Biology subjects

Prague, M.

Publications and source records attributed to Prague, M..

6 recordsLinked to original sources

bioRxiv2026

Joint mechanistic modeling of viral and antibody responses to vaccines in non-human primates to quantify SARS-CoV-2 mechanistic correlates of protection

In a global SARS-CoV-2 landscape of hybrid immunity resulting from a vaccinated worldwide population and the emergence of new variants able to escape immunity, the identification and quantification of correlates of protection (CoPs) is crucial for the adaptation of next-generation SARS-CoV-2 vaccines. Antibodies and their neutralizing capacity have been identified as reliable mechanistic CoPs. Here, we proposed an original mechanistic model jointly describing viral and antibody dynamics, and their mutual interactions, observed after SARS-CoV-2 infection in non-human primates (NHPs) with distinct immunological backgrounds. From the model, the concentration of neutralizing antibodies being protective against viral infection spreading was derived using the reproduction number. Counterfactual simulations were also performed to better understand and validate immune mechanisms driving immune control. The model was estimated on viral, binding (bAb), and neutralizing (nAb) antibody dynamics, with data collected in 34 naive and convalescent NHPs. Animals were involved in a preclinical study evaluating two next-generation protein-based vaccines targeting the RBD of the Spike protein to CD40-expressing cells, and the original BNT162b2 mRNA vaccine against Delta SARS-CoV-2 infection. Our model validated the functionality of nAb to neutralize viruses as the primary mechanism of protection. An inhibitory antibody concentration against Delta variant of 20 AU/mL was deemed protective against Delta infection in naive animals. Moreover, we showed the strong benefit of hybrid immunity to induce faster and more protective antibody responses pointing out the crucial role of the memory B-cell immune response in viral control. Finally, an additional effect of natural immunity beyond antibodies and enhancing the elimination of infected cells was identified, suggesting the potential role of the T-cell response in viral control. Our model showed the benefit of combining information from both viral and antibody responses to better qualitatively and quantitatively inform on their protective capacity against Delta SARS-CoV-2 infection.

immunology↗

bioRxiv2022

Impact of variants of concern on SARS-CoV-2 viral dynamics in non-human primates.

The impact of variants of concern (VoC) on SARS-CoV-2 viral dynamics remains poorly understood and essentially relies on observational studies subject to various sorts of biases. In contrast, experimental models of infection constitute a powerful model to perform controlled comparisons of the viral dynamics observed with VoC and better quantify how VoC escape from the immune response. Here we used molecular and infectious viral load of 78 cynomolgus macaques to characterize in detail the effects of VoC on viral dynamics. We first developed a mathematical model that recapitulate the observed dynamics, and we found that the best model describing the data assumed a rapid antigen-dependent stimulation of the immune response leading to a rapid reduction of viral infectivity. When compared with the historical variant, all VoC except beta were associated with an escape from this immune response, and this effect was particularly sensitive for delta and omicron variant (p<10-6 for both). Interestingly, delta variant was associated with a 1.8-fold increased viral production rate (p=0.046), while conversely omicron variant was associated with a 14-fold reduction in viral production rate (p<10-6). During a natural infection, our models predict that delta variant is associated with a higher peak viral RNA than omicron variant (7.6 log10 copies/mL 95% CI 6.8 - 8 for delta; 5.6 log10 copies/mL 95% CI 4.8 - 6.3 for omicron) while having similar peak infectious titers (3.7 log10 PFU/mL 95% CI 2.4 - 4.6 for delta; 2.8 log10 PFU/mL 95% CI 1.9 - 3.8 for omicron). These results provide a detailed picture of the effects of VoC on total and infectious viral load and may help understand some differences observed in the patterns of viral transmission of these viruses.

microbiology↗

bioRxiv2021

Design, immunogenicity, and efficacy of a pan-sarbecovirus dendritic-cell targeting vaccine

The emergence of SARS-CoV-2 variants of concern (VOCs) that escape pre-existing antibody neutralizing responses increases the need for vaccines that target conserved epitopes and induce cross-reactive B- and T-cell responses. We used a computational approach and sequence alignment analysis to design a new-generation subunit vaccine targeting conserved sarbecovirus B- and T-cell epitopes from Spike (S) and Nucleocapsid (N) to antigen-presenting cells expressing CD40 (CD40.CoV2). We demonstrate the potency of CD40.CoV2 to elicit high levels of cross-neutralizing antibodies against SARS-CoV-2, VOCs, and SARS-CoV-1 in K18-hACE2 transgenic mice, associated with improved viral control and survival after challenge. In addition, we demonstrate the potency of CD40.CoV2 in vitro to recall human multi-epitope, functional, and cytotoxic SARS-CoV-2 S- and N-specific T-cell responses that are unaffected by VOC mutations and cross-reactive with SARS-CoV-1 and, to a lesser extent, MERS epitopes. Overall, these findings provide a framework for a pan-sarbecovirus vaccine.

immunology↗

bioRxiv2021

SARS-CoV-2 mechanistic correlates of protection: insight from modelling response to vaccines

The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. Here, we propose a new framework 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. One Sentence SummaryA framework for modelling the immune control of viral dynamics is applied to quantify the effect of several SARS-CoV-2 vaccine platforms and to define mechanistic correlates of protection.

immunology↗

bioRxiv2019

Viral rebound kinetics following single and combination immunotherapy for HIV/SIV

HIV infection can be treated but not cured with antiretroviral therapy, motivating the development of new therapies that instead target host immune responses. Three such immunotherapies were recently tested in non-human primates - a TLR7-agonist, therapeutic vaccine, and broadly-neutralizing antibody - and cured a subset of animals by preventing or controlling viral rebound after antiretrovirals were stopped. However, their mechanism of action remains unknown; for example, whether they reduced the pool of latently-infected cells versus boosted antiviral immunity, and whether they acted independently or synergistically. Here we conduct a detailed analysis of the kinetics of viral rebound after immunotherapy, and use mathematical models combined with rigorous statistical fitting to quantify the impact of these interventions on viral dynamics. We find that the vaccine reduced reactivation of latent virus by 4-fold, and boosted the avidity of antiviral immune responses by 17-fold when alone and 210-fold when combined with the TLR7-agonist. In the context of later initiation of antiretroviral therapy only, the TLR7-agonist reduced latent reservoir reactivation by 8-fold, but also slightly increased target cell availability (1.5-fold). The antibody boosted immune response avidity (8-fold) and displayed no detectable synergy with the TLR7-agonist. To predict the impact of these immunotherapies in clinical trials, we calibrated a model of HIV rebound to human treatment interruption trials and simulated the effect of adding each therapy. Overall, our results provide a framework for understanding the relative contributions of different mechanisms of preventing viral rebound and highlight the multifaceted roles of TLR7-agonists for HIV/SIV cure.

systems biology↗

bioRxiv2019

Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer

PurposeFor patients with early-stage breast cancer, prediction of the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (e.g. Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for the time to metastatic relapse. MethodsThe data consisted of 642 patients with 21 clinicopathological variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter ) and dissemination (parameter ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of 5 covariates with best predictive power. These were further considered to individually predict the model parameters, by using a backward selection approach. Predictive performances were compared to classical Cox regression and machine learning algorithms. ResultsThe mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with (p=0.001) and EGFR with (p=0.009). Achieving a c-index of 0.65 (0.60-0.71), the model had similar predictive performance as the random survival forest (c-index 0.66-0.69) and Cox regression (c-index 0.62 - 0.67), as well as machine learning classification algorithms. ConclusionBy providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool of help for routine management of breast cancer patients.

cancer biology↗