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

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 SARS-CoV-2 virus 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 co-morbidities. 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, co-morbidities such as obesity, diabetes, and hypertension, and dysregulated immune response 1,2. 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 pre-infection 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.

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

“…This is being done using multiscale simulation-based models. 78,79,80 These models keep track of the complex molecular interactions involved in viral infection and virus replication within cells as well as spatial spread of the virus, the production of cytokines and chemokines, and the population dynamics of various types of immune cells, such as macrophages, neutrophils, CD4 + T cells, CD8 + T cells, etc. This modeling approach offers a unique opportunity to integrate existing knowledge about viral infection and the induced response of various immune molecules and cells.…”

“…In contrast to the approaches above, where the models focused only on key aspects of the immune response and their impact on viral dynamics, another approach is to incorporate much more detailed information about the innate and adaptive immune responses as well as comorbidities induced by the virus. This is being done using multiscale simulation‐based models 78,79,80 . These models keep track of the complex molecular interactions involved in viral infection and virus replication within cells as well as spatial spread of the virus, the production of cytokines and chemokines, and the population dynamics of various types of immune cells, such as macrophages, neutrophils, CD4 + T cells, CD8 + T cells, etc.…”

Impairment of T cells’ antiviral and anti-inflammation immunities dominates death from COVID-19

“…In conclusion, we have quantified the adaptive-immune-response heterogeneity from non-survivors to survivors of COVID-19, using a dynamical motif with 19 measurable parameters beyond the overcomplication of the previous multiscale model 24 . For the first time, this model provides an accurate description of real-time clinical data involving hundreds of patients, which then reliably clarifies T cells' dominant roles in the antiviral and anti-inflammatory immune responses.…”

“…The difficulty of previous multiscale simulations [22][23][24] due to considerable parameter value uncertainties stems from the fact that, in the bottom-up strategy, the immune response to infectious disease is modeled as a complex network of numerous factors, resulting in the so-called 'curse of dimensionality' 25 . In contrast, a recent successful model of a classical complex system, namely, fluid turbulence, one of us has demonstrated that the global motions composed of numerous components typically display a symmetry-breaking which can be quantitatively modeled with finite functional variables, called order functions 26,27 .…”

“…However, most recent studies focus on the simple viral dynamics and its interactions with immune responses 10,11 and antiviral drugs [12][13][14][15][16][17][18][19][20][21] , without considerations of organ damages and disease progression. On the other hand, some multiscale simulations [22][23][24] incorporate existing knowledge about the viral dynamics, immune responses, and comorbidities to simulate the clinical outcomes. However, these approaches involve hundreds of model parameters, which have considerable value uncertainties that limit the reliability of predictions and systematic comparisons with clinical data.…”