Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals

TL;DR

This study demonstrates that color flow imaging microscopy combined with deep learning significantly improves the identification of stress sources in protein aggregate particles, enhancing biopharmaceutical quality control.

Contribution

It introduces the use of color FIM images with deep learning for stress source classification, outperforming monochrome imaging methods.

Findings

Color FIM with deep learning outperforms monochrome FIM in stress source identification.

A new dataset of 16,000 SvPs from monoclonal antibodies was curated for this study.

Deep learning models, including CNNs and vision transformers, effectively classify stress sources using color FIM images.

Abstract

Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible particles (SvPs), which can compromise their efficacy and trigger immunological responses, emphasizing the critical need for robust monitoring techniques. Flow Imaging Microscopy (FIM) has been a significant advancement in detecting SvPs, evolving from monochrome to more recently incorporating color imaging. Complementing SvP images obtained via FIM, deep learning techniques have recently been employed successfully for stress source identification of monochrome SvPs. In this study, we explore the potential of color FIM to enhance the characterization of stress sources in SvPs. To achieve this, we curate a new dataset comprising 16,000 SvPs from eight commercial monoclonal antibodies subjected to heat and mechanical stress. Using both supervised and self-supervised convolutional neural networks, as well as vision transformers in large-scale experiments, we demonstrate that deep learning with color FIM images consistently outperforms monochrome images, thus highlighting the potential of color FIM in stress source classification compared to its monochrome counterparts.