Subvisible Particulate Contamination in Cell Therapy Products—Can We Distinguish?

Abstract

Cell therapy products represent an exciting new class of medicinal products, which must be parenterally administered. Thus, compliance with parenteral preparation guidelines is required. One requirement for parenteral products is the characterization of particle contaminations. As cell-based products are turbid suspensions, containing particles, the cells, characterization and control of foreign particle impurities remain a challenge. Within this study, we evaluated a flow imaging microscopy method for the detection and characterization of subvisible particle contaminations in cell-based products. We found that flow imaging microscopy is a potential method where subvisible particle contaminations can be differentiated from the cells in cell therapy products.

Particulate impurities in cell-based medicinal products traced by flow imaging microscopy combined with deep learning for image analysis

“…The verification of viability and total concentration of cells used in our study was performed by using a hemocytometer. As shown in previous studies [12,21], cell concentrations determined by using FlowCam were lower compared with manual counting, and in our case the difference was~20%. Furthermore, the concentration of Dynabeads detected by FlowCam deviated from the reference concentrations stated by the manufacturer, that is, the recovery was always <100% especially for Dynabeads at lower concentrations.…”

“…Different particle populations can be discriminated based on particle structure and appearance [17,18]. Recently, FIM has been applied to study cell viability and confluency in cell culture as well as quality of CBMPs [12,[19][20][21]. However, comprehensive particle characterization reaches a limit when using the morphological parameters derived from the instruments' operating software because of the complexity of CBMPs, which may consist of multiple particulate populations of highly heterogeneous morphologies.…”

Particles in Biopharmaceutical Formulations, Part 2: An Update on Analytical Techniques and Applications for Therapeutic Proteins, Viruses, Vaccines and Cells

“…226,228,229 For differentiation of cells and other particulate components in the sample, FIM is particularly suited, owing to the high-resolution images. Morphological filters can be developed in the instrument's software to differentiate between cells, 220,221 cell debris, 221,230 and process-related impurities such as SO droplets, glass and rubber particles 230 . Furthermore, machine learning tools can improve the discrimination between cells, debris, aggregates and magnetic beads, and potentially other particulate impurities, in FIM images in comparison to classical FIM analysis approaches.…”