%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 2019 %R 10.5731/pdajpst.2018.009324 %J PDA Journal of Pharmaceutical Science and Technology %P pdajpst.2018.009324 %X Understanding the contribution of relevant factors to the analytical variability of micro-flow imaging (MFI) technique is of prime importance due to 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 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 < 10μm, ≥10μm, ≥25μm and ≥50μm size ranges along with the total particle count and maximum and mean particle size. The contribution of the vial to the variability of subvisible particle counts was found to be higher than other factors evaluated in the current study. The analytical method variability in terms of % relative standard deviation (%RSD) with respect to the particle count in the <10μm, ≥10μm and ≥25μm 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 i.e. by increasing the number of vials, samples and measurements. The learnings of the current study may be leveraged for the optimization of analytical method or to minimize the analytical variability with the MFI technique. %U https://journal.pda.org/content/pdajpst/early/2019/09/13/pdajpst.2018.009324.full.pdf