Application of one-class classification using deep learning technique improves the classification of subvisible particles

Abstract

Capturing subvisible particles using flow imaging microscopy is useful for evaluating protein aggregates that may induce immunogenicity. Automated labeling is desirable to distinguish harmless components such as silicone oil (SO) from subvisible particles. The one-class classifier, which requires only target class data for model establishment, is suitable for machine learning and proposes a useful solution for distinguishing a subject with heterogeneous but stable distributions, such as SO. However, the effectiveness of the application of one-class classifiers to subvisible particles remains unclear. In this study, we investigated whether deep learning techniques can improve the performance on a variety of images. We prepared datasets using SO and two types of protein aggregates: immunoglobulin G-derived aggregates (Agg_IgG) and albumin-derived aggregates (Agg_Alb). The deep-learning technique improved the classification scores for both Agg_IgG and Agg_Alb. The classification scores for Agg_IgG were more satisfactory than those for Agg_Alb. Cluster analysis revealed that one-class classification using deep learning techniques achieved excellent effectiveness across almost all clusters in classifying Agg_IgG. Collectively, the deep learning technique remarkably improved the one-class classification of subvisible particles of Agg_IgG and Agg_Alb. Combined with deep learning, one-class classification can contribute to the evaluation of subvisible particles, particularly for Agg_IgG.