Towards quantification and differentiation of protein aggregates and silicone oil droplets in the low micrometer and submicrometer size range by using oil-immersion flow imaging microscopy and convolutional neural networks

Abstract

Biopharmaceutical product characterization benefits from the quantification and differentiation of unwanted protein aggregates and silicone oil droplets to support risk assessment and control strategies as part of the development. Flow imaging microscopy is successfully applied to differentiate the two impurities in the size range larger than about 5 µm based on their morphological appearance. In our study we applied the combination of oil-immersion flow imaging microscopy and convolutional neural networks to extend the size range below 5 µm. It allowed to differentiate and quantify heat stressed therapeutic monoclonal antibody aggregates from artificially generated silicone oil droplets with misclassification rates of about 10% in the size range between 0.3 and 5 µm. By comparing the misclassifications across the tested size range, particles in the low submicron size range were particularly difficult to differentiate as their morphological appearance becomes very similar.

Keywords: Convolutional neural network; Flow imaging microscopy; Machine learning; Monoclonal antibody; Protein aggregation; Silicone oil.