November 04, 2021 | 11:00am ET
Quantitation of Subvisible Particles by Class in Cell-Based Medicinal Products (CBMPs)
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
About the Presenter
Christine Probst works as a Senior Scientist within the Particle Characterization Core at KBI Biopharma in Boulder, Colorado. She holds a Master’s degree in Bioengineering from University of Washington and a Bachelor’s degree in Bioengineering from the University of California Los Angeles. She has contributed numerous papers and presentations to characterization of subvisible particles in protein biologics. Her current work focuses on characterization of particulate matter within next generation therapeutics and devices such as cell-based or vaccine therapeutics.