Analysis and optimal design of batch and two-column continuous chromatographic frontal processes for monoclonal antibody purification

Developing physics-informed neural networks for model predictive control of periodic counter-current chromatography

The applications of continuous manufacturing technology in biopharmaceuticals require advanced design, monitoring, and control due to its complexity. Traditional mechanistic models, which rely on numerical solutions, suffer from long computational times, making them unsuitable for the timely demands of continuous processes and digital twin applications in biomanufacturing. This issue significantly limits the capability for real-time optimization and control. To overcome this challenge, this study proposes a Physics-Informed Neural Network (PINN) based General Rate Model (GRM) approach that greatly reduces computation time while maintaining high accuracy and reliability in simulations. The developed PINN is applicable for different parameters across wide ranges and is capable of parameter estimation. It presents excellent performance in both offline simulation of single-column breakthrough curves and online optimization of load conditions for four-column periodic counter-current chromatography (4C-PCC), achieving significant reductions in fitting time from 2608.6 to 110.7 s for offline simulations, and completing online simulations within 12 to 14 s. The results demonstrate the potential of PINN for real-time model predictive control and digital twin applications, offering a promising solution to the limitations of traditional numerical methods.

Hydrophobic interaction chromatography in continuous flow-through mode for product-related variant removal

Product-related impurities are challenging to remove during monoclonal antibody (mAb) purification process due to molecular similarity. Frontal chromatography on hydrophobic interaction resins has demonstrated its capability to effectively remove such impurities. However, process improvements geared towards purity level comes as a trade-off with the yield loss. In this work, we present a hydrophobic interaction chromatography process using multicolumn continuous chromatography (MCC) concept and frontal analysis to remove a high prevalence product related impurity. This design uses a two-column continuous system where the two columns are directly connected during product chase step to capture product wash loss without any in-process adjustment. This polish MCC operation resulted in a 10 % increase in yield while maintaining 99 % purity, despite the presence of 20 % product-related impurities in the feed material. One challenge associated with polish MCC design is that the accumulation of the impurities renders a non-steady state recycling. To surmount this issue and ensure a robust process, a mechanistic model was developed and validated to predict multicomponent breakthrough. This model was capable to predict multiple cycle behavior and accounts for increased impurity concentration. Assisted by the model, the optimized operation parameters and conditions can be determined to account for variation in product load quality. The simulated results demonstrate an effective doubling of productivity compared to conventional batch chromatography.

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.

Mathematical modeling and process analytical technology for continuous chromatography of biopharmaceutical products

Chromatography is a widely used separation method that is inherently a batch operation. However, the demand for higher productivity and lower cost and labor has prompted industries such as the petrochemical and food industries to transition from batch to continuous chromatography. Growing market competition in the biopharmaceutical industry and the rise of novel biotherapeutics modalities have brought about promising continuous chromatography methods as well as next-generation tools to enable continuous operation in bioprocessing. While these continuous chromatography methods outperform their batch counterpart, their implementation presents challenges due to their greater complexity. This review discusses two key technologies that are essential for the implementation of continuous chromatography: mathematical modeling and novel process analytical technologies. Mechanistic-based models not only aid in process development and optimization but also allow for greater process control and automation.

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.

Current research approaches in downstream processing of pharmaceutically relevant proteins

Biopharmaceuticals and their production are on the rise. They are needed to treat and to prevent multiple diseases. Therefore, an urgent need for process intensification in downstream processing (DSP) has been identified to produce biopharmaceuticals more efficiently. The DSP currently accounts for the majority of production costs of pharmaceutically relevant proteins. This short review gathers essential research over the past 3 years that addresses novel solutions to overcome this bottleneck. The overview includes promising studies in the fields of chromatography, aqueous two-phase systems, precipitation, crystallization, magnetic separation, and filtration for the purification of pharmaceutically relevant proteins.