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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},
}