Evaluation of bioequivalence for highly variable drugs with scaled average bioequivalence

Bioequivalence studies are performed to demonstrate in vivo that two pharmaceutically equivalent products (in the US) or alternative pharmaceutical products (in the EU) are comparable in their rate and extent of absorption. By definition, for highly variable drugs (HVDs), the estimated within-subject variability is >30%. HVDs often fail to meet current regulatory acceptance criteria for average bioequivalence (ABE). The determination of the bioequivalence of HVDs has been a vexing problem since the inception of the current regulations. It is of concern not only to the generic industry but also to the innovator industry. This article reviews the definition of HVDs, the present regulatory recommendations and the approaches proposed in the literature to deal with the bioequivalence problems of HVDs. The approach of scaled ABE (SABE) is proposed as the most adequate procedure to solve the problem. It is demonstrated that SABE has firm theoretical foundations. In fact, statistical tests similar to SABE are used in various fields, such as psychology and quality control. Algorithms and numerical examples are presented to calculate SABE from the data in conventional two-period and replicate-design studies. The most important feature of SABE is that a fixed sample size is adequate to demonstrate bioequivalence regardless of within-subject variability. The conditions for reaching consistent regulatory decisions with SABE are discussed. The required sample size, for a given statistical power, depends on the regulatory criteria. Sample sizes with different criteria are demonstrated and compared with those arising from a recent informal US FDA proposal. Pragmatic considerations lead to modifications of the theoretical concept of SABE. Several modifications are proposed, including reference scaling, restriction on the estimated geometric mean ratios and possibly limiting SABE to only secondary bioequivalence metrics such as the maximum concentration. Each proposal has its own merit but is also a source of new controversy. Overall, the statistical evaluation of SABE is more complex than that of ABE, which means higher regulatory burden. Standardized open software could be very useful in this regard. A small program script is presented to calculate SABE confidence limits.