Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries

Abstract

Background Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross-correlation of various media components and artifacts throughout the process. Main Methods A systematic Raman calibration workflow for perfusion processes enabling highly specific and fast model calibration was developed. Harvest libraries consisting of frozen harvest samples from multiple CHO cell culture bioreactors collected at different process times were established. Model calibration was subsequently performed in an offline setup using a flow cell by spiking process harvest with glucose, raffinose, galactose, mannose, and fructose. Major Results In a screening phase, Raman spectroscopy was proven capable not only to distinguish sugars with similar chemical structures in perfusion harvest but also to quantify them independently in process-relevant concentrations. In a second phase, a robust and highly specific calibration model for simultaneous glucose (root mean square error prediction [RMSEP] = 0.32 g L−1) and raffinose (RMSEP = 0.17 g L−1) real-time monitoring was generated and verified in a third phase during a perfusion process. Implication The proposed novel offline calibration workflow allowed proper Raman peak decoupling, reduced calibration time from months down to days, and can be applied to other analytes of interest including lactate, ammonia, amino acids, or product titer. Graphical Abstract and Lay Summary Building accurate and robust Raman models for online monitoring of cell culture processes remains a difficult and time-consuming process, particularly for perfusion processes. In this study, the authors developed a novel offline calibration approach based on design-of-experiment spiking and a harvesting library. The Raman spectra of these spiked harvest samples allowed proper peak decoupling and model generation within days instead of weeks or even months. The approach has been successfully applied to monitor various sugars in perfusion bioreactors and other compounds as well as process modes may equally benefit from the described workflow.