Performing Reference-scaled Average Bioequivalence (RSABE) in Phoenix
WinNonlin®. H. Schuetz 1, A. Henry 2, L. Hughes 2. 1 BEBAC, 2 Certara.
Purpose.
Performing Reference-scaled Average Bioequivalence (RSABE) in Phoenix WinNonlin® H. Schuetz 1, A. Henry 2, L. Hughes 2 1 BEBAC, 2 Certara Purpose Traditional average bioequivalence (ABE) methodology, when used with highly variable drugs and drug products (HVDs/HVDPs), requires sample sizes that are prohibitively large, which increases the expense of BE studies, places more study subjects at risk, and ultimately limits the availability of generics. Hence, proof of bioequivalence for HVDs/HVDPs increasingly utilizes RSABE methodology. This methodology might vary between regulatory bodies, but all require that subjects receive the reference drug more than once, and that BE analysis accounts for within-subject variability. Although the Phoenix WinNonlin software provides a BE module to perform average bioequivalence, this module is not currently designed for a complete RSABE analysis. The purpose of this work is to show how RSABE can be performed in Phoenix WinNonlin using workflows. Methods Phoenix workflows in general provide a means of creating a collection of data-processing and computational tools that can be reused with different datasets. Workflows specific to RSABE start with data-processing tools for managing reference drug data, a linear mixed effects modeling tool to compute the within-subject variance for the reference drug, and more data processing tools to compute intra-subject CV%. HVDs/HVDPs are defined as those with intra-subject CV% of the reference greater than 30%. Workflows specific to EMA methods use this within-subject variance to compute a scaled acceptance range, then confidence intervals that result from an average BE tool in the workflow are compared to the scaled range. Workflows specific to FDA methods use data processing tools and linear mixed effects modeling tools to compute a BE limit scaled to the within-subject variance and an upper confidence limit, as well as a point estimate for the difference in means. Results RSABE analysis of full replicate and partial replicate designs was performed using Phoenix WinNonlin workflows, using datasets provided by EMA. Key results obtained with Phoenix matched results reported by EMA and obtained using SAS code. Conclusion Although numerous steps were required, Phoenix WinNonlin has the capability to perform RSABE workflows, and to save the workflows as templates that can be reused with different datasets. The Phoenix projects and templates presented in this poster are available for download by contacting the authors.
