Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

Abstract

SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. Empirical evidence points to the prominent roles of innate immune and CD8 T-cell responses in determining the outcomes. However, how these immune arms act in concert to elicit the outcomes remains unclear. Here, we developed a mathematical model of within-host SARS-CoV-2 infection that incorporates the essential features of the innate immune and CD8 T-cell responses. Remarkably, by varying the strengths and timings of the two immune arms, the model recapitulated the entire spectrum of outcomes realized. Furthermore, model predictions offered plausible explanations of several confounding clinical observations, including the occurrence of multiple peaks in viral load, viral recrudescence after symptom loss, and prolonged viral positivity. We applied the model to analyze published datasets of longitudinal viral load measurements from patients exhibiting diverse outcomes. The model provided excellent fits to the data. The best-fit parameter estimates indicated a nearly 80-fold stronger innate immune response and an over 200-fold more sensitive CD8 T-cell response in patients with mild compared to severe infection. These estimates provide quantitative insights into the likely origins of the dramatic inter-patient variability in the outcomes of SARS-CoV-2 infection. The insights have implications for interventions aimed at preventing severe disease and for understanding the differences between viral variants.

A simple model of COVID-19 explains disease severity and the effect of treatments

“…However, some of the more complex models reported in the literature did give rise to interesting hypotheses that may warrant further investigation, such as the role of monocyte-to-macrophage differentiation or the role of anti-inflammatory cytokines 11 , 12 . Further, more complex models have provided complementary insight on the conditional impact of immunomodulatory treatment on disease severity that are consistent with the results reported herein 61 , 62 .…”

Relationship between the inclusion/exclusion criteria and sample size in randomized controlled trials for SARS-CoV-2 entry inhibitors

“…Attempts to quantify the inter-individual variability in SARS-CoV-2 infection dynamics have been made in studies using various models and data ( Goyal et al, 2020 , Chatterjee et al, 2022 , Néant, et al, 2021 , Gonçalves, 2020 ). These quantitative models have been used to predict the efficacy of antiviral treatment and to investigate the factors affecting individual differences in outcomes in clinical trials as we did in this study.…”

“…In our model, we considered only that administration of an entry inhibitor inhibits de novo viral infection of uninfected cells. A mathematical modeling study suggested that there is a large difference in the sensitivity of immune responses related to CD8T cell exhaustion due to viral infection between patients with mild and severe cases ( Chatterjee et al, 2022 ). If suppression of viral load with antiviral treatment restores such immune cell activity and leads to a greater rate of elimination of infected cells, viral load may be greatly reduced as our previous mathematical modeling study have shown ( Kim, 2021 ).…”

Modelling how the altered usage of cell entry pathways by the SARS-CoV-2 Omicron variant may affect the efficacy and synergy of TMPRSS2 and Cathepsin B/L inhibitors

“…Quantifying the changes in the preference of the Omicron variant for different entry routes may help evaluate the efficacies of entry inhibitors in clinical development and optimise entry inhibitor-based treatments (Gunst et al, 2021; Zhuravel et al, 2021). Mathematical models of SARS-CoV-2 kinetics have provided valuable insights into COVID-19 disease progression, drug action, and the effectiveness of vaccines and treatments (Amidei and Dobrovolny, 2022; Chatterjee et al, 2022; Desikan et al, 2021; Garg et al, 2021; Gonçalves et al, 2020; Goyal et al, 2020; Ke et al, 2021; Kissler et al, 2021; Néant et al, 2021; Padmanabhan et al, 2022; Perelson and Ke, 2020). Here, adapting a previously developed mathematical model of SARS-CoV-2 entry (Padmanabhan et al, 2020) to the analysis of in vitro data on variants (Garcia-Beltran et al, 2021; Hoffmann and et al, 2021), we quantified the altered usage of host proteases required for entry by the Omicron variant relative to the original strain and the Delta variant.…”