Cell-based medicinal products (CBMPs) face unique challenges in manufacturing and quality control due to donor variability, limited shelf-life, and inability to sterile-filter samples. Subvisible particles including live cells, dead cells, and intrinsic and extrinsic contaminants are considered important quality attributes for CBMPs. However, robust methods to characterize subvisible particles in CBMPs are still being developed. KBI’s Particle Characterization Core has developed a novel CBMP classification model by combining an imaging flow cytometry- an emerging analytical method- with deep learning using convolutional neural networks. The model was trained to sort particles into live cells, dead cells (via Trypan Blue viability staining), doublets, clusters, protein aggregates, and silicone oil droplets. We will present case studies demonstrating how the classification model can be applied for optimization of CBMP storage, shipment, and administration.
During this webinar, participants will learn:
Learn how imaging flow cytometry and convolutional neural networks can be combined to characterize CBMP particle profiles
Review case studies optimizing cell storage, shipment, and administration though analysis of CBMP particle profiles
