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Updated: Sep 5, 2025

Purification and Analytics of a Monoclonal Antibody from Chinese Hamster Ovary Cells Using an Automated Microbioreactor System
Published on: May 1, 2019
Design and optimization of membrane chromatography for monoclonal antibody charge variant separation
Sathish Nadar1, Balaji Somasundaram2, Marcela Charry2
1Australian Research Council Training Centre for Biopharmaceutical Innovation, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
Abstract:
The manufacturing scale implementation of membrane chromatography to purify monoclonal antibodies has gradually increased with the shift in industry focus toward flexible manufacturing and disposable technologies. Membrane chromatography are used to remove process-related impurities such as host cell proteins (HCPs) and DNA, leachates, and endotoxins, with improved productivity and process flexibility. However, application of membrane chromatography to separate product-related variants such as charge variants has not gained major traction due to low-binding capacity. The work reported here demonstrates that a holistic process development strategy to optimize static binding (pH and salt concentration) and dynamic process (membrane loading, flowrate, and gradient length) parameters can alleviate the capacity limitations. The study employed high throughput screening tools and scale-down membranes for intermediate and polishing purification of the model monoclonal antibody. An optimized process consisting of anion exchange and cation exchange membrane chromatography reduced the acidic variants present in Protein A eluate from 89.5% to 19.2% with 71% recovery of the target protein. The membrane chromatography process also cleared HCP to below limit of detection with 6- to 30-fold higher membrane loading, compared to earlier reported values. The results confirm that membrane chromatography is effective in separating closely related product variants when supported by a well-defined process development strategy.
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