ABSTRACT
Understanding the particulate content of formulated drug products is essential for ensuring patient safety. In particular, it is critical to assess the presence of aggregated proteins or extraneous particles (e.g. fibres) that pose potential dangers. Additionally, it is useful to be able to distinguish non-proteinaceous particles, such as silicone oil droplets that commonly occur in formulations stored in pre-filled syringes. Standard particle counting methods (e.g. light obscuration) provide only total numbers of particles of a given size, but provide no mechanism for particle classification. Significant recent work has focused on the use of flow imaging microscopy to enable simultaneous classification and counting of particles using machine learning (ML) models including convolutional neural networks (CNN). In this paper we expand upon this theme by exploring techniques for achieving high prediction accuracy when the size of the labeled dataset used for model training is limited. We demonstrate that maximum performance can be achieved by combining multiple techniques such as data augmentation, transfer learning, and novel (to this field) models combining imaging and tabular data.
