To evaluate the effect of components in Guanxin Ⅱ prescription on the pharmacokinetic profiles of paeoniflorin. Plasma concentration of Paeoniflorin in rats after intravenous injection of Paronia Pall Extract （PPE） and oral administration of PPE and three types of decoctions in Guanxin Ⅱ prescription, respectively, were determined by HPLC analyses. NONMEM （nonlinear mixed-effect modeling） method was used to analyze full set of pharmacokinetic data directly. A two-compartment model with first-order degradation in absorption compartment was employed for the data analysis. The mean of population parameters, CL1, V1, CL2, V2, Ka0, and Kal, were measured to be 0.509 L/h, 0.104 L, 0.113 L/h, 0.123 L, 0.135/h, and 0.0135/h, respectively. Inter-individual variabilities were estimated and dose formulation （DF） was identified as a significant covariate of Ka 1, Ka0, and V1. It is concluded that the pharmacokinetic behaviors of paeoniflorin in rats can alter with different dose formulations.
Aim To develop a method to estimate population pharmacokinetic parameters with the limited sampling time points provided clinically during therapeutic drug monitoring. Methods Various simulations were attempted using a one-compartment open model with the first order absorption to determine PK parameter estimates with different sampling strategies as a validation of the method. The estimated parameters were further verified by comparing to the observed values. Results The samples collected at the single time point close to the non-informative sampling time point designed by this method led to bias and inaccurate parameter estimations. Furthermore, the relationship between the estimated non-informative sampling time points and the values of the parameter was examined. The non-informative sampling time points have been developed under some typical occasions and the results were plotted to show the tendency. As a result, one non-informative time point was demonstrated to be appropriate for clearance and two for both volume of distribution and constant of absorption in the present study. It was found that the estimates of the non-informative sampling time points developed in the method increase with increases of volume of distribution and the decrease of clearance and constant of absorption. Conclusion A rational sampling strategy during therapeutic drug monitoring can be established using the method present in the study.
