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Friday, September 14
Fri, Sep 14, 1:30 PM - 2:45 PM
Thurgood Marshall East
Challenges and Innovations in Bioequivalence Studies for Veterinary Drugs

Testing for Bioequivalence of Highly Variable Drugs from TR-RT Crossover Designs with Heterogeneous Residual Variances (300645)

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Qing Kang, The Statistical Intelligence Group 
*Christopher I. Vahl, Kansas State University 

Keywords: bioequivalence, highly variable drugs, veterinary medicine, crossover design, generalized pivotal quantity

Traditional bioavailability studies assess average bioequivalence (ABE) between the test (T) and reference (R) products under the crossover design with TR and RT sequences. With highly variable (HV) drugs whose intra-subject coefficient of variation in pharmacokinetic measures is 30% or greater, assertion of ABE becomes difficult due to the large sample sizes needed to achieve adequate power. In 2011, the FDA adopted a more relaxed, yet complex, ABE criterion and supplied a procedure to assess this criterion exclusively under TRR-RTR-RRT and TRTR-RTRT designs. However, designs with more than two periods are not always feasible. This present work investigates how to evaluate HV drugs under TR-RT designs. A mixed model with heterogeneous residual variances is used to fit data from TR-RT designs. Under the assumption of zero subject-by-formulation interaction, this basic model is comparable to the FDA-recommended model for TRR-RTR-RRT and TRTR-RTRT designs, suggesting the conceptual plausibility of our approach. To overcome the distributional dependency among summary statistics of model parameters, we develop statistical tests via the generalized pivotal quantity (GPQ). A real-world data example is given to illustrate the utility of the resulting procedures. Our simulation study identifies a GPQ-based testing procedure that evaluates HV drugs under practical TR-RT designs with desirable type I error rate and reasonable power. In comparison to the FDA’s approach, this GPQ-based procedure gives similar performance when the product’s inter-subject standard deviation is low (<0.4) and is most useful when practical considerations restrict the crossover design to two periods.