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

Chromatography in Downstream Processing of Recombinant Adeno-Associated Viruses: A Review of Current and Future Practises

Recombinant adeno-associated virus (rAAV) has emerged as an attractive gene delivery vector platform to treat both rare and pervasive diseases. With more and more rAAV-based therapies entering late-stage clinical trials and commercialization, there is an increasing pressure on the rAAV manufacturing process to accelerate drug development, account for larger trials, and commercially provide high doses. Still, many of the pre-clinical and clinical manufacturing processes are tied to outdated technologies, which results in substantial production expenses. Those processes face challenges including low productivity and difficult scalability, which limits its ability to provide for required dosages which in turn influences the accessibility of the drug. And as upstream efforts are expected to increase productivities, the downstream part needs to adapt with more scalable and efficient technologies. In this review, both traditional and novel rAAV downstream technologies are presented and discussed. Traditional rAAV downstream processes are based on density gradient ultracentrifugation and have been shown to effectively purify rAAVs with high yields and purities. However, those processes lack scalability and efficiency, which is why novel rAAV downstream processes based on column-chromatography have emerged as an attractive alternative and show potential for integration in continuous processes, following the principle of next-generation manufacturing.

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.

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.

Reviewing the process intensification landscape through the introduction of a novel, multitiered classification for downstream processing

A demand for process intensification in biomanufacturing has increased over the past decade due to the ever-expanding market for biopharmaceuticals. This is largely driven by factors such as a surge in biosimilars as patents expire, an aging population, and a rise in chronic diseases. With these market demands, pressure upon biomanufacturers to produce quality products with rapid turnaround escalates proportionally. Process intensification in biomanufacturing has been well received and accepted across industry based on the demonstration of its benefits of improved productivity and efficiency, while also reducing the cost of goods. However, while these benefits have been shown empirically, the challenges of adopting process intensification into industry remain, from smaller independent start-up to big pharma. Traditionally, moving from batch to a process intensification scheme has been viewed as an "all or nothing" approach involving continuous bioprocessing, in which the factors of complexity and significant capital costs hinder its adoption. In addition, the literature is crowded with a variety of terms used to describe process intensification (continuous, periodic counter-current, connected, intensified, steady-state, etc.). Often, these terms are used inappropriately or as synonyms, which generates confusion in the field. Through a detailed review of current state-of-the-art systems, consumables, and process intensification case studies, we herein propose a defined approach in the implementation of downstream process intensification through a standardized nomenclature and viewing it as distinct independent levels. These can function separately as intensified single-unit operations or be built upon by integration with other process steps allowing for simple, incremental, cost-effective implementation of process intensification in the manufacturing of biopharmaceuticals.

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.

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.