Binary adsorption of globular proteins on ion-exchange media

Coupling cation and anion exchange chromatography for fast separation of monoclonal antibody charge variants

A design procedure for the separation of charge variants of a monoclonal antibody (mAb) was developed, which was based on the coupling of cation-exchange chromatography (CEX) and anion-exchange chromatography (AEX) under high loading conditions. The design of the coupled process was supported by a dynamic model. The model was calibrated on the basis of band profiles of variants determined experimentally for the mAb materials of different variant compositions. The numerical simulations were used to select the coupling configuration and the loading conditions that allowed for efficient separation of the mAb materials into three products enriched with each individual variant: the acidic (av), main (mv) and basic (bv) one. In the CEX section, a two-step pH gradient was used to split the loaded mass of mAb into a weakly bound fraction enriched with av and mv, and a strongly bound fraction containing the bv-rich product. The weakly bound fraction was further processed in the AEX section, where the mv-rich product was eluted in flowthrough, while the av-rich product was collected by a step change in pH. The choice of flow distribution and the number of columns in the CEX and AEX sections depended on the variant composition of the mAb material. For the selected configurations, the optimized mAb loading density in the CEX columns ranged from 10 to 26 mg mL-1, while in the AEX columns it was as high as 300 or 600 mg mL-1, depending on the variant composition of the mAb material. By proper selection of the loading condition, a trade-off between yield and purity of the products could be reached.

Coupling of chromatography and precipitation for adjusting acidic variant content in a monoclonal antibody pool

The acidic charge variants (av) of monoclonal antibodies (mAb) are often reported to have reduced therapeutic potency compared with the main (mv) and basic variants (bv), therefore reduction in the av content in mAb pools is often prioritized over reduction in the bv content. In previous studies we described two different methods for reducing the av content, which were based on either ion exchange chromatography or selective precipitation in polyethylene glycol (PEG) solutions. In this study, we have developed a coupled process, in which advantages of simplicity and ease in realization of PEG-aided precipitation and high separation selectivity of anion exchange chromatography (AEX) were exploited. The design of AEX was supported by the kinetic-dispersive model, which was supplemented with the colloidal particle adsorption isotherm, whereas the precipitation process and its coupling with AEX was quantified by simple mass balance equations and underlying thermodynamic dependencies. The model was used to assess the performance of the coupling of AEX and precipitation under different operating conditions. The advantage of the coupled process over the stand-alone AEX depended on the demand for the av reduction as well as the initial variant composition of the mAb pool, e.g., the improvement in the throughput provided by the optimized sequence of AEX and PREC varied from 70 to 600% for the initial av content changed from 35 to 50% w/w, and the reduction demand changed from 30 to 60%.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Robust mechanistic modeling of protein ion-exchange chromatography

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.

Protein adsorption on ion exchange adsorbers: A comparison of a stoichiometric and non-stoichiometric modeling approach

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.

Surface charge distribution: a key parameter for understanding protein behavior in chromatographic processes

Multi-component adsorption of proteins still requires a better understanding of local phenomena to improve the development of predictive models. In this work, all-atom Molecular Dynamics (MD) simulations were used to investigate the influence of protein charge distribution on the adsorption capacity. The simultaneous adsorption of α-chymotrypsin and lysozyme on a cation exchanger, SP Sepharose FF, was studied through MD simulations and compared to macroscopic isotherm experiments. It appears that the charge distribution is a relevant information to better understand specific phenomena, such as a multilayer adsorption caused by the particular electrostatic profile of α-chymotrypsin. Therefore, MD simulations seem to be an interesting way to visualize and highlight these behaviors.

Mechanistic Modeling of Preparative Column Chromatography for Biotherapeutics

Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.