This talk will review recent advances in applying machine learning/AI to characterize images of particles in biologics. The approach aims to utilize the wealth of morphological information encoded in images (augmenting size and count information) produced in the outputs of many commercially available imaging platforms (flow imaging microscopy, backgrounded membrane imaging, etc.). Case studies from protein therapeutics and other biologic drugs will be discussed. The technology presented underlies ParticleSentryAI, a recently released software characterization product.
Learning Objectives:
Describe how machine learning can increase information gained from common commercial high-throughput microscopy platforms for monitoring subvisible particles in the biopharmaceuitical industry.
Discuss the potential of machine learning and AI in formulation screening and quality control applications involving biologic drugs.
Define improved subvisible particle characterization strategies that harness machine learning, develop machine learning applications for other techniques in pharmaceutical development, and identify commercial software for assisting in these tasks.