Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation.

The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.

Mechanistic modeling of empty-full separation in recombinant adeno-associated virus production using anion-exchange membrane chromatography.

Recombinant adeno-associated viral vectors (rAAVs) have become an industry-standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids-those that contain no genetic material-and enrichment of full rAAV using anion-exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single-step or two-step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

Methodology for fast development of digital solutions in integrated continuous downstream processing.

The methodology for production of biologics is going through a paradigm shift from batch-wise operation to continuous production. Lot of efforts are focused on integration, intensification, and continuous operation for decreased foot-print, material, equipment, and increased productivity and product quality. These integrated continuous processes with on-line analytics become complex processes, which requires automation, monitoring, and control of the operation, even unmanned or remote, which means bioprocesses with high level of automation or even autonomous capabilities. The development of these digital solutions becomes an important part of the process development and needs to be assessed early in the development chain. This work discusses a platform that allows fast development, advanced studies, and validation of digital solutions for integrated continuous downstream processes. It uses an open, flexible, and extendable real-time supervisory controller, called Orbit, developed in Python. Orbit makes it possible to communicate with a set of different physical setups and on the same time perform real-time execution. Integrated continuous processing often implies parallel operation of several setups and network of Orbit controllers makes it possible to synchronize complex process system. Data handling, storage, and analysis are important properties for handling heterogeneous and asynchronous data generated in complex downstream systems. Digital twin applications, such as advanced model-based and plant-wide monitoring and control, are exemplified using computational extensions in Orbit, exploiting data and models. Examples of novel digital solutions in integrated downstream processes are automatic operation parameter optimization, Kalman filter monitoring, and model-based batch-to-batch control.

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.

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.

Binary separation control in preparative gradient chromatography using iterative learning control.

Purification of biopharmaceuticals has shifted toward continuous and integrated processes, in turn bringing along a need for monitoring and control to maintain a desired separation between the target pharmaceutical and any impurities it may carry. In this study, a cycle-to-cycle control of the retention volumes of two compounds in a chromatographic, ion exchange purification step was developed, allowing the process to maintain the desired retention volumes in the separation. The controller made use of a model-based, multivariate iterative learning control (ILC) algorithm that used a quadratic-criterion objective function for optimal set point control, along with feed-forward control based on direct model inversion for preemptive control of set point changes. The model was calibrated using 3 experiments, allowing for fast setup. The controller was tested by introducing three different disturbances to a sequence of otherwise identical ion exchange separation processes: a change in the salt concentration of the elution buffer, a change in set point, and a change in the pH of the elution buffer. It was capable of correcting for all disturbances within at most 3 cycles, proving its efficacy. The successful application of ILC for separation control in biopharmaceutical purification paves the way for the development of further ILC-based control strategies within the field, as well as combination with other control strategies.

Integrated continuous biomanufacturing on pilot scale for acid-sensitive monoclonal antibodies.

In this study, we demonstrated the first, to our knowledge, integrated continuous bioprocess (ICB) designed for the production of acid-sensitive monoclonal antibodies, prone to aggregate at low pH, on pilot scale. A high cell density perfusion culture, stably maintained at 100 × 106 cells/ml, was integrated with the downstream process, consisting of a capture step with the recently developed Protein A ligand, ZCa ; a solvent/detergent-based virus inactivation; and two ion-exchange chromatography steps. The use of a mild pH in the downstream process makes this ICB suitable for the purification of acid-sensitive monoclonal antibodies. Integration and automation of the downstream process were achieved using the Orbit software, and the same equipment and control system were used in initial small-scale trials and the pilot-scale downstream process. High recovery yields of around 90% and a productivity close to 1 g purified antibody/L/day were achieved, with a stable glycosylation pattern and efficient removal of impurities, such as host cell proteins and DNA. Finally, negligible levels of antibody aggregates were detected owing to the mild conditions used throughout the process. The present work paves the way for future industrial-scale integrated continuous biomanufacturing of all types of antibodies, regardless of acid stability.

Design of an integrated continuous downstream process for acid-sensitive monoclonal antibodies based on a calcium-dependent Protein A ligand.

Monoclonal antibodies (mAb) are used as therapeutics and for diagnostics of a variety of diseases, and novel antibodies are continuously being developed to find treatments for new diseases. Therefore, the manufacturing process must accommodate a range of mAb characteristics. Acid-sensitive mAbs can severely compromise product purity and yield in the purification process due to the potential formation of aggregates. To address this problem, we have developed an integrated downstream process for the purification of pH-sensitive mAbs at mild conditions. A calcium-dependent Protein A-based ligand, called ZCa, was used in the capture step in a 3-column periodic counter-current chromatography operation. The binding of ZCa to antibodies is regulated by calcium, meaning that acidic conditions are not needed to break the interaction and elute the antibodies. Further, the virus inactivation was achieved by a solvent/detergent method, where the pH could remain unchanged. The polishing steps included a cation and an anion exchange chromatography step, and screening of the capture and polishing steps was performed to allow for a seamless integration of the process steps. The process was implemented at laboratory scale for 9 days obtaining a high yield, and a consistently pure drug substance, including high reduction values of the host cell protein and DNA concentrations, as well as aggregate levels below the detection limit, which is attributed to the mild conditions used in the process.

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 PrismATM, 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 study on periodic counter-current chromatography integrated in a monoclonal antibody downstream process.

An optimization study of an integrated periodic counter-current chromatography (PCC) process in a monoclonal antibody (mAb) downstream process at lab scale, is presented in this paper. The optimization was based on a mechanistic model of the breakthrough curve in the protein-A capture step. Productivity and resin utilization were the objective functions, while yield during the loading of the capture column was set as a constraint. Different integration approaches were considered, and the effect of the feed concentration, yield and the protein-A resin was studied. The breakthrough curve and the length of the product recovery, which depended on the integration approach, determined the process scheduling. Several optimal Pareto solutions were obtained. At 0.5 mg mL-1 and 99% yield, a maximum productivity of 0.38 mg mL-1 min-1 with a resin utilization of 60% was obtained. On the other hand, the maximum resin utilization was 95% with a productivity of 0.10 mg mL-1 min-1. Due to the constraint of the process scheduling, a lower productivity could be achieved in the integration approaches with higher recovery time, which was more remarkable at higher concentrations. Therefore, it was shown that a holistic approach, where all the purification steps are considered in the process optimization, is needed to design a PCC in a downstream process.

Model-based design and control of a small-scale integrated continuous end-to-end mAb platform.

A continuous integrated bioprocess available from the earliest stages of process development allows for an easier, more efficient and faster development and characterization of an integrated process as well as production of small-scale drug candidates. The process presented in this article is a proof-of-concept of a continuous end-to-end monoclonal antibody production platform at a very small scale based on a 200 ml alternating tangential flow filtration perfusion bioreactor, integrated with the purification process with a model-based design and control. The downstream process, consisting of a periodic twin-column protein A capture, a virus inactivation, a CEX column and an AEX column, was compactly implemented in a single chromatography system, with a purification time of less than 4 hr. Monoclonal antibodies were produced for 17 days in a high cell density perfusion culture of CHO cells with titers up to 1.0 mg/ml. A digital twin of the downstream process was created by modelling all the chromatography steps. These models were used for real-time decision making by the implementation of control strategies to automatize and optimize the operation of the process. A consistent glycosylation pattern of the purified product was ensured by the steady state operation of the process. Regarding the removal of impurities, at least a 4-log reduction in the HCP levels was achieved. The recovery yield was up to 60%, and a maximum productivity of 0.8 mg/ml/day of purified product was obtained.

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.

Integration of a complete downstream process for the automated lab-scale production of a recombinant protein.

In this work, an automated downstream process for the purification and formulation of a recombinant protein was integrated at lab scale in a single chromatography unit. The purification chain consists of three bind-and-elute chromatography columns, a flow-through membrane chromatography step, and a final ultrafiltration-diafiltration (UFDF) step to concentrate and formulate the sample. An integrated downstream process increases productivity and decreases process time and hold-up volume. In addition, the automation of the process allows reducing the manual work and increases reproducibility. To integrate the downstream steps, all the intermediate tanks are removed, and the eluate of a column is loaded directly onto the next one. This makes it necessary to design the process in order to minimize the column volumes and the process time. A research software called Orbit was used to automate the purification process and implement a UFDF step in the chromatography unit. The whole downstream sequence was successfully implemented at lab scale, getting a pure concentrated and formulated product with a productivity of 1.09 mg mL-1 h-1, achieving a time reduction from almost two to one working day, while getting a similar yield and purity. Regarding the UFDF operation, the sample was concentrated 10 times, and 97% of the old buffer was exchanged by the formulation buffer with a sequential diafiltration.

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants.

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.

Mechanistic Modeling of Reversed-Phase Chromatography of Insulins within the Temperature Range 10-40 °C.

In the many published theories on the retention in reversed-phase chromatography (RPC), the focus is generally on the effect of the concentration of the mobile phase modulator(s), although temperature is known to have a significant influence both on the retention and on the selectivity between the adsorbates. The aim of this study was to investigate and model the combined effects of the temperature and the modulator concentrations on RPC of three insulin variants. KCl and ethanol were used as mobile phase modulators, and the experiments were performed on two different adsorbents, with C18 and C4 ligands. The temperature dependence was investigated for the interval 10-40 °C and at two different concentrations of each modulator. The model is derived from the expression for the adsorption equilibrium, which assumes that ethanol is adsorbed to the ligands and displaced by the insulin molecules, similar to the displacement of counterions in the steric mass-action model for ion-exchange chromatography. A good model fit to the new linear-range retention data was achieved by only adding and calibrating three parameters for the temperature dependence of the equilibrium. We found that a lower temperature results in a longer retention time for all adsorbates, adsorbents, and modulator concentrations used in this study, indicating that the adsorption process is enthalpy-driven. A comparison of the different contributions to the temperature dependence revealed that the large contribution from the equilibrium constant is dampened by the significant contributions of the opposite sign from the changes in activity coefficients of insulins and ethanol. Neglect of these effects when comparing different adsorbents and modulators might yield incorrect conclusions because the equilibrium constant varies with both, whereas the activity coefficients should be independent of the adsorbent. As expected, the conditions that promote higher retention also give a higher selectivity between the adsorbates. Nonetheless, in relation to its effect on the retention, the influence of the KCl concentration on the selectivity was significantly stronger than that of the temperature or that of the ethanol concentration.

Pareto-optimal reversed-phase chromatography separation of three insulin variants with a solubility constraint.

With the shift of focus of the regulatory bodies, from fixed process conditions towards flexible ones based on process understanding, model-based optimization is becoming an important tool for process development within the biopharmaceutical industry. In this paper, a multi-objective optimization study of separation of three insulin variants by reversed-phase chromatography (RPC) is presented. The decision variables were the load factor, the concentrations of ethanol and KCl in the eluent, and the cut points for the product pooling. In addition to the purity constraints, a solubility constraint on the total insulin concentration was applied. The insulin solubility is a function of the ethanol concentration in the mobile phase, and the main aim was to investigate the effect of this constraint on the maximal productivity. Multi-objective optimization was performed with and without the solubility constraint, and visualized as Pareto fronts, showing the optimal combinations of the two objectives productivity and yield for each case. Comparison of the constrained and unconstrained Pareto fronts showed that the former diverges when the constraint becomes active, because the increase in productivity with decreasing yield is almost halted. Consequently, we suggest the operating point at which the total outlet concentration of insulin reaches the solubility limit as the most suitable one. According to the results from the constrained optimizations, the maximal productivity on the C4 adsorbent (0.41 kg/(m3 column h)) is less than half of that on the C18 adsorbent (0.87 kg/(m3 column h)). This is partly caused by the higher selectivity between the insulin variants on the C18 adsorbent, but the main reason is the difference in how the solubility constraint affects the processes. Since the optimal ethanol concentration for elution on the C18 adsorbent is higher than for the C4 one, the insulin solubility is also higher, allowing a higher pool concentration. An alternative method of finding the suggested operating point was also evaluated, and it was shown to give very satisfactory results for well-mapped Pareto fronts.

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

This work is a proof of concept of how a sequence of industrial batch separation steps together are used to form an integrated autonomous downstream process. The sequence in this case study consisted of an anion chromatography step, virus inactivation and finally a hydrophobic chromatography step. Moving from batch to integrated separation minimizes hold-up times, storage tanks, and required equipment. The conversion from batch to integrated mode is achieved by extracting operating points and separation data from batch chromatograms. The integrated separation process is realized on an ÄKTA Pure controlled by an open research software called Orbit, making it possible to operate complex process configurations including multiple steps. The results from this case study is the principle and method of the steps taken to automation, achieving a more continuous and efficient downstream process.

Design of two-column batch-to-batch recirculation to enhance performance in ion-exchange chromatography.

Preparative liquid chromatography is a separation technique widely used in the manufacturing of fine chemicals and pharmaceuticals. A major drawback of traditional single-column batch chromatography step is the trade-off between product purity and process performance. Recirculation of impure product can be utilized to make the trade-off more favorable. The aim of the present study was to investigate the usage of a two-column batch-to-batch recirculation process step to increase the performance compared to single-column batch chromatography at a high purity requirement. The separation of a ternary protein mixture on ion-exchange chromatography columns was used to evaluate the proposed process. The investigation used modelling and simulation of the process step, experimental validation and optimization of the simulated process. In the presented case the yield increases from 45.4% to 93.6% and the productivity increases 3.4 times compared to the performance of a batch run for a nominal case. A rapid concentration build-up product can be seen during the first cycles, before the process reaches a cyclic steady-state with reoccurring concentration profiles. The optimization of the simulation model predicts that the recirculated salt can be used as a flying start of the elution, which would enhance the process performance. The proposed process is more complex than a batch process, but may improve the separation performance, especially while operating at cyclic steady-state. The recirculation of impure fractions reduces the product losses and ensures separation of product to a high degree of purity.