A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions
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Abstract
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 mAb and antiviral 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 time points 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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SciScore for 10.1101/2021.12.07.21267277: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Model Simulation: The model was simulated in MATLAB 2019a, and ode15s was used to integrate the model differential equations. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are number of limitations to our approach. Chiefly, while model components directly describing the viral load dynamics are calibrated against both hospitalized and outpatient datasets, the majority of immune states are informed by data in hospitalized …
SciScore for 10.1101/2021.12.07.21267277: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Model Simulation: The model was simulated in MATLAB 2019a, and ode15s was used to integrate the model differential equations. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are number of limitations to our approach. Chiefly, while model components directly describing the viral load dynamics are calibrated against both hospitalized and outpatient datasets, the majority of immune states are informed by data in hospitalized COVID-19 subjects. Additionally, the model does not distinguish between distinct compartments of infection, such as the upper and lower respiratory tract, and further does not account for the mechanistic influence of excessive immune activation on the incidence of systemic complications or the impact of systemic comorbidities on disease severity. Finally, we do not comprehensively account for the endogenous humoral SARS-CoV-2 antibody response dynamics, which is found to be associated with baseline viral load. Subsequent releases of the model will focus on addressing these limitations and extending the model to other patient-care settings, such as in the case of high-risk vaccinated subjects with pre-existing immunity and the development of immunomodulatory treatments in hospitalized patients.
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04427501 Active, not recruiting A Study of LY3819253 (LY-CoV555) and LY3832479 (LY-CoV016) i… NCT04425629 Recruiting Safety, Tolerability, and Efficacy of Anti-Spike (S) SARS-Co… NCT04405570 Completed A Safety, Tolerability and Efficacy of Molnupiravir (EIDD-28… Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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