Model-based process development and evaluation of twin-column continuous capture processes with Protein A affinity resin

Exploring the effects of resin particle sizes on enhancing antibody binding capacity of a hybrid biomimetic ligand

Particle size is a critical parameter of chromatographic resins that significantly affects protein separation. In this study, effects of resin particle sizes (31.26 μm, 59.85 μm and 85.22 μm named Aga-31, Aga-60 and Aga-85, respectively) on antibody adsorption capacity and separation performance of a hybrid biomimetic ligand were evaluated. Their performance was investigated through static adsorption and breakthrough assays to quantify static and dynamic binding capacity (Qmax and DBC). The static adsorption results revealed that the Qmax for hIgG was 152 mg/g resin with Aga-31, 151 mg/g resin with Aga-60, and 125 mg/g resin with Aga-85. Moreover, the DBC at 10% breakthrough for hIgG with a residence time of 2 min was determined to be 49.4 mg/mL for Aga-31, 45.9 mg/mL for Aga-60, and 38.9 mg/mL for Aga-85. The resins with smaller particle sizes exhibited significantly higher capacity compared to typical commercial agarose resins and a Protein A resin (MabSelect SuRe). Furthermore, the Aga-31 resin with the hybrid biomimetic ligand demonstrated exceptional performance in terms of IgG purity (>98%) and recovery (>96%) after undergoing 20 separation cycles from CHO cell supernatant. These findings are helpful in further chromatographic resin design for the industrial application of antibody separation and purification.

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.

Physics-informed neural networks to solve lumped kinetic model for chromatography process

Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.

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.

Computational framework for the techno-economic analysis of monoclonal antibody capture chromatography platforms

We developed a computational framework that integrates commercial software components to perform customizable technoeconomic feasibility analyses. The use of multiple software packages overcomes the shortcomings of each to provide a detailed simulation that can be used for sensitivity analyses and optimizations. In this paper, the framework was used to evaluate the performance of monoclonal antibody capture processes. To this end, the simulation framework incorporated dynamic models for the affinity chromatography step that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. As proof of concept, the framework was used to perform a sensitivity analysis and optimization for a case study in which we sought to compare membrane and resin chromatography for disposable and reusable batch capture platforms. Two membranes and one resin were selected for the capture media, which yielded six process alternatives to compare. The objective functions were set to be cost of goods, process time, and buffer utilization. The results of the optimization of these process alternatives were a set of operating conditions that display tradeoffs between competing objectives. From this application exercise, we conclude that the framework can handle multiple variables and objectives, and it is adaptable to platforms with different chromatography media and operating modes. Additionally, the framework is capable of providing ad hoc analyses for decision making in a specific production context.

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.

Converting a mAb downstream process from batch to continuous using process modeling and process analytical technology

The biopharmaceutical market is driving the revolution from traditional batch processes to continuous manufacturing for higher productivity and lower costs. In this work, a batch mAb downstream process has been converted into an integrated continuous process with the combination of multiple techniques. For process intensification, two batch mode unit operations (protein A capture chromatography, ultrafiltration/diafiltration) are converted into continuous ones; For continuity, surge tanks were used between adjacent steps, and level signals were used to trigger process start or stop, forming a holistic continuous process. For process automation, manual operations (e.g., pH and conductivity adjustment) were changed into automatic operation and load mass was controlled with process analytical technology (PAT). A model-based simulation was applied to estimate the loading conditions for the continuous capture process, resulting in 21% resin capacity utilization and 28% productivity improvement as compared to the batch process. Automatic load mass control of cation exchange chromatography was achieved through a customized in-line protein quantity monitoring system, with a difference of less than 1.3% as compared to off-line analysis. Total process time was shortened from 4 days (batch process) to less than 24 hours using the continuous downstream process with the overall productivity of 23.8 g mAb /day for the bench-scale system. Comparable yield and quality data were obtained in three test runs, indicating a successful conversion from a batch process to a continuous process. The insight of this work could be a reference to other similar situations. This article is protected by copyright. All rights reserved.

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.

Downstream processing of virus-like particles with aqueous two-phase systems: applications and challenges

The advancement of recombinant virus-like particle-based vaccines has attracted global attention owing to substantially safety and high efficacy in provoking a protective immunity against various chronic and infectious diseases in humans and animals. A robust, low-cost and scalability separation and purification technology is of utmost importance in the downstream processing of recombinant virus-like particles to produce affordable and safe vaccines. Being a relatively simple, environmentally friendly and efficient biomolecules recovery approach, aqueous two-phase systems have received great attention from researchers worldwide. This review aims to highlight the challenges and outlook in addition to the current applications of aqueous two-phase systems in downstream processing of virus-like particles. The efforts will confidently reinforce scholars' knowledge and fill in the valuable research gap in the aspect of concerning recombinant virus-like particle-based vaccines development, particularly related to the virus-like particles downstream production processes. This article is protected by copyright. All rights reserved.

A novel framework of surrogate-based feasibility analysis for establishing design space of twin-column continuous chromatography

Multi-column periodic counter-current chromatography (PCC) has attracted wide attention for the primary capture for the purpose of achieving continuous biomanufacturing. Consequently, determining the design space of the continuous capture process is very important to facilitate process understanding and improving product quality. In this work, we proposed a novel approach to identify the design space of continuous chromatography to balance the computational complexity and model predictions. Specifically, surrogate-based feasibility analysis with adaptive sampling is applied to establish the design space of twin-column CaptureSMB process. The surrogate model is constructed based on the developed mechanistic model for the identification of the design space. The effects of process variables (including interconnected loading time, interconnected flowrate, and batch flowrate) on the design space are comprehensively examined based on an active set strategy. Besides, essential factors like recovery-regeneration time and constraints of column performance parameters (yield, productivity, and capacity utilization) are thoroughly investigated. The impact of design variables such as column length is also studied.

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

Continuous chromatography is a promising technology for downstream processing of biopharmaceuticals. The operation of continuous processes is significantly different to batch-mode chromatography and needs comprehensive evaluation. In this work, the performances of four Protein A affinity resins were studied systematically for twin-column continuous capture processes. A model-based approach was used to evaluate the process performance (productivity and capacity utilization) under varying operation conditions, and the objective was to reveal the crucial resin properties for continuous capture. The trade-off between productivity and capacity utilization was found, and it is necessary to select appropriate resins for different feedstock and operation conditions. The capacity utilization heavily depends on mass transfer, and steep breakthrough curves are favorable for high capacity utilization. The productivity is determined by both equilibrium binding capacity and mass transfer, and the balance of feed amount and feed time is critical. Moreover, the influence of binding capacity and mass transfer on process productivity and parameter sensitivity with two important resin properties (equilibrium binding capacity q_max and effective pore diffusion coefficient D_e) were assessed by the model, and suitable resin parameter ranges for twin-column continuous capture were determined. The model-based approach is an effective and useful tool to evaluate the complex performance of different resins and guide the design of next-generation resins for continuous processes.

Process development and optimization of continuous capture with three-column periodic counter-current chromatography

Continuous capture with affinity chromatography is one of the most important units for continuous manufacturing of monoclonal antibody (mAb). Due to the complexity of three-column periodic counter-current chromatography (3C-PCC), three approaches (experimental, model-based, and simplified approaches) were studied for process development and optimization. The effects of residence time for interconnected load (RT _C ), breakthrough percentage of the first column for interconnected load (s) and feed protein concentration (c ₀ ) on productivity and capacity utilization were focused. The model-based approach was found superior to the experimental approach in process optimization and evaluation. Two phases of productivity were observed and the optimal RT _C for the maximum productivity was located at the boundary of the two phases. The comprehensive effects of the operating parameters (RT _C , s, and c ₀ ) were evaluated by the model-based approach, and the operation space was predicted. The best performance of 34.5 g/L/h productivity and 97.6% capacity utilization were attained for MabSelect SuRe LX resin under 5 g/L concentration at RT _C  = 2.8 min and s = 87.5%. Moreover, a simplified approach was suggested to obtain the optimal RT _C for the maximum productivity. The results demonstrated that model-assisted tools are useful to determine the optimum conditions for 3C-PCC continuous capture with high productivity and capacity utilization.

Recent trends in sorbents for bioaffinity chromatography

Isolation or enrichment of biological molecules from complex biological samples is mostly a prerequisite in proteomics, genomics, and glycomics. Different techniques have been used to advance the efficiency of the purification of biological molecules. Bioaffinity chromatography is one of the most powerful technique that plays an important role in the isolation of target biological molecules by the specific interactions with ligands that are immobilized on different support materials. This review examines the recent developments in bioaffinity chromatography particularly over the past 5 years in the literature. Also properties of supports, immobilization techniques, types of binding agents, and methods used in bioaffinity chromatography applications are summarized.

Model-assisted approaches for continuous chromatography: Current situation and challenges

Continuous bioprocessing is a promising trend in biopharmaceutical production, and multi-column continuous chromatography shows advantages of high productivity, high resin capacity utilization, small footprint, low buffer consumption and less waste. Due to the complexity and dynamic nature of continuous processing, traditional experiment-based approaches are often time-consuming and inefficient. In this review, model-assisted approaches were focused and their applications in continuous chromatography process development, validation and control were discussed. Chromatographic models are useful in describing particular process performances of continuous capture and polishing with multi-column chromatography. Model-assisted tools showed powerful ability in evaluating multiple operating parameters and identifying optimal points over the entire design space. The residence time distribution models, model-assisted process analytical technologies and model-predictive control strategies were also developed to reveal the propagation of disturbances, enhance real time monitor and achieve adaptive control to match the transient disturbances and deviations of continuous processes. Moreover, artificial neural networks and machine learning concepts were integrated into modeling approaches to improve data treatment. In general, further development in research and applications of model-assisted approaches for continuous chromatography are needed urgently to support the continuous manufacturing.