Monitoring of amino acids and antibody N-glycosylation in high cell density perfusion culture based on Raman spectroscopy

• Main Authors: Castan, A. (Author), Chotteau, V. (Author), Mäkinen, M.E (Author), Schwarz, H. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: CHO cells; Monoclonal antibody; Perfusion process; PLS model; Process analytical technology; Raman spectroscopy
• Online Access: View Fulltext in Publisher

 Raman spectrum based predictive models provide a process analytical technology (PAT) tool for monitoring and control of culture parameters in bioprocesses. Steady-state perfusion cultures generate a relatively stable metabolite profile, which is not conducive to modeling due to the absence of variations of culture parameters. Here we present an approach where different steady-states obtained by variation of the cell specific perfusion rate (CSPR) between 10 and 40 pL/(cell * day) with cell densities up to 100 × 106 cells/mL during the process development provided a dynamic culture environment, favorable for the model calibration. The cell density had no effect on the culture performance at similar CSPR, however a variation in the CSPR had a strong influence on the metabolism, mAb productivity and N-glycosylation. Predictive models were developed for multiple culture parameters, including cell density, lactate, ammonium and amino acids; and then validated with new runs performed at multiple or single steady-states, showing high prediction accuracy. The relationship of amino acids and antibody N-glycosylation was modeled to predict the glycosylation pattern of the product in real time. The present efficient process development approach with integration of Raman spectroscopy provides a valuable PAT tool for later implementation in steady-state perfusion production processes. © 2022 The Authors