气候变化领域集成服务门户(CCPortal): In silico dynamics of COVID-19 phenotypes for optimizing clinical management

CCPortal

DOI	10.1073/pnas.2021642118
	In silico dynamics of COVID-19 phenotypes for optimizing clinical management
	Voutouri C.; Nikmaneshi M.R.; Corey Hardin C.; Patel A.B.; Verma A.; Khandekar M.J.; Dutta S.; Stylianopoulos T.; Munn L.L.; Jain R.K.
发表日期	2021
ISSN	00278424
卷号	118 期号:3
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	SARS-CoV-2 \| COVID-19 \| mathematical model \| simulation
语种	英语
scopus关键词	angiotensin converting enzyme 2; angiotensin receptor antagonist; antivirus agent; dexamethasone; dipeptidyl carboxypeptidase inhibitor; heparin; siltuximab; tocilizumab; cytokine; adaptive immunity; adult; aged; Article; blood clotting; CD8+ T lymphocyte; comorbidity; computer model; controlled study; coronavirus disease 2019; diabetes mellitus; disease course; groups by age; health status; human; human cell; hypertension; immune response; immunocompetent cell; innate immunity; middle aged; molecular dynamics; nonhuman; obesity; oxygen saturation; pathogenesis; patient care; phenotype; prediction; priority journal; renin angiotensin aldosterone system; retrospective study; risk factor; Severe acute respiratory syndrome coronavirus 2; T lymphocyte activation; thrombosis; treatment outcome; virus entry; virus load; computer simulation; disease exacerbation; drug effect; drug therapy; genetics; immunology; phenotype; physiology; theoretical model; virology; Computer Simulation; COVID-19; Cytokines; Disease Progression; Humans; Immunity, Innate; Models, Theoretical; Phenotype; SARS-CoV-2
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180980
作者单位	Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, 1678, Cyprus; Department of Mechanical Engineering, Sharif University of Technology, Tehran, 11155, Iran; Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States; Department of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States; Department of Medicine, Renal Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
推荐引用方式 GB/T 7714	Voutouri C.,Nikmaneshi M.R.,Corey Hardin C.,et al. In silico dynamics of COVID-19 phenotypes for optimizing clinical management[J],2021,118(3).
APA	Voutouri C..,Nikmaneshi M.R..,Corey Hardin C..,Patel A.B..,Verma A..,...&Jain R.K..(2021).In silico dynamics of COVID-19 phenotypes for optimizing clinical management.Proceedings of the National Academy of Sciences of the United States of America,118(3).
MLA	Voutouri C.,et al."In silico dynamics of COVID-19 phenotypes for optimizing clinical management".Proceedings of the National Academy of Sciences of the United States of America 118.3(2021).