# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 1 Introduction  
Therapeutic protein formulations are among the fastest-developing pharmaceutical classes. Pharmaceutical formulations always contain subvisible particles owing to the limitations of the production process and the instability of the proteins [1]. According to the discussion in USP1790 [2], particles in protein formulations can usually be classified as \"extrinsic\", \"intrinsic\", or \"inherent\" particles. Extrinsic particles can be considered foreign to the manufacturing process, including hair, non-process-related fibers, starch, minerals, and similar inorganic and organic materials. Intrinsic particles are those related to the manufacturing process, which may originate from processing equipment or packaging materials, including seals, gaskets, packaging glass and elastomers, fluid transport tubing, and silicone lubricant. Inherent particles are associated with the production formulationsand primarily contain protein aggregates. Protein aggregation mainly results from changes in product stability caused by stress in industrial production, including freeze-thawing, mechanism shock, shaking and shearing, elevated temperatures, oxidation, or a combination of these stresses [3; 4; 5; 6]. Because particles may cause sterile failure or even produce severe immunogenic reactions [7; 8; 9], endangering patient safety [10], the FDA and other regulatory agencies have imposed strict quantitative limits on protein aggregates and other particles in pharmaceutical formulations. USP<788> [11] requires pharmaceutical formulation product manufacturing departments to monitor particle content using light obscuration or microscopy, and to set a firm upper limit for the number of particles above 10 \(\upmu\)m and 25 \(\upmu\)m to ensure that the formulation is free of particles. Because of the complexity of the sources of particles and their possible adverse effects, it is imperative to identify the particles, which helps to trace their sources and thus avoid them in the manufacturing process, thereby reducing the potential risk of adverse reactions caused by these particles at the source. Over the past few years, flow imaging microscopy (FIM) has been widely used as an increasingly popular imaging technology in cutting-edge research in environmental science, biology, pharmacology, and other disciplines. This is mainly because FIM instruments, such as FIM or FlowCam, can image particles/microorganisms between 2 \(\upmu\)m and 200 \(\upmu\)m in diameter in rapid batches [12] and obtain high-quality images. FlowCam consists of a 10\(\times\) microscope, camera, software, and flow cell with a flow rate controlled by a vacuum pump. After injecting the liquid, the liquid flows through the flow cell at a low speed under the action of the pump, while the camera captures images at a high framerate. Additionally, the software extracts independent particle images from the camera images through threshold extractions. Such an FIM instrument can perform particle counting and provide more than 1000 single-particle images with a large number of morphological features in each experiment. Because of the difference in imaging principles, FIM is more sensitive than the light obscuration method in counting translucent particles [13], such as protein particles, and has been increasingly used to monitor the particle content in protein preparations. USP<1788> [14] even introduced a revised chapter using FIM to support light obscuration and microscopy methods for determining subvisible particles. However, most studies using FIM have focused on analyzing particle concentration or simple particle parameters. For example, FIM is used to determine the relationship between the stress type and particle quantity [15], or identify simple morphological features to distinguish between protein and silicone oil [16]. However, using simple morphological features extracted by image-based algorithms often leads to the loss of a great deal of information in the complete grayscale image provided by FIM.