[Preparation of a macroporous anion exchange chromatographic medium by polyamine grafting and evaluation of its protein-adsorption behavior].

The macroporous anion exchange chromatographic medium (FastSep-PAA) was prepared through grafting polyallylamine (PAA) onto polyacrylate macroporous microspheres (FastSep-epoxy). The effects of the synthesis conditions, including the PAA concentration, reaction time, and reaction solution pH, on the ion exchange (IC) of the medium were investigated in detail. When the PAA concentration, reaction time, and reaction solution pH were increased, the IC of the medium increased, and optimal synthesis conditions were then selected in combination with changes of protein binding capacity. A scanning electron microscope was used to examine the surface morphology of the medium. The medium possessed high pore connectivity. Furthermore, the pore structure of the medium was preserved after the grafting of PAA onto the macroporous microspheres. This finding demonstrates that the density of the PAA ligands does not appear to have any discernible impact on the structure of the medium; that is, no difference in the structure of the medium is observed before and after the grafting of PAA onto the microspheres. The pore size and pore-size distribution of the medium before and after grafting were determined by mercury intrusion porosimetry and the nitrogen adsorption method to investigate the relationship between pore size (measured in the range of 300-1000 nm) and protein adsorption. When the pore size of the medium was increased, its protein binding capacity did not exhibit any substantial decrease. An increase in pore size may hasten the mass transfer of proteins within the medium. Among the media prepared, that with a pore size of 400 nm exhibited the highest dynamic-binding capacity (DBC: 70.3 g/L at 126 cm/h). The large specific surface area of the medium and its increased number of protein adsorption sites appeared to positively influence its DBC. When the flow rate was increased, the protein DBC decreased in media with original pore sizes of less than 700 nm. In the case of the medium with an original pore size of 1000 nm, the protein DBC was independent of the flow rate. The protein DBC decreased by 3.5% when the flow rate was increased from 126 to 628 cm/h. In addition, the protein DBC was maintained at 57.7 g/L even when the flow velocity was 628 cm/h. This finding reveals that the diffusion rate of protein molecules at this pore size is less restricted and that the prepared medium has excellent mass-transfer performance. These results confirm that the macroporous polymer anion exchange chromatographic medium developed in this study has great potential for the high-throughput separation of proteins.

Mixed-mode size-exclusion silica resin for polishing human antibodies in flow-through mode.

The polishing step in the downstream processing of therapeutic antibodies removes residual impurities from Protein A eluates. Among the various classes of impurities, antibody fragments are especially challenging to remove due to the broad biomolecular diversity generated by a multitude of fragmentation patterns. The current approach to fragment removal relies on ion exchange or mixed-mode adsorbents operated in bind-and-gradient-elution mode. However, fragments that bear strong similarity to the intact product or whose biophysical features deviate from the ensemble average can elude these adsorbents, and the lack of a chromatographic technology enabling robust antibody polishing is recognized as a major gap in downstream bioprocessing. Responding to this challenge, this study introduces size-exclusion mixed-mode (SEMM) silica resins as a novel chromatographic adsorbent for the capture of antibody fragments irrespective of their biomolecular features. The pore diameter of the silica beads features a narrow distribution and is selected to exclude monomeric antibodies, while allowing their fragments to access the pores where they are captured by the mixed-mode ligands. The static and dynamic binding capacity of the adsorbent ranged respectively between 30-45 and 25-33 gs of antibody fragments per liter of resin. Selected SEMM-silica resins also demonstrated the ability to capture antibody aggregates, which adsorb on the outer layer of the beads. Optimization of the SEMM-silica design and operation conditions - namely, pore size (10 nm) and ligand composition (quaternary amine and alkyl chain) as well as the linear velocity (100 cm/h), ionic strength (5.7 mS/cm), and pH (7) of the mobile phase - afforded a significant reduction of both fragments and aggregates, resulting into a final antibody yield up to 80% and monomeric purity above 97%.

Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation.

The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.

Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow.

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Digital twin in high throughput chromatographic process development for monoclonal antibodies.

The monoclonal antibody (mAb) industry is becoming increasingly digitalized. Digital twins are becoming increasingly important to test or validate processes before manufacturing. High-Throughput Process Development (HTPD) has been progressively used as a tool for process development and innovation. The combination of High-Throughput Screening with fast computational methods allows to study processes in-silico in a fast and efficient manner. This paper presents a hybrid approach for HTPD where equal importance is given to experimental, computational and decision-making stages. Equilibrium adsorption isotherms of 13 protein A and 16 Cation-Exchange resins were determined with pure mAb. The influence of other components in the clarified cell culture supernatant (harvest) has been under-investigated. This work contributes with a methodology for the study of equilibrium adsorption of mAb in harvest to different protein A resins and compares the adsorption behavior with the pure sample experiments. Column chromatography was modelled using a Lumped Kinetic Model, with an overall mass transfer coefficient parameter (kov). The screening results showed that the harvest solution had virtually no influence on the adsorption behavior of mAb to the different protein A resins tested. kov was found to have a linear correlation with the sample feed concentration, which is in line with mass transfer theory. The hybrid approach for HTPD presented highlights the roles of the computational, experimental, and decision-making stages in process development, and how it can be implemented to develop a chromatographic process. The proposed white-box digital twin helps to accelerate chromatographic process development.

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.

In-line buffer exchange in the coupling of Protein A chromatography with weak cation exchange chromatography for the determination of charge variants of immunoglobulin G derived from chinese hamster ovary cell cultures.

Immunoglobulin G (IgG) is the most common monoclonal antibody (mAb) grown for therapeutic applications. While IgG is often selectively isolated from cell lines using protein A (ProA) chromatography, this is only a stepping stone for complete characterization. Further classification can be obtained from weak cation exchange chromatography (WCX) to determine IgG charge variant distributions. The charge variants of monoclonal antibodies can influence the stability and efficacy in vivo, and deviations in charge heterogeneity are often cell-specific and sensitive to upstream process variability. Current methods to characterize IgG charge variants are often performed off-line, meaning that the IgG eluate from the ProA separation is collected, diluted to adjust the pH, and then transferred to the WCX separation, adding time, complexity, and potential contamination to the sample analysis process. More recently, reports have appeared to streamline this separation using in-line two-dimensional liquid chromatography (2D-LC). Presented here is a novel, 2D-LC coupling of ProA in the first dimension (1D) and WCX in the second dimension (2D) chromatography. As anticipated, the initial direct column coupling proved to be challenging due to the pH incompatibility between the mobile phases for the two stages. To solve the solvent compatibility issue, a size exclusion column was placed in the switching valve loop of the 2D-LC instrument to act as a means for the on-line solvent exchange. The efficacy of the methodology presented was confirmed through a charge variant determination using the NIST monoclonal antibody standard (NIST mAb), yielding correct acidic, main, and basic variant compositions. The methodology was employed to determine the charge variant profile of IgG from an in-house cultured Chinese hamster ovary (CHO) cell supernatant. It is believed that this methodology can be easily implemented to provide higher-throughput assessment of IgG charge variants for process monitoring and cell line development.

Evaluation of a novel stationary phase for the separation of lactose and isomers in lactose-free products using high-performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD).

Lactose intolerance is a widespread condition, which prevents a large number of people from consuming dairy products as a part of their daily diet. It is estimated that an average of 65% of the global population is suffering from lactose intolerance. The global market for 'lactose-free' dairy products is rapidly growing and the criteria for 'lactose-free' labelled products are becoming stricter. To check the lactose contents in these products there is a need for fast, sensitive, and selective analytical method. A method is presented for fast and sensitive determination of lactose and its isomers using High-Performance Anion Exchange Chromatography in combination with Pulsed Amperometric Detection (HPAEC-PAD). The use of a new anion-exchange column, SweetSep™ AEX200, which is a strong anion-exchange column with highly monodisperse 5 µm particles, allowed the separation of all compounds of interest in less than 8 min with high resolution. A variety of dairy products were analyzed to demonstrate the versatility of the method.

Universal ion chromatography method for anions in active pharmaceutical ingredients enabled by computer-assisted separation modeling.

Ion Chromatography (IC) is one of the most widely used methods for analyzing ionic species in pharmaceutical samples. A universal IC method that can separate a wide range of different analytes is highly desired as it can save a lot of time for method development and validation processes. Herein we report the development of a universal method for anions in active pharmaceutical ingredients (APIs) using computer-assisted chromatography modeling tools. We have screened three different IC columns (Dionex IonPac AS28-Fast 4 µm, AS19 4 µm and AS11-HC 4 µm) to determine the best suitable column for universal IC method development. A universal IC method was then developed using an AS11-HC 4 µm column to separate 31 most common anionic substances in 36 mins. This method was optimized using LC Simulator and a model which precisely predicts the retention behavior of 31 anions was established. This model demonstrated an excellent match between predicted and experimental analyte retention time (R2 =0.999). To validate this universal IC method, we have studied the stability of sulfite and sulfide analytes in ambient conditions. The method was then validated for a subset of 29 anions using water and organic solvent/water binary solvents as diluents for commercial APIs. This universal IC method provides an efficient and simple way to separate and analyze common anions in APIs. In addition, the method development process combined with LC simulator modeling can be effectively used as a starting point during method development for other ions beyond those investigated in this study.

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation.

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

Mechanistic modeling of empty-full separation in recombinant adeno-associated virus production using anion-exchange membrane chromatography.

Recombinant adeno-associated viral vectors (rAAVs) have become an industry-standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids-those that contain no genetic material-and enrichment of full rAAV using anion-exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single-step or two-step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

Tuning mobile phase properties to improve empty and full particle separation in adeno-associated virus productions by anion exchange chromatography.

In the past decade, recombinant adeno-associated virus (rAAV) has gained increased attention as a prominent gene therapy technology to treat monogenetic diseases. One of the challenges in rAAV production is the enrichment of full rAAV particles containing the gene of interest (GOI) payload. By adjusting the mobile phase properties of anion-exchange chromatography (AEX), it was demonstrated that empty and full separation of rAAV was improved in monolith based preparative AEX chromatography. When compared to the baseline method using NaCl, the use of tetraethylammonium acetate (TEA-Ac) in the AEX mobile phase resulted in enhanced resolution from 0.75 to 1.23 between "Empty" and "Full" peaks by salt linear gradient elution, as well as increased the percentage of full rAAV particles from 20% to 36% and genome recovery from 59% to 62%. Furthermore, a dual wash plus step elution AEX method was developed. Wherein, the first wash step harnesses TEA-Ac to separate empty and full capsids, which is followed by a second wash step that ensures no TEA-Ac salt is carried over into AEX eluate. The resulting optimized AEX purification method has the potential to be adapted for manufacturing and purification processes involving various rAAV production platforms that experience empty and full rAAV separation challenges.

Determination of inorganic ions and carbohydrates in cardioplegia and nephroplegia solutions by ion chromatography.

In this study, methods for analyzing inorganic ions and carbohydrates in cardioplegia and nephroplegia solutions were developed and validated using ion chromatography with both conductivity and pulsed amperometric detection. The inorganic ions such as sodium, potassium, and calcium were separated by a cation-exchange column with 27 mM methanesulfonic acid as mobile phase at 0.5 mL/min. The anion (chloride) and carbohydrates (mannitol and glucose) were analyzed by an anion-exchange column using a mobile phase of 20 mM sodium hydroxide at 1.0 mL/min. The methods showed a high sensitivity for all analytes, with quantification limits from 0.0002 to 0.06 mg/L. Good linearities between the peak areas and concentrations were found for all analytes within the selected concentration range (R2  > 0.999). Relative standard deviation values for repeatability and interday precision were 0.1%-1.0% and 0.7%-1.6%, respectively. The accuracy was validated by determining the percentage recovery, which was between 98.0% and 101.3% for all analytes, indicating good accuracy of the methods. The robustness was verified by using an experimental design. Finally, real samples were analyzed to determine the content of the analytes. All assay values were between 96.8% and 102.5%.

A novel method for continuous chromatographic separation of monoclonal antibody charge variants by combining displacement mode chromatography and step elution.

Charge heterogeneity of monoclonal antibodies is considered a critical quality attribute and hence needs to be monitored and controlled by the manufacturer. Typically, this is accomplished via separation of charge variants on cation exchange chromatography (CEX) using a pH or conductivity based linear gradient elution. Although an effective approach, this is challenging particularly during continuous processing as creation of linear gradient during continuous processing adds to process complexity and can lead to deviations in product quality upon slightest changes in gradient formation. Moreover, the long length of elution gradient along with the required peak fractionation makes process integration difficult. In this study, we propose a novel approach for separation of charge variants during continuous CEX chromatography by utilizing a combination of displacement mode chromatography and salt-based step elution. It has been demonstrated that while the displacement mode of chromatography enables control of acidic variants ≤26% in the CEX eluate, salt-based step gradient elution manages basic charge variant ≤25% in the CEX eluate. The proposed approach has been successfully demonstrated using feed materials with varying compositions. On comparing the designed strategy with 2-column concurrent (CC) chromatography, the resin specific productivity increased by 95% and resin utilization increased by 183% with recovery of main species >99%. Further, in order to showcase the amenability of the designed CEX method in continuous operation, the method was examined in our in-house continuous mAb platform.

Preparation of two zwitterionic polymer functionalized stationary phases and comparative evaluation under mixed-mode of reversed phase/ hydrophilic interaction/ion exchange chromatography.

Zwitterions are a promising choice to prepare separation materials because of their hydrophilicity and biocompatibility. We described the preparation of two zwitterionic polymer functionalized stationary phases and evaluation under mixed-mode chromatography. A zwitterionic monomer, S-(4-vinylbenzyl) cysteine (SVC), was synthesized and bonded to silica via reversible addition fragmentation chain transfer (RAFT) polymerization to afford a zwitterionic stationary phase, Sil-SVC. A hydrophobic monomer, N-(4-phenylbutan-2-yl) acrylamide (NPA), was copolymerized with SVC onto the stationary phase (Sil-SVCNPA) for comparison. The stationary phases were characterized with FT-IR, TGA, EA, and zeta-potential measurements. Mobile phase composition (ACN content, pH and salt concentration) was varied to study the retention property. Linear solvation energy relationship and Van't Hoff plot were used to investigate the retention mechanism and how chromatographic conditions influenced it. Both stationary phases showed a mixed-mode of RPLC/HILIC/IEC and satisfactory performance in separating hydrophobic analytes (alkylbenzenes and polycyclic aromatic hydrocarbons), hydrophilic nucleotide and bases, and anions, high column efficiency of 60,000 plates·m-1 was achieved. In summary, zwitterionic polymers are attractive options to prepare stationary phases and the retention property can be easily regulated by copolymer.

Predictive mechanistic modeling of loading and elution in protein A chromatography.

Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.

Selective immunoglobulin aggregates removal in antivenoms by a simple chromatographic step based on a monolithic stationary phase.

Antivenom therapy is a critical intervention for treating the more than 5.000.000 envenomation accidents that occur each year around the world. These immunotherapeutic drugs are mostly produced following techniques developed more than fifty years ago with minor changes. Aggregate content has been described as one of the main causes of early adverse effects after intravenous administration of antivenoms. In this work we propose the introduction of a final polishing step to traditional antivenom manufacturing processes aimed at lowering the aggregate content in the final product. The refinement step proposed in this work is based on the selective capture of immunoglobulin aggregates by a cation exchange monolithic stationary phase. We show that this media can effectively remove aggregates in the final product under isotonic ion-strength and mildly acidic conditions following a negative chromatography strategy, thus making it a useful technique for producing higher quality products.

Enhancing Selectivity of Protein Biopharmaceuticals in Ion Exchange Chromatography through Addition of Organic Modifiers.

Charge heterogeneity among therapeutic monoclonal antibodies (mAbs) is considered an important critical quality attribute and requires careful characterization to ensure safe and efficacious drug products. The charge heterogeneity among mAbs is the result of chemical and enzymatic post-translational modifications and leads to the formation of acidic and basic variants that can be characterized using cation exchange chromatography (CEX). Recently, the use of mass spectrometry-compatible salt-mediated pH gradients has gained increased attention to elute the proteins from the charged stationary phase material. However, with the increasing antibody product complexity, more and more selectivity is required. Therefore, in this study, we set out to improve the selectivity by using a solvent-enriched mobile phase composition for the analysis of a variety of mAbs and bispecific antibody products. It was found that the addition of the solvents to the mobile phase appeared to modify the hydrate shell surrounding the protein and alter the retention behavior of the studied proteins. Therefore, this work demonstrates that the use of solvent-enriched mobile phase composition could be an attractive additional method parameter during method development in CEX.