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Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification

The identification of optimal process parameters for the isolation of a target component from multicomponent mixtures is especially challenging in industrial applications. With constantly increasing time-to-market pressure, screening a large parameter space is not feasible and design-of-experiment a...

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Main Authors: Hahn, Tobias (Author) , Huuk, Thiemo (Author) , Osberghaus, Anna (Author) , Doninger, Katharina (Author) , Nath, Susanne (Author) , Hepbildikler, Stefan (Author) , Heuveline, Vincent (Author) , Hubbuch, Jürgen (Author)
Format: Article (Journal)
Language:English
Published: 2016
In: Engineering in life sciences
Year: 2015, Volume: 16, Issue: 2, Pages: 107-113
ISSN:1618-2863
DOI:10.1002/elsc.201400248
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/elsc.201400248
Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/elsc.201400248
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Author Notes:Tobias Hahn, Thiemo Huuk, Anna Osberghaus, Katharina Doninger, Susanne Nath, Stefan Hepbildikler, Vincent Heuveline, Jürgen Hubbuch
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Summary:The identification of optimal process parameters for the isolation of a target component from multicomponent mixtures is especially challenging in industrial applications. With constantly increasing time-to-market pressure, screening a large parameter space is not feasible and design-of-experiment approaches with few experiments might fail due to dynamic and nonlinear reactions to small parameter changes. Model-based optimization can determine optimal operating conditions, once the model has been calibrated to the specific process step. In this work, parameters for the steric mass action model were estimated for the target protein and three impurities of an industrial antibody cation-exchange purification step using only chromatograms at different wavelengths and additional fraction analyses with size exclusion chromatography. Information on the molar or mass concentrations in the feed are not available. The model-based optimization results coincide with conventional chromatogram-based optimization.
Item Description:First published: 21 May 2015
Gesehen am 10.09.2020
Physical Description:Online Resource
ISSN:1618-2863
DOI:10.1002/elsc.201400248

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