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@ARTICLE{Kumar:904484,
author = {Kumar, Vijesh and Leweke, Samuel and Heymann, William and
von Lieres, Eric and Schlegel, Fabrice and Westerberg, Karin
and Lenhoff, Abraham M.},
title = {{R}obust mechanistic modeling of protein ion-exchange
chromatography},
journal = {Journal of chromatography / A},
volume = {1660},
issn = {0021-9673},
address = {Amsterdam [u.a.]},
publisher = {Elsevier},
reportid = {FZJ-2021-06054},
pages = {462669},
year = {2021},
abstract = {Mechanistic models for ion-exchange chromatography of
proteins are well-established and a broad consensus exists
on most aspects of the detailed mathematical and physical
description. A variety of specializations of these models
can typically capture the general locations of elution
peaks, but discrepancies are often observed in peak position
and shape, especially if the column load level is in the
non-linear range. These discrepancies may prevent the use of
models for high-fidelity predictive applications such as
process characterization and development of high-purity and
-productivity process steps. Our objective is to develop a
sufficiently robust mechanistic framework to make both
conventional and anomalous phenomena more readily
predictable using model parameters that can be evaluated
based on independent measurements or well-accepted
correlations. This work demonstrates the implementation of
this approach for industry-relevant case studies using both
a model protein, lysozyme, and biopharmaceutical product
monoclonal antibodies, using cation-exchange resins with a
variety of architectures (SP Sepharose FF, Fractogel EMD
SO3−, Capto S and Toyopearl SP650M). The modeling employs
the general rate model with the extension of the surface
diffusivity to be variable, as a function of ionic strength
or binding affinity. A colloidal isotherm that accounts for
protein-surface and protein-protein interactions
independently was used, with each characterized by a
parameter determined as a function of ionic strength and pH.
Both of these isotherm parameters, along with the variable
surface diffusivity, were successfully estimated using
breakthrough data at different ionic strengths and pH. The
model developed was used to predict overloads and elution
curves with high accuracy for a wide variety of gradients
and different flow rates and protein loads. The in-silico
methodology used in this work for parameter estimation,
along with a minimal amount of experimental data, can help
the industry adopt model-based optimization and control of
preparative ion-exchange chromatography with high accuracy.},
cin = {IBG-1},
ddc = {540},
cid = {I:(DE-Juel1)IBG-1-20101118},
pnm = {2171 - Biological and environmental resources for
sustainable use (POF4-217)},
pid = {G:(DE-HGF)POF4-2171},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:34800897},
UT = {WOS:000720438400004},
doi = {10.1016/j.chroma.2021.462669},
url = {https://juser.fz-juelich.de/record/904484},
}