Discrimination between silicone oil droplets and protein aggregates in biopharmaceuticals: a novel multiparametric image filter for sub-visible particles in microflow imaging analysis.

PURPOSE: Accurate monitoring of the sub-visible particle load in protein biopharmaceuticals is increasingly important to drug development. Manufacturers are expected to characterize and control sub-visible protein particles in their products due to their potential immunogenicity. Light obscuration, the most commonly used analytical tool to count microscopic particles, does not allow discrimination between potentially harmful protein aggregates and harmless pharmaceutical components, e.g. silicone oil, commonly present in drug products. Microscopic image analysis in flow-microscopy techniques allows not only counting, but also classification of sub-visible particles based on morphology. We present a novel approach to define software filters for analysis of particle morphology in flow-microscopic images enhancing the capabilities of flow-microscopy.
METHODS: Image morphology analysis was applied to analyze flow-microscopy data from experimental test sets of protein aggregates and silicone oil suspensions.
RESULTS: A combination of four image morphology parameters was found to provide a reliable basis for automatic distinction between silicone oil droplets and protein aggregates in protein biopharmaceuticals resulting in low misclassification errors.
CONCLUSIONS: A novel, custom-made software filter for discrimination between proteinaceous particles and silicone oil droplets in flow-microscopy imaging analysis was successfully developed.