Adrien Mitard1,2, Cécile Hérate3, Romain Marlin3, Flora Donati4, Yannis Rahou4, Sylvie Van der Werf4, Etienne Simon-Loriere4, Roger Le Grand3, Mélanie Prague2,5, Jérémie Guedj1
1Université Paris Cité, IAME, INSERM, 2Université de Bordeaux, Inserm, Inria, BPH Research Center, SISTM Team, UMR 1219, 3Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), 4National Reference Center for Respiratory Viruses, Institut Pasteur, 5Vaccine Research Institute
Introduction: COVID-19 vaccination has dramatically reduced the risk of severe disease and prevented around 60% of expected deaths in Europe [1]. In the Omicron context, bivalent vaccines targeting the latest strain as well as the historical one have been developed. They offer a better protection than the monovalent ones against severe disease [2] but their effect on viral replication, and thus on infection and transmission risks [3,4], is still uncertain. Despite this effort, part of the population, mainly immunocompromised individuals, remains at risk. Therefore, assessing the pertinence of binding and neutralisation as Correlates of Protection (CoP) against infection is crucial [5]. Moreover, we lack of understanding on the differences between protections offered by vaccination (mono or bivalent) or infection. Experimental challenges offer the ideal setting to address those questions thanks to a controlled environment, regular measurements of viral and immune markers and a known infection date. We used a non-human primate (NHP) study designed to look into viral replication given different immunological backgrounds. Many modelling studies have studied viral [6,7] or antibodies dynamics [8] but very few fitted both jointly, especially with rich longitudinal data. Objectives: •Develop a model bridging mechanistically viral and immune dynamics. •With a simulation work, investigate neutralizing and binding antibodies levels as CoP against infection and transmission. Methods: NHP data: Our study includes 22 NHP challenged with BQ.1.1 that are split in 4 groups having different exposure histories: •N: 6 naive NHP •M/M (6 NHP): 2 doses of monovalent BNT162b2 vaccine •M/B (6): 1 dose of monovalent and 1 dose of bivalent (Wuhan/BA.4-BA.5) vaccine •C/B (4): Infected by a previous omicron strain (BA.2) and then received a dose of bivalent vaccine Viral kinetics and immune response model: We used a target cell limited model to characterize the viral load of infected animals [6,7]. Regarding the immune response, Short (S) and long-lived B Cells (L) replicate proportionally to L, a proxy of the memory cells. Their recruitment is triggered either through vaccination or by the viral load. They produce binding antibodies that form immune complexes with free virions inducing a quicker elimination [9,10]. A fraction ? of them is fully neutralized and cannot infect new cells. Those mechanisms allow our model to decouple the information given by binding and neutralisation assays. Results: We developed a model fitting viral load and antibodies dynamics over multiple patterns of infections and vaccinations. It enables us to highlight the extra-information brought by neutralisation comparing to binding levels. However, the antibody response isn’t enough to explain how exposure history does shape viral dynamics. Including covariates, we found that an infection increases the loss rate of infected cells by 24.5 [95% CI: 4.2 – 90] (respectively by 1.2 [1.1 – 1.4] and 1.9 [1.6 – 2.3] after a monovalent or bivalent vaccination) protecting completely convalescents from infection. We also highlight a lower neutralisation ability against BQ1.1 of antibodies elicited by a monovalent vaccination comparing to those following an infection or a bivalent dose (44% [20 – 66]). We simulated infections with a much lower inoculum varying the neutralisation and binding levels at the time of the challenge to characterise viral kinetics in a more general setting. The threshold of complete protection – ie peak viral load below the limit of detection is found at 2*10^4 [6*10^3 – 5*10^4] for the M/B and 10^5 [3*10^4 – 3*10^5] for the M/M. Simulations of the AUC of infectious viral load also inform on the reduced risk of transmission. Conclusion: This study demonstrates that antibodies conferred by vaccination and/or infection reduce viral replication through both binding and neutralisation. However, the strong covariate effect on the loss rate following an infection hints towards a crucial role of the T response that needs to be further characterised. A strength of this model is that it could be used in more complex settings without needs for more parameters a priori. It could then be used to predict virus circulation in the general population and assess the impact of a vaccination campaign. More directly, it could select individuals in need of a booster.
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Reference: PAGE 33 (2025) Abstr 11577 [www.page-meeting.org/?abstract=11577]
Poster: Drug/Disease Modelling - Other Topics