Browse views: by Year, by Function, by GLF, by Subfunction, by Conference, by Journal

Estimating Shelf Life Through Tolerance Intervals

Quinlan, Michelle and Forenzo, Patrick and James, Schwenke and Walter, Stroup (2020) Estimating Shelf Life Through Tolerance Intervals. AAPS PharmSciTech, 21 (290). pp. 1-22. ISSN 10.1208/s12249-020-01800-2


This paper is a continuation of the research published by the Stability Shelf Life
Working Group as chartered under the Product Quality Research Institute. The
Working Group was formed in 2006 and disbanded in late 2019. Following the
philosophy presented by the Working Group on how to characterize the stability shelf
life paradigm (Capen et al., 2012), shelf life is estimated here in terms of defining risk
as a specified proportion of the pharmaceutical stability distribution of interest being out
of specification. Shelf life can be defined for the batch mean distribution for regulatory
issues, as well as for the product distributions for patient interests. Estimates of shelf
life are proposed corresponding to each stability distribution through the use of
statistical tolerance intervals. Appropriate estimates of the between‑batch and
within‑batch variance components are obtained through a random coefficient mixed
regression model analysis based on the best fit to batch stability response data.
Tolerance interval estimates are computed as part of the mixed model analysis and
computed directly using the statistical definition of the stability distributions. A proposed
rationale is offered on how to select an appropriate proportion allowed out of
specification to define a meaningful shelf life. Examples of the proposed shelf life
estimates are presented using industry stability batch data. For each example, the
traditional ICH shelf life estimate is given for comparison.

Item Type: Article
Keywords: stability analysis; shelf life estimation; random coefficient regression; tolerance intervals
Date Deposited: 05 Nov 2020 00:45
Last Modified: 05 Nov 2020 00:45


Email Alerts

Register with OAK to receive email alerts for saved searches.