Imaging Flow Cytometry for Sizing and Counting of Subvisible Particles in Biotherapeutics.

Imaging flow cytometry (IFC), a technique originally designed for cellular imaging, is featured by the parallel acquisition in brightfield (BF), fluorescence (FL), and side scattering channels. Introduced to the field of subvisible particle analysis in biopharmaceuticals roughly ten years ago, it has the potential to yield additional information, e.g., on particle origin. Here, we present an extensive, systematic development of masks for IFC image analysis to optimize the accuracy of size determination of polystyrene beads and pharmaceutically relevant particles (protein, silicone oil) in BF and FL channels. Based on the developed masks, particle sizing and counting by IFC are compared to flow imaging microscopy (FIM). Mask verification based on fluorescent polystyrene particles revealed good agreement between sizes obtained from IFC and FIM. In the evaluation of counting accuracy, IFC reported lower concentrations for polystyrene particle standards than FIM. For the analysis of fluorescently stained silicone oil and protein particles however, IFC FL imaging reported higher particle concentrations in the low micrometer size range. Overall, we identified IFC as suitable tool to generate supportive data for particle characterization purposes or trouble shooting activities, but not as routine quantitative technique, e.g., for subvisible particle analysis during drug product development or quality control.

Forced degradation of cell-based medicinal products guided by flow imaging microscopy: Explorative studies with Jurkat cells.

Cell-based medicinal products (CBMPs) offer ground-breaking opportunities to treat diseases with limited or no therapeutic options. However, the intrinsic complexity of CBMPs results in great challenges with respect to analytical characterization and stability assessment. In our study, we submitted Jurkat cell suspensions to forced degradation studies mimicking conditions to which CBMPs might be exposed from procurement of cells to administration of the product. Flow imaging microscopy assisted by machine learning was applied for determination of cell viability and concentration, and quantification of debris particles. Additionally, orthogonal cell characterization techniques were used. Thawing of cells at 5 °C was detrimental to cell viability and resulted in high numbers of debris particles, in contrast to thawing at 37 °C or 20 °C which resulted in better stability. After freezing of cell suspensions at -18 °C in presence of dimethyl sulfoxide (DMSO), a DMSO concentration of 2.5% (v/v) showed low stabilizing properties, whereas 5% or 10% was protective. Horizontal shaking of cell suspensions did not affect cell viability, but led to a reduction in cell concentration. Fetal bovine serum (10% [v/v]) protected the cells during shaking. In conclusion, forced degradation studies with application of orthogonal analytical characterization methods allow for CBMP stability assessment and formulation screening.

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

Cell-based medicinal products (CBMPs) are rapidly gaining importance in the treatment of life-threatening diseases. However, the analytical toolbox for characterization of CBMPs is limited. The aim of our study was to develop a method based on flow imaging microscopy (FIM) for the detection, quantification and characterization of subvisible particulate impurities in CBMPs. Image analysis was performed by using an image classification approach based on a convolutional neural network (CNN). Jurkat cells and Dynabeads were used in our study as a representation of cellular material and non-cellular particulate impurities, respectively. We demonstrate that FIM assisted with CNN is a powerful method for the detection and quantification of Dynabeads and cells with other process related impurities, such as cell agglomerates, cell-bead adducts and debris. By using CNN, we achieved a more than 50-fold lower misclassification rate compared with the use of output parameters from the FIM software. The limit of detection was ~15 000 beads/mL in the presence of ~500 000 cells/mL, making this approach suitable for the detection of these particulate impurities in CBMPs. In conclusion, CNN-assisted FIM is a powerful method for the detection and quantification of cells, Dynabeads and other subvisible process impurities potentially present in CBMPs.