Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis.

Abstract

Polysorbate 20 (PS20) is commonly used as an excipient in therapeutic protein formulations. However, over the course of a therapeutic protein product's shelf life, minute amounts of co-purified host-cell lipases may cause slow hydrolysis of PS20, releasing fatty acids (FAs). These FAs may precipitate to form subvisible particles that can be detected and imaged by various techniques, e.g., flow imaging microscopy (FIM). Images of particles can then be classified using supervised convolutional neural networks (CNNs). However, CNNs should be trained on representative images of particles which, as we demonstrate in this work, may be challenging to obtain. Here, we tested several rapid techniques to create FA particles and examined whether CNNs trained on microscopy images of these rapidly formed particles could accurately classify images of particles that had been produced by kinetically slower lipase-catalyzed hydrolysis of PS20. CNNs trained on images of rapidly produced particles were less accurate in classifying images of FA particles that had been produced by enzymatic hydrolysis of PS20 than CNNs trained with images of particles generated by the same slow hydrolysis, highlighting the importance of using representative image data sets for training CNN classifiers.