Modeling of dual gradient elution in ion exchange and mixed-mode chromatography

Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data

In this work, we have examined an array of isotherm formalisms and characterized them based on their relative complexities and predictive abilities with multimodal chromatography. The set of isotherm models studied were all based on the stoichiometric displacement framework, with considerations for electrostatic interactions, hydrophobic interactions, and thermodynamic activities. Isotherm parameters for each model were first determined through twenty repeated fits to a set of mAb - Capto MMC batch isotherm data spanning a range of loading, ionic strength, and pH as well as a set of mAb - Capto Adhere batch data at constant pH. The batch isotherm data were used in two ways-spanning the full range of loading or consisting of only the high concentration data points. Predictive ability was defined through the model's capacity to capture prominent changes in salt gradient elution behavior with respect to pH for Capto MMC or unique elution patterns and yield losses with respect to gradient slope for Capto Adhere. In both cases, model performance was quantified using a scoring metric based on agreement in peak characteristics for column predictions and accuracy of fit for the batch data. These scores were evaluated for all twenty isotherm fits and their corresponding column predictions, thereby producing a statistical distribution of model performances. Model complexity (number of isotherm parameters) was then considered through use of the Akaike information criterion (AIC) calculated from the score distributions. While model performance for Capto MMC benefitted substantially from removal of low protein concentration data, this was not the case for Capto Adhere; this difference was likely due to the qualitatively different shapes of the isotherms between the two resins. Surprisingly, the top-performing (high accuracy with minimal number of parameters) isotherm model was the same for both resins. The extended steric mass action (SMA) isotherm (containing both protein-salt and protein-protein activity terms) accurately captured both the pH-dependent elution behavior for Capto MMC as well as loss in protein recovery with increasing gradient slope for Capto Adhere. In addition, this isotherm model achieved the highest median score in both resin systems, despite it lacking any explicit hydrophobic stoichiometric terms. The more complex isotherm models, which explicitly accounted for both electrostatic and hydrophobic interaction stoichiometries, were ill-suited for Capto MMC and had lower AIC model likelihoods for Capto Adhere due to their increased complexity. Interestingly, the ability of the extended SMA isotherm to predict the Capto Adhere results was largely due to the protein-salt activity coefficient, as determined via isotherm parameter sensitivity analyses. Further, parametric studies on this parameter demonstrated that it had a major impact on both binding affinity and elution behavior, therein fully capturing the impact of hydrophobic interactions. In summary, we were able to determine the isotherm formalisms most capable of consistently predicting a wide range of column behavior for both a multimodal cation-exchange and multimodal anion-exchange resin with high accuracy, while containing a minimized set of model parameters.

Understanding adsorption behavior of antiviral labyrinthopeptin peptides in anion exchange chromatography

Lantipeptides from bacterial sources are increasingly important as biopharmaceuticals because of their broad range of applications. However, the availability of most lantipeptides is low, and systematic approaches for downstream processing of this group of peptides is still lacking. Model-based development for chromatographic separations has proven to be a useful tool for developing reliable purification processes. One important compound of such a model is the adsorption behavior of the components of interest. In ion-exchange chromatography, the adsorption equilibrium between salt and proteins can be described using the steric mass action (SMA) formalism. Beyond, the model parameters may be related to the lanthipeptides physico-chemical properties. In this study, the antiviral lantipeptides labyrinthopeptin A1 and A2, purified from Actinomadura namibiensis culture broth, were characterized for their adsorption behavior in anion-exchange chromatography in the range from pH 5.0-7.4. The experiments necessary to determine the three SMA parameters were chosen in a way to limit the amount of peptides needed. Linear gradient elution was applied successfully to separate A1 and A2 and to determine the characteristic charge ν_i and the equilibrium constant [Formula: see text] . Batch adsorption experiments using a robotic workstation for high throughput and accuracy provided non-linear adsorption isotherms and the steric factor σ_i. Labyrinthopeptin A1 and A2 show a very different adsorption behavior even though the fundamental structure of the two peptides is similar. k_eq of A1 ranging from 0.18 to 0.88 are approximately one order of magnitude smaller than that of A2 ranging from 3.44 to 9.73 indicating the higher affinity of A2 to the stationary phase. At pH 7.0 σ was 1.12 and 0.60 for A1 and A2, respectively which was expected based on the molecular weight of the peptides. The characteristic charge for both peptides was also theoretically estimated from the amino acids involved in electrostatic interactions which was in good agreement with experimental data. Thereby, this work provides an useful approach to estimate SMA parameters based on simple structural information that can be applied early in chromatographic ion-exchange process development for peptides and may help adapting the processes for future designed lanthipeptides.

Standardized method for mechanistic modeling of multimodal anion exchange chromatography in flow through operation

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics.

Mechanistic modeling, simulation, and optimization of mixed-mode chromatography for an antibody polishing step

Mixed-mode chromatography combines features of ion-exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow-through operations on traditional unmixed resins or as a pH-controlled bind-and-elute step, the use of both interaction modes promises a better removal of product-specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer-optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion-exchange chromatography. In a case study, a model for a pH-controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed-mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.

Fractionated charge variants of biosimilars: A review of separation methods, structural and functional analysis

The similarity between originator and biosimilar monoclonal antibody candidates are rigorously assessed based on primary, secondary, tertiary, quaternary structures, and biological functions. Minor differences in such parameters may alter target-binding, potency, efficacy, or half-life of the molecule. The charge heterogeneity analysis is a prerequisite for all biotherapeutics. Monoclonal antibodies are prone to enzymatic or non-enzymatic structural modifications during or after the production processes, leading to the formation of fragments or aggregates, various glycoforms, oxidized, deamidated, and other degraded residues, reduced Fab region binding activity or altered FcR binding activity. Therefore, the charge variant profiles of the monoclonal antibodies must be regularly and thoroughly evaluated. Comparative structural and functional analysis of physically separated or fractioned charged variants of monoclonal antibodies has gained significant attention in the last few years. The fraction-based charge variant analysis has proved very useful for the biosimilar candidates comprising of unexpected charge isoforms. In this report, the key methods for the physical separation of monoclonal antibody charge variants, structural and functional analyses by liquid chromatography-mass spectrometry, and surface plasmon resonance techniques were reviewed.

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.

Increased production of human fibroblast growth factor 17 in Escherichia coli and proliferative activity in NIH3T3 cells

Fibroblast growth factor 17 (FGF17) is a novel member of the FGFs family, which is essential for cell development, tissue repair, tumor growth and invasion. The aim of the current study was to obtain a high expression level of recombinant human FGF17 (rhFGF17), including soluble proteins and inclusion bodies. An optimized rhFGF17 cDNA sequence was cloned into a pET3a vector, then the pET3a‑hFGF17 vector was transformed into BL21(DE3)pLysS Escherichia coli cells. Expression was induced by optimizing the conditions using isopropyl β‑D‑1‑thiogalactopyranoside (IPTG) and it was confirmed that a 24‑h exposure to 0.8 mM IPTG at 16˚C provided the optimal condition for soluble hFGF17. Furthermore, for the inclusion bodies, the optimal condition was a 4‑h exposure to 0.4 mM IPTG at 37˚C. Two forms of rhFGF17 protein were purified by heparin affinity and SP Sepharose Fast Flow chromatography. MTT assays demonstrated that the purified rhFGF17 exerted an important effect on the proliferative activity of NIH3T3 cells, although there was no significant difference when compared with standard rhFGF17. Thus, an optimal and economic expression system was created in the present study for rhFGF17 in E. coli. This expression strategy enables the preparation of sufficient and highly bioactive rhFGF17 for further investigation of underlying mechanisms.

High-yield of biologically active recombinant human fibroblast growth factor-16 in E. coli and its mechanism of proliferation in NCL-H460 cells

Fibroblast growth factor-16 (FGF16) is a member of FGF9 subfamily, which plays key role in promoting mitosis and cell survival, and also involved in embryonic development, cell growth, tissue repair, morphogenesis, tumor growth, and invasion. However, the successful high-yield purification of recombinant human fibroblast growth factor-16 (rhFGF16) protein has not been reported. In addition, lung cancer is a major cause of cancer-related deaths, which threats people's lives and its incidence has continued to rise. Learning pathways or proteins, which involved in lung tumor progression will contribute to the development of early diagnosis and targeted therapy. FGF16 promoted proliferation and invasion behavior of SKOV-3 ovarian cancer cells, whose function may be similar in lung cancer. The hFGF16 was cloned into pET-3d and expressed in Escherichia coli BL21 (DE3) pLysS. Finally, obtained two forms of FGF16 that exhibited remarkable biological activity and the purity is over 95%, meanwhile, the yield of soluble 130 mg/100 g and insoluble 240 mg/100 g. Experiments demonstrated FGF16 could promote proliferation of NCL-H460 cells by activating Akt, Erk1/2, and p38 MAPK signaling, whereas JNK had no significant effect. In total, this optimized expression strategy enables significant quantity and activity of rhFGF16, thereby meeting its further pharmacological and clinical usages.