Methods development in multimodal chromatography with mobile phase modifiers using the steric mass action model

All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.

Predicting multimodal chromatography of therapeutic antibodies using multiscale modeling

Multimodal chromatography has emerged as a powerful method for the purification of therapeutic antibodies. However, process development of this separation technique remains challenging because of an intricate and molecule-specific interaction towards multimodal ligands, leading to time-consuming and costly experimental optimization. This study presents a multiscale modeling approach to predict the multimodal chromatographic behavior of therapeutic antibodies based on their sequence information. Linear gradient elution (LGE) experiments were performed on an anionic multimodal resin for 59 full-length antibodies, including five different antibody formats at pH 5.0, 6.0, and 7.0 that were used for parameter determination of a linear adsorption model at low loading density conditions. Quantitative structure-property relationship (QSPR) modeling was utilized to correlate the adsorption parameters with up to 1374 global and local physicochemical descriptors calculated from antibody homology models. The final QSPR models employed less than eight descriptors per model and demonstrated high training accuracy (R² > 0.93) and reasonable test set prediction accuracy (Q² > 0.83) for the adsorption parameters. Model evaluation revealed the significance of electrostatic interaction and hydrophobicity in determining the chromatographic behavior of antibodies, as well as the importance of the HFR3 region in antibody binding to the multimodal resin. Chromatographic simulations using the predicted adsorption parameters showed good agreement with the experimental data for the vast majority of antibodies not employed during the model training. The results of this study demonstrate the potential of sequence-based prediction for determining chromatographic behavior in therapeutic antibody purification. This approach leads to more efficient and cost-effective process development, providing a valuable tool for the biopharmaceutical industry.

A comprehensive method for modeling and simulating ion exchange chromatography of complex mixtures

In recent years, many biological-based products have been developed, representing a significant fraction of income in the pharmaceutical market. Ion exchange chromatography is an important downstream step for the purification of target recombinant proteins present in clarified cell extracts, together with many other unknown impurities. This work develops a robust approach to model and simulate the purification of untagged heterologous proteins, so that the improved conditions to carry out an ion exchange chromatography are identified in a rational basis prior to the real purification run itself. Purification of the pneumococcal surface protein A (PspA4Pro) was used as a case study. This protein is produced by recombinant Escherichia coli and is a candidate for the manufacture of improved pneumococcal vaccines. The developed method combined experimental and computational procedures. Different anion exchange operating conditions were mapped in order to gather a broad range of representative experimental data. The equilibrium dispersive and the steric mass action equations were used to model and simulate the process. A training strategy to fit the model and separately describe the elution profiles of PspA4Pro and other proteins of the cell extract was applied. Based on the simulation results, a reduced ionic strength was applied for PspA4Pro elution, leading to increases of 14.9% and 11.5% for PspA4Pro recovery and purity, respectively, compared to the original elution profile. These results showed the potential of this method, which could be further applied to improve the performance of ion exchange chromatography in the purification of other target proteins under real process conditions.

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.

Protein association on multimodal chromatography media

The phenomenon of protein-protein association on multimodal chromatography resins was described for two different case study examples. The adsorption pattern of single-component solutions of calcium-rich alpha-lactalbumin (aLaCa) and calcium-depleted alpha-lactalbumin (aLa) and their mixtures with bovine serum albumin was determined on a multimodal anion-exchange chromatography medium. In single-component solutions, both aLaCa and aLa exhibited identical adsorption behavior at low resin loadings, whereas at high loadings the adsorption strength of aLa markedly exceeded that of alaCa. In binary mixtures, the adsorption of BSA enhanced at high concentrations of aLa or aLaCa in the adsorbed phase. The unusual adsorption patterns observed were attributed to the tendency of the proteins for molecular association in the adsorbed phase in single and binary solutions. The phenomena was examined for different pH of the solution: pH 6, 7, 8, and different solvent environments: phosphate buffer (PB), bis tris buffer (BT), 100 mM NaCl in BT and bis tris propane buffer (BTP). The strongest effect was observed for PB and for 100 mM NaCl in BT. Its occurrence was also evidenced for other case study example, i.e., adsorption of single-component solutions and binary mixtures of a monoclonal antibody (mAb) and lysozyme (LYZ) on a multimodal cation-exchange chromatography medium. The enhancement of adsorption of mAb was observed at high concentrations of LYZ in the adsorbed phase. To quantify the underlying effects, a mechanistic model was used, which accounted for both protein association and exclusion resulting from attractive and repulsive protein-protein iterations in the adsorbed phase.

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.

Understanding the effects of system differences for parameter estimation and scale-up of high throughput chromatographic data

In this paper, we evaluate how employing fraction collection and multistep gradients with RoboColumns® (Repligen, formally Atoll) affects both comparison to benchtop experimental data and column simulation parameter estimation. These operational differences arise from the RoboColumn® system (operated on an automated liquid handling device) requiring offline analysis for determination of elution profiles rather than the continuous in-line UV curves obtained with larger scale systems. In addition, multistep gradients are used to model the smooth linear gradients of larger scale systems because sequential injections are used to provide liquid flow. Comparisons of two sets of column simulations was first carried out to demonstrate that fraction collection reduced the first moments of the elution peaks by 1/2 of the fraction volumes. Additional column simulations determined that the effect of a multistep gradient approximation on retention volume was dependent upon the gradient step length. An empirical transformation was then developed to correct the first moments obtained from gradient experimental data using the RoboColumn® system. These corrected values provided a more direct comparison of the experimental data at different scales and resulted in a significant improvement in agreement with results obtained using a 20 mL benchtop column. Linear steric mass-action (SMA) parameters were then estimated using the corrected values and employed to successfully predict the performance of the benchtop system data. Finally, these parameters were demonstrated to be well suited for modeling the RoboColumn® gradient data when properly accounting for multistep gradients and fraction collection. This work continues previous investigations into understanding system differences associated with robotic liquid handling devices and proposes a methodology for properly accounting for operational differences to predict operation at larger scales using conventional chromatography systems.

A framework for calculating orthogonal selectivities in multimodal systems directly from cell culture fluid

This paper presents a straightforward approach for measuring and quantifying orthogonality directly in complex cell culture fluids (CCFs) without the requirement for tracking the retention behaviors of large sets of proteins. Null-producing CCFs were fractionated using linear salt gradients at constant pH on a set of multimodal resins. Fractions were then analyzed by ultraperformance-reversed phase liquid chromatography and the resulting chromatograms provided host cell protein (HCP) "fingerprints." Using these fingerprints, an inner product vector-based approach was employed to quantify the degree of orthogonality between pairs of resins and operating conditions for these large HCP protein sets. To compare resin orthogonality behavior in different expression systems, the Chinese hamster ovary and Pichia pastoris null-producing CCFs were examined. Orthogonality in multimodal systems was found to strongly depend on the expression system and the HCPs being screened. We also identified several unexpected pairs of multimodal resins within the same family that exhibited significant orthogonality. Furthermore, "self-orthogonality" was evaluated between resins operated at different pHs, and important operating regimes were identified for maximizing orthogonal selectivities. The framework developed in this paper for calculating orthogonality without the need for labor-intensive HCP tracking has important implications for efficient process development and resin/operating condition selection for both monoclonal antibody (mAb) polishing steps and non-mAb processes. In addition, this study provides a tool to unlock the untapped potential of multimodal resins by aiding in their rational selection and incorporation. Finally, the orthogonality framework here can facilitate the development of sets of next-generation multimodal resins specifically designed to provide highly orthogonal and efficient separations tailored for different expression systems.

Isocratic reporter-exclusion immunoassay using restricted-access adsorbents

We introduce analyte-dependent exclusion of reporter reagents from restricted-access adsorbents as the basis of an isocratic reporter-exclusion immunoassay for viruses, proteins, and other analytes. Capto™ Core 700 and related resins possess a noninteracting size-selective outer layer surrounding a high-capacity nonspecific mixed-mode capture adsorbent core. In the absence of analyte, antibody-enzyme reporter conjugates can enter the adsorbent and be captured, and their signal is lost. In the presence of large or artificially-expanded analytes, reporter reagents bind to analyte species to form complexes large enough to be excluded from the adsorbent core, allowing their signal to be observed. This assay principle is demonstrated using M13 bacteriophage virus and human chorionic gonadotropin as model analytes. The simple isocratic detection approach described here allows a rapid implementation of immunoassay for detection of a wide range of analytes and uses inexpensive, generally-applicable, and stable column materials instead of costly analyte-specific immunoaffinity adsorbents.

Using multimodal chromatography for post-conjugation antibody-drug conjugate purification: A methodology from high throughput screening to in-silico process development

In this paper, a methodology for the development of a multimodal chromatography process is presented that is aimed at removal of under-conjugated antibody-drug conjugate (ADC) species. Two ADCs are used as case studies: One ADC results from site-directed conjugation to inserted cysteine residues and has a drug-to-antibody ratio (DAR) of two, the other is the product of conjugation to interchain disulfide bonds with a DAR of eight. First, filter plate screening studies are designed for the unconjugated antibody and the ADCs. Different metrics for the analysis of these data sets are presented and discussed. From this analysis, the selected process conditions are then carried out using a benchtop chromatography system to confirm the separations observed in the filter plate studies while simultaneously generating data to estimate steric mass-action isotherm and mass transport parameters for process simulation. This column model is then employed to develop separation processes in-silico for the removal of the unconjugated parent antibody and under-conjugated product variants. The optimized process conditions identified using the model are then verified experimentally. The methodology presented in this work utilizes multimodal chromatography for ADC purification and provides the framework for a streamlined systematic approach to process development.

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.

Retention Mechanism of Proteins in Hydroxyapatite Chromatography - Multimodal Interaction Based Protein Separations: A Model Study

BACKGROUND

Retention mechanism of proteins in hydroxyapatite chromatography (HAC) was investigated by linear gradient elution experiments (LGE).

MATERIALS AND METHODS

Several mobile phase (buffer) solution strategies and solutes were evaluated in order to probe the relative contributions of two adsorption sites of hydroxyapatite (HA) particles, C-site due to Ca (metal affinity) and P-site due to PO4 (cation-exchange). When P-site was blocked, two basic proteins, lysozyme (Lys) and ribonuclease A(RNase), were not retained whereas cytochrome C(Cyt C) and lactoferrin (LF) were retained and also retention of acidic proteins became stronger as the repulsion due to P-site was eliminated. The number of the binding site B values determined from LGE also increased, which also showed reduction of repulsion forces.

CONCLUSION

The selectivity (retention) of four basic proteins (RNase, Lys, Cyt C, LF) in HAC was different from that in ion-exchange chromatography. Moreover, it was possible to tune the selectivity by using NaCl gradient.

Solvent modulated linear pH gradient elution for the purification of conventional and bispecific antibodies: Modeling and application

Classical ion-exchange chromatography using a linear salt gradient to elute the adsorbed protein at fixed pH is the most common method to separate product-related impurities during downstream processing of biopharmaceuticals. Linear pH gradient elution provides a useful alternative by separating proteins in a linear pH gradient at fixed salt concentration. Although linear pH gradient elution provides excellent selectivity, it is rarely encountered in industrial purification processes. Here, a stoichiometric displacement model is used to characterize pH gradient elution based on simple linear gradient elution experiments. Protein retention behavior is described with respect to the pH dependencies of the characteristic binding charge and the equilibrium constant of the ion exchange reaction. Furthermore, the influence of solvent composition using PEG as a mobile phase modifier is investigated. Validity and applicability of the model are demonstrated for the purification of a conventional monoclonal antibody from soluble aggregates and for a novel bispecific antibody format containing a unique product-related impurity profile. pH step elution protocols are derived from model calculations without further optimization experiments necessary.