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Multivariate Assessment for Bioequivalence Based on the Correlation of Random Effect
Hyungmi An1, Dongseong Shin2,3
1Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea.
Drug Design, Development and Therapy
|September 1, 2021
Summary
A new multivariate hierarchical generalized linear model (HGLM) offers narrower confidence intervals for bioequivalence testing compared to traditional separate linear mixed models (LMMs). This advanced method improves the assessment of drug product bioavailability, especially for highly variable medications.
Area of Science:
- Pharmacokinetics and Pharmacodynamics
- Statistical Modeling in Drug Development
- Bioequivalence Study Design
Background:
- Bioequivalence testing is crucial for demonstrating therapeutic equivalence between drug products.
- Current gold standard involves separate linear mixed models (LMMs) for key pharmacokinetic parameters like area under the concentration-time curve (AUC) and peak concentration (Cmax).
- These LMMs analyze responses independently, potentially missing correlations between them.
Purpose of the Study:
- To apply a recently developed multivariate hierarchical generalized linear model (HGLM) to practical bioequivalence testing.
- To compare the performance of the multivariate HGLM against conventional separate LMMs.
- To evaluate the impact of modeling correlated random effects on bioequivalence assessment.
Main Methods:
- Analysis of three real-world pharmacokinetic datasets: fixed-dose combination (naproxen and esomeprazole), tramadol, and fimasartan.
- Comparison of 90% confidence intervals (CIs) for the geometric mean ratio (GMR) between test and reference products.
- Utilized both the multivariate HGLM and two conventional separate LMMs for comparative analysis.
Main Results:
- The multivariate HGLM consistently produced narrower 90% CIs for GMRs of both AUC and Cmax compared to separate LMMs across all three datasets.
- Observed narrower CIs for Cmax (esomeprazole) were (0.843, 1.152) vs (0.825, 1.177) and for AUC (fimasartan) were (1.163, 1.332) vs (1.009, 1.341).
- The superiority of HGLM was attributed to highly correlated random subject effects observed in the datasets (r = 0.883, 0.966, 0.832).
Conclusions:
- The multivariate HGLM demonstrates strong performance in bioequivalence testing involving multiple endpoints.
- This method offers a more refined approach by incorporating correlation parameters, leading to reduced CI width.
- The HGLM provides a significant advantage for evaluating the bioequivalence of highly variable drugs, potentially leading to more conclusive results.
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