Image Classification of Degraded Polysorbate, Protein and Silicone Oil Sub-Visible Particles Detected by Flow-Imaging Microscopy in Biopharmaceuticals Using a Convolutional Neural Network Model

Abstract

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.