Equilibrium Theory Based Design Space for the Multicolumn Countercurrent Solvent Gradient Purification Process

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.

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.

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.

Design space and robustness analysis of batch and counter-current frontal chromatography processes for the removal of antibody aggregates

Increasing molecular diversity and market competition requires biopharmaceutical manufacturers to intensify their processes. In this respect, frontal chromatography on cation exchange resins has shown its potential to effectively remove aggregates. However, yield losses during the wash step need to be accepted in order to ensure robust product quality. In this work, we present a novel counter-current frontal chromatography process called Flow2, which uses inline dilution during an interconnected wash phase to allow high monomer recovery without contaminating the product pool with impurities. Its model-based design spaces under purity and yield constraints are compared with those corresponding to traditional batch processes in terms of size and process attributes yield and productivity. The Flow2 process shows the largest extent of feasible operating points independent of feed conditions. Thereby, it allows the implementation of higher ionic strength wash, thus widening the range of operating conditions resulting in yields above 95% compared to batch processes. Productivities of batch and counter-current processes are the same at short regeneration times and equal residence time. However, long regeneration times, while influencing the size of the Flow2 design space, are not detrimental for its productivity resulting in twice as high values as obtained for the batch process. Furthermore, process robustness is evaluated by the ability of the process to maintain the required product quality when subjected to process parameter perturbations. It is found that the Flow2 process is able to retain a larger design space associated also with higher yields showing its ability to improve process attributes without sacrificing robustness at the same time.