期刊
JOURNAL OF CHROMATOGRAPHY A
卷 1653, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.chroma.2021.462397
关键词
Protein purification; Mechanistic modeling; Colloidal particle adsorption model; Steric mass action model; Adsorption isotherm; Scaled particle theory
The article introduces a new Colloidal Particle Adsorption (CPA) model for mechanistic modeling of ion exchange processes, which provides a more accurate description of nonlinear protein adsorption behavior. Comparison with the Stoichiometric Model (SMA) shows that the CPA model is better suited for describing nonlinear adsorption effects.
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. (C) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据