Establishment of an Equivalence Acceptance Criterion for Accelerated Stability Studies

Abstract

In this article, the use of statistical equivalence testing for providing evidence of process comparability in an accelerated stability study is advocated over the use of a test of differences. The objective of such a study is to demonstrate comparability by showing that the stability profiles under nonrecommended storage conditions of two processes are equivalent. Because it is difficult at accelerated conditions to find a direct link to product specifications, and hence product safety and efficacy, an equivalence acceptance criterion is proposed that is based on the statistical concept of effect size. As with all statistical tests of equivalence, it is important to collect input from appropriate subject-matter experts when defining the acceptance criterion.

Quality by Design for Biopharmaceutical Drug Product Development

“…Given how much one can sample from a manufactured batch, the level chosen in Eq. 26.3 was found to be working well in practice; however, study owner may find the use of other levels cited in (Burdick and Sidor 2013), more appropriate for their application. …”

Statistical considerations for design and analysis of stability, comparability and formulation tests

“…An equivalence test declares equivalence when the confidence interval for the difference of degradation rates is contained entirely within where is the pre‐specified equivalence margin. See Burdick and Sidor 11 for a discussion on how to set acceptance criteria. In this procedure, a narrower confidence interval will increase the power of the test, i.e., increase the probability of declaring equivalency when the processes are truly equivalent.…”

“…For accelerated conditions that are likely to approach the significant change criteria, ICH recommends a minimum of four time points, however it does not recommend locations for the time points. Our Base condition for accelerated‐time studies, consists of four equally spaced time points as considered in Burdick and Sidor 11 …”

“…Variability in an attribute's measurements is due to assay variation, quantified by intermediate precision, see ICH Q2(R1) 10 . When regression models use the correct functional form, assay variance is the primary component of the regression variance 11 . Analyses of these experiments typically assume independent measurements.…”

“…10 When regression models use the correct functional form, assay variance is the primary component of the regression variance. 11 Analyses of these experiments typically assume independent measurements. The assumption of independence is questionable because samples taken at the same time point are typically assayed immediately in a single assay session; this is done for convenience and to enable interim analyses.…”