Cabelguenne D, Bleyzac N, Pivot C, Maire P
ADCAPT, Service Pharmaceutique, Hôpital A. Charial, Hospices Civils de Lyon, France.
Therapie. 2000 Mar-Apr;55(2):277-82.
The aim of this study is to evaluate the use of a pharmacokinetic population model built by the two-stage method and individual parameters determined by a Bayesian estimation instead of nonlinear regression. We performed a retrospective analysis on 32 patient files (mean age: 82 years). First, we analysed prediction of amikacin serum levels for the Bayesian method (MAP) and nonlinear regression (MLS). Second, we compared pharmacokinetic parameter values for each patient with MAP and MLS methods for a one- or two-compartment model. For the one-compartment model, no difference in prediction performance was found (correlation coefficient: rMLS = 0.911, rMAP = 0.903, p > 0.05; precision: pMLS = 134.3, pMAP = 147, p > 0.05). A significant difference was observed only for systematic error (eMLS = -4.47, eMAP = -3.34, p < 0.05). For a two-compartment model, the Bayesian method was better for long-term prediction: 4-8 days (rMLS = 0.877, rMAP = 0.886, p > 0.05; eMLS = 5.26, eMAP = 0.04, p < 0.01; pMLS = 441.7, pMAP = 149, p < 0.05). The comparison of MAP and MLS estimated pharmacokinetic parameter values for a one-compartment model showed that the Bayesian method used to built a pharmacokinetic population in two stages does not influence pharmacokinetic parameter estimation (p > 0.05 for Vd, Kslope, Kel and t1/2). We conclude that we can use a Bayesian method to build a pharmacokinetic population in two steps in order to perform adaptative control of a drug-dosage regimen.
本研究的目的是评估使用两阶段法构建的药代动力学群体模型以及通过贝叶斯估计而非非线性回归确定的个体参数。我们对32份患者病历（平均年龄：82岁）进行了回顾性分析。首先，我们分析了贝叶斯方法（MAP）和非线性回归（MLS）对阿米卡星血清水平的预测。其次，我们比较了单室或双室模型中每位患者采用MAP和MLS方法时的药代动力学参数值。对于单室模型，预测性能未发现差异（相关系数：rMLS = 0.911，rMAP = 0.903，p>0.05；精密度：pMLS = 134.3，pMAP = 147，p>0.05）。仅在系统误差方面观察到显著差异（eMLS = -4.47，eMAP = -3.34，p<0.05）。对于双室模型，贝叶斯方法在长期预测（4 - 8天）方面表现更好：（rMLS = 0.877，rMAP = 0.886，p>0.05；eMLS = 5.26，eMAP = 0.04，p<0.01；pMLS = 441.7，pMAP = 149，p<0.05）。单室模型中MAP和MLS估计的药代动力学参数值比较表明，用于两阶段构建药代动力学群体的贝叶斯方法不影响药代动力学参数估计（Vd、Kslope、Kel和t1/2的p>0.05）。我们得出结论，我们可以使用贝叶斯方法分两步构建药代动力学群体，以便对药物剂量方案进行适应性控制。
