A novel approach to identify 5 types of simulated stresses that induce protein aggregation in prefilled syringeetype biopharmaceuticals was developed. Principal components analyses of texture metrics extracted from flow imaging microscopy images were used to define subgroups of particles. Supervised machine learning methods, including convolutional neural networks, were used to train classifiers to identify subgroup membership of constituent particles to generate distribution profiles. The applicability of the stress-specific signatures for distinguishing stress source types was verified. The high classification efficiencies (100%) precipitated the collection of data from more than 20 independent experiments to train support vector machines, k-nearest neighbors, and ensemble classifiers. The performances of the trained classifiers were validated. High classification efficiencies for friability (80%-100%) and heating at 90 C (85%-100%) are indicative of high reliability of these methods for stress-stability assays while extreme variations in freeze-thawing (2%-100%) and heating at 60 C (2.25%-98.25%) indicate the unpredictability of particle composition profiles for these forced degradation conditions. We also developed subvisible particle classifiers using convolutional neural network to automatically identify silicone oil droplets, air bubbles, and protein aggregates. The developed classifiers will contribute to mitigating aggregation in biopharmaceuticals via the identification of stress sources.

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