Zhang Runcong, Fan Jing, Han Lu, Mao Juehui, Sun Liang, Yu Yuetian, Fan Weibin, Xie Jiao, Lin Bin, Lin Nengming
Department of Pharmacy, Changxing People's Hospital, Changxing, People's Republic of China.
Key Laboratory of Intelligent Pharmacy and Individualized Therapy of Huzhou, Changxing, People's Republic of China.
Drug Des Devel Ther. 2024 Dec 2;18:5517-5527. doi: 10.2147/DDDT.S479561. eCollection 2024.
Nirmatrelvir/ritonavir (N/R) is the first drug to receive emergency authorization for the treatment of COVID-19 infection. We aimed to develop a population pharmacokinetic (PopPK) model to evaluate the effects of potential covariates and explore dosing regimen.
Sparse data of serum concentrations of N/R were obtained from 129 patients with COVID-19 infection receiving oral 300/100 mg N/R twice daily for 5 days. Plasma samples were assayed using ultra-high-performance liquid chromatography-tandem mass spectrometry. The PopPK model was developed using a nonlinear mixed effects approach utilizing the NONMEM 7.4 software. Monte Carlo simulation was conducted to optimize the dosage regimen.
A one-compartment model with first-order absorption and first-order elimination provided the best fit for the data. Allometric scaling of parameters on creatinine clearance (CrCl) and body weight were identified as covariates that significantly influenced exposure-efficacy after oral administration of nirmatrelvir. Monte Carlo simulation using the final model generated concentration-time profiles for virtual patients (1,000 per group) with varying renal functions and body weight. Furthermore, we developed a web-based dashboard to visualize the dynamic changes in nirmatrelvir concentration and provide individualized dosage regimens.
This study showed that dosing regimen optimization of nirmatrelvir should be based on CrCl and body weight. Moreover, a web-based dashboard has been developed to facilitate individualized pharmacotherapy.
奈玛特韦/利托那韦（N/R）是首个获得紧急授权用于治疗新冠病毒感染的药物。我们旨在建立一个群体药代动力学（PopPK）模型，以评估潜在协变量的影响并探索给药方案。
从129例新冠病毒感染患者中获取N/R血清浓度的稀疏数据，这些患者接受口服300/100mg N/R，每日两次，共5天。血浆样本采用超高效液相色谱-串联质谱法进行检测。使用NONMEM 7.4软件，采用非线性混合效应方法建立PopPK模型。进行蒙特卡洛模拟以优化给药方案。
具有一级吸收和一级消除的单室模型对数据拟合最佳。肌酐清除率（CrCl）和体重对参数的异速缩放被确定为口服奈玛特韦后显著影响暴露-疗效的协变量。使用最终模型进行的蒙特卡洛模拟为具有不同肾功能和体重的虚拟患者（每组1000例）生成了浓度-时间曲线。此外，我们开发了一个基于网络的仪表盘，以可视化奈玛特韦浓度的动态变化并提供个体化给药方案。
本研究表明，奈玛特韦的给药方案优化应基于CrCl和体重。此外，已开发出基于网络的仪表盘以促进个体化药物治疗。
