ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Estimation of Variance Components and Use of Tolerance Interval for Accuracy Measure on Assay Qualification and Validation

11 September 2017, 16:20 – 16:40

Abstract

Submitted by
Dan Lin
Authors
Dan Lin (GSK), Bernard Francq (GSK), Walter Hoyer (GSK)
Abstract
During development of a vaccine, different analytical methods for determining the antigen concentration, the (relative) potency or the level of impurities in the produced vaccine batches need to be developed. In this paper, we focus on evaluating two aspects of the desired method performance: precision and accuracy in the process of assay development and validation. Precision is a measure of the variability in a series of measurements obtained from repeated samplings within and between assay runs. Historically, repeatability (only intra-assay variability) and intermediate precision (combined inter- and intra-assay variability) have been studied separately, see e.g. ICH guideline Q2(R1) for a number of samples spanning the intended working range of the analytical method. More recently, regulatory authorities expect a comprehensive approach including a variance decomposition to clearly distinguish the different contributions to the total variability, as described in United States Pharmacopoeia chapter <1033>. A linear mixed model across all samples will be used to estimate the variance components and the construction of total variability with its confidence interval. The particular emphasis of this presentation is to study the effect of pooling some factors inside the statistical model, e.g. including a single factor “session” (i.e., the combination of “Day” and “Operator”) as opposed to the factors “Day”, “Operator”, and their interaction. Some simulation results will be shown to illustrate the effect of under-estimation for the intermediate precision in case of model mis-specification. Accuracy is defined as the closeness between individual test results and accepted reference value. Its measure should take into account the systematic error (namely, trueness, i.e., the ratio between mean test result and the true theoretic value) and random error (precision). For this purpose, the tolerance interval for the accuracy measure will be used to capture the total error (trueness and precision) from the linear mixed model. An automated tool with a graphical user interface will be presented in which a user can upload their validation data and receive a complete pdf report within seconds.

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