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| Hauptseite > Publikationsdatenbank > Mechanistic Modeling of Ion-Exchange Process Chromatography of Charge Variants of Monoclonal Antibody Products |
| Hauptseite > Publikationsdatenbank > Mechanistic Modeling of Ion-Exchange Process Chromatography of Charge Variants of Monoclonal Antibody Products |
| Journal Article | FZJ-2015-06754 |
; ; ;
2015
Science Direct
New York, NY [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.chroma.2015.11.062
Abstract: Ion-exchange chromatography (IEX) is universally accepted as the optimal method for achieving process scale separation of charge variants of a monoclonal antibody (mAb) therapeutic. These variants are closely related to the product and a baseline separation is rarely achieved. The general practice is to fractionate the eluate from the IEX column, analyze the fractions and then pool the desired fractions to obtain the targeted composition of variants. This is, however, a very cumbersome and time consuming exercise. A mechanistic model that is capable of simulating the peak profile will be a much more elegant and effective way to make a decision on the pooling strategy. This paper proposes a mechanistic model, based on the general rate model, to predict elution peak profile for separation of the main product from its variants. The proposed approach uses inverse fit of process scale chromatogram for estimation of model parameters using the initial values that are obtained from theoretical correlations. The packed bed column has been modeled along with the chromatographic system consisting of the mixer, tubing and detectors as a series of dispersed plug flow and continuous stirred tank reactors. The model uses loading ranges starting at 25% to a maximum of 70% of the loading capacity and hence is applicable to process scale separations. Langmuir model has been extended to include the effects of salt concentration and temperature on the model parameters. The extended Langmuir model that has been proposed uses one less parameter than the SMA model and this results in a significant ease of estimating the model parameters from inverse fitting. The proposed model has been validated with experimental data and has been shown to successfully predict peak profile for a range of load capacities (15–28 mg/mL), gradient lengths (10–30 CV), bed heights (6–20 cm), and for three different resins with good accuracy (as measured by estimation of residuals). The model has been also validated for a two component mixture consisting of the main mAb product and one of its basic charge variants. The proposed model can be used for optimization and control of preparative scale chromatography for separation of charge variants.
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