Host Factors Modulate Nirmatrelvir-Ritonavir Efficacy in COVID-19 Patients: A Viral Dynamics Modeling Study
Liao, Y.; Wang, Y.; Wang, Y.; Ai, J.; Law, B. K.; Lim, D.; Zhou, J.; Wang, H.; Wu, Y.; Chia, P. Y.; Chua, H. K.; Chan, C. E. Z.; Schiffer, J. T.; Owens, K.; Esmaeili, S.; Cowling, B. J.; Cove, M. E.; Saito, H.; Wee, L. E.; Young, B. E.; Ng, T. M.; Chan, E. C. Y.; Ajelli, M.; Zhang, W.; Yu, H.; Ejima, K.
Show abstract
Antiviral therapies such as nirmatrelvir-ritonavir are widely used for COVID-19, yet their real-world effectiveness and sources of heterogeneity in treatment response remain incompletely understood. Here, we integrate longitudinal viral load data from a large cohort of SARS-CoV-2 BA.2-infected patients in Shanghai (n=48,243) with a mechanistic within-host viral dynamics model coupled to pharmacokinetic/pharmacodynamic principles to quantify in vivo antiviral efficacy. We estimate that nirmatrelvir-ritonavir reduces viral production by approximately 55% on average. Treatment response exhibits substantial heterogeneity, with higher efficacy observed in vaccinated individuals and reduced efficacy in older adults. Sensitivity analyses demonstrate that the vaccination effect is robust across model specifications, whereas age-related differences depend on assumptions about early viral kinetics, highlighting structural identifiability challenges when analyzing sparse real-world data. These findings provide a mechanistic interpretation of heterogeneous treatment effects and establish a generalizable framework for integrating real-world clinical data with within-host models to inform antiviral optimization and personalized treatment strategies.
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