COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes

Jenner, Adrianne L.; Aogo, Rosemary A.; Alfonso, Sofia; Crowe, Vivienne; Deng, Xiaoyan; Smith, Amanda; Morel, Penelope A.; Davis, Courtney L.; Smith, Amber M.; Craig, Morgan

doi:10.1371/journal.ppat.1009753

Cited by 79 publications

(119 citation statements)

References 129 publications

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“…Assembly of longitudinal datasets is a challenging task that requires periodical sampling of patients over an extended period of time. Consequently, analysis of longitudinal data has only recently begun to emerge [35]. In order to bypass this limitation, mathematical representations of the immunopathology of COVID-19 and development of virtual patient cohorts were developed, elucidating potential relationships between immune response and disease severity [35,36].…”

Section: Introductionmentioning

confidence: 99%

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

et al. 2022

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The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

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Section: Introductionmentioning

confidence: 99%

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

et al. 2022

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“…Less attention has been paid to the predictive models of disease progression in heterogeneity outcome. Recently, a mechanistic, within-host ODE model was established to study the immune response to SARS-CoV-2 and the impact of delayed IFN on infection dynamics [ 55 ]. Virtual patient cohorts were generated based on an algorithm of random parameter sampling, and dynamics of how immune mechanisms drive disease outcomes was discussed.…”

Section: Discussionmentioning

confidence: 99%

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

et al. 2021

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Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.

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“…were recently developed to elucidate the relative importance of biological processes underlying COVID-19 pathophysiology and evaluate the efficacy of various therapeutic interventions. These mathematical models provide mechanistic support for a link between the disease severity and the timing of Type-1 interferon (IFN) activation after infection [11], an impaired CD8+ T-cell-dependent adaptive immune response [12], and post-hospitalization viral load dynamics [13]. Furthermore, mechanistic model-based analyses suggest that the efficacy of virus-targeting therapeutic interventions declines sharply with the time of intervention relative to symptom onset, [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 93%

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

Rao

Musante

2021

Preprint

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A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.

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COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes

Cited by 79 publications

References 129 publications

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

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