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Bioreactor Process Parameter Screening Utilizing a Plackett-Burman Design for a Model Monoclonal Antibody

Published

Author(s)

John E. Schiel, Cyrus D. Agarabi, Scott C. Lute, Brittany K. Chavez, Michael T. Boyne, Kurt A. Brorson, Mansoor A. Kahn, Erik K. Read

Abstract

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 than desirable quality. Therefore, changes proposed to currently validated processes usually require justification and are reported to the FDA for approval. Recently, Design of Experiments (DoE) 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 products critical quality attributes. Here, we present a lab-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. We found engineering changes relating to culture temperature and non-essential amino acid supplementation significantly impacted glycan profiles associated with fucosylation, β-galactosylation, and sialylation, which are important for monoclonal antibody product quality.
Citation
Journal of Pharmaceutical Sciences
Volume
104
Issue
6

Keywords

Design of Experiments, Monoclonal Antibody, Glycosylation, Quality by Design, Mammalian Cell Culture

Citation

Schiel, J. , Agarabi, C. , Lute, S. , Chavez, B. , Boyne, M. , Brorson, K. , Kahn, M. and Read, E. (2015), Bioreactor Process Parameter Screening Utilizing a Plackett-Burman Design for a Model Monoclonal Antibody, Journal of Pharmaceutical Sciences, [online], https://doi.org/10.1002/jps.24420 (Accessed April 18, 2024)
Created March 11, 2015, Updated November 10, 2018