Shengyuan Zhang 1, Joe Standing 1,2
1 Infection, Immunity and Inflammation, Great Ormond Street Institute of Child Health, University College London (London, United Kingdom), 2 Great Ormond Street Hospital for Children NHS Trust (London, United Kingdom)
Introduction: To evaluate antiviral effectiveness, the PANORAMIC trial, a large open-label platform study, was conducted in outpatients at increased risk of severe COVID-19, assessing molnupiravir and nirmatrelvir/ritonavir1. Although no significant reduction in hospitalisation or death was observed due to high vaccine derived protection, both treatments were associated with reductions in viral load2,3. However, the extent to which accelerated viral clearance translates into improved clinical outcomes remains unclear, raising questions about the clinical value of antivirals in this setting.
Previous studies have suggested that viral load at initial sampling is predictive of disease severity, and higher viral load has been associated with fewer post-COVID-19 symptoms4,5. However, variability in the time from infection may substantially influence these measurements. Transmission risk is also linked to viral load, particularly in household contacts, where transmission probability was estimated as 48% at viral loads exceeding 10^10 copies/mL6. These findings support viral load as a mechanistic link between antiviral effects and clinical outcomes, but observed time point measurements are limited. Viral dynamic modelling enables prediction of viral load trajectories and derivation of viral exposure metrics (e.g., area under the curve, AUC), providing a more robust and objective basis for linking viral kinetics to symptom resolution and transmission risk in open-label studies.
In this study, a mechanistic model in which viral load and spike antibody responses were jointly modelled was applied, enabling prediction of viral trajectories with or without treatment. The aim is to quantify the degree of viral load reduction required for faster symptom resolution and evaluate the relationship between viral load and household transmission risk.
Methods: Data from 1,226 participants enrolled in the PANORAMIC virology sub-studies were analysed, including 155 participants in the intensive sampling group. Viral load was measured daily on days 1–5 and day 14 (intensive group) or days 1, 5, and 14 (less intensive group); spike antibodies were collected on days 1, 5, and 14. All participants had Omicron infection and received 0–8 vaccine doses (median 3). Symptoms were self-reported daily.
A previously reported mechanistic model built on molnupiravir trial data was reconstructed in nlmixr2 and extended with additional clinical data from the same trial7. Antibody responses were modelled using a Gompertz function via modulating infected cell clearance. The time from infection to symptom onset was fixed at 3 days8. Viral clearance and infected cell productivity, were fixed based on prior literature9. Antiviral effects were incorporated on viral clearance as time-varying or exposure-driven Emax models using plasma or intracellular concentrations, with dosing based on individual records to account for adherence.
A stepwise covariate modelling approach was applied. Patient level covariates (age, sex, vaccination dose number, and comorbidities with >10% prevalence, including lung diseases, obesity, and hypertension) and viral subvariant were tested with inclusion guided by the Bayesian Information Criterion. Model fitting was performed using the FOCEi algorithm in nlmixr2 (version 5.0.0).
Model-based predictions of viral load were generated from the final model. Logistic regression was used to evaluate the association between model predicted viral load and viral culture positivity. Associations between viral exposure (viral load area under the curve, AUC) and household transmission risk were also assessed using logistic regression. Multivariable cox proportional hazards models were used to evaluate the effects of viral and antibody exposure, along with patient covariates and viral subvariant, on time to symptom resolution and sustained recovery using the survival package.
Results: A mechanistic model adequately described viral load and antibody dynamics across treatment arms was established. Antiviral treatment effects for both molnupiravir and nirmatrelvir/ritonavir were incorporated as time-varying covariates on viral production. Age (effect size: 2.87) was identified as a significant covariate on the antibody level associated with 90% of the maximal spike antibody effect, with increasing age associated with higher required antibody levels. Infection with the BA.2 subvariant (effect size: -0.687) was associated with lower maximum spike antibody levels. This effect may be confounded by differences in baseline characteristics, as a greater proportion of participants in the nirmatrelvir/ritonavir arm were infected with the BA.2 subvariant and had received a higher number of vaccine doses.
Model predicted viral load was significantly associated with viral culture positivity, with higher predicted viral loads corresponding to an increased probability of detecting replication competent virus (p<0.001). Higher viral exposure (viral load AUC over 28 days since symptom onset) was associated with an increased risk of household transmission. A one log10 increase in viral load AUC was associated with a 1.28-fold increase in the odds of household transmission (odd ratio, OR = 1.28, p<0.001).
Time-to-event analyses showed that higher viral exposure was associated with delayed symptom reduction(reduction of all symptoms by at least one grade; HR = 0.88, p = 0.03) and all symptom alleviation(hazard ratio, HR = 0.92, p = 0.03), regardless of treatment allocation. No significant association was observed between antibody exposure and symptom resolution. Among patient-level covariates, sex was identified as significant predictors of symptom reduction, symptom alleviation and sustained recovery, with higher hazards observed in males compared to females(HR = 1.38, P = 0.001; HR = 1.50, p = 0.08; HR = 1.63, p = 0.04). Age was a significant predictor of symptom alleviation only (HR = 1.13, p < 0.001). The age effect could also be confounded by the fact that patients with higher age were suggested to take more doses of vaccines. Lung disease was found significantly associated with reduced symptom and symptom alleviation(HR = 0.79, P = 0.001; HR = 0.72, p < 0.001). Subvariant XBB.1 was associated with the hazard of sustained recovery (HR = 1.28, p = 0.04) and BA.2 was associated with symptom alleviation (HR = 1.23, p = 0.02).
Conclusion: A mechanistic model integrating antiviral effects and host immune responses adequately characterised SARS-CoV-2 viral dynamics in a highly vaccinated outpatient population. Model derived viral load trajectories and exposure metrics, together with patient and pathogen level covariates, served as predictors of clinically relevant outcomes, including infectiousness, household transmission, symptom resolution, and sustained recovery, supporting model derived metrics as robust endpoints for antiviral evaluation.
References:
1. Gbinigie, O. 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 13, e069176 (2023).
2. Butler, C. C. et al. Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): An open-label, platform-adaptive randomised controlled trial. The Lancet 401, 281–293 (2023).
3. Standing, J. F. et al. Randomized controlled trial of molnupiravir SARS-CoV-2 viral and antibody response in at-risk adult outpatients. Nature Communications 15, 1652 (2024).
4. Ringlander, J. et al. Influence of viral load on severity and mortality in COVID-19. Infectious Diseases 57, 811–818 (2025).
5. Girón Pérez, D. A. et al. Post-COVID-19 Syndrome in Outpatients and Its Association with Viral Load. International Journal of Environmental Research and Public Health 19, 15145 (2022).
6. Marc, A. et al. Quantifying the relationship between SARS-CoV-2 viral load and infectiousness. eLife 10, e69302 (2021).
7. Nguyen, B. T. et al. A Viroimmunologic Model to Characterize the Antiviral Effect of Molnupiravir in Outpatients Infected With SARS-CoV-2: Implication for Treatment Duration. The Journal of Infectious Diseases jiaf158 (2025).doi:10.1093/infdis/jiaf158
8. Wu, Y. et al. Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Network Open 5, e2228008 (2022).
9. Néant, N. et al. Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort. Proceedings of the National Academy of Sciences 118, e2017962118 (2021).
Reference: PAGE 34 (2026) Abstr 12290 [www.page-meeting.org/?abstract=12290]
Poster: Drug/Disease Modelling - Other Topics