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首页> 外文期刊>Journal of pharmacokinetics and biopharmaceutics >Predictive performance of a semiparametric method to estimate population pharmacokinetic parameters using NONMEM.
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Predictive performance of a semiparametric method to estimate population pharmacokinetic parameters using NONMEM.

机译：使用NONMEM估计群体药代动力学参数的半参数方法的预测性能。

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摘要

Routine clinical pharmacokinetic (PK) data collected from patients receiving inulin were analyzed to estimate population PK parameters; 560 plasma concentration determinations for inulin were obtained from 90 patients. The data were analyzed using NONMEM. The population PK parameters were estimated using a Constrained Longitudinal Splines (CLS) semiparametric approach and a first-order conditional method (FOCE). The mean posterior individual clearance values were 7.73 L/hr using both parametric and semiparametric methods. This estimation was compared with clearances estimated using standard nonlinear weighted least squares approach (reference value, 7.64 L/hr). The bias was not statistically different from zero and the precision of the estimates was 0.415 L/hr using parametric method and 0.984 L/hr using semiparametric method. To evaluate the predictive performances of the population parameters, 17 new subjects were used. First, the individual inulin clearance values were estimated from drug concentration-time curve using a nonlinear weighted least-squares method then they were estimated using the NONMEM POSTHOC method obtained using parametric and CLS methods as well as an alternative method based on a Monte Carlo simulation approach. The population parameters combined with two individual inulin plasma concentrations (0.25 and 2 hr) led to an estimation of individual clearances without bias and with a good precision. This paper not only evaluates the relative performance of the parametric and the CLS methods for sparse data but also introduces a new method for individual estimation.
机译：分析从接受菊粉的患者收集的常规临床药代动力学（PK）数据，以估计群体PK参数;从90例患者中获得560种菊粉血浆浓度测定值。使用NONMEM分析数据。人口PK参数使用约束纵向样条（CLS）半参数方法和一阶条件方法（FOCE）进行估算。使用参数和半参数方法后平均个体后清除值为7.73 L / hr。将该估算值与使用标准非线性加权最小二乘法估算的电气间隙（参考值，7.64 L / hr）进行了比较。偏差在统计上与零没有差异，使用参数方法的估算精度为0.415 L / hr，使用半参数方法的估算精度为0.984 L / hr。为了评估总体参数的预测性能，使用了17个新对象。首先，使用非线性加权最小二乘法从药物浓度-时间曲线估算各个菊粉清除率值，然后使用通过参数和CLS方法以及基于蒙特卡洛模拟的替代方法获得的NONMEM POSTHOC方法进行估算方法。群体参数与两个菊粉血浆浓度（0.25和2 hr）相结合，可以估计个体清除率而无偏倚且精确度高。本文不仅评估了稀疏数据的参数和CLS方法的相对性能，而且介绍了一种用于个体估计的新方法。

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