Aurélien Marc (1), Marion Kerioui (1), Charles Margossian (2), Julie Bertrand (1), Pauline Maisonnasse (3), Yoann Aldon (4), Rogier W Sanders (4), Marit Van Gils (4), Roger Le Grand (3), Jérémie Guedj (1)
(1) Université de Paris, IAME, INSERM, F-75018 Paris, France, (2) Columbia University, New York, USA, (3) 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, (4) Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, 1105 AZ, Amsterdam, The Netherlands
Introduction: After more than a year of an unprecedented pandemic, it remains critical to find effective anti-viral strategies against SARS-CoV-2 infections. As several monoclonal antibodies have already shown good efficacy as treatment for SARS-CoV-2 infection (1–3), it is crucial to better understand the pharmacokinetics (PK) and pharmacodynamics (PD) of such antibodies. Recently a pre-clinical study conducted in Non-Human Primates (NHP) has shown the new monoclonal antibody COVA1-18 to have great effectiveness in reducing SARS-CoV-2 viral load in nasopharynx and trachea (4). Using the data of this study, we developed a model of viral kinetics, characterized the PK-PD of COVA1-18 and estimated its effectiveness.
We used Bayesian modelling in Stan to take advantage of all available information from previous experiments (5). As COVA1-18 efficiently blocks the entry of SARS-CoV-2 inside the cell, we should be able to estimate previously non-identifiable viral kinetics parameter such has SARS-CoV-2 in vivo clearance rate.
Objectives:
– To develop a model of SARS-CoV-2 viral dynamics and characterize the PK-PD of a new monoclonal antibody.
– To estimate COVA1-18 effectiveness is NHPs
– To provide an estimation of SARS-CoV-2 in vivo clearance rate.
Methods: NHP Data
Treated and control cynomolgus macaques (N = 5 per group) were challenged on day 0 with 106 PFU of SARS-CoV-2 via combined intranasal and intratracheal routes. Treated animals received a dose of 10 mg.kg-1 of COVA1-18 24 h prior to viral challenge. Both genomic (n = 7) and subgenomic (n = 3) RNA samples were performed and COVA1-18 concentrations (n varying from 5 to 8) were measured until day 28 post-infection (4).
Viral kinetics model
We used a target cell model to describe the viral kinetics in both nasopharyngeal and tracheal compartments. We used a linear PK model to describe COVA1-18 concentrations and an Emax model to characterize the effect in blocking viral entry into target cells. Unlike previous models (5), we used the subgenomic data as a proxy of the number of infected cells. We implemented this viral dynamic model in Stan and we used previously gathered information as prior knowledge. We used normal and lognormal distribution as priors with estimates and standard errors from (5) as mean and standard deviations.
Results: The Stan estimation was run with 3000 iterations and 4 chains. The estimation showed good convergence with Rhats close to 1 and good efficiency with effective sample size > 2000. The model well replicated data in both nasopharynx and trachea, as well as the COVA1-18 pharmacokinetics. We estimated a half-life for COVA1-18 of 12 days, consistent with other monoclonal antibodies (6). Among control animals, the mean time to viral peak was 1.3- and 1-days post infection in nasopharynx and trachea respectively. Viral loads of treated animals fell under undetectable levels in 2.8- and 2.2-days post-infection in nasopharynx and trachea respectively. Then, we found that our model predicted a 1 log difference between genomic and subgenomic RNA, as observed in the data. Given the drug plasma concentration, we estimated an efficacy of in the nasopharynx and in the trachea.
Finally, as COVA1-18 efficiently blocks the entry of the virus inside cells, we are able to estimate otherwise non-identifiable parameters, such as SARS-CoV-2 clearance rate. We estimated the in vivo clearance rate of SARS-CoV-2 at translating to a half-life of 1.8 hours (). When using different priors distributions, centered around 5 and 20 d-1, respectively, the posterior values were equal to 7 and ), corresponding to a half-life of 2.2 and 1.1 hours.
Conclusion:
– We estimate an efficacy of COVA1-18 of 97.7% and 99.9% in the nasopharynx and the trachea respectively.
– The SARS-CoV-2 half-life is estimated to 1.8 hours ranging from 1.1 hours to 2.2 hours.
This high level of antiviral efficacy makes COVA1-18 a relevant clinical candidate for treatment, including post-exposure prophylaxis (7). The model developed could be relevant to analyse the viral dynamics with other mAbs that have already received a compassionate authorization of use or are currently in phase 3 of clinical trials.
References:
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Reference: PAGE 29 (2021) Abstr 9726 [www.page-meeting.org/?abstract=9726]
Poster: Drug/Disease Modelling - COVID-19