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Zonal rate model for stacked membrane chromatography part II: Characterizing ion‐exchange membrane chromatography under protein retention conditions
Author(s) -
Francis Patrick,
von Lieres Eric,
Haynes Charles
Publication year - 2012
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.24349
Subject(s) - chemistry , chromatography , membrane , elution , volumetric flow rate , mass transfer , ion chromatography , analytical chemistry (journal) , biological system , thermodynamics , physics , biochemistry , biology
The Zonal Rate Model (ZRM) has previously been shown to accurately account for contributions to elution band broadening, including external flow nonidealities and radial concentration gradients, in ion‐exchange membrane (IEXM) chromatography systems operated under nonbinding conditions. Here, we extend the ZRM to analyze and model the behavior of retained proteins by introducing terms for intra‐column mass transfer resistances and intrinsic binding kinetics. Breakthrough curve (BTC) data from a scaled‐down anion‐exchange membrane chromatography module using ovalbumin as a model protein were collected at flow rates ranging from 1.5 to 20 mL min −1 . Through its careful accounting of transport nonidealities within and external to the membrane stack, the ZRM is shown to provide a useful framework for characterizing putative protein binding mechanisms and models, for predicting BTCs and complex elution behavior, including the common observation that the dynamic binding capacity can increase with linear velocity in IEXM systems, and for simulating and scaling separations using IEXM chromatography. Global fitting of model parameters is used to evaluate the performance of the Langmuir, bi‐Langmuir, steric mass action (SMA), and spreading‐type protein binding models in either correlating or fundamentally describing BTC data. When combined with the ZRM, the bi‐Langmuir, and SMA models match the chromatography data, but require physically unrealistic regressed model parameters to do so. In contrast, for this system a spreading‐type model is shown to accurately predict column performance while also providing a realistic fundamental explanation for observed trends, including an observed increase in dynamic binding capacity with flow rate. Biotechnol. Bioeng. 2012; 109:615–629. © 2011 Wiley Periodicals, Inc.