Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2. Issue 8 (17th April 2019)

• Record Type: Journal Article
• Authors: Sauer, Dominik Georg; Melcher, Michael; Mosor, Magdalena; Walch, Nicole; Berkemeyer, Matthias; Scharl‐Hirsch, Theresa; Leisch, Friedrich; Jungbauer, Alois; Dürauer, Astrid
• Is Part Of: Biotechnology and bioengineering. Volume 116:Issue 8(2019)
• Page Start: 1999
• Page End: 2009
• Publication Date: 2019-04-17
• Subjects: ATR‐FTIR -- dsDNA -- fluorescence -- HCP -- MALS -- online sensors; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.26984
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: 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 double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia 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 attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively. Abstract : A methodology with several online sensors for real‐time monitoring of chromatographic steps was developed. Based on the correlation of the observed online data with corresponding offline analytics of relevant critical quality attributes mathematical models were established for product concentration and impurity content e.g.host cell proteins and dsDNA. Those predictive models are the basis for process control such as starting / stopping fraction collection in accordance to pre‐set quality criteria. This set‐up reduces holding times and increases process efficiency.