Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.

UV-based dynamic control improves the robustness of multicolumn countercurrent solvent gradient purification of oligonucleotides

Therapeutic oligonucleotides (ONs) have great potential to treat many diseases due to their ability to regulate gene expression. However, the inefficiency of standard purification techniques to separate the target sequence from molecularly similar variants is hindering development of large scale ON manufacturing at a reasonable cost. Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) is a valuable process able to bypass the purity-yield tradeoff typical of single-column operations, and hence to make the ON production more sustainable from both an economic and environmental point of view. However, operating close to the optimum of MCSGP can be challenging, resulting in unstable process performance and in a drift in product quality, especially when running a continuous process for extended periods where process parameters such as temperature are prone to variation. In this work, we demonstrate how greater process robustness is introduced in the design and execution of MCSGP for the purification of a 20mer single-stranded DNA sequence through the implementation of UV-based dynamic control. With this novel approach, the cyclic steady state was reached already in the third cycle and disturbances coming from fluctuations in the feed quality, loading amount and temperature were effectively compensated allowing a stable operation close to the optimum. In response to the perturbations, the controlled process kept the standard deviation on product recovery below 3.4%, while for the non-controlled process it increased up to 27.5%.

Integration of rapid bioburden testing into production quality management systems and process control

The move to integrated continuous bioprocessing (ICB), while providing a means for process intensification, can put added strain on process analytics when conventional methods are used. For instance, traditional microbial methods provide minimal value to ICB processes given that the time required for data to become available is much longer than a typical full cycle of the manufacturing process. Although rapid microbial detection has been in discussion for over 30 years, it is still not routinely deployed in commercial biopharmaceutical manufacturing. One contributing factor is the ability to integrate this technology into a process control strategy and existing quality systems. An understanding of the capability of microbial detection technology available today can be leveraged to implement a control strategy for bioburden monitoring in real time for process intermediates. One key tenet of this proposed control strategy is the use of a "two-tiered approach" wherein a fast (but possibly less sensitive) test is used to monitor the process and trigger further action for a second, longer duration test which is used to confirm and quantify the presence of bioburden and identify the organism. This approach, presented here alongside several case studies for microbial monitoring, can have broader application for other process analytical technologies where fit for purpose methods could be employed to establish process control alongside real time continuous processes.

Robust method for chromatogram shape analysis to improve early detection of performance drifts and adverse changes in process parameters during purification column operations

The biopharmaceutical industry is under increased pressure to maximize efficiency, enhance quality compliance, and reduce the cost of drug substance manufacturing. Ways to reduce costs associated with manufacturing of complex biological molecules include maximizing efficiency of chromatography purification steps. For example, process analytical technology (PAT) tools can be employed to improve column resin life, prevent column operating failures, and decrease the time it takes to solve investigations of process deviations. We developed a robust method to probe the shape of the chromatogram for indications of column failure or detrimental changes in the process. The approach herein utilizes raw data obtained from manufacturing followed by a pre-processing routine to align chromatograms and patch together the different chromatogram phases in preparation for multivariate analysis. A principal component analysis (PCA) was performed on the standardized chromatograms to compare different batches, and resulted in the identification specific process change that affected the profile. In addition, changes in the chromatogram peaks were used to create predictive models for impurity clearance. This approach has the potential for early detection of column processing issues, improving timely resolution in large-scale chromatographic operations.

Automating CAR-T Transfection with Micro and Nano-Technologies

Cancer poses a significant health challenge, with traditional treatments like surgery, radiotherapy, and chemotherapy often lacking in cell specificity and long-term curative potential. Chimeric antigen receptor T cell (CAR-T) therapy,utilizing genetically engineered T cells to target cancer cells, is a promising alternative. However, its high cost limits widespread application. CAR-T manufacturing process encompasses three stages: cell isolation and activation, transfection, and expansion.While the first and last stages have straightforward, commercially available automation technologies, the transfection stage lags behind. Current automated transfection relies on viral vectors or bulk electroporation, which have drawbacks such as limited cargo capacity and significant cell disturbance. Conversely, micro and nano-tool methods offer higher throughput and cargo flexibility, yet their automation remains underexplored.In this perspective, the progress in micro and nano-engineering tools for CAR-T transfection followed by a discussion to automate them is described. It is anticipated that this work can inspire the community working on micro and nano transfection techniques to examine how their protocols can be automated to align with the growing interest in automating CAR-T manufacturing.

Current challenges and recent advances on the path towards continuous biomanufacturing

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Automated quality analysis in continuous downstream processes for small-scale applications

Development of integrated, continuous biomanufacturing (ICB) processes brings along the challenge of streamlining the acquisition of data that can be used for process monitoring, product quality testing and process control. Manually performing sample acquisition, preparation, and analysis during process and product development on ICB platforms requires time and labor that diverts attention from the development itself. It also introduces variability in terms of human error in the handling of samples. To address this, a platform for automatic sampling, sample preparation and analysis for use in small-scale biopharmaceutical downstream processes was developed. The automatic quality analysis system (QAS) consisted of an ÄKTA Explorer chromatography system for sample retrieval, storage, and preparation, as well as an Agilent 1260 Infinity II analytical HPLC system for analysis. The ÄKTA Explorer system was fitted with a superloop in which samples could be stored, conditioned, and diluted before being sent to the injection loop of the Agilent system. The Python-based software Orbit, developed at the department of chemical engineering at Lund university, was used to control and create a communication framework for the systems. To demonstrate the QAS in action, a continuous capture chromatography process utilizing periodic counter-current chromatography was set up on an ÄKTA Pure chromatography system to purify the clarified harvest from a bioreactor for monoclonal antibody production. The QAS was connected to the process to collect two types of samples: 1) the bioreactor supernatant and 2) the product pool from the capture chromatography. Once collected, the samples were conditioned and diluted in the superloop before being sent to the Agilent system, where both aggregate content and charge variant composition were determined using size-exclusion and ion-exchange chromatography, respectively. The QAS was successfully implemented during a continuous run of the capture process, enabling the acquisition of process data with consistent quality and without human intervention, clearing the path for automated process monitoring and data-based control.

An automated buffer management system for small-scale continuous downstream bioprocessing

Buffer management for biopharmaceutical purification processes include buffer preparation, storage of buffers and restocking the buffers when needed. This is usually performed manually by the operators for small scale operations. However, buffer management can become a bottleneck when running integrated continuous purification processes for prolonged times, even at small scale. To address this issue, a buffer management system for the application in continuous lab-scale bioprocessing is presented in this paper. For this purpose, an ÄKTA™ explorer chromatography system was reconfigured to perform the buffer formulation. The system formulated all buffers from stock solutions and water according to pre-specified recipes. A digital twin of the physical system was introduced in the research software Orbit, written in python. Orbit was also used for full automation and control of the buffer system, which could run independently without operator input and handle buffer management for one or several connected buffer-consuming purification systems. The developed buffer management system performed automatic monitoring of buffer volumes, buffer order handling as well as buffer preparation and delivery. To demonstrate the capability of the developed system, it was integrated with a continuous downstream process and supplied all 9 required buffers to the process equipment during a 10-day operation. The buffer management system processed 55 orders and delivered 38 L of buffers, corresponding to 20% of its capacity. The pH and conductivity profiles observed during the purification steps were consistent across the cycles. The deviation in conductivity and pH from the measured average value was within ±0.89% in conductivity and ±0.045 in pH, well within the typical specification for buffer release, indicating that the prepared buffers had the correct composition. The operation of the developed buffer management system was robust and fully automated, and provides one solution to the buffer management bottleneck on lab scale for integrated continuous downstream bioprocessing.

Comprehensive studies on building a scalable downstream process for mRNAs to enable mRNA therapeutics

In recent years, mRNA-based therapeutics have been a fast-growing new class of biologics that can, in principle, encode any protein(s) directly in patients to treat various diseases. mRNA vaccines have been proven to work efficiently, have high potency, and can be rapidly developed and deployed, which is critical for a quick responses in the case of a pandemic. Such agile development is enabled by rapid synthesis of RNA in vitro using recombinant enzymes rather than relying on lengthy and complex cell culture processes. mRNA exhibits physical and chemical properties differing from protein-based therapeutics. It is highly negatively charged and the hydroxyl group makes mRNA less stable and more susceptible to hydrolysis and nucleophilic cleavage. This novel work shares comprehensive studies carried out to compare the performance of various mRNA purification strategies by considering its scalability and critical quality attributes. In addition, the paper provides insights on how to establish a scalable mRNA purification process that consists of ultrafiltration/diafiltration and chromatography steps with good recoveries. Alternative Oligo(dT) based columns were further explored aiming to improve total process recovery. With Oligo(dT) as a capture step, overall recoveries of 70% can be achieved for mRNAs studied here that encode anti-influenza immunoglobulin G monoclonal antibodies.

Toward Autonomous Production of mRNA-Therapeutics in the Light of Advanced Process Control and Traditional Control Strategies for Chromatography

mRNA-based therapeutics are predicted to have a bright future. Recently, a B2C study was published highlighting the critical bottlenecks of mRNA manufacturing. The study focused on supply bottlenecks of various chemicals as well as shortages of skilled personnel. The assessment of existing messenger ribonucleic acid (mRNA) vaccine processing shows the need for continuous manufacturing processes that are capable of about 80% chemical reduction and more than 70% personnel at factor five more efficient equipment utilization. The key technology to solve these problems is both a higher degree of automation and the maximization of process throughput. In this paper, the application of a quality-by-design process development approach is demonstrated, using process models as digital twins. Their systematic application leads to both robust optimized process parameters, with an increase in productivity of up to 108%, and sophisticated control concepts, preventing batch failures and minimizing the operating workload in terms of personnel and chemicals’ consumption. The approach thereby provides a data-driven decision basis for the industrialization of such processes, which fulfills the regulatory requirements of the approval authorities and paves the way for PAT integration. In the process investigated, it was shown that conventional PID-based controls can regulate fluctuations in the input streams sufficiently well. Model-based control based on digital twins may have potential above all in a further increase in productivity, but is not mandatory to implement for the industrialization of continuous mRNA manufacturing.

Process Automation and Control Strategy by Quality-by-Design in Total Continuous mRNA Manufacturing Platforms

Vaccine supply has a bottleneck in manufacturing capacity due to operation personnel and chemicals needed. Assessment of existing mRNA (messenger ribonucleic acid) vaccine processing show needs for continuous manufacturing processes. This is enabled by strict application of the regulatory demanded quality by design process based on digital twins, process analytical technology, and control automation strategies in order to improve process transfer for manufacturing capacity, reduction out-of-specification batch failures, qualified personnel training and number, optimal utilization of buffers and chemicals as well as speed-up of product release. In this work, process control concepts, which are necessary for achieving autonomous, continuous manufacturing, for mRNA manufacturing are explained and proven to be ready for industrialization. The application of the process control strategies developed in this work enable the previously pointed out benefits. By switching from batch-wise to continuous mRNA production as was shown in previous work, which was the base for this study, a potential cost reduction by a factor 5 (i.e., from EUR 0.380 per dose to EUR 0.085 per dose) is achievable. Mainly, based on reduction of personnel (factor 30) and consumable (factor 7.5) per campaign due to the significant share of raw materials in the manufacturing costs (74–97). Future research focus following this work may be on model-based predictive control to gain further optimization potential of potential batch failure and out of specification (OOS) number reduction.

Online optimization of dynamic binding capacity and productivity by model predictive control

In preparative and industrial chromatography, the current viewpoint is that the dynamic binding capacity governs the process economy, and increased dynamic binding capacity and column utilization are achieved at the expense of productivity. The dynamic binding capacity in chromatography increases with residence time until it reaches a plateau, whereas productivity has an optimum. Therefore, the loading step of a chromatographic process is a balancing act between productivity, column utilization, and buffer consumption. This work presents an online optimization approach for capture chromatography that employs a residence time gradient during the loading step to improve the traditional trade-off between productivity and resin utilization. The approach uses the extended Kalman filter as a soft sensor for product concentration in the system and a model predictive controller to accomplish online optimization using the pore diffusion model as a simple mechanistic model. When a soft sensor for the product is placed before and after the column, the model predictive controller can forecast the optimal condition to maximize productivity and resin utilization. The controller can also account for varying feed concentrations. This study examined the robustness as the feed concentration varied within a range of 50%. The online optimization was demonstrated with two model systems: purification of a monoclonal antibody by protein A affinity and lysozyme by cation-exchange chromatography. Using the presented optimization strategy with a controller saves up to 43% of the buffer and increases the productivity together with resin utilization in a similar range as a multi-column continuous counter-current loading process.

Scalable, Robust and Highly Productive Novel Convecdiff Membrane Platform for mAb Capture

The recombinant monoclonal antibody capture step represents the current bottleneck in downstream processing. Protein A resins are diffusion-limited chromatography materials which require low flow rates to achieve a binding capacity above 30 g L⁻¹ with the result of low productivity. Here, we present a novel chromatography membrane combining superior binding capacities with high flow rates for high productivity while achieving comparable product quality as state-of-the-art protein A resins. Further, we demonstrate full scalability of this convecdiff technology with experimental data demonstrating suitability for bioprocessing at different scales. This technology results in more than 10-fold higher productivity compared to Protein A resins, which is maintained during scale up. We demonstrate the influence of residence times, feed titers and the cleaning regime on productivity and indicate optimal utilization of the convecdiff membrane based on feed titer availability. The underlying high productivity and short cycle times of this material enable the purification of monoclonal antibodies with 10-times less chromatography material used per batch and utilization of the membrane within one batch. Provided in disposable consumables, this novel technology will remove column handling in bioprocesses and resin re-use over multiple batches.

A decade in review: use of data analytics within the biopharmaceutical sector

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.

Bioprocess Control: Current Progress and Future Perspectives

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.