Monitoring protein particles is increasingly emphasized in the development of biopharmaceuticals due to potential immunogenicity. Accurate quantitation of protein particles is complicated by silicone oil droplets, a common pharmaceutical component in pre-filled syringes. Though silicone oil is typically regarded as harmless, numerous reports have indicated protein adsorption may render these particles with immunostimulatory properties. Imaging flow cytometry (IFC) is an emerging pharmaceutical method capable of capturing high-resolution brightfield and fluorescence imagery from samples in suspension. In this study, we created a data analysis strategy using artificial intelligence (AI) software to classify brightfield images collected with IFC as protein or silicone oil. The AI software performs image classification using deep learning with a convolutional neural network architecture, for identification of subtle morphological phenotypes. The AI model yielded robust classification of particles >2 μm across various sources of protein and silicone oil particles and over the instrument life cycle. Next, the AI model was combined with IFC fluorescence images to differentiate potentially immunogenic protein-adsorbed silicone and innocuous native silicone. The methods reported herein provide added analytical capability for characterization of particulate matter in therapeutic formulations, and may be applied for optimization of protein formulations and evaluation of product consistency.
Keywords: Adsorption; Formulation; Image analysis; Morphology; Neural network(s); Physical stability; Protein aggregation; Protein formulation(s); Stability; Surfactant(s).
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