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Monoclonal antibodies are mainly produced by mammalian cell culture, which due to its complexity, results in a wide range of product variants/isoforms. With the growing implementation of Quality by Design (QbD) and Process Analytical Technology (PAT) in drug manufacturing, monitoring and controlling quality attributes within a predefined range during manufacturing may provide added consistency to product quality. To implement these concepts, more robust analytical tools could reduce the time needed for monitoring quality attributes during upstream processing. The formation of protein aggregates is one such quality attribute that can lead to safety and efficacy issues in the final drug product. Described in this study is a fully automated two-dimensional high performance liquid chromatography (2D-HPLC) method for characterizing protein aggregation of crude in-process bioreactor samples. It combines protein A purification and separation by size exclusion into a single analytical module that has the potential to be employed at-line within a bioprocessing system. This method utilizes a novel in-line fraction collection device allowing for the collection of up to twelve fractions from a single sample or peak which facilitates the subsequent linked analysis of multiple protein peaks of interest in one chromatography module., (Published by Elsevier B.V.)

Consistent high-quality antibody yield is a key goal for cell culture bioprocessing. This endpoint is typically achieved in commercial settings through product and process engineering of bioreactor parameters during development. When the process is complex and not optimized, small changes in composition and control may yield a finished product of less desirable quality. Therefore, changes proposed to currently validated processes usually require justification and are reported to the US FDA for approval. Recently, design-of-experiments-based approaches have been explored to rapidly and efficiently achieve this goal of optimized yield with a better understanding of product and process variables that affect a product's critical quality attributes. Here, we present a laboratory-scale model culture where we apply a Plackett-Burman screening design to parallel cultures to study the main effects of 11 process variables. This exercise allowed us to determine the relative importance of these variables and identify the most important factors to be further optimized in order to control both desirable and undesirable glycan profiles. We found engineering changes relating to culture temperature and nonessential amino acid supplementation significantly impacted glycan profiles associated with fucosylation, β-galactosylation, and sialylation. All of these are important for monoclonal antibody product quality., (© 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.)