Marie Alexandre 1, Romain Marlin 2, Mireille Centlivre 3, Roger Le Grand 2, Rodolphe Thiébaut 1, Yves Lévy 3,4, Mélanie Prague 1
1 University of Bordeaux, Department of Public Health, Inserm Bordeaux Population Health Research Center, Inria SISTM (Bordeaux, France), 2 Center for Immunology of Viral, Auto- immune, Hematological and Bacterial Diseases (IMVA- HB/IDMIT), CEA (Fontenay-aux-Roses, France), 3 Vaccine Research Institute, Paris-Est Créteil University, Faculté de Médecine, INSERM U955 (Créteil, France), 4 Assistance Publique-Hôpitaux de Paris, Groupe Henri-Mondor Albert-Chenevier, Service Immunologie Clinique (Créteil, France)
Introduction and objectives: SARS-CoV-2 evolves constantly and rapidly. The identification of correlates of protection (CoPs) against the pathogen could accelerate vaccine adaptation in the current landscape of hybrid immunity. We previously identified antibody inhibiting the binding between the ACE2 receptor and the viral receptor-binding domain (RBD) as a consistent mechanistic CoP [1]. Here, we propose to extend this work by developing mathematical modeling using preclinical data testing SARS-CoV-2 vaccines to quantify this CoP.
Methods: A within-host mechanistic model based on ordinary differential equations was set up to quantify the protective threshold of this CoP against within-host viral infection’s spreading. We proposed a new mechanistic model jointly describing viral and antibody dynamics following infection, along with their mutual interactions: i) production of antibodies by antibody-secreting cells in presence of virions, and ii) their ability to block new cell infections. Afterwards, we used model parameters, estimated by the SAEM algorithm implemented in Monolix, to derive the protective threshold from the within-host basic reproduction number (R0), indicative of the secondary infections one infected cell can cause. Finally, we performed counterfactual simulations to deeper understand the immune mechanisms driving viral control in our model. We used data from non-human primate (NHP) preclinical studies evaluating three SARS-CoV-2 vaccines (two next-generation protein-based vaccines targeting the RBD of spike protein to CD40-expressing cells, and the original BNT162b2 mRNA vaccine) in 34 NHPs (11 naïve, 23 Wuhan convalescent, including 6 and 17 vaccinated, respectively). Four weeks after vaccination, animals were challenged with B.1.617.2 Delta SARS-CoV-2 variant and intensively monitored with viral loads and antibody responses longitudinally measured for 30 days post-infection.
Results: Our results first validated the blockage of new target-cell entry as the primary mechanism against infection captured by the ACE2/RBD binding inhibition marker. Secondly, we showed that an inhibitory antibody concentration against Delta variant of 20 AU/mL was deemed protective against within-host virus spreading after Delta challenge in naïve animals. Additionally, we pointed out the impact of the immunological background on the inhibitory functionality of antibodies, showing their stronger efficacy in case of hybrid immunity. Finally, counterfactual scenarios highlighted the role of memory B-cells to raise rapid and efficient antibody responses in immunized animals, as well as the major role of target-cell depletion and humoral responses in viral control in naïve and immunized animals, respectively.
Conclusions: This original modeling study identified a protective threshold relying on binding inhibitory antibodies generated after Delta infection or vaccination. Results were consistent regardless of vaccines. A broader description of B-cell responses and the role of T-cell responses in viral control might enhance our understanding of natural- and vaccine-induced protection. This modeling work can be applied to other preclinical platforms and infectious diseases, and could be extended to human infection for identification of CoP.
References:
[1] Alexandre M et al. (2022). Elife, 11, e75427
Reference: PAGE 34 (2026) Abstr 12248 [www.page-meeting.org/?abstract=12248]
Poster: Drug/Disease Modelling - Infection