【摘要】 本研究以基于D最优设计的最大后验贝叶斯法(MAPB)估算个体药动学参数,并与多元线性回归(MLR)法比较。以吡格列酮为模型药物,非线性混合效应模型(NONMEM)法考察药物的群体药动学特征。WinPOPT软件进行D最优采样设计,获得1～4点的采样方案。采用蒙特卡罗法产生模拟数据集,对估算方法进行评估。结果显示:随采样点数量的下降,MAPB估算CL和V的准确度和精密度均下降;随CL和V个体间变异增高,基于2点D最优设计的MAPB估算CL和V的精密度下降;随残差变异增高,MAPB估算的准确度和精密度均下降。与MLR比较结果显示:MAPB 2点D最优方案和MLR的2点估算AUC的准确度和精密度较接近,但在最佳采样点前后调整1 h采样,MAPB估算准确度和精密度优于MLR法。总体而言,MAPB法估算AUC的能力与MLR较为接近,但较MLR更具采样灵活性。更多还原

【Abstract】 This study was aimed to develop a maximum a posteriori Bayesian(MAPB) estimation method to estimate individual pharmacokinetic parameters based on D-optimal sampling strategy.Meanwhile,the performance of MAPB was compared with the multiple linear regression(MLR) method in terms of accuracy and precision.Pharmacokinetic study of pioglitazone was employed as the example case.The population pharmacokinetics was characterized by nonlinear mixed effects model(NONMEM).The sparse sampling strategy(1 - 4 points) was identified by D-optimal algorithm using WinPOPT software.The simulated data generated by Monte Carlo method were used to access the performance of MAPB and MLR.As the number of samples per subject decreased,the accuracy and precision of MAPB method tended to get worse.The estimation for CL and V by MAPB using D-optimal two-point design had less bias with low inter-individual variability,and had more bias and imprecision with high residue variability.The estimation of AUC by MAPB using D-optimal 2 points design had similar accuracy and precision to MLR.However,MAPB estimation was better than MLR while adjusting the sampling time to one hour.Overall,the MAPB method had similar predictive performance as MLR,but MAPB could provide more pharmacokinetic information with higher sampling flexibility.更多还原