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Modeling the Impact of Ensitrelvir on SARS-CoV-2 Dynamics and Its Application for Assessment of Transmission
Daichi Yamaguchi1, Masaya M Saito2, Ayano Hata3
1Clinical Pharmacology & Pharmacokinetics, Shionogi & Co., Ltd., 3-13, Imabashi 3-Chome, Chuo-ku, Osaka, 541-0042, Japan. daichi.yamaguchi@shionogi.co.jp.
Introduction:
Mathematical modeling can provide quantitative understanding of the viral dynamics and viral reduction effects of drugs and enable simulations of the dynamics in various scenarios. In this study, a drug effect model of ensitrelvir was developed to describe the viral reduction effect. Using the model, we also estimated the impact of treatment with ensitrelvir on the reduction in the number of infected patients at the population level in Japan.
Methods:
The drug effect model of ensitrelvir was developed based on a viral dynamic model for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a population pharmacokinetic model of ensitrelvir using 10,477 samples of viral load from 1447 patients with coronavirus disease 2019 (COVID-19) in a phase 2/3 study. It was assumed that the drug effect on SARS-CoV-2 promoted the viral clearance depending on the free plasma concentrations. We estimated the impact of ensitrelvir treatment on the reduction in the number of infected patients at the population level in Japan using the susceptible-infectious-recovered-susceptible (SIRS) model including transmission mitigation.
Results:
The viral reduction effect of ensitrelvir was characterized as a promotion of viral clearance depending on the plasma ensitrelvir concentrations using the Emax model. The maximum reduction effect was considered to depend on the time from symptom onset to treatment. The maximum transmission mitigation effect was observed when treatment was initiated within 12-24 h of symptom onset, and secondary infections could be reduced by administering ensitrelvir as soon as possible after symptom onset.
Conclusion:
The viral reduction by ensitrelvir could be characterized based on the viral dynamics, and the dynamics could be estimated using the drug effect model. Furthermore, the drug effect on population level transmission based on the dynamics could be estimated. Thus, the simulation could be conducted for various conditions.
