Shengyuan Zhang1, David Lowe2,3, Sofia Morfopoulou1,4, Judith Breuer1,5, Joseph Standing1,5
1Infection, Immunity and Inflammation, Great Ormond Street Institute of Child Health, University College London, 2Institute of Immunity and Transplantation, University College London, 3Department of Clinical Immunology, Royal Free London NHS Foundation Trust, 4Section for Paediatrics, Department of Infectious Diseases, Faculty of Medicine, Imperial College London, 5Great Ormond Street Hospital for Children NHS Trust
Introduction The COVID-19 pandemic has been marked by the continuous evolution of SARS-CoV-2, with emerging variants exhibiting diverse transmissibility and virulence. The variant of concern, Omicron, is characterised by its lower virulence but better ability to immune escape1-3. With such a change, the focus shifted to the elderly and those with chronic conditions that make them at high risk of developing severe disease4. Vaccine boosters are recommended for this population to keep the effectiveness5. These boosters lead to higher antibody levels, which helps with virus clearance6. Under such conditions, the antiviral response becomes less obvious in clinical settings. To accurately assess the effectiveness of antivirals, a thorough understanding of these high antibody levels and subsequent viral load dynamics become important. Mathematical modelling of viral load and antibody dynamics offers a powerful approach to elucidate such relationship6,7. Objectives To develop a mathematical model that integrates viral load and antibody kinetics to elucidate their dynamic relationship during SARS-CoV-2 infection. Methods Data from 636 participants enrolled in the PANORAMIC virology sub-studies (molnupiravir and nirmatrelvir-ritonavir arms) received only usual care without any antivirals were analysed, including 79 patients allocated to the intensive sampling group6. Viral load samples were collected daily from days 1 to 5 and on day 14 for intensive sampling group, while samples from less intensive group were collected on days 1, 5, and 14. Antibody samples were collected at days 1, 5 and 14. All participants were infected with the Omicron variant and had received between 0 and 6 vaccine doses (median = 3). A target cell-limited model, with and without an eclipse phase, was fitted to the viral load data. Innate immune responses were incorporated via a refractory cell compartment. Adaptive immune responses were modelled using linear or Gompertz functions to describe changes in antibody levels. The impact of antibodies on viral dynamics was tested by incorporating their effect on either the virus infection rate (neutralising viruses) or the clearance rate of infected cells (antibody-dependent cell-mediated cytotoxicity, ADCC). The time from infection to symptom onset was fixed at 3 days, consistent with reported values for Omicron8. Viral clearance rate, infected cell production rate, and the proportion of infectious virus were fixed based on a previously published study9. Model performance was evaluated with numerical (BIC and AIC) and graphical (goodness-of-fit plots and visual predictive checks) methods. Model fitting was performed using the FOCEi algorithm in nlmixr2 (version 3.0.1), and visual predictive checks were generated using the tidyvpc package. Results A target cell-limited model incorporating an eclipse phase and a refractory cell compartment provided the best fit to the viral load data. Antibody dynamics were best described by a Gompertz equation and then incorporated to the viral model via increasing infected cell clearance rate. The final model demonstrated observed viral load and antibody kinetics well. Conclusions In conclusion, we built a mathematical model that captures the dynamic interplay between viral load and antibody responses in SARS-CoV-2 infected high risk outpatients. This provides a foundation for investigations into actual effectiveness of antiviral therapies in clinical setting.
1.Senevirathne TH, Wekking D, Swain JWR, Solinas C, De Silva P. COVID-19: From emerging variants to vaccination. Cytokine & Growth Factor Reviews. 2024;76:127-141. doi:10.1016/j.cytogfr.2023.11.005 2.Araf Y, Akter F, Tang Y dong, et al. Omicron variant of SARS-CoV-2: Genomics, transmissibility, and responses to current COVID-19 vaccines. Journal of Medical Virology. 2022;94(5):1825-1832. doi:10.1002/jmv.27588 3.Carabelli AM, Peacock TP, Thorne LG, et al. SARS-CoV-2 variant biology: immune escape, transmission and fitness. Nat Rev Microbiol. 2023;21(3):162-177. doi:10.1038/s41579-022-00841-7 4.Gbinigie O, Ogburn E, Allen J, et al. Platform adaptive trial of novel antivirals for early treatment of COVID-19 In the community (PANORAMIC): protocol for a randomised, controlled, open-label, adaptive platform trial of community novel antiviral treatment of COVID-19 in people at increased risk of more severe disease. BMJ Open. 2023;13(8):e069176. doi:10.1136/bmjopen-2022-069176 5.Andrews N, Stowe J, Kirsebom F, et al. Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. New England Journal of Medicine. 2022;386(16):1532-1546. doi:10.1056/NEJMoa2119451 6.Standing JF, Buggiotti L, Guerra-Assuncao JA, et al. Randomized controlled trial of molnupiravir SARS-CoV-2 viral and antibody response in at-risk adult outpatients. Nat Commun. 2024;15(1):1652. doi:10.1038/s41467-024-45641-0 7.Esmaeili S, Owens K, Wagoner J, Polyak SJ, White JM, Schiffer JT. A unifying model to explain high nirmatrelvir therapeutic efficacy against SARS-CoV-2, despite low post-exposure prophylaxis efficacy and frequent viral rebound. Published online August 24, 2023. doi:10.1101/2023.08.23.23294505 8.Wu Y, Kang L, Guo Z, Liu J, Liu M, Liang W. Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Network Open. 2022;5(8):e2228008. doi:10.1001/jamanetworkopen.2022.28008 9.Néant N, Lingas G, Le Hingrat Q, et al. Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort. Proc Natl Acad Sci USA. 2021;118(8):e2017962118. doi:10.1073/pnas.2017962118
Reference: PAGE 33 (2025) Abstr 11767 [www.page-meeting.org/?abstract=11767]
Poster: Drug/Disease Modelling - Infection