Maxime Beaulieu (1), Alexandre Gaymard (2,3), Clément Massonnaud (4), Nathan Peiffer-Smadja (1,5,6), Maude Bouscambert-Duchamp (2,3), Guislaine Carcelain (7,8), Guillaume Lingas (1), France Mentré (1,4), Florence Ader (9,10), Maya Hites (11), Pascal Poignard (12,13,14), Jérémie Guedj (1) on behalf the DisCoVeRy Study group
(1) Université Paris Cité, IAME, INSERM, F-75018 Paris, France. (2) Hospices Civils de Lyon, Laboratoire de Virologie, Institut des Agents Infectieux de Lyon, Centre National de Référence des virus respiratoires France Sud, F-69317, Lyon, France. (3) Université Claude Bernard Lyon 1, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, F69372, Lyon, France. (4) AP-HP, Hôpital Bichat, Département d’Épidémiologie, Biostatistique et Recherche Clinique, F75018 Paris, France. (5) AP-HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France. (6) National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK. (7) Immunology Department, Robert Debré Hospital, Assistance Publique Hôpitaux de Paris, Paris, France. (8) Université Paris Cité, INSERM U976, Paris, France. (9) Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Département des Maladies Infectieuses et Tropicales, F-69004, Lyon, France. (10) Université Claude Bernard Lyon 1, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, F-69372, Lyon, France. (11) Clinic of Infectious Diseases, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles, Brussels, Belgium. (12) Groupe de Recherche en Infectiologie Clinique CIC-1406, Inserm - CHUGA - Université Grenoble Alpes, Grenoble, France. (13) Univ. Grenoble Alpes, CEA, CNRS, Institut de Biologie Structurale (IBS), Grenoble, France. (14) Laboratoire de Virologie, Center Hospitalier Universitaire Grenoble-Alpes, Grenoble, France.
Introduction. Antiviral treatments for SARS-CoV-2 have been shown to reduce the risk of hospitalization in outpatients [1,2]. However, the use of antiviral therapy in patients hospitalized with COVID-19 remains controversial [3].
Evusheld (AZD7442), a cocktail of two human monoclonal antibodies (mAbs), tixagevimab and cilgavimab, reduces the risk of hospitalization or death by 50% if administered within the first week after the symptom onset [4]. The clinical efficacy of this mAb was evaluated in hospitalized patients in 2021-2022 in the randomized, placebo-controlled, phase III, DisCoVeRy clinical trial (NCT04315948) [5]. Evusheld administration led to a large and rapid increase in the serum neutralization activity but no significant difference in clinical or virological outcomes was found [5]. However, the pre-specified statistical analysis was largely underpowered due to the premature interruption of inclusions caused by the emergence of Omicron variants that were not sensitive to Evusheld.
In order to gain more insights into the antiviral efficacy of Evusheld, which is likely a prerequisite to clinical efficacy [6], we here aimed to analyze by modeling the viral and immunological dynamics in the Discovery trial.
Methods. We analyzed the evolution of both the nasopharyngeal viral load and the serum neutralization activity against the variant of infection in 199 hospitalized patients (109 treated with Evusheld, 90 treated with placebo) infected with the SARS-CoV-2 virus and included in the DisCoVeRy trial.
Using a mechanistic mathematical model, we simultaneously reconstructed the trajectories of individual viral kinetics and how they are modulated by the increase in serum neutralization activity during Evusheld treatment. We modeled the progressive increase in the neutralization activity, from the time of infection, using an adapted sigmoid Gompertz model [7]. We incorporated the individual neutralization activity level in a viral dynamic model with an eclipse phase and a refractory compartment to describe the viral load dynamics from time to infection to viral clearance [8]. We accounted for inter-individual variability with a non-linear mixed-effects framework and estimated the parameter estimates with Monolix [9].
The impact of Evusheld on neutralization evolution and viral load dynamics was evaluated through simulations of 5,000 in silico Evusheld-treated profiles using the estimated parameters. Then, for each virtual patient, we calculated the viral kinetics with and without treatment, and we calculated for each individual the gain in viral load decline as well as in the time to reach undetectable viral load (TTU).
Results. Our model identified that the neutralization activity was associated with the elimination rate of viral particles. Reflecting the variant-dependent neutralization activity of Evusheld, the antiviral activity of Evusheld at hours 12-post-treatment initiation measured in ED50 was equal to 6,369 (95% PI: 1650-25,165), 467 (95% PI: 108-7,244), and 5,143 (95% PI: 1,306-18,453) in pre-Omicron, Omicron BA.1, and Omicron BA.2 treated patients, respectively, as compared to 36 (95% PI: 1-4,921), 72 (95% PI: 1-6,918), and 113 (95% PI: 1-10,437) in untreated patients. These higher levels of neutralization activity induced by Evusheld led to a more rapid viral decline. The model predicted that Evusheld reduced the median TTU compared to placebo-treated patients by more than 5 days in patients infected by pre-Omicron (median: 5.9; 95% PI: 0.8-24.2) or Omicron BA.2 (median: 5.4; 95% PI: 0.9-25.0), respectively. The effect was more modest in patients infected by the Omicron BA.1 variant, reducing the median TTU by 2 days (median: 2.2; 95% PI: 0.2-15.5).
Conclusions. Mathematical modeling is critical to identify an antiviral treatment effect in COVID-19 hospitalized patients. Our results show that antiviral treatment can reduce the time to SARS-CoV-2 viral clearance in hospitalized patients and suggest that evaluation of antiviral treatment in this population is warranted.
References:
[1] Najjar-Debbiny R, Gronich N, Weber G, et al. Effectiveness of Paxlovid in Reducing Severe Coronavirus Disease 2019 and Mortality in High-Risk Patients. Clin Infect Dis. 2023; 76(3):e342–e349.
[2] Abani O, Abbas A, Abbas F, et al. Casirivimab and imdevimab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. The Lancet. 2022; 399(10325):665–676.
[3] Lingas G, Néant N, Gaymard A, et al. Effect of remdesivir on viral dynamics in COVID-19 hospitalized patients: a modelling analysis of the randomized, controlled, open-label DisCoVeRy trial. J Antimicrob Chemother. 2022; 77(5):1404–1412.
[4] Hobbs FDR, Montgomery H, Padilla F, et al. Outpatient Treatment with AZD7442 (Tixagevimab/Cilgavimab) Prevented COVID-19 Hospitalizations over 6 Months and Reduced Symptom Progression in the TACKLE Randomized Trial. Infect Dis Ther. 2023; 12(9):2269–2287.
[5] Hites et al. Tixagevimab-cilgavimab (AZD7442) for the treatment of patients hospitalized with COVID-19 (DisCoVeRy): a phase 3, randomized, double-blind, placebo-controlled trial. J Infect. 2024; .
[6] Parienti JJ, Grooth HJ de. Clinical relevance of nasopharyngeal SARS-CoV-2 viral load reduction in outpatients with COVID-19. J Antimicrob Chemother. 2022; 77(7):2038–2039.
[7] Tjørve KMC, Tjørve E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. Merks RMH, editor. PLOS ONE. 2017; 12(6):e0178691.
[8] Phan T, Zitzmann C, Chew KW, et al. Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody [Internet]. Immunology; 2023 Sep. Available from: http://biorxiv.org/lookup/doi/10.1101/2023.09.14.557679
[9] Lixoft. Antony, France. Monolix version 2018R2. 2018; .
Reference: PAGE 32 (2024) Abstr 10854 [www.page-meeting.org/?abstract=10854]
Poster: Drug/Disease Modelling - Other Topics