叶红波 郑宏 张兴安 池信锦 陈文瑛 徐建国 李金恒 芮建中. 多中心异丙酚群体药物动力学评价J. 药学学报, 2010,45(12): 1550-1558.
引用本文: 叶红波 郑宏 张兴安 池信锦 陈文瑛 徐建国 李金恒 芮建中. 多中心异丙酚群体药物动力学评价J. 药学学报, 2010,45(12): 1550-1558.
XIE Gong-Bei, ZHENG Hong, ZHANG Xin-An, CHE Shen-Jin, CHEN Wen-Ying, XU Jian-Guo, LI Jin-Heng, RUI Jian-Zhong. Population pharmacokinetic modeling and evaluation of propofol from multiple centersJ. 药学学报, 2010,45(12): 1550-1558.
Citation: XIE Gong-Bei, ZHENG Hong, ZHANG Xin-An, CHE Shen-Jin, CHEN Wen-Ying, XU Jian-Guo, LI Jin-Heng, RUI Jian-Zhong. Population pharmacokinetic modeling and evaluation of propofol from multiple centersJ. 药学学报, 2010,45(12): 1550-1558.

Population pharmacokinetic modeling and evaluation of propofol from multiple centers

  • 摘要:

    本项研究旨在分析中国人异丙酚血管内给药的群体药动学特征, 建立群体模型预测药物浓度。收集5个中心的手术患者在异丙酚麻醉给药过程中不同时程的血样并测定药物浓度, NONMEM程序进行群体药动学分析, 建立三房室药动学模型 (个体间变异用指数模型、残差变异用常系数模型), 考察体重等协变量对参数CLV1Q2, V2Q3V3的影响。用自举验证、拟合优度、直观预测检验 (VPC) 评价最终模型的性能, 并模拟6个亚组群体时间相关半衰期和给药维持速率。CLV1Q2V2Q3V3的群体典型值分别是0.965 × (1 + 0.401 × VESS) × (BW/59)0.578 L·min−113.4 × (AGE/45)0.317 L0.659 × (1 + GENDER × 0.385) L·min−128.8 L0.575 × (1 + GENDER × 0.367) × (1 0.369 × VESS) L·min−1196 L, 个体间变异系数分别是29.2%46.9%35.2%40.4%67.0%49.9%。分析仪器HPLC-UVGC-MSHPLC-FLU的残差变异系数分别是24.7%16.1%22.5%, 最终模型表明体重 (BW) 正相关影响CL, 年龄 (AGE) 负相关影响V1, (GENDER = 1) Q2Q3分别比女性 (GENDER = 0) 升高38.5% 36.7%, 动脉采血 (VESS = 1) CLQ3比静脉采血 (VESS = 0) 别升高40.1% 和降低36.9%。最终模型能很好地预测异丙酚浓度, 浓度观察值 (DV) 与个体预测值 (IPRED) 的决定系数r2 = 0.91。自举验证、拟合优度、VPC的评价结果都表明模型稳定、预测结果可靠。模拟的6种亚组群体的时间相关半衰期和给药维持速率差异明显。异丙酚血管内给药符合三房室模型, 固定效应包括体重、年龄、性别、采血部位, 各亚组群体的模拟结果可以用于临床。根据最终模型得出的患者个体药动学参数可用于中国人异丙酚靶控输注给药的麻醉监控。

     

    Abstract:

    In order to successfully develop the effective population pharmacokinetic model to predict the concentration of propofol administrated intravenously, the data including the concentrations across both distribution and elimination phases from five hospitals were analyzed using nonlinear mixed effect model (NONMEM).  Three-compartment pharmacokinetic model was applied while the exponential model was used to describe the inter-individual variability and constant coefficient model to the intra-individual variability, accordingly.  Covariate effect including the body weight on the parameter CL, V1, Q2, V2, Q3 and V3 were investigated.  The performance of final model was assessed by Bootstrapping, goodness-of-fit and visual predictive checking (VPC).  The context-sensitive half-times and the infusion rates necessary to maintain the concentration of 1 μg·mL−1 were simulated to six subpopulations.  The results were as follows: the typical value of CL, V1, Q2, V2, Q3 and V3 were 0.965×(1+0.401×VESS)×(BW/59)0.578 L·min−1, 13.4×(AGE/45)0.317 L, 0.659×(1+GENDER×0.385) L·min−1, 28.8 L, 0.575×(1+GENDER×0.367)×(10.369×VESS) L·min−1 and 196 L respectively.  Coefficients of the inter-individual variability of CL, V1, Q2, V2, Q3 and V3 were 29.2%, 46.9%, 35.2%, 40.4%, 67.0% and 49.9% respectively, and the coefficients of residual variability were 24.7%, 16.1% and 22.5%, the final model indicated a positive influence of a body weight on CL, and also that a negative correlation of age with V1.  Q2 and Q3 in males were higher than those in females at 38.5% and 36.7%.  The CL and Q3 were 40.1% increased and 36.9% decreased in arterial samples compared to those in venous samples.  The determination coefficient of observations (DV)-individual predicted value (IPRED) by the final model was 0.91 which could predict the propofol concentration fairly well.  The stability and the predictive performance were accepted by Bootstrapping, the goodness-of-fit and VPC.  The context-sensitive half-times and infusion rates necessary to maintain the concentration of 1 μg·mL−1 were different obviously among the 6 sub-populations obviously.  The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well.  The involved fixed effects are age, body weight, gender and sampling site.  The simulations in 6 subpopulations were available in clinical anesthesia.  The propofol anesthesia monitor care could be improved by individualization of pharmacokinetic parameter estimated from the final model.

     

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