III-36 Aurélien MARC

Characterization of SARS-CoV-2 variants of concern viral dynamics in non-human primates

Aurélien Marc1, Romain Marlin2, Marion Kerioui1, Cécile Hérate2, Pauline Maisonnasse2, Julie Bertrand1, Mélanie Prague3,4, Flora Donati5, Vanessa Contreras2, Sylvie Behillil6, Sylvie Van Der Werf 6,7, Roger Le Grand2, Jérémie Guedj1

1 Université de Paris, IAME, INSERM F-75018 Paris, France 2 Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France 3 Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, University of Bordeaux, Bordeaux, France 4 Vaccine Research Institute, Créteil, France 5 Institut Pasteur, Université de Paris, CNRS UMR 3528, Structural Bioinformatics Unit, Paris, France. 6 National Reference Center for Respiratory Viruses, Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris, France. 7 Molecular Genetics of RNA Viruses Unit, Institut Pasteur, UMR3569, CNRS, Université de Paris, 28 rue du Docteur Roux, 75015 Paris, France.

Introduction

Since the beginning of the pandemic several SARS-CoV-2 variants have emerged and although most of them vanished quickly, some have caused epidemic rebound in many countries and have spread worldwide. The variants of concern (VOC), namely alpha, beta, gamma, delta and omicron, have acquired specific mutations enhancing their infectious capacities and outcompete other variants locally or even globally (1). Even though some studies focus on a small number of very detailed patients (2,3), as most studies rely on large cross-sectional analysis with few data points per patient(4–6), it is difficult to fully characterize the dynamic of the infection in humans. Non-human primate (NHP) models offer a unique opportunity to fully describe a natural infection of SARS-CoV-2 (7–9).

Here we used data generated in cynomolgus macaques infected with strains of variants of concerns (beta, gamma and delta) to develop a mathematical model of SARS-CoV-2 infection and identify key differences in both viral kinetics parameters and infectiousness between SARS-CoV-2 variants of concerns.

Objectives

  • – Characterize viral dynamics of SARS-CoV-2 variants of concern in non-human primates.
  • – Simulate a human infection and quantify the impact of variants on viral load and infectiousness

Methods

Non-human primate data

Our study includes 69 NHP infected with doses ranging from 7×104 to 106 PFU of different SARS-CoV-2 strains. Animals are infected via both nasopharyngeal and intratracheal route with 10% of the initial volume administered in the nose and 90% in the trachea. The study is composed of 4 groups, each infected with a different SARS-CoV-2 strain: 44 Historical, 9 Bêta, 5 Gamma and 11 Delta. For each group both genomic RNA and subgenomic RNA were quantified using PCR. For 32 animals (13 Historical, 3 Bêta, 5 Gamma and 11 Delta) infectious titers were measured at 2 time points, early (2 or 3 days post infection) and late (5 or 7 days post infection).

Viral kinetics model

We used a target cell model to characterize the nasopharyngeal viral load of infected animals. Unlike previous models (8), we described conjointly the genomic RNA, subgenomic RNA and infectious titers using the total viral dynamics (infectious viruses and non-infectious viruses), the productive infected cells and the infectious titers (infectious viruses) respectively. Several models were tested, the following results are obtained using our best model accounting for an exponential decrease in the ration infectious particles. We performed an iterative covariate selection using both likelihood-ratio test and Wald test to identify potential variant specific effect on population parameters. Finally, we simulated a human infection considering both the uncertainty in the estimation and the inter-individual variability. Parameter estimation was performed in a non-linear mixed effect framework and the likelihood was maximized using the SAEM algorithm implemented in Monolix software (10,11).

 

Results

The model well replicated all 3 three biomarkers of nasopharyngeal viral load. We estimated variant specific covariate effect on certain parameters compared to the historical strain. For the beta strain, the infectivity parameter is increased by 10-fold (95% prediction interval (PI) [5-23]) and the loss rate of infected cells is decreased by a factor of 0.7 [0.6-0.9]. For the gamma strain, the infectivity parameter is decreased by a factor 0.3 [0.2- 0.6]. Finally, for the delta strain, the viral production parameter is increased by 7-fold [2-17].

Interestingly, a temporal effect on the ratio of infectious particle was found on both the historical and the beta strain. In order to better characterize this effect, we simulated a human infection by assuming infection with 10 viral particle (12).

The mean time to viral load clearance was found to 12 days for the historical strain (12) and to 13, 18 and 14 days in beta, gamma and delta strains respectively. We also observe a relative increase compared to the historical variant in the area under infectious titers curve of 18%, 40% and 91% for the beta, gamma and delta strains respectively.

 

Conclusion

  • – We characterized the infection of SARS-CoV-2 variants of concern in non-human primates
  • – Found an increase in the area under infectious titers curve of 90% for the delta strain
  • – Identified a temporal factor in the ratio of infectious viruses in the historical and beta strain

References:

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  3. Kissler SM, Fauver JR, Mack C, Tai CG, Breban MI, Watkins AE, et al. Viral Dynamics of SARS-CoV-2 Variants in Vaccinated and Unvaccinated Persons. New England Journal of Medicine. 23 déc 2021;385(26):2489-91.
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  9. Maisonnasse P, Aldon Y, Marc A, Marlin R, Dereuddre-Bosquet N, Kuzmina NA, et al. COVA1-18 neutralizing antibody protects against SARS-CoV-2 in three preclinical models. Nat Commun. 20 oct 2021;12(1):6097.
  10. Comets E, Lavenu A, Lavielle M. Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm. Journal of Statistical Software. 29 août 2017;80(1):1-41.
  11. Monolix [Internet]. Lixoft. [cité 24 mai 2020]. Disponible sur: http://lixoft.com/products/monolix/
  12. Killingley B, Mann AJ, Kalinova M, Boyers A, Goonawardane N, Zhou J, et al. Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults. Nat Med. 31 mars 2022;1-11.

Reference: PAGE 30 (2022) Abstr 10222 [www.page-meeting.org/?abstract=10222]

Poster: Drug/Disease Modelling - Infection