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

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