Designing an Autonomous Integrated Downstream Sequence From a Batch Separation Process − An Industrial Case Study

Real-time detection of mAb aggregates in an integrated downstream process

The implementation of continuous processing in the biopharmaceutical industry is hindered by the scarcity of process analytical technologies (PAT). To monitor and control a continuous process, PAT tools will be crucial to measure real-time product quality attributes such as protein aggregation. Miniaturizing these analytical techniques can increase measurement speed and enable faster decision-making. A fluorescent dye (FD)-based miniaturized sensor has previously been developed: a zigzag microchannel which mixes two streams under 30 s. Bis-ANS and CCVJ, two established FDs, were employed in this micromixer to detect aggregation of the biopharmaceutical monoclonal antibody (mAb). Both FDs were able to robustly detect aggregation levels starting at 2.5%. However, the real-time measurement provided by the microfluidic sensor still needs to be implemented and assessed in an integrated continuous downstream process. In this work, the micromixer is implemented in a lab-scale integrated system for the purification of mAbs, established in an ÄKTA™ unit. A viral inactivation and two polishing steps were reproduced, sending a sample of the product pool after each phase directly to the microfluidic sensor for aggregate detection. An additional UV sensor was connected after the micromixer and an increase in its signal would indicate that aggregates were present in the sample. The at-line miniaturized PAT tool provides a fast aggregation measurement, under 10 min, enabling better process understanding and control.

An integrated continuous downstream process with real-time control: A case study with periodic countercurrent chromatography and continuous virus inactivation

Integrated continuous downstream processes with process analytical technology offer a promising opportunity to reduce production costs and increase process flexibility and adaptability. In this case study, an integrated continuous process was used to purify a recombinant protein on laboratory scale in a two-system setup that can be used as a general downstream setup offering multiproduct and multipurpose manufacturing capabilities. The process consisted of continuous solvent/detergent virus inactivation followed by periodic countercurrent chromatography in the capture step, and a final chromatographic polishing step. A real-time controller was implemented to ensure stable operation by adapting the downstream process to external changes. A concentration disturbance was introduced to test the controller. After the disturbance was applied, the product output recovered within 6 h, showing the effectiveness of the controller. In a comparison of the process with and without the controller, the product output per cycle increased by 27%, the resin utilization increased from 71.4% to 87.9%, and the specific buffer consumption was decreased by 21% with the controller, while maintaining a similar yield and purity as in the process without the disturbance. In addition, the integrated continuous process outperformed the batch process, increasing the productivity by 95% and the yield by 28%.

Optimal loading flow rate trajectory in monoclonal antibody capture chromatography

In this paper, we determined the optimal flow rate trajectory during the loading phase of a mAb capture column. For this purpose, a multi-objective function was used, consisting of productivity and resin utilization. Several general types of trajectories were considered, and the optimal Pareto points were obtained for all of them. In particular, the presented trajectories include a constant-flow loading process as a nominal approach, a stepwise trajectory, and a linear trajectory. Selected trajectories were then applied in experiments with the state-of-the-art protein A resin mAb Select PrismA^TM, running in batch mode on a standard single-column chromatography setup, and using both a purified mAb solution as well as a clarified supernatant. The results show that this simple approach, programming the volumetric flow rate according to either of the explored strategies, can improve the process economics by increasing productivity by up to 12% and resin utilization by up to 9% compared to a constant-flow process, while obtaining a yield higher than 99%. The productivity values were similar to the ones obtained in a multi-column continuous process, and ranged from 0.23 to 0.35 mg/min/mL resin. Additionally, it is shown that a model calibration carried out at constant flow can be applied in the simulation and optimization of flow trajectories. The selected processes were scaled up to pilot scale and simulated to prove that even higher productivity and resin utilization can be achieved at larger scales, and therefore confirm that the trajectories are generalizable across process scales for this resin.

Optimization of integrated chromatography sequences for purification of biopharmaceuticals

With continued development of integrated and continuous downstream purification processes, tuning and optimization become increasingly complicated with additional parameters and codependent variables over the sequence. This article offers a novel perspective of nonlinear optimization of integrated sequences with regard to individual column sizes, flow rates, and scheduling. The problem setup itself is a versatile tool to be used in downstream design which is demonstrated in two case studies: a four‐column integrated sequence and a continuously loaded twin‐capture setup with five columns.

Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.