Exploring the Capabilities of Fluorometric Online Monitoring on Chinese Hamster Ovary Cell Cultivations Producing a Monoclonal Antibody

Online monitoring of Chinese hamster ovary fed-batch cell cultures via two-dimensional fluorescence spectroscopy (2DFS) was evaluated in this work. Particular attention was directed toward different process strategies regarding the use of nutrient-rich feed media and temperature shifts. These intentionally performed process manipulations broadened the variances in the obtained fluorescence spectra and this was suspected to hamper the generation of reliable soft sensors. Principal component analysis of the obtained fluorescence data showed that temperature shift and feeding strategy had a considerable impact on the fluorescence signals. Partial least square regression models were calculated for the prediction of glucose, lactate, monoclonal antibody (mAb), and viable cell concentrations (VCC). It was aimed to integrate all 2DFS datasets in the respective calibration models regardless of the process-strategy-dependent diversity. Contrary to the expectations, it was feasible to calibrate soft sensors for the online prediction of glucose (7 latent variables (LVs), R-cal(2) = 0.97, rout mean squared error of prediction (RMSEP) = 1.1 g L-1), lactate (5 LV; R-cal(2) = 0.96; RMSEP = 0.5 g L-1) and mAb concentrations (4 LV; R-cal(2) = 0.99; RMSEP = 11.4 mg L-1). Feeding and temperature shifts had the highest impact on the VCC model (3 LV; R-cal(2) = 0.94; RMSEP 3.8 x 10(5) mL(-1)), nevertheless the prediction of VCC from the fed-batch 2DFS data was feasible. The results strongly indicate that variances in the datasets due to the process strategy can be tolerated to some extent by the respective soft sensors. (C) 2016 American Institute of Chemical Engineers