Abstract

Objective: This study was aimed at exploring the accuracy of population pharmacokinetic method in evaluating the bioequivalence of pidotimod with sparse data profiles and whether this method is suitable for bioequivalence evaluation in special populations such as children with fewer samplings. Methods: In this single-dose, two-period crossover study, 20 healthy male Chinese volunteers were randomized 1 : 1 to receive either the test or reference formulation, with a 1-week washout before receiving the alternative formulation. Noncompartmental and population compartmental pharmacokinetic analyses were conducted. Simulated data were analyzed to graphically evaluate the model and the pharmacokinetic characteristics of the two pidotimod formulations. Various sparse sampling scenarios were generated from the real bioequivalence clinical trial data and evaluated by population pharmacokinetic method. Results: The 90% confidence intervals (CIs) for AUC_0–12h, AUC_0–∞, and C_max were 97.3 – 118.7%, 96.9 – 118.7%, and 95.1 – 109.8%, respectively, within the 80 – 125% range for bioequivalence using noncompartmental analysis. The population compartmental pharmacokinetics of pidotimod were described using a one-compartment model with first-order absorption and lag time. In the comparison of estimations in different dataset, the estimation of random three- and< fixed four-point sampling strategies can provide results similar to those obtained through rich sampling. The nonlinear mixed-effects model requires fewer data points. Moreover, compared with the noncompartmental analysis method, the pharmacokinetic parameters can be more accurately estimated using nonlinear mixed-effects model. Conclusions: The population pharmacokinetic modeling method was used to assess the bioequivalence of two pidotimod formulations with relatively few sampling points and further validated the bioequivalence of the two formulations. This method may provide useful information for regulating bioequivalence evaluation in special populations.

*Jihan Huang and Mengying Li contributed equally to this work.

Author Details

Authors

• Jihan Huang*
• Mengying Li*
• Yinghua Lv
• Juan Yang
• Ling Xu
• Jingjing Wang
• Junchao Chen
• Kun Wang
• Yingchun He
• Qingshan Zheng

Departments

• Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Address

Kun Wang, MD
or
Yingchun He
Center for Drug Clinical Research
Shanghai University of Traditional Chinese Medicine
1200#, Cailun Rd, Pudong New District, Shanghai, 201203, China
Email: [email protected] or [email protected]

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