Model-based approach for two-stage group sequential or adaptive designs in bioequivalence studies using parallel and crossover designs

In pharmacokinetic (PK) bioequivalence (BE) analysis, the recommended approach is the two one-sided tests (TOSTs) on non-compartmental analysis (NCA) estimates of area under the plasma drug concentration versus time curve and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math> (NCA-TOST). Sample size estimation for a BE study requires assumptions on between/within subject variability (B/WSV). When little prior information is available, interim analysis using two-stage group sequential (GS) or adaptive designs (ADs) may be beneficial. GS fixes the second stage size, while AD requires sample re-estimation based on first-stage results. Recent research has proposed model-based (MB) TOST, using nonlinear mixed effects models, as an alternative to NCA-TOST. This work extends GS and AD approaches to MB-TOST. We evaluated these approaches on simulated parallel and two-way crossover designs for a one-compartment PK model, considering three variability levels for initial sample size calculation. We compared final sample size, type I error, and power estimates from one-stage, GS, and AD designs using NCA-TOST and MB-TOST. Results showed both NCA-TOST and MB-TOST reasonably controlled type I error while maintaining adequate power in two-stage GS and AD approaches, based on our limited computation power. Two-stage designs reduced sample size compared to traditional designs, especially for highly variable drugs, with many trials stopping at Stage 1 in AD designs. Our findings suggest MB-TOST may serve as a viable alternative to NCA-TOST for BE assessment in two-stage designs, especially when B/WSV impacts BE results.