Comparing antiviral strategies against COVID-19 via multiscale within-host modelling

Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.


Dear Dr Twarock
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Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) The authors successfully answered a number of my questions. However, I am still unconvinced about some aspects of the statistical analysis and model fitting. I believe these question require a new round of review: 1/ You argue that estimation with Monolix was not possible thus you fitted the model using individual by individual approach in Matlab. First, to avoid any confusion, I believe that the references to Monolix should be removed to avoid confusions -just present the methods you used in more details. Second, can you properly check the identifiability of your model (for exemple the method of Castro and de boer 2020 could be used). I am concerned by estimates stability and over-interpretation. It may be that some parameters compensate each other and it would be more suitable to fix some parameters to some values in order to achieve identifiability. 2/ Even if you do individual by individual fittings, it is perfectly possible to get confidence intervals. This is possible doing bootstrap using Monte Carlo simulations. You can samplevalues on the multivariate posterior distribution of parameters and sample possible trajectories. Then just take the 2.5% and 97.5% percentile of the sampled trajectories.

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Decision letter (RSOS-210082.R1)
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Review -Fatehi et al. -Comparing antiviral strategies against 1 COVID-19 via multi-scale within host 2 modelling
The article uses a stochastic agent-based models and ODE-based dynamical models to help understanding the within-host infection dynamics of COVID-19. They investigate in silico the effect of remdesivir and convalescent plasma therapy and their possible synergetic interaction.
The supplemental information file was not available (it was a duplicate of the main manuscript). It made it difficult to understand the details of simulations and results. This file will need to be provided for further review.
The author calibrated their model on 12 patients from a study in Singapore (Young et al.) using the Monolix software. They also based their model on available experimental data. For some parameters, they investigate the robustness of their conclusion to variation in the parameters value using sensibility analysis. Although the approach is valid, I have remaining question regarding the identifiability of the model and the robustness of their estimation. In particular, more confidence interval should be provided to appreciate the quality of the results.
Their primary endpoints to compare scenario for the study is the viral load that is secreted from infected cells and tissue damage. The later notion is not very well defined in the manuscript. Trade-off between the two aspects are also not very much discussed. Finally, discussion can be expended to discuss more clinical implication in particular on symptoms and disease severity.
The results provided on the effect of convalescent plasma and antiviral therapies are expected. In particular, the fact that these therapies are most effective if administered early post infection. However, the modeling work in itself provide quantification and represent a good platform for further experiments. In silico simulation of the counterfactual effect of therapies in the 12 patients from the Singapore study could be further developed (although partially developed).
In general, article will benefit from some writing efforts. The modeling and the findings are interesting, but their impact is decreased by the number of assumptions made in the modeling. The effort made by the authors to make these assumptions clear is good, but they are given here and there in the text which impacts the clarity. Organization of the paper should be revised so that modeling is presented all at once (especially for the CP and antiviral therapies effects) with its assumptions. To do so, material and method section could be expended.

Major comments
1. More references need to be added in the introduction. First there is no string review of existing models of immune response to viral infection. Second, there is a growing existing literature on clinical results of remdesivir and plasma convalescent therapy effect. This should be reviewed to expose in the introduction the scientific and medical interest of this in silico research.
2. Section 2.1/2.2, there is a gap between the modeling presented and the results. Please explain what is done more explicitly in the main manuscript which should stand alone without the appendix. Part of the material of this section could be described in Material and methods. 3. In material and method, you mention "However, considering a higher basal level does not change model outcome as a and μ which determine the proliferation rate of effector cells and removal rate of infected cells by effector cells are estimated using data fitting." This is not clear to me at all. Do you refer to nonidentifiability? Did you perform an identifiability analysis for you model? Same question also applies for the estimation of other parameters. 4. You use article in HBV to set parameters values in your model, for example for antibodies death rate (as well as other parameters). Can you discuss how realistic this choice is? 5. When using Monolix did you fit each patients' data separately? If yes, this is known to be suboptimal compared to a population fitting using mixed effects, which I believe you did. Please clarify. 6. Regarding the use of Monolix and table 1, can you provide the standard deviation for parameter estimation. This indicator is different from the std you reported in the table. You get them by computing the Fisher information matrix. On top of providing information about the model identifiability, this would provide insight on uncertainty in parameters estimation. Can you also provide in appendix an idea of the fit to the data (visual predictive check and/or individual data fits)?
The analysis would also benefit from a thorough convergence assessment procedure as proposed by Monolix. 7. Is the multi-peak behavior of the viral load something reported in clinical data /other studies? Could it be some well-known overshooting/oscillatory behavior only due to the mechanics of ODE systems? 8. The assumption of Remdesivir efficacy is very strong. 0.99 seems to be very high compared to values found in the literature (Gonçalves, A., Bertrand, J., Ke, R., Comets, E., De Lamballerie, X., Malvy, D., ... & Smith, P. (2020). Timing of antiviral treatment initiation is critical to reduce SARS-CoV-2 viral load. CPT: pharmacometrics & systems pharmacology,9(9), 509-514). 9. In Figure 3, the discussion of the number of days before starting the treatment is not convincing. Why the dotted line (dpi=6 days) does not appear in most graphics? AUC is advocated as a good indicator. Can the author provide the values of AUC as well as confidence interval for them? For example, l281 p10/16, you mention that AUC is reduced significantly. Please provide quantification of it as well as for the significance. 10. Confidence intervals are never provided making it difficult to appreciate the uncertainty in the results. Please add them, including in graphics. 11. Individual prediction. Simulation in silico of CP and antiviral therapy in the 12 patients of the Singapore study is interesting. Please provide more details in the main manuscript. 12. The severity of symptoms is known to be associated with a higher antibodies level in patients. How does this information relate with your model? Please at least mention some discussion points in the discussion part.