Modeling the Impact of Ensitrelvir on SARS-CoV-2 Dynamics and Its Application for Assessment of Transmission Mitigation of Patients with COVID-19.

IF 4.7 3区 医学 Q1 INFECTIOUS DISEASES
Infectious Diseases and Therapy Pub Date : 2024-11-01 Epub Date: 2024-10-07 DOI:10.1007/s40121-024-01046-6
Daichi Yamaguchi, Masaya M Saito, Ayano Hata, Ryosuke Shimizu, Shogo Miyazawa, Takamichi Baba, Ryuji Kubota, Yoshitake Kitanishi
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Abstract

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.

建立恩西特韦对 SARS-CoV-2 动态影响的模型及其在评估 COVID-19 患者传播减缓中的应用。
引言数学模型可以定量地了解病毒的动态变化和药物的减毒效果,并能模拟各种情况下的动态变化。在本研究中,我们建立了恩西特韦的药物效应模型来描述病毒减毒效应。利用该模型,我们还估算了使用恩西特韦治疗对减少日本感染者人数的影响:方法:根据严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)的病毒动态模型,以及使用 2/3 期研究中 1447 名冠状病毒病 2019(COVID-19)患者的 10,477 份病毒载量样本建立的安斯瑞韦群体药代动力学模型,建立了安斯瑞韦的药物效应模型。假设药物对SARS-CoV-2的作用会促进病毒清除,这取决于游离血浆浓度。我们利用包括传播缓解在内的易感-感染-康复-易感(SIRS)模型,估算了恩西特韦治疗对减少日本人群感染人数的影响:结果:使用Emax模型,根据血浆中恩替雷韦的浓度,将恩替雷韦的病毒减少效应描述为促进病毒清除。最大减毒效果取决于从症状出现到接受治疗的时间。如果在症状出现后12-24小时内开始治疗,则可观察到最大的传播缓解效果:结论:可以根据病毒的动态变化来描述恩西特韦减少病毒感染的效果,并利用药物效应模型来估算病毒的动态变化。此外,还可以根据动态估计药物对人群传播的影响。因此,可以在各种条件下进行模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Infectious Diseases and Therapy
Infectious Diseases and Therapy Medicine-Microbiology (medical)
CiteScore
8.60
自引率
1.90%
发文量
136
审稿时长
6 weeks
期刊介绍: Infectious Diseases and Therapy is an international, open access, peer-reviewed, rapid publication journal dedicated to the publication of high-quality clinical (all phases), observational, real-world, and health outcomes research around the discovery, development, and use of infectious disease therapies and interventions, including vaccines and devices. Studies relating to diagnostic products and diagnosis, pharmacoeconomics, public health, epidemiology, quality of life, and patient care, management, and education are also encouraged. Areas of focus include, but are not limited to, bacterial and fungal infections, viral infections (including HIV/AIDS and hepatitis), parasitological diseases, tuberculosis and other mycobacterial diseases, vaccinations and other interventions, and drug-resistance, chronic infections, epidemiology and tropical, emergent, pediatric, dermal and sexually-transmitted diseases.
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