New Statistical Method Could Speed Up Drug Approval for Hard-to-Test Formulations
Researchers have developed a faster way to prove that generic drugs work as well as brand-name versions, even when clinical trials involve limited data. The technique could accelerate approvals for difficult-to-test products like eye drops and inhalers, potentially reducing development costs and getting cheaper medicines to patients sooner.
Originaltitel: Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods
<p>Conventional approaches for establishing bioequivalence (BE) between test andreference formulations using non-compartmental analysis (NCA) may demon-strate low power in pharmacokinetic (PK) studies with sparse sampling. In thiscase, model-integrated evidence (MIE) approaches for BE assessment have beenshown to increase power, but may suffer from selection bias problems if modelsare built on the same data used for BE assessment. This work presents modelaveraging methods for BE evaluation and compares the power and type I errorof these methods to conventional BE approaches for simulated studies of oraland ophthalmic formulations. Two model averaging methods were examined:bootstrap model selection and weight-based model averaging with parameteruncertainty from three different sources, either from a sandwich covariance ma-trix, a bootstrap, or from sampling importance resampling (SIR). The proposedapproaches increased power compared with conventional NCA-based BE ap-proaches, especially for the ophthalmic formulation scenarios, and were simul-taneously able to adequately control type I error. In the rich sampling scenarioconsidered for oral formulation, the weight-based model averaging method withSIR uncertainty provided controlled type I error, that was closest to the target of5%. In sparse-sampling designs, especially the single sample ophthalmic scenar-ios, the type I error was best controlled by the bootstrap model selection method.</p>