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

Authors:
Dominik Georg Sauer
Dominik Georg Sauer
Austrian Centre of Industrial Biotechnology
Michael Melcher
Michael Melcher
Institute of Science and Technology in Art
Austria
Magdalena Mosor
Magdalena Mosor
Austrian Centre of Industrial Biotechnology
Friedrich Leisch
Friedrich Leisch
Institute for Applied Statistics and Computing
Alois Jungbauer
Alois Jungbauer
University of Natural Resources and Applied Life Sciences Vienna
Austria

Biotechnol Bioeng 2019 Apr 1. Epub 2019 Apr 1.

Austrian Centre of Industrial Biotechnology, Vienna, Austria.

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 coliOnline 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.

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http://dx.doi.org/10.1002/bit.26984DOI Listing
April 2019
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References

(Supplied by CrossRef)
Infrared spectroscopy of proteins
Barth A. et al.
Biochimica et Biophysica Acta 2007
Boosting with the L2 loss: Regression and classification
Bühlmann P. et al.
Journal of the American Statistical Association 2003

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