Random sparse sampling strategy using stochastic simulation and estimation for a population pharmacokinetic study.
The purpose of this study was to use the stochastic simulation and estimation method to evaluate the effects of sample size and the number of samples per individual on the model development and evaluation. The pharmacokinetic parameters and inter- and intra-individual variation were obtained from a population pharmacokinetic model of clinical trials of amlodipine. Stochastic simulation and estimation were performed to evaluate the efficiencies of different sparse sampling scenarios to estimate the compartment model. Simulated data were generated a 1000 times and three candidate models were used to fit the 1000 data sets. Fifty-five kinds of sparse sampling scenarios were investigated and compared. The results showed that, 60 samples with three points and 20 samples with five points are recommended, and the quantitative methodology of stochastic simulation and estimation is valuable for efficiently estimating the compartment model and can be used for other similar model development and evaluation approaches.
Center of Pharmacokinetics, College of Pharmacy, Anhui Medical University, Hefei 230032, Anhui, China.
Xiao-Hui
XH
Huang
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Kun
K
Wang
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Ji-Han
JH
Huang
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Ling
L
Xu
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Lu-Jin
LJ
Li
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Yu-Cheng
YC
Sheng
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 20123, China.
Qing-Shan
QS
Zheng
Amlodipine; Model estimation; Population pharmacokinetics; Sparse sampling