Selection of optimal study design based on the bioequivalence simulation study in highly variable drugs
Eunjung Song1, Woojoo Lee2, Bo-Hyung Kim1,3
1 Department of Clinical Pharmacology and Therapeutics, Kyung Hee University Hospital, Seoul, Republic of Korea; 2 Department of Statistics, lnha University, Incheon, Republic of Korea; 3 Department of Biomedical Science and Technology, Kyung Hee University, Seoul, Republic of Korea
Objectives: Highly variable drugs (HVD) indicate drugs with within-subject standard deviation (SWR) > 0.294. For HVD, bioequivalence tests are conducted with the reference-scaled average bioequivalence approach using SWR. Therefore, reference replicated bioequivalence study designs, such as partially replicated 3-way design (TRR, RTR, and RRT) or fully replicated 4-way design (TRTR and RTRT), are recommended to estimate SWR. However, it is difficult to decide which study design is the most efficient design between the designs. As an approach to solving this difficulty, the current study is planned to suggest an appropriate study design by comparing simulation results from various study designs including reference-replicated designs.
Methods: Given the population pharmacokinetic (PopPK) model with known parameters, a Monte Carlo simulation is performed to compare power for bioequivalence study designs for HVD. First, we generated simulation data from PopPK models with known parameters. In particular, we tried to have almost same total number of observations for each design to compare different study designs fairly. Second, we conducted bioequivalence tests for HVD according to reference-scaled average bioequivalence approach and mixed scaling approach. Then, we calculated power from the results of the tests for each study design. The procedure was performed by using SAS version 9.4.
Results: The power (%) in bioequivalence study was affected by the following factors: total number of observations, geometric mean ratio, covariance matrix structure for Cmax or AUC. In detail, the SWR of Cmax or AUC was important factor increasing power. As the correlation within subjects decreases, the power for the study design tends to increase.
Conclusions: The covariance structure was a critical factor to reliably compare the power between the various bioequivalence study designs. From comparison of power for bioequivalence study designs under the proper covariance structure and appropriate parameters, we can select a study design which have the smallest the number of observations while maintaining pre-defined power.
 Lawrence, X. Y. and Li, B. V. (Eds.). (2014). FDA bioequivalence standards (Vol. 13). Springer.