Comparison of Protein A affinity resins for twin-column continuous capture processes: Process performance and resin characteristics

Comparison of batch and continuous multi-column capture of monoclonal antibodies with convective diffusive membrane adsorbers

Protein A affinity membrane adsorbers are a promising alternative to resins to intensify the manufacturing of monoclonal antibodies. This study examined the process performance of convective diffusive membrane adsorbers operated in batch and continuous multi-column mode. Therefore, three different processes were compared regarding membrane utilization, productivity, and buffer consumption: the batch process, the rapid cycling parallel multi-column chromatography process, and the rapid cycling simulated moving bed process. The influence of the monoclonal antibody loading concentration (between 0.5 g L-1 and 5.2 g L-1) and the loading flow rate (between 1.25 MV min-1 and 10 MV min-1) on the monoclonal antibody binding behavior of the membrane adsorber were studied with breakthrough curve experiments. The determined breakthrough curves were used to calculate the monoclonal antibody dynamic binding capacity, the duration of the loading steps for each process, and the number of required membrane adsorbers for the continuous processes rapid cycling parallel multi-column chromatography and rapid cycling simulated moving bed. The highest productivity for the batch (176 g L-1 h-1) and rapid cycling parallel multi-column chromatography process (176 g L-1 h-1) was calculated for high monoclonal antibody loading concentrations and low loading flow rates. In contrast, the rapid cycling simulated moving bed process achieved the highest productivity (217 g L-1 h-1) for high monoclonal antibody loading concentrations and loading flow rates. Furthermore, due to the higher membrane utilization, the buffer consumption of the rapid cycling simulated moving bed process (1.1 L g-1) was up to 1.9 times lower than that of the batch or rapid cycling parallel multi-column chromatography operation (2.1 L g-1).

Integrated continuous downstream process of monoclonal antibody developed by converting the batch platform process based on the process characterization

A continuous downstream process of monoclonal antibody was developed based on the process characterization. Periodic-counter current chromatography (PCCC) with two protein A columns was used for the capture step. For low pH virus inactivation (VI), a batch reactor was employed, which can work as a surge (buffer) tank. Flow-through chromatography (FTC) with two connected columns of different separation modes (anion-mixed mode and cation exchange) was designed as a polish step. After 24 h PCCC run, the collected pool was processed for VI. After adjusting pH and electric conductivity, the solution was fed to the two connected FTC columns for 24 h. Virus filter was also connected to the exit of the connected-column. PCCC and FTC were run in parallel. Six runs of different feed rates (0.5-10 L/day) and feed concentrations (1-3.2 g/L) were performed with protein A columns of 1-5 mL and FTC columns of 3-10 mL. The largest run (feed rate 10 L/day, feed concentration 2 g/L) was carried out at a GMP facility with 15 mL protein A columns and 100 mL FTC columns. Good recovery and purity values were obtained for all runs. The process was found to be flexible and stable for feed fluctuations. Only three surge or pool tanks were needed in addition to the final product pool tank.

Continuous multi-column capture of monoclonal antibodies with convective diffusive membrane adsorbers

Downstream processing is the bottleneck in the continuous manufacturing of monoclonal antibodies (mAbs). To overcome throughput limitations, two different continuous processes with a novel convective diffusive protein A membrane adsorber (MA) were investigated: the rapid cycling parallel multi-column chromatography (RC-PMCC) process and the rapid cycling simulated moving bed (RC-BioSMB) process. First, breakthrough curve experiments were performed to investigate the influence of the flow rate on the mAb dynamic binding capacity and to calculate the duration of the loading steps. In addition, customized control software was developed for an automated MA exchange in case of pressure increase due to membrane fouling to enable robust, uninterrupted, and continuous processing. Both processes were performed for 4 days with 0.61 g L-1 mAb-containing filtrate and process performance, product purity, productivity, and buffer consumption were compared. The mAb was recovered with a yield of approximately 90% and productivities of 1010 g L-1 d-1 (RC-PMCC) and 574 g L-1 d-1 (RC-BioSMB). At the same time, high removal of process-related impurities was achieved with both processes, whereas the buffer consumption was lower for the RC-BioSMB process. Finally, the attainable productivity for perfusion bioreactors of different sizes with suitable MA sizes was calculated to demonstrate the potential to operate both processes on a manufacturing scale with bioreactor volumes of up to 2000 L.

Computer-aided design space identification for screening of protein A affinity chromatography resins

The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date. Examining performance alongside flexibility is crucial for the design of processes that can handle disturbances arising from the feed stream. In this work, we present a model-based approach for the identification of design spaces, enhanced by machine learning. We demonstrate its capabilities on the design of a Protein A chromatography unit, screening five industrially relevant resins. The computational results favourably compare to experimental data and a resin performance comparison is presented. An improvement on the computational time by a factor of 300,000 is achieved using the machine learning aided methodology. This allowed for the identification of 5,120 different design spaces in only 19 h.

Progress of ligand-modified agarose microspheres for protein isolation and purification

Proteins are the material basis of life and the primary carriers of life activities, containing various impurities that must be removed before use. To keep pace with the increasing complexity of protein samples, it is essential to constantly work on developing new purification technologies for downstream processes. While traditional downstream purification methods rely heavily on protein A affinity chromatography, there is still a lot of interest in finding safer and more cost-effective alternatives to protein A. Many non-affinity ligands and technologies have also been developed in biological purification recently. Here, the current status of biotechnology and the progress of protein separation technology from 2018 to 2023 are reviewed from the aspects of new preparation methods and new composite materials of commonly used separation media. The research status of new ligands with different mechanisms of action was reviewed, including the expanded application of affinity ligands, the development prospect of biotechnology such as polymer grafting, continuous column technology, and its new applications.

Evaluation of dynamic control of continuous capture with periodic counter-current chromatography under feedstock variations

Multi-column periodic counter-current chromatography is a promising technology for continuous antibody capture. However, dynamic changes due to disturbances and drifts pose some potential risks for continuous processes during long-term operation. In this study, a model-based approach was used to describe the changes in breakthrough curves with feedstock variations in target proteins and impurities. The performances of continuous capture of three-column periodic counter-current chromatography under ΔUV dynamic control were systematically evaluated with modeling to assess the risks under different feedstock variations. As the concentration of target protein decreased rapidly, the protein might not breakthrough from the first column, resulting in the failure of ΔUV control. Small reductions in the concentrations of target proteins or impurities would cause protein losses, which could be predicted by the modeling. The combination of target protein and impurity variations showed complicated effects on the process performance of continuous capture. A contour map was proposed to describe the comprehensive impacts under different situations, and nonoperation areas could be identified due to control failure or protein loss. With the model-based approach, after the model parameters are estimated from the breakthrough curves, it can rapidly predict the process stability under dynamic control and assess the risks under feedstock variations or UV signal drifts. In conclusion, the model-based approach is a powerful tool for continuous process evaluation under dynamic changes and would be useful for establishing a new real-time dynamic control strategy.

Comparison of multi-column chromatography configurations through model-based optimization

Integrated continuous bioprocessing has been identified as the next important phase of evolution in biopharmaceutical manufacturing. Multiple platform technologies to enable continuous processing are being developed. Multi-column counter-current chromatography is a step in this direction to provide increased productivity and capacity utilization to capture biomolecules like monoclonal antibodies (mAbs) present in the reactor harvest and remove impurities. Model-based optimization of two prevalent multi-column designs, 3-column and 4-column periodic counter-current chromatography (PCC) was carried out for different concentrations of mAbs in the feed, durations of cleaning-in-place and equilibration protocols. The multi-objective optimization problem comprising three performance measures, namely, product yield, productivity, and capacity utilization was solved using the Radial basis function optimization technique. The superficial velocities during load, wash, and elute operations, along with durations of distinct stages present in the multi-column operations were considered as decision variables. Optimization results without the constraint on number of wash volumes showed that 3-Column PCC performs better than 4-Column PCC. For example, at a feed concentration of 1.2 mg/mL, productivity, yield and capacity utilization, respectively, were 0.024 mg/mL.s, 0.94, and 0.94 for 3-Column PCC and 0.017 mg/mL.s, 0.87, and 0.83 for 4-column PCC. Similar trends were observed at higher feed concentrations also. However, when the constraint on number of wash volumes is included, 4-Column PCC was found to result in consistent productivity and product yield under different operating conditions but at the expense of reduced capacity utilization.

Model-assisted process development, characterization and design of continuous chromatography for antibody separation

Continuous manufacturing in monoclonal antibody production has generated increased interest due to its consistent quality, high productivity, high equipment utilization, and low cost. One of the major challenges in realizing continuous biological manufacturing lies in implementing continuous chromatography. Given the complex operation mode and various operation parameters, it is challenging to develop a continuous process. Due to the process parameters being mainly determined by the breakthrough curves and elution behaviors, chromatographic modeling has gradually been used to assist in process development and characterization. Model-assisted approaches could realize multi-parameter interaction investigation and multi-objective optimization by integrating continuous process models. These approaches could reduce time and resource consumption while achieving a comprehensive and systematic understanding of the process. This paper reviews the application of modeling tools in continuous chromatography process development, characterization and design. Model-assisted process development approaches for continuous capture and polishing steps are introduced and summarized. The challenges and potential of model-assisted process characterization are discussed, emphasizing the need for further research on the design space determination strategy and parameter robustness analysis method. Additionally, some model applications for process design were highlighted to promote the establishment of the process optimization and process simulation platform.

Model-based evaluation and model-free strategy for process development of three-column periodic counter-current chromatography

Multi-column counter-current chromatography is an advanced technology used for continuous capture processes to improve process productivity, resin capacity utilization and product consistency. However, process development is difficult due to process complexity. In this work, some general and convenient guidances for three-column periodic counter-current chromatography (3C-PCC) were developed. Boundaries and distributions of operating windows of 3C-PCC processes were clarified by model-based predictions. Interactive effects of feed concentration (c₀), resin properties (q_max and D_e), recovery and regeneration times (t_RR) were evaluated over a wide range for maximum productivity (P_max). Furthermore, variation of P_max was analyzed considering the constraint factors (capacity utilization target and flow rate limitation). The plateau value of P_max was determined by q_max and t_RR. The operating conditions for P_max were controlled by q_max, t_RR and c₀ interactively, and a critical concentration existed to judge whether the operating conditions of P_max under constraints. Based on the comprehensive understanding on 3C-PCC processes, a model-free strategy was proposed for process development. The optimal operating conditions could be determined based on a set of breakthrough curves, which was used to optimize process performance and screen resins. The approach proposed was validated using monoclonal antibody (mAb) capture with a 3C-PCC system under various mAb and feed concentrations. The results demonstrated that a thorough model-based process understanding on multi-column counter-current chromatography is important and could improve process development and establish a model-free strategy for more convenient applications.

Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Theoretical considerations and experimental verification

Ion exchange chromatography (IEC) is one of the most widely-used techniques for protein separation and has been characterized by mechanistic models. However, the time-consuming and cumbersome model calibration hinders the application of mechanistic models for process development. A new methodology called "parameter-by-parameter method (PbP)" was proposed with mechanistic derivations of the steric mass action (SMA) model of IEC. The protocol includes four steps: (1) first linear regression (LR1) for characteristic charge; (2) second linear regression (LR2) for equilibrium coefficient; (3) linear approximation (LA) for shielding factor; (4) inverse method (IM) for kinetic coefficient. Four SMA parameters could be one-by-one determined in sequence, reducing the number of unknown parameters per species from four to one, and predicting almost consistent retention. Numerical single-component experiments were investigated firstly, and the PbP method showed excellent agreement between experiments and simulations. The effects of loadings on the PbP and Yamamoto methods were compared. It was found that the PbP method had higher accuracy and robustness than the Yamamoto method. Moreover, a five-experiment strategy was suggested to implement the PbP method, which is straightforward to reduce the cost of calibration experiments. Finally, a real-world multi-component separation was challenged and further confirmed the feasibility of the PbP method. In general, the proposed method can not only reliably estimate the SMA parameters with comprehensive physical understanding but also accurately predict retention over a wide range of loading conditions.

Online optimization of dynamic binding capacity and productivity by model predictive control

In preparative and industrial chromatography, the current viewpoint is that the dynamic binding capacity governs the process economy, and increased dynamic binding capacity and column utilization are achieved at the expense of productivity. The dynamic binding capacity in chromatography increases with residence time until it reaches a plateau, whereas productivity has an optimum. Therefore, the loading step of a chromatographic process is a balancing act between productivity, column utilization, and buffer consumption. This work presents an online optimization approach for capture chromatography that employs a residence time gradient during the loading step to improve the traditional trade-off between productivity and resin utilization. The approach uses the extended Kalman filter as a soft sensor for product concentration in the system and a model predictive controller to accomplish online optimization using the pore diffusion model as a simple mechanistic model. When a soft sensor for the product is placed before and after the column, the model predictive controller can forecast the optimal condition to maximize productivity and resin utilization. The controller can also account for varying feed concentrations. This study examined the robustness as the feed concentration varied within a range of 50%. The online optimization was demonstrated with two model systems: purification of a monoclonal antibody by protein A affinity and lysozyme by cation-exchange chromatography. Using the presented optimization strategy with a controller saves up to 43% of the buffer and increases the productivity together with resin utilization in a similar range as a multi-column continuous counter-current loading process.

An integrated and continuous downstream process for microbial virus-like particle vaccine biomanufacture

In this study, we present the first integrated and continuous downstream process for the production of microbial virus‐like particle vaccines. Modular murine polyomavirus major capsid VP1 with integrated J8 antigen was used as a model virus‐like particle vaccine. The integrated continuous downstream process starts with crude cell lysate and consists of a flow‐through chromatography step followed by periodic counter‐current chromatography (PCC) (bind‐elute) using salt‐tolerant mixed‐mode resin and subsequent in‐line assembly. The automated process showed a robust behavior over different inlet feed concentrations ranging from 1.0 to 3.2 mg ml⁻¹ with only minimal adjustments needed, and produced continuously high‐quality virus‐like particles, free of nucleic acids, with constant purity over extended periods of time. The average size remained constant between 44.8 ± 2.3 and 47.2 ± 2.9 nm comparable to literature. The process had an overall product recovery of 88.6% and a process productivity up to 2.56 mg h⁻¹ ml_resin⁻¹ in the PCC step, depending on the inlet concentration. Integrating a flow through step with a subsequent PCC step allowed streamlined processing, showing a possible continuous pathway for a wide range of products of interest.