The ICH E5 is currently focusing on the effective utilization of overseas data as the bridging data package. In consideration of the bridging strategy, we have to provide the simlarities in pharmacokinetic (PK) and pharmacodynamic (PD) profiles between the new region’s population and the original region's. These profiles are correlated to each other. The currently recommended bridging strategy is the following: In the first step, we have to evaluate the PK profile of the new region to judge the necessity of dose adjustment due to the ethnic differences in PK. Secondly, a dose-response study is usually conducted as bridging study, in which we have to evaluate PD profiles which include efficacy and safety. It is generally useful to incorporate the relationship among PK profile, disease progression and drug response into the proper population PK/PD model in bridging studies. Demonstrating similarity of bridging studies is diffcult because the obtained information from patients is often incomplete and biased. In order to confirm effcacy and to demonstrate similarity between ethnic groups in clinical studies, effcacy should be clinically evaluated from multiple perspectives through refined analysis based on a model, which includes mechanism-based population PK/PD model. Construction and evaluation of the mechanistic PK/PD model in effcacy and safety may be able to explain the ethnic similarity, even though the external circumstances are different.
For bridging clinical data, a study is additionally conducted in new region to demonstrate some similarity in treatment effects between two regions. An alternative bridging strategy is that new region participates in a multi-regional study as one site. This paper focuses on a possibility of bridging through joining multi-regional study, and statistical inference for an interaction term between treatment and region to detect non-similarity of treatment effects among regions/countries. Applying a logistic model with the terms of treatment effect, regional effect and their interaction to dichotomous response data, a simulation study is performed to investigate statistical power to detect significant treatment-by-region interaction, and treatment effect for a variety of sample size configurations. The result suggests that moderate or large sample sizes are required in new region to achieve sufficient power, if a substantial (qualitative or quantitative) interaction exists.