Improved process analytical technology for protein a chromatography using predictive principal component analysis tools.
Protein A chromatography is widely employed for the capture and purification of antibodies and Fc-fusion proteins. Due to the high cost of protein A resins, there is a significant economic driving force for using these chromatographic materials for a large number of cycles. The maintenance of column performance over the resin lifetime is also a significant concern in large-scale manufacturing. In this work, several statistical methods are employed to develop a novel principal component analysis (PCA)-based tool for predicting protein A chromatographic column performance over time. A method is developed to carry out detection of column integrity failures before their occurrence without the need for a separate integrity test. In addition, analysis of various transitions in the chromatograms was also employed to develop PCA-based models to predict both subtle and general trends in real-time protein A column yield decay. The developed approach has significant potential for facilitating timely and improved decisions in large-scale chromatographic operations in line with the process analytical technology (PAT) guidance from the Food and Drug Administration (FDA).
Hou
Ying
Y
Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
Jiang
Canping
C
Shukla
Abhinav A
AA
Cramer
Steven M
SM
D000906:Antibodies / Q000302:isolation & purification*; D001709:Biotechnology / Q000379:methods*; D002846:Chromatography, Affinity / Q000379:methods*; D025341:Principal Component Analysis; D011485:Protein Binding; D011994:Recombinant Proteins / Q000302:isolation & purification; D013205:Staphylococcal Protein A / Q000378:metabolism*
D000906:Antibodies; D011994:Recombinant Proteins; D013205:Staphylococcal Protein A