(82b) Using Particle Size Distributions to Predict the Filterability of Materials throughout the Processing of Monoclonal Antibodies

Throughout the downstream processing of monoclonal antibodies, filtration is used to remove particulates from various materials. As these materials can vary widely in their filterability, robust process development is necessary to prevent filter failure prior to reaching the desired filter loading at the manufacturing scale. Therefore, a method that can predict a material’s filterability based on easily measurable material characteristics would benefit development and process transfer. For example, turbidity has previously been used as a rough measure to predict a material’s filterability but is typically not an accurate predictor on its own. We explore the role of both turbidity and particle size distribution in the filtration of materials obtained from commercial-scale manufacturing batches of a monoclonal antibody. Ultimately, the results of this work may allow us to develop a model to predict a material's filterability based on its turbidity and particle size distribution.