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

A Practical Discussion on Estimating Shelf Life Through Tolerance Intervals.

Forenzo, Patrick and Quinlan, Michelle (2021) A Practical Discussion on Estimating Shelf Life Through Tolerance Intervals. AAPS PharmSciTech, 22 (8). p. 273. ISSN 1530-9932

Abstract

This paper is a companion article to the research originally presented in "Estimating Shelf Life through Tolerance Intervals" (Schwenke et al., 21:290, 2020) published in AAPS PharmSciTech where tolerance intervals are introduced as an alternative methodology for estimating pharmaceutical shelf life. An industry stability shelf life example data set was used to demonstrate the proposed methods. Although using industry data does give relevance to examples demonstrating shelf life estimation, measures of how well the proposed methods accurately and effectively estimate shelf life cannot be obtained because the true shelf life values are not known for example data sets. In this current paper, the results of a computer simulation are reported where the tolerance interval estimates of shelf life are compared to theoretically known true shelf life values. Various factors that affect a tolerance interval estimate of pharmaceutical shelf life are investigated. A critical decision factor is the choice of the proportion of the stability distribution allowed out of specification at expiry to define the pharmaceutical risk. The number of stability batches available for shelf life estimation and the storage time at which the estimate is made are also considered in this simulation study. The industry example data are again used as the basis for the simulation study to give relevance to this research.

Item Type: Article
Keywords: Computer Simulation Drug Stability Drug Storage Models, Statistical Time Factors
Date Deposited: 12 Jul 2022 00:45
Last Modified: 12 Jul 2022 00:45
URI: https://oak.novartis.com/id/eprint/45623

Search

Email Alerts

Register with OAK to receive email alerts for saved searches.