Content uniformity testing for stratified samples via parametric tolerance interval testing. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Novick, Steven; Hudson-Curtis, Buffy
• Is Part Of: Journal of biopharmaceutical statistics. Volume 28:Issue 3(2018)
• Page Start: 463
• Page End: 474
• Publication Date: 2018-05-04
• Subjects: Generalized pivotal quantity -- PTI-TOST -- USP<905>; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2017.1321005
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: Historically in the biopharmaceutical setting, USP&lt;905&gt; has been used to establish that a batch of drug product has acceptable content uniformity. More recently, alternative approaches such as the two one-sided parametric tolerance interval test (PTI-TOST) have been proposed to establish content uniformity. Traditionally, the PTI-TOST is implemented as a sequential, two-tiered test, under the generally accepted assumption that the data are independently and identically distributed. Since the material is sequenced through the manufacturing process over a period of time, there are conceptually arguable locations within each batch, for instance: beginning, middle, and end. In such a situation, a practitioner may wish to evaluate potential effects of these batch locations, for example, during process validation. If location (stratified) differences exist within the batch and if multiple samples are taken from each location, significant within-location correlations may be induced in the data. In such a case, the traditional PTI-TOST underestimates the total variability, thereby improperly boosting the power of the test method. When there is reason to believe that location variances exist, the batch may be evaluated using stratified sampling, and the location effect may be modeled. In this paper, a two-tiered PTI-TOST that accounts for both between-location and within-location variance components is introduced. Operating characteristic curves and practical advice are given to aid the practitioner&#39;s uptake of the proposed method.