Performance of the Population Bioequivalence (PBE) Statistical Test Using an IPAC-RS Database of Delivered Dose from Metered Dose Inhalers

Abstract

This article reports performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled products. A PBE Working Group of the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) assembled and considered a database comprising delivered dose measurements from 856 individual batches across 20 metered dose inhaler products submitted by industry. A review of the industry dataset identified variability between batches and a systematic lifestage effect that was not included in the FDA-prescribed model for PBE. A simulation study was designed to understand PBE performance when factors identified in the industry database were present. Neglecting between-batch variability in the PBE model inflated errors in the equivalence conclusion: (i) The probability of incorrectly concluding equivalence (type I error) often exceeded 15% for non-zero between-batch variability, and (ii) the probability of incorrectly rejecting equivalence (type II error) for identical products approached 20% when product and between-batch variabilities were high. Neglecting a systematic through-life increase in the PBE model did not substantially impact PBE performance for the magnitude of lifestage effect considered. Extreme values were present in 80% of the industry products considered, with low-dose extremes having a larger impact on equivalence conclusions. The dataset did not support the need for log-transformation prior to analysis, as requested by FDA. Log-transformation resulted in equivalence conclusions that depended on the direction of product mean differences. These results highlight a need for further refinement of in vitro equivalence methodology.

Between-Batch Bioequivalence (BBE): a Statistical Test to Evaluate In Vitro Bioequivalence Considering the Between-Batch Variability

“…, the type I error or false positive rate, FPR) in the presence of between-batch variations. In addition, Morgan et al ( 9 ) confirmed the increase of at least 15% of the probability to incorrectly conclude on equivalence (type I error) when neglecting between-batch variability in PBE studies. This same study also highlights an increase of the probability to incorrectly reject equivalence ( i.e.…”

“…In vitro bioequivalence testing is not considered and evaluated in the same way by all instances taking part in the process ( 7 , 8 ). Indeed, in the USA, FDA recommends the use of population bioequivalence (PBE) ( 9 , 10 ), whereas Europe recommends using average bioequivalence (ABE) ( 11 , 12 ).…”

“…In the last decade, PBE has been challenged by several studies ( 1 , 2 , 9 , 21 ), especially in the context of in vitro bioequivalence testing. The main limitation is the asymmetry of the method, promoting situations with higher variability on the Reference product than on the Test product ( 9 , 21 ). In addition, the bioequivalence limit is fixed by the guidance on what may be questionable ( 21 , 24 ).…”

“…In addition, the bioequivalence limit is fixed by the guidance on what may be questionable ( 21 , 24 ). Finally, as highlighted in Morgan et al (2018), the IPAC-RS PBE working group recommends further investigation into the appropriateness of a log transformation for in vitro data in equivalence assessments, ( 9 , 21 ). Therefore, the asymmetrical formula of PBE may lead to accept the bioequivalence, even when both products appear instinctively not equivalent.…”

“…While both ABE and PBE tests consider the device total variability, the between-batch variability, which is part of the last, is not considered individually in the mathematical definitions proposed by the FDA and EMA guidelines. Recent works recommended to consider the between-batch variability in the bioequivalence tests ( 2 , 9 , 21 , 27 , 28 ). Indeed, both methods do not consider the between-batch variability.…”