In silico dynamics of COVID-19 phenotypes for optimizing clinical management

Significance

A distinctive feature of COVID-19 is its extreme heterogeneity—illness ranges from minimally symptomatic to life threatening. Heterogeneity results from a poorly understood combination of patient factors, viral dynamics, antiviral and immune modulating therapies, and dynamics of the innate and adaptive immune responses. In order to better understand clinical heterogeneity and optimal treatment, we developed a comprehensive mathematical model incorporating elements of the innate and adaptive immune responses, the renin−angiotensin system (which the virus exploits for cellular entry), rates of viral replication, inflammatory cytokines, and the coagulation cascade. Our model reveals divergent treatment responses and clinical outcomes as a function of comorbidities, age, and details of the innate and adaptive immune responses which can provide a framework for understanding individual patients’ trajectories.

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.