Objective: The objective of this study is to evaluate the relative performance of individual or population compartmental analysis (ICA or PCA) vs. noncompartmental analysis (NCA) in estimating the systemic exposures of drugs to assess bioequivalence (BE) between original and generic formulations in the case of limited datasets.
Methods: BE study data of adefovir, finasteride, and tiropramide were chosen. The analyses were performed for the 1) original dataset, 2) limited dataset with small size for which the number of subjects was decreased to half, and 3) limited dataset with minimal-sampling timepoint of 9 samples. As for NCA and ICA, the Cmax and AUCinf were estimated using WinNonlin®. The PCA was implemented in NONMEM® and then Monte Carlo simulation was utilized to generate 10,000 sets of Cmax and AUCinf.
Results: The 90% confidence intervals (CIs) of the original datasets of the 3 drugs were all within BE acceptance criteria regardless of the analysis method. For small-sample-size datasets of adefovir and finasteride, BE results were maintained. In tiropramide, the lower boundary of CI computed from ICA or PCA results was less than 0.800 for the 3 small sample sizes (n = 22, 16, 10), but that of NCA results was less than 0.800 for only the smallest sample size (n = 10). As for the minimal-sampling timepoint, results were within the BE acceptance criteria for all of the 3 analyses.
Conclusions: Compartmental approaches can provide a complementary method for BE assessment, as well as being used for restricted-design studies.