%0 Journal Article
%A Kai Zhang
%A Thomas Wrzosek
%A Kashappa Goud Desai
%A Myrna Monck
%T Micro-Flow Imaging: Estimation of the Contribution of Key Factors to the Variability of Subvisible Particle Count Measurement by a Nested Statistical Analysis
%D 2020
%R 10.5731/pdajpst.2018.009324
%J PDA Journal of Pharmaceutical Science and Technology
%P 15-26
%V 74
%N 1
%X Understanding the contribution of relevant factors to the analytical variability of the micro-flow imaging (MFI) technique is of prime importance because of the significance of the subvisible particulate data in biopharmaceutical product development. The current study was performed to determine the contribution of several key variables to the variability of the subvisible particle counts (e.g., day-to-day, vial-to-vial, sample-to-sample, and measurement-to-measurement variabilities) using a nested statistical analysis. The variability was measured in the <10 μm, ≥10 μm, ≥25 μm, and ≥50 μm size ranges along with the total particle count and the maximum and the mean particle size. The contribution of the vial to the variability of the subvisible particle counts was found to be greater than those of the other factors evaluated in the current study. The analytical method variability in terms of percent relative standard deviation with respect to the particle count in the <10 μm, ≥10 μm, and ≥25 μm size ranges was found to be 16%, 40%, and 44%, respectively. A thorough understanding of the contribution of key factors to the analytical variability revealed how the corresponding contribution can be minimized, that is, by increasing the number of vials, samples, and measurements. The results of the current study may be leveraged for the optimization of the analytical method or for minimization of the analytical variability with the MFI technique.
%U https://journal.pda.org/content/pdajpst/74/1/15.full.pdf