Microflow Imaging Analyses Reflect Mechanisms of Aggregate Formation: Comparing Protein Particle Data Sets Using the...

Subvisible particles in therapeutic protein formulations are an increasing manufacturing and regulatory concern because of their potential to cause adverse immune responses. Flow imaging microscopy is used extensively to detect subvisible particles and investigate product deviations, typically by comparing imaging data using histograms of particle descriptors. Such an approach discards much information and requires effort to interpret differences, which is problematic when comparing many data sets. We propose to compare imaging data using the Kullback-Leibler divergence, an information theoretic measure of the difference of distributions (Kullback S, Leibler RA. 1951. Ann Math Stat. 22:79-86). We use the divergence to generate scatter plots representing the similarity between data sets and to classify new data into previously determined categories. Our approach is multidimensional, automated, and less biased than traditional techniques. We demonstrate the method with FlowCAM® imagery of protein aggregates acquired from monoclonal antibody samples subjected to different stresses. The method succeeds in classifying aggregated samples by stress condition and, once trained, is able to identify the stress that caused aggregate formation in new samples. In addition to potentially detecting subtle incipient manufacturing faults, the method may have applications to verification of product uniformity after manufacturing changes, identification of counterfeit products, and development of closely matching bio-similar products.