用彩色流式成像提升生物药蛋白聚集物应力源识别精度
Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals
- 结合彩色流式成像与深度学习，实现应力源精准分类
- 16000个蛋白颗粒数据集验证，彩色图像识别效果更优
- 适合药物研发中质量控制与稳定性评估人员参考
基于蛋白的治疗药物在现代医学中至关重要，但易形成亚可见颗粒（SvPs），影响疗效并引发免疫反应，亟需有效监测手段。流式成像显微镜（FIM）在SvP检测中已取得进展，从黑白图像发展到彩色成像。本研究构建了包含8种商业单克隆抗体在热与机械应力下产生的16,000个SvPs的新数据集，采用监督与自监督卷积神经网络及视觉变换器进行大规模实验，结果表明：使用彩色FIM图像的深度学习模型在应力源分类任务中持续优于黑白图像，证实彩色FIM在提升蛋白聚集物应力源识别能力方面具有显著优势。
原文摘要 · Abstract (English)
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.
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