Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.


Sample-size estimation
 You should state whether an appropriate sample size was computed when the study was being designed  You should state the statistical method of sample size computation and any required assumptions  If no explicit power analysis was used, you should describe how you decided what sample (replicate) size (number) to use Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: No data were generated for this study. Data from three different studies were used in this analysis, as referenced below. For each of the three studies, only data collected from cynomolgus macaques were used and no data have excluded. All information about sample-size estimation for the different studies can be found in their related papers and their respective reporting summary. Information about laboratory animals can also be found in section Materials and Methods in subsection Experimental model and subjects details, as well as in the legend of Figure 1A ([data related to Marlin, 2021]) and in the legend of Figure 5A (data related to [Brouwer, 2021]).
In each figure displaying raw data or dynamics predicted by the model, sample size within each group of treatment is provided in the legend.

Replicates
 You should report how often each experiment was performed  You should include a definition of biological versus technical replication  The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates  If you encountered any outliers, you should describe how these were handled  Criteria for exclusion/inclusion of data should be clearly stated  High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: No data were generated for this study. Data from three different studies were used in this analysis, as referenced below. For each of the three studies, only data collected from cynomolgus macaques were used and no data have been excluded. All information about technical replication for the different studies can be found in their related papers and their respective reporting summary.
In addition, information about materials and experimental systems can be found in section Material and Methods subsection Evaluation of anti-spike, anti-RBD and neutralizing IgG antibodies for the quantification of antibody response as well as in the legend of the Figure 3, Figure 1-figure supplement 3 and 4 (data related to [Marlin, 2021] and the Figure 5 (data related to [Brouwer, 2021]) for PCR, PCR, ELISA, ELISPOT, Luminex, ICS and (pseudo-) neutralization assays. Statistical reporting  Statistical analysis methods should be described and justified  Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10)  For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d)  Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.) Data from three different studies were used in this analysis, as referenced below. All statistical analysis performed on raw data (differences between groups, correlation between viral and immune markers) can be directly found in their related papers.
Statistical analysis were performed in this paper related to the proposed model-based framework evaluating mechanistic correlate of protection.
-Mechanistic models developed in the paper were estimated by Monolix® software, version 2019R1. In those models, inter-individual variability was taken into account by considering statistical models (Mixed effects model) on each model parameter, which can depend on covariates.
-Statistical significance of the group of intervention as covariate in the models were evaluated by a Wald test directly performed in Monolix and were reported in Supplementary file 1 and 2 via p-values.
-A flow chart of the algorithm implemented for an automatic selection of time-varying covariates in our mechanistic models is given in Figure 4 -figure supplement 2 and the algorithm is described in the section Materials and Methods in the subsection Algorithm for automatic selection of biomarkers as CoP. The statistical significance of the timevarying covariate tested by this algorithm was evaluated via 3 criteria: 1) the value of the Bayesian Information Criteria BICc evaluated for the model after adjustment for the time-varying covariate, 2) the statistical significance of the covariate group when the model was adjusted for the tested covariate and the group, evaluated by a Wald-test and 3) the explained variability induced by the time-varying covariate. These information can be found in the Supplementary file 1. In addition, we ensure the statistical difference from 0 of the regression coefficient related to the covariate (in the mixed-effects model) by calculating its confidence interval. These information can be found in Figure 4B and Group allocation  Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied  Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: No data were generated for this study. Data from three different studies were used in this analysis, as referenced below. For each of the three studies, only data collected from cynomolgus macaques were used and no data have been excluded. In addition, allocation into experimental groups were kept has defined in the studies.
-In [Brouwer, 2021] the 10 cynomolgus macaques were allocated into the two groups of treatment referred as "Naïve" (n=4) and "vaccinated" (n=6), as reported in Figure 5A and the Supplementary file 1.
-In [Corbett, 2021], animals were splitted into three group of treatment referred as "placebo", "10μg" and "100μg" as reported in the Supplementary file 1.
Group allocation and sample size were provided in the legend of each figure involving a stratification of the results according to the group of intervention.