Yamaguchi Daichi, Saito Masaya M, Hata Ayano, Shimizu Ryosuke, Miyazawa Shogo, Baba Takamichi, Kubota Ryuji, Kitanishi Yoshitake
Clinical Pharmacology & Pharmacokinetics, Shionogi & Co., Ltd., 3-13, Imabashi 3-Chome, Chuo-ku, Osaka, 541-0042, Japan.
Department of Information Security, Faculty of Information Systems, University of Nagasaki, 1-1-1, Manabino, Nagayocho, Nishisonogigun, Nagasaki, 851-2195, Japan.
Infect Dis Ther. 2024 Nov;13(11):2377-2393. doi: 10.1007/s40121-024-01046-6. Epub 2024 Oct 7.
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.
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.
The viral reduction effect of ensitrelvir was characterized as a promotion of viral clearance depending on the plasma ensitrelvir concentrations using the E 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.
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.
数学建模能够提供对病毒动力学以及药物的病毒抑制效果的定量理解,并能够模拟各种情况下的动力学过程。在本研究中,开发了恩赛特韦的药物效应模型以描述其病毒抑制效果。利用该模型,我们还估计了在日本人群水平上,恩赛特韦治疗对感染患者数量减少的影响。
基于严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的病毒动力学模型和恩赛特韦的群体药代动力学模型,利用一项2/3期研究中1447例2019冠状病毒病(COVID-19)患者的10477份病毒载量样本,开发了恩赛特韦的药物效应模型。假设药物对SARS-CoV-2的作用取决于游离血浆浓度,促进病毒清除。我们使用包括传播缓解的易感-感染-康复-易感(SIRS)模型,估计了恩赛特韦治疗对日本人群水平上感染患者数量减少的影响。
使用E模型,恩赛特韦的病毒抑制效应表现为取决于血浆恩赛特韦浓度的病毒清除促进作用。最大抑制效应被认为取决于从症状出现到治疗的时间。当在症状出现后12 - 24小时内开始治疗时,观察到最大传播缓解效应,并且在症状出现后尽快给予恩赛特韦可以减少二次感染。
恩赛特韦的病毒抑制作用可根据病毒动力学进行表征,并且可以使用药物效应模型估计其动力学过程。此外,可以估计基于该动力学的药物对人群水平传播的影响。因此,可以针对各种情况进行模拟。