Performance of the Population Bioequivalence (PBE) Statistical Test with Impactor Sized Mass Data.

Autor: Chen S; Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA., Morgan B; Inhalation Product Development Statistics, AstraZeneca, Raleigh, North Carolina, USA., Beresford H; Drug Delivery Systems Division, 3M Company, Loughborough, Leicestershire, UK., Burmeister Getz E; Clinical Pharmacology, Oriel/Novartis, Emeryville, California, USA., Christopher D; Statistics, Merck & Co. Inc., West Point, Pennsylvania, USA., Långström G; Pharmaceutical Technology & Development, AstraZeneca Gothenburg, Mölndal, Sweden., Strickland H; Statistics, GlaxoSmithKline, Raleigh, North Carolina, USA., Wiggenhorn C; Drug Delivery Systems Division, 3M Company, St. Paul, Minnesota, USA., Lyapustina S; Pharmaceutical Practice Group, Drinker Biddle & Reath LLP, 1500 K Street, N.W.; Suite 1100, Washington, District of Columbia, 20005-1209, USA. Svetlana.Lyapustina@dbr.com.
Jazyk: angličtina
Zdroj: AAPS PharmSciTech [AAPS PharmSciTech] 2019 Aug 23; Vol. 20 (7), pp. 296. Date of Electronic Publication: 2019 Aug 23.
DOI: 10.1208/s12249-019-1507-8
Abstrakt: This article extends previous work studying performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled and nasal drug products. Based on analysis of a metered dose inhaler database for impactor sized mass, a simulation study was designed to compare performance of the recommended PBE approach with several modified or alternative approaches. These included an extended PBE that separately modeled within-batch (can) and between-batch (batch) variability and average bioequivalence (ABE) tests that modeled with or without between-batch variability and with or without log-transformation. This work showed that separately modeling within- and between-batch variability while increasing the number of sampled batches addressed previously identified issues of the PBE approach when between-batch variability was present, namely, (a) increased risk for falsely concluding equivalence and (b) low probability of correctly concluding equivalence. The same modifications were also required of the ABE to achieve expected performance. However, these modifications did not successfully address the issue of equivalence conclusions that depended on the direction of product mean differences (asymmetric performance). This work highlights the importance of understanding decision-making error rates in developing regulatory recommendations to standardize bioequivalence outcomes across products.
Databáze: MEDLINE
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