Chemometrics and in-line near infrared spectroscopic monitoring of a biopharmaceutical Chinese hamster ovary cell culture: Prediction of multiple cultivation variables

Abstract

In the present study near infrared (NIR) spectroscopy was used to monitor the cultivation of mammalian Chinese hamster ovary (CHO) cells producing a monoclonal antibody in a fed-batch cell culture process. A temperature shift was applied during the cultivation. The cells were incubated at 37°C and 33°C. The Fourier transform near infrared (FT-NIR) multiplex process analyzer spectroscopy was investigated to monitor cultivation variables of the CHO cell culture from 10 independent batches using two channels of the FT-NIR. The measurements were performed on production scale bioreactors of 12,500L. The cell cultures were analyzed with the spectrometer coupled to a transflection sterilizable fiber optic probe inserted into the bioreactors. Multivariate data analysis (MVDA) employing unsupervised principal component analysis (PCA) and partial least squares regression methods (PLS) were applied. PCA demonstrated that 96% of the observed variability was explained by the process trajectory and the inter-batch variability. PCA was found to be a significant tool in identifying batch homogeneity between lots and in detecting abnormal fermentation runs. Seven different cell culture parameters such as osmolality, glucose concentration, product titer, packed cell volume (PCV), integrated viable packed cell volume (ivPCV), viable cell density (VCD), and integrated viable cell count (iVCC) were monitored inline and predicted by NIR. NIR spectra and reference analytics data were computed using control charts to evaluate the monitoring abilities. Control charts of each media component were under control by NIR spectroscopy. The PLS calibration plots offered accurate predictive capabilities for each media. This paper underlines the capability for inline prediction of multiple cultivation variables during bioprocess monitoring.