| Summary: | Hyungmi An,1 Dongseong Shin2,3 1Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea; 2Department of Pharmacology, Gachon University College of Medicine, Incheon, Korea; 3Clinical Trials Center, Gachon University Gil Medical Center, Incheon, KoreaCorrespondence: Dongseong ShinDepartment of Pharmacology, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, KoreaTel +82-32-460-9459Fax +82-32-460-9443Email dsshin@gilhospital.comBackground and Objective: Bioequivalence tests are fundamental step in assessing the equivalence in bioavailability between a test and reference product. In practice, two separate linear mixed models (LMMs) with random subject effects, which have an area under the concentration-time curve (AUC) and the peak concentration (Cmax) as the responses, have become the gold standard for evaluating bioequivalence. Recently, Lee et al developed a multivariate hierarchical generalized linear model (HGLM) for several responses that modeled correlations among multivariate responses via correlated random effects. The objective of this study was to apply this multivariate analysis to the bioequivalence test in practice and to compare the performance of multivariate HGLM and separate LMMs.Methods: Three pharmacokinetic datasets, fixed-dose combination (naproxen and esomeprazole), tramadol and fimasartan data were analyzed. We compared the 90% confidence interval (CI) for the geometric mean ratio (GMR) of a test product to a reference product using the multivariate HGLM and two conventional separate LMMs.Results: We found that the 90% CIs for the GMRs of both AUC and Cmax from the multivariate HGLM were narrower than those from the separate LMMs: (0.843, 1.152) vs (0.825, 1.177) for Cmax of esomeprazole in fixed-dose combination data; (0.805, 0.931) vs (0.797, 0.941) for Cmax in tramadol data; (0.801, 1.501) vs (0.762, 1.578) for Cmax and (1.163, 1.332) vs (1.009, 1.341) for AUC in fimasartan data, consistent with the random subject effects from two separate LMMs being highly correlated in the three datasets (correlation coefficient r = 0.883; r = 0.966; r = 0.832).Conclusion: This multivariate HGLM had good performance in the bioequivalence test with multiple endpoints. This method would provide a more reasonable option to reduce the 90% CI by adding correlation parameters and thus an advantage especially in evaluating the bioequivalence of highly variable drugs with broad 90% CIs.Keywords: bioequivalence test, correlated multiple responses, multivariate random effects model, H-likelihood, multivariate HGLM
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