Fedorowicz Filip M, Chalus Pascal, Kirschenbühler Kyra, Drewes Sarah, Koulov Atanas
Lonza AG, Drug Product Services, Hochbergerstrasse 60G, 4057 Basel, Switzerland; Current affiliation: Clear Solutions Laboratories AG, Mattenstrasse 22, 4058 Basel, Switzerland.
Lonza AG, Drug Product Services, Hochbergerstrasse 60G, 4057 Basel, Switzerland.
J Pharm Sci. 2023 Dec;112(12):3099-3108. doi: 10.1016/j.xphs.2023.07.003. Epub 2023 Jul 6.
Degradation of polysorbates in biopharmaceutical formulations can induce the formation of sub-visible particles (SvPs) in the form of free-fatty acids (FFAs) and potentially protein aggregates. Flow-imaging microscopy (FIM) is one of the most common techniques for enumerating and characterizing the SvPs, allowing for collection of image data of the SvPs in the size ranges of two to several hundred micrometers. The vast amounts of data obtained with FIM do not allow for rapid manual characterization by an experienced analyst and can be ambiguous. In this work, we present the application of a custom convolutional neural network (CNN) for classification of SvP images of FFAs, proteinaceous particles and silicon oil droplets, by FIM. The network was then used to predict the composition of artificially pooled test samples of unknown and labeled data with varying compositions. Minor misclassifications were observed between the FFAs and proteinaceous particles, considered tolerable for application to pharmaceutical development. The network is considered to be suitable for fast and robust classification of the most common SvPs found during FIM analysis.
生物制药制剂中聚山梨酯的降解会诱导以游离脂肪酸（FFA）和潜在蛋白质聚集体形式存在的亚可见颗粒（SvP）的形成。流动成像显微镜（FIM）是用于计数和表征SvP的最常用技术之一，可收集尺寸范围在2至数百微米的SvP的图像数据。通过FIM获得的大量数据无法由经验丰富的分析师进行快速手动表征，并且可能含糊不清。在这项工作中，我们展示了一种定制卷积神经网络（CNN）在通过FIM对FFA、蛋白质颗粒和硅油滴的SvP图像进行分类中的应用。然后使用该网络预测具有不同组成的未知和标记数据的人工混合测试样品的组成。在FFA和蛋白质颗粒之间观察到轻微的错误分类，认为这对于应用于药物开发是可以接受的。该网络被认为适用于对FIM分析过程中发现的最常见SvP进行快速且可靠的分类。
