LC‐HRMS‐based targeted metabolomics for high‐throughput and quantitative analysis of 21 growth inhibition‐related metabolites in Chinese hamster ovary cell fed‐batch cultures. (8th February 2022)

• Record Type: Journal Article
• Authors: Lai, Zijuan; Choudhury, Feroza K.; Tang, Danming; Liang, Xiaorong; Dean, Brian; Misaghi, Shahram; Sangaraju, Dewakar
• Is Part Of: Biomedical chromatography. Volume 36:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-08
• Subjects: bioprocessing, Chinese hamster ovary cell culture, LC–HRMS, quantitative analysis, targeted metabolomics; Chromatographic analysis -- Periodicals; Biology -- Periodicals; Medicine -- Periodicals; Biology -- Periodicals; Chromatography -- methods -- Periodicals; Medicine -- Periodicals; 543.089
• DOI: 10.1002/bmc.5348
• Languages: English
• ISSNs: 0269-3879

Abstract

Abstract: Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed‐batch cultures produce various amino acid–derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and, more interestingly, antibody drug productivity. Herein, we developed an LC‐HRMS‐based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition‐related metabolites generated from 14 different amino acids in CHO cell fed‐batch cultures. High throughput derivatization procedures, matrix‐matched calibration curves, stable isotope‐labeled internal standards, and accurate mass full MS scan were utilized to achieve our goal for a wide range of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M + 1 ion as a quantification analog in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis, and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell‐based monoclonal antibody (mAb) production process with fed‐batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched‐chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.