Advancing Subvisible Particle Characterization With AI

By Daniel Weinbuch, Ph.D., Business Development Manager, Coriolis Pharma

Characterizing subvisible particles is a vital component of ensuring the safety and stability of biopharmaceutical products. While traditional methods like light obscuration provide essential counts and sizing, they often fall short of offering the deep morphological insights needed to differentiate between particle types, such as protein aggregates and silicone oil droplets. Integrating artificial intelligence into flow imaging microscopy transforms this process by leveraging convolutional neural networks to analyze complex datasets.

This approach creates unique "fingerprints" for particle populations, allowing for precise classification and the detection of subtle outliers that manual analysis might miss. By identifying the root causes of particle formation early in development, researchers can refine formulations with greater confidence and speed. These advancements not only enhance batch-to-batch consistency but also provide a robust framework for navigating modern regulatory expectations. Explore how AI-driven analysis is redefining the boundaries of particle characterization to support the next generation of safe, high-quality therapeutics.