Therapeutic protein drugs are subjected to numerous stresses throughout their manufacturing, storage and shipping that may result in subvisible particles being formed.
Convolutional neural networks can be used to classify particles generated to mAbs by common manufacturing stresses.
Machine learning approaches using convolutional neural networks will be used to extract useful information from collections of images of particles measured after biopharmaceutical fill-finish operations
By implementing machine learning techniques to detect out of specification particulate matter in biopharmaceutical processing, an organization could reduce the costs associated with product recalls and waste by up to 30%, enabling the company to maintain high product quality standards and increase operational efficiency.
Image set development for normal and stressed conditions to create a library to support root-cause analysis.
Applied machine learning approach using convolutional neural networks to extract information from particles measured after biopharmaceutical fill-finish operations.
Greenblott, D. N., Wood, C. V., Zhang, J., Viza, N., Chintala, R., Calderon, C. P., & Randolph, T. W. (2024). Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation. Biotechnology and Bioengineering, 121(5), 1626-1641. https://doi.org/10.1002/bit.28671
Greenblott, David, “Machine Learning Approaches to Characterization and Root-Cause Analysis of Particulates in Monoclonal Antibody Formulations,” poster presented at the AAPS ParmSci 360, October 17-20, 2021
Randolph, T. (Feb 24, 2022) Machine Learning and Flow Imaging Microscopy for Characterization and Root-Cause Analysis of Particulates in Biopharmaceutical Formulations. Presented to NIIMBL Member Forum.
Randolph, T., Greenblott, D., Machine Learning Approaches to Characterization and Root-Cause Analysis of Particulates in Monoclonal Antibody Formulations, NIIMBL Annual Meeting, Washington, DC, July 14, 2021.
Randolph, T., Machine Learning and Flow Imaging Microscopy for Characterization and Root-Cause Analysis of Particulates in Biopharmaceutical Formulations. Presented to Merck, Jan 27, 2022.
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Regents of the University of Colorado (Boulder)
Merck Sharp & Dohme LLC
SentrySciences LLC
