Protein adsorption on ion exchange adsorbers: A comparison of a stoichiometric and non-stoichiometric modeling approach.
Adsorption isotherm
Colloidal particle adsorption model
Mechanistic modeling
Protein purification
Scaled particle theory
Steric mass action model
Journal
Journal of chromatography. A
ISSN: 1873-3778
Titre abrégé: J Chromatogr A
Pays: Netherlands
ID NLM: 9318488
Informations de publication
Date de publication:
13 Sep 2021
13 Sep 2021
Historique:
received:
17
04
2021
revised:
02
07
2021
accepted:
05
07
2021
pubmed:
21
7
2021
medline:
14
10
2021
entrez:
20
7
2021
Statut:
ppublish
Résumé
For mechanistic modeling of ion exchange (IEX) processes, a profound understanding of the adsorption mechanism is important. While the description of protein adsorption in IEX processes has been dominated by stoichiometric models like the steric mass action (SMA) model, discrepancies between experimental data and model results suggest that the conceptually simple stoichiometric description of protein adsorption provides not always an accurate representation of nonlinear adsorption behavior. In this work an alternative colloidal particle adsorption (CPA) model is introduced. Based on the colloidal nature of proteins, the CPA model provides a non-stoichiometric description of electrostatic interactions within IEX columns. Steric hindrance at the adsorber surface is considered by hard-body interactions between proteins using the scaled-particle theory. The model's capability of describing nonlinear protein adsorption is demonstrated by simulating adsorption isotherms of a monoclonal antibody (mAb) over a wide range of ionic strength and pH. A comparison of the CPA model with the SMA model shows comparable model results in the linear adsorption range, but significant differences in the nonlinear adsorption range due to the different mechanistic interpretation of steric hindrance in both models. The results suggest that nonlinear adsorption effects can be overestimated by the stoichiometric formalism of the SMA model and are generally better reproduced by the CPA model.
Identifiants
pubmed: 34284263
pii: S0021-9673(21)00521-5
doi: 10.1016/j.chroma.2021.462397
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
462397Informations de copyright
Copyright © 2021. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors affiliated to GoSilico declare that the software ChromX used in this study is a commercial product of GoSilico. The presented research does not depend on the usage of ChromX, the model can be implemented in any kind of modeling software.