Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Dominik Georg Sauer, Michael Melcher, Magdalena Mosor, Nicole Walch, Matthias Berkemeyer, Theresa Scharl-Hirsch, Friedrich Leisch, Alois Jungbauer, Astrid Dürauer
Biotechnology and Bioengineering 2019 April 1
Process analytical technology combines understanding and control of the process with real-time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study a chromatographic workstation was equipped with additional online sensors, such as multi-angle light scattering, refractive index, attenuated total reflection Fourier-transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and dsDNA content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in E. coli. Online data and corresponding offline data for product quantity and co-eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/mL (range in training data: 0.1-28 mg/mL). For HCP and dsDNA additional fluorescence and/or ATR-FTIR spectral information was important to achieve prediction errors of 200 ppm (2-6579 ppm) and 340 ppm (8-3773 ppm), respectively. This article is protected by copyright. All rights reserved.

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