Abstract：
OBJECTIVE: To establish a population pharmacokinetics (PopPK) model of nirmatrelvir in Chinese COVID-19 patients and provide reference for refining the dosing strategy of nirmatrelvir in patients confirmed to be infected with SARS-CoV-2.
METHODS: A total of 80 blood samples were obtained from 35 mild to moderate COVID-19 patients who were orally administered nirmatrelvir/ritonavir tablets. The PopPK model of nirmatrelvir was developed using a nonlinear mixed effects modelling approach. The stability and prediction of the final model were assessed through a combination of goodness-of-fit and bootstrap method. The exposure of nirmatrelvir across various clinical scenarios was simulated using Monte Carlo simulations.
RESULTS: The pharmacokinetics of nirmatrelvir was well characterised by a one-compartment model with first-order absorption, and with creatinine clearance (Ccr) as the significant covariate. Typical population parameter estimates of apparent clearance and distribution volume for a patient with a Ccr of 95.5 mL·min-1were 3.45 L·h-1 and 48.71 L, respectively. The bootstrap and visual predictive check procedures demonstrated satisfactory predictive performance and robustness of the final model.
CONCLUSIONS: The final model was capable of offering an early prediction of drug concentration ranges for different nirmatrelvir dosing regimens and optimise the dose regimen of nirmatrelvir in individuals with confirmed SARS-CoV-2 infection.
METHODS: A total of 80 blood samples were obtained from 35 mild to moderate COVID-19 patients who were orally administered nirmatrelvir/ritonavir tablets. The PopPK model of nirmatrelvir was developed using a nonlinear mixed effects modelling approach. The stability and prediction of the final model were assessed through a combination of goodness-of-fit and bootstrap method. The exposure of nirmatrelvir across various clinical scenarios was simulated using Monte Carlo simulations.
RESULTS: The pharmacokinetics of nirmatrelvir was well characterised by a one-compartment model with first-order absorption, and with creatinine clearance (Ccr) as the significant covariate. Typical population parameter estimates of apparent clearance and distribution volume for a patient with a Ccr of 95.5 mL·min-1were 3.45 L·h-1 and 48.71 L, respectively. The bootstrap and visual predictive check procedures demonstrated satisfactory predictive performance and robustness of the final model.
CONCLUSIONS: The final model was capable of offering an early prediction of drug concentration ranges for different nirmatrelvir dosing regimens and optimise the dose regimen of nirmatrelvir in individuals with confirmed SARS-CoV-2 infection.
摘要：
目的：建立中国COVID-19患者尼马特雷韦的群体药动学(PopPK)模型，为完善SARS-CoV-2感染患者尼马特雷韦的给药策略提供参考。
方法：从35例轻度中度COVID-19患者中采集80份血液样本，这些患者口服尼马特雷韦/利托那韦片。Nirmatrelvir的PopPK模型是使用非线性混合效应建模方法开发的。通过拟合优度和Bootstrap方法的组合评估最终模型的稳定性和预测。使用蒙特卡洛模拟模拟了各种临床情景中nirmatrelvir的暴露。
结果：尼马特雷韦的药代动力学通过一级吸收的单室模型得到了很好的表征，以肌酐清除率(Ccr)为显著协变量。Ccr为95.5mL·min-1的患者的表观清除率和分布体积的典型人群参数估计分别为3.45L·h-1和48.71L，分别。引导和视觉预测检查程序证明了最终模型的令人满意的预测性能和鲁棒性。
结论：最终模型能够提供不同尼马特雷韦给药方案的药物浓度范围的早期预测，并优化确诊SARS-CoV-2感染个体的尼马特雷韦剂量方案。
方法：从35例轻度中度COVID-19患者中采集80份血液样本，这些患者口服尼马特雷韦/利托那韦片。Nirmatrelvir的PopPK模型是使用非线性混合效应建模方法开发的。通过拟合优度和Bootstrap方法的组合评估最终模型的稳定性和预测。使用蒙特卡洛模拟模拟了各种临床情景中nirmatrelvir的暴露。
结果：尼马特雷韦的药代动力学通过一级吸收的单室模型得到了很好的表征，以肌酐清除率(Ccr)为显著协变量。Ccr为95.5mL·min-1的患者的表观清除率和分布体积的典型人群参数估计分别为3.45L·h-1和48.71L，分别。引导和视觉预测检查程序证明了最终模型的令人满意的预测性能和鲁棒性。
结论：最终模型能够提供不同尼马特雷韦给药方案的药物浓度范围的早期预测，并优化确诊SARS-CoV-2感染个体的尼马特雷韦剂量方案。
