pnas.2021642118] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment-or combination of treatments-depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.
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Affiliation(s)
- Chrysovalantis Voutouri
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus
| | - Mohammad Reza Nikmaneshi
- Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran, 11155
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - C Corey Hardin
- Department of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Ankit B Patel
- Department of Medicine/Renal Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Ashish Verma
- Department of Medicine/Renal Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Melin J Khandekar
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Sayon Dutta
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus;
| | - Lance L Munn
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114;
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114;
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