Applying machine learning to flow imaging of pharmaceutical products can assist in defining the criticality of product quality attributes, as well as establishing an integrated control strategy for characterization and control of drug products.
TORONTO (PRWEB) March 10, 2020
Flow Imaging is a proven method for the characterization of particulates in therapeutic products. It is routinely performed alongside the United States Pharmacopeia (USP) 788/787 Light Obscuration methods to more accurately quantify and characterize the particle subpopulations in drug products (silicone oil, protein aggregate, extrinsic material, etc.). Typical classifications of imaging data use single parameter filters such as aspect ratio to quantify silicone oil compared to protein. However, machine learning provides a sophisticated approach to more accurately classify particles in therapeutic products by leveraging the information present in the raw particle images.
This free webinar will demonstrate how various machine learning algorithms facilitate improved classification compared to the traditional approach, leading to superior sample descriptions. It will also showcase examples of the benefits that machine learning provides for protein products and cell therapy products. Flow Imaging has tremendous potential to monitor particle size distributions, aggregates/agglomerates and extrinsic contaminants from batch to batch. Applying machine learning to flow imaging of pharmaceutical products can assist in defining the criticality of product quality attributes, as well as establishing an integrated control strategy for characterization and control of drug products.
For more information or to register for this event, visit Machine Learning Applications for the Characterization of Particle Profiles of Therapeutic Products.
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