Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process. (16th November 2021)

• Record Type: Journal Article
• Authors: A. Gibbons, Luke; Rafferty, Carl; Robinson, Kerry; Abad, Marta; Maslanka, Francis; Le, Nikky; Mo, Jingjie; Clark, Kevin; Madden, Fiona; Hayes, Ronan; McCarthy, Barry; Rode, Christopher; O'Mahony, Jim; Rea, Rosemary; O'Mahony Hartnett, Caitlin
• Is Part Of: Biotechnology progress. Volume 38:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-16
• Subjects: chemometrics -- multivariate data analysis -- Raman spectroscopy; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.3223
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract: The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono‐glycated, % non‐glycated, % G0F‐GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.