Presentation Information
[SS09-07]The estimation of the transmission mitigation in patients with COVID-19 by ensitrelvir treatment based on SARS-CoV-2 viral dynamic model
*Daichi Yamaguchi1 (1. Shionogi & Co., Ltd. (Japan))
Keywords:
Modeling and simulation,Viral dynamic model,Transmission mitigation,SARS-CoV-2,Ensitrelvir
Mathematical modeling and simulation (M&S) provides additional information for new drugs which have limited observed data from nonclinical or clinical studies. In this presentation, I will introduce M&S studies for utilization in the development of ensitrelvir, anti-SARS-CoV-2 drug. The mathematical model for SARS-CoV-2 dynamics in human was developed [1] and the impact of ensitrelvir treatment for the reduction in the number of COVID-19 patients was estimated [2] based on the viral reduction effect of ensitrelvir including those pharmacokinetic (PK) profiles [3].
First, the viral dynamic model for SARS-CoV-2 was developed using data from placebo cohort in a clinical study for ensitrelvir. A target cell-limited model with immune function was refined to characterize both the dynamics of viral RNA and viral titer. Vaccination status was identified as a significant covariate affecting viral dynamics, and this was incorporated into the model. Second, the population PK model of ensitrelvir was developed using plasma concentration data in clinical studies. A two-compartment model with a first-order absorption model was selected and influencing factors (body weight and food conditions) were incorporated in this model. Third, a drug effect model of ensitrelvir was constructed by integrating the viral dynamic model and the population PK model. it was assumed that the antiviral effect of ensitrelvir was promoting viral clearance depending on the plasma concentrations predicted by the population PK model. The relationship between the time from symptom onset to the start of treatment and the maximum viral reduction effect was characterized using a linear model in this drug effect model.
The relative reduction in the area under the curve for viral RNA of patients with the ensitrelvir treatment compared to patients without the treatment was simulated using developed models, and it was translated to the transmission-mitigation effect. Based on the susceptible-infectious-recovered-susceptible (SIRS) model, commonly used to express the epidemic dynamics in infectious diseases, we constructed a model to predict the potential impact of ensitrelvir treatment on reducing patients counts. Various scenario analyses (immune duration and treatment rate) were performed to forecast the number of patients with COVID-19 infection over 20 years in Japan.
In these studies, M&S was used to connect the micro world (viral dynamics in human) to the macro world (population level impact), and to predict the future impact of ensitrelvir treatment for healthcare in Japan.
[1] Yamaguchi D, et al. CPT Pharmacometrics Syst Pharmacol. 2024; 13: 1354-1365. [2] Yamaguchi D, et al. Infect Dis Ther. 2024; 13: 2377-2393. [3] Ishibashi T, et al. Clin Pharmacokinet. 2024; 63: 1723-1734.
First, the viral dynamic model for SARS-CoV-2 was developed using data from placebo cohort in a clinical study for ensitrelvir. A target cell-limited model with immune function was refined to characterize both the dynamics of viral RNA and viral titer. Vaccination status was identified as a significant covariate affecting viral dynamics, and this was incorporated into the model. Second, the population PK model of ensitrelvir was developed using plasma concentration data in clinical studies. A two-compartment model with a first-order absorption model was selected and influencing factors (body weight and food conditions) were incorporated in this model. Third, a drug effect model of ensitrelvir was constructed by integrating the viral dynamic model and the population PK model. it was assumed that the antiviral effect of ensitrelvir was promoting viral clearance depending on the plasma concentrations predicted by the population PK model. The relationship between the time from symptom onset to the start of treatment and the maximum viral reduction effect was characterized using a linear model in this drug effect model.
The relative reduction in the area under the curve for viral RNA of patients with the ensitrelvir treatment compared to patients without the treatment was simulated using developed models, and it was translated to the transmission-mitigation effect. Based on the susceptible-infectious-recovered-susceptible (SIRS) model, commonly used to express the epidemic dynamics in infectious diseases, we constructed a model to predict the potential impact of ensitrelvir treatment on reducing patients counts. Various scenario analyses (immune duration and treatment rate) were performed to forecast the number of patients with COVID-19 infection over 20 years in Japan.
In these studies, M&S was used to connect the micro world (viral dynamics in human) to the macro world (population level impact), and to predict the future impact of ensitrelvir treatment for healthcare in Japan.
[1] Yamaguchi D, et al. CPT Pharmacometrics Syst Pharmacol. 2024; 13: 1354-1365. [2] Yamaguchi D, et al. Infect Dis Ther. 2024; 13: 2377-2393. [3] Ishibashi T, et al. Clin Pharmacokinet. 2024; 63: 1723-1734.