An overview of mechanistic modeling of liquid chromatography

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.

Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.

Using the reactive/transport dispersive models to simulate a monolithic anion exchanger: Experimental parameter determination, simultaneous model evaluation, and validation

Selecting an adequate model to represent the mass transfer mechanisms occurring in a chromatographic process is generally complicated, which is one of the reasons why monolithic chromatography is scarcely simulated. In this study, the chromatographic separation of model proteins bovine serum albumin (BSA), β-lactoglobulin-A, and β-lactoglobulin-B on an anion exchange monolith was simulated based on experimental parameter determination, simultaneous model testing, and validation under three statistical criteria: retention time, dispersion accuracies, and Pearson correlation coefficient. Experimental characterization of morphologic, physicochemical, and kinetic parameters was performed through volume balances, pressure drop analysis, breakthrough curve analysis, and batch adsorptions. Free Gibbs energy indicated a spontaneous adsorption process for proteins and counterions. Dimensionless numbers were estimated based on height equivalent to a theoretical plate analysis, finding that pore diffusion controlled β-lactoglobulin separation, whereas adsorption/desorption kinetics was the dominant mechanism for BSA. The elution profiles were modeled using the transport dispersive model and the reactive dispersive model coupled with steric mass action (SMA) isotherms because these models allowed to consider most of the mass transport mechanisms that have been described. RDM-SMA presented the most accurate simulations at pH 6.0 and at low (250 mM) and high (400 mM) NaCl concentrations. This simulation will be used as reference to forecast the purification of these proteins from bovine whey waste and to extrapolate this methodology to other monolith-based separations using these three statistical criteria that have not been used previously for this purpose.

Application of mechanistic modelling in membrane and fiber chromatography for purification of biotherapeutics - A review

Mechanistic modelling is a simulation tool which has been effectively applied in downstream bioprocessing to model resin chromatography. Membrane and fiber chromatography are newer approaches that offer higher rates of mass transfer and consequently higher flow rates and reduced processing times. This review describes the key considerations in the development of mechanistic models for these unit operations. Mass transfer is less complex than in resin columns, but internal housing volumes can make modelling difficult, particularly for laboratory-scale devices. Flow paths are often non-linear and the dead volume is often a larger fraction of the overall volume, which may require more complex hydrodynamic models to capture residence time distributions accurately. In this respect, the combination of computational fluid dynamics with appropriate protein binding models is emerging as an ideal approach.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

Theoretical Investigation of the Nonlinear General Rate Model with the Bi-Langmuir Adsorption Isotherm Using Core-Shell Adsorbents

Core-shell particles enable the separation of intricate mixtures in a highly efficient and rapid manner. The porous shell particles increased the separation efficiency with expedited flow rates due to an abatement in the pore volume accessible for longitudinal diffusion and a decrease in diffusion path length. This study focuses on the numerical approximation of a nonlinear isothermal general rate model applied to stationary bed columns that are replete with inert core adsorbents featuring double adsorption sites. The transport of solute in heterogeneous porous media can be modeled by a nonlinear convection acquiescent partial differential equation system together with a specific nonlinear algebraic relation: the bi-Langmuir adsorption isotherm. Therefore, it is important to develop accurate and reliable numerical techniques that can perform accurate numerical simulations of these models. We extended and implemented a second-order, semidiscrete, high-resolution finite volume method to simulate the governing equations of the model. Single solute flow and multi component mixture flows are assessed through a series of numerical experiments to theoretically illustrate the repercussions of intraparticle diffusion, film mass resistance, axial dispersion, and the size of the inert core radius upon simulated elution curves. Standard performance criteria are assessed to determine the optimal core radius fraction range for optimizing the separation performance.

Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk

Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.

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.

Comparison of multi-column chromatography configurations through model-based optimization

Integrated continuous bioprocessing has been identified as the next important phase of evolution in biopharmaceutical manufacturing. Multiple platform technologies to enable continuous processing are being developed. Multi-column counter-current chromatography is a step in this direction to provide increased productivity and capacity utilization to capture biomolecules like monoclonal antibodies (mAbs) present in the reactor harvest and remove impurities. Model-based optimization of two prevalent multi-column designs, 3-column and 4-column periodic counter-current chromatography (PCC) was carried out for different concentrations of mAbs in the feed, durations of cleaning-in-place and equilibration protocols. The multi-objective optimization problem comprising three performance measures, namely, product yield, productivity, and capacity utilization was solved using the Radial basis function optimization technique. The superficial velocities during load, wash, and elute operations, along with durations of distinct stages present in the multi-column operations were considered as decision variables. Optimization results without the constraint on number of wash volumes showed that 3-Column PCC performs better than 4-Column PCC. For example, at a feed concentration of 1.2 mg/mL, productivity, yield and capacity utilization, respectively, were 0.024 mg/mL.s, 0.94, and 0.94 for 3-Column PCC and 0.017 mg/mL.s, 0.87, and 0.83 for 4-column PCC. Similar trends were observed at higher feed concentrations also. However, when the constraint on number of wash volumes is included, 4-Column PCC was found to result in consistent productivity and product yield under different operating conditions but at the expense of reduced capacity utilization.

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.

Serially coupled column liquid chromatography: An alternative separation tool

Despite the rapid development of liquid chromatography (LC) in recent decades, it remains a challenge to achieve the desired chromatographic separation of complex matrices using a single column. Multi-column LC techniques, particularly serially coupled column LC (SCC-LC), have emerged as a promising solution to overcome this challenge. While more attention has been focused on heart-cutting or comprehensive two-dimensional LC, reviews specifically focusing on SCC-LC, which offers advantages in terms of precision and facile instrumentation, are scarce. Here, our concerns are devoted to the progress summary regarding the instrumentation and applications of SCC-LC. Emphasis is placed on column selection aiming to enlarge peak capacity, selectivity, or both through the optimization of combination types (e.g. RPLC-RPLC, -RPLC-HILIC, and achiral-chiral LC), connection devices (e.g. zero dead volume connector, tubing, and T-type connector), elution program (i.e. isocratic or gradient) and detectors (e.g. mass spectrometer, ultraviolet detector, and fluorescence detector). The application of SCC-LC in pharmaceutical, biological, environmental, and food fields is also reviewed, and future perspectives and potential directions for SCC-LC are discussed. We envision that the review can give meaningful information to analytical scientists when facing heavy chromatographic separation tasks for complicated matrices.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

Understanding Sorption Mechanisms Directly from Isotherms

Currently, more than 100 isotherm models coexist for the six IUPAC isotherm types. However, no mechanistic insights can be reached when several models, each claiming a different mechanism, fit an experimental isotherm equally well. More frequently, popular isotherm models [such as the site-specific models like Langmuir, Brunauer-Emmett-Teller (BET), and Guggenheim-Anderson-de Boer (GAB)] have been applied to real and complex systems that break their basic assumptions. To overcome such conundrums, we establish a universal approach to model all isotherm types, attributing the difference to the sorbate-sorbate and sorbate-surface interactions in a systematic manner. We have generalized the language of the traditional sorption models (such as the monolayer capacity and the BET constant) to the model-free concepts of partitioning and association coefficients that can be applied across the isotherm types. Through such a generalization, the apparent contradictions, caused by applying the site-specific models alongside with cross-sectional area of sorbates for the purpose of surface area determination, can be eliminated straightforwardly.

Surrogate-based Optimization of Capture Chromatography Platforms for the Improvement of Computational Efficiency

In this work, we discuss the use of surrogate functions and a new optimization framework to create an efficient and robust computational framework for process design. Our model process is the capture chromatography unit operation for monoclonal antibody purification, an important step in biopharmaceutical manufacturing. Simulating this unit operation involves solving a system of non-linear partial differential equations, which can have high computational cost. We implemented surrogate functions to reduce the computational time and make the framework more attractive for industrial applications. This strategy yielded accurate results with a 93% decrease in processing time. Additionally, we developed a new optimization framework to reduce the number of simulations needed to generate a solution to the optimization problem. We demonstrate the performance of our new framework, which uses MATLAB built-in tools, by comparing its performance against individual optimization algorithms for problems with integer, continuous, and mixed-integer variables.

Computational framework for the techno-economic analysis of monoclonal antibody capture chromatography platforms

We developed a computational framework that integrates commercial software components to perform customizable technoeconomic feasibility analyses. The use of multiple software packages overcomes the shortcomings of each to provide a detailed simulation that can be used for sensitivity analyses and optimizations. In this paper, the framework was used to evaluate the performance of monoclonal antibody capture processes. To this end, the simulation framework incorporated dynamic models for the affinity chromatography step that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. As proof of concept, the framework was used to perform a sensitivity analysis and optimization for a case study in which we sought to compare membrane and resin chromatography for disposable and reusable batch capture platforms. Two membranes and one resin were selected for the capture media, which yielded six process alternatives to compare. The objective functions were set to be cost of goods, process time, and buffer utilization. The results of the optimization of these process alternatives were a set of operating conditions that display tradeoffs between competing objectives. From this application exercise, we conclude that the framework can handle multiple variables and objectives, and it is adaptable to platforms with different chromatography media and operating modes. Additionally, the framework is capable of providing ad hoc analyses for decision making in a specific production context.

Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Simplified estimation for steric shielding factor

Mechanistic models play a crucial role in the process development and optimization of ion-exchange chromatography (IEC). Recent researches in steric mass action (SMA) model have heightened the need for better estimation of nonlinear parameter, steric shielding factor σ. In this work, a straightforward approach combination of simplified linear approximation (SLA) and inverse method (IM) was proposed to initialize and further determine σ, respectively. An existed, unique, and positive σ can be derived from SLA. Compared with linear approximation (LA) developed in our previous study, σ of the multi-component system can be calculated easily without solving the complex system of linear equations, leading to a time complexity reduction from O(n³) to O(n). The proposed method was verified first in numerical experiments about the separation of three charge variants. The calculated σ was more reasonable than that of LA, and the error of elution profiles with the parameters estimated by SLA+IM was only one-sixth of that by LA in numerical experiments. Moreover, the error accumulation effect could also be reduced. The proposed method was further confirmed in real-world experiments about the separation of monomer-dimer mixtures of monoclonal antibody. The results gave a lower error and better physical understanding compared to LA. In conclusion, SLA+IM developed in the present work provides a novel and straightforward way to determine σ. This simplification would help to save the effort of calibration experiments and accelerate the process development for the multi-component IEC separation.

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.

Separation of closely related monoclonal antibody charge variant impurities using poly(ethylenimine)-grafted cation-exchange chromatography resin

The removal of protein charge variants due to complex chemical and enzymatic modifications like glycosylation, fragmentation and deamidation presents a significant challenge in the purification of monoclonal antibodies (mAb) and complicates downstream processing. These protein modifications occur either in vivo or during fermentation and downstream processing. The presence of charge variants can lead to diminished biological activity, differences in pharmacokinetics, pharmacodynamics, stability and efficacy. Therefore, these different product variants should be appropriately controlled for the consistency of product quality and to ensure patient safety. This investigation focuses on the development of a chromatography step for the removal of the charge variants from a recombinant single-chain variable antibody fragment (scFv-Fc-Ab). Poly(ethyleneimine)-grafted cation-exchange resins (Poly CSX and Poly ABX) were evaluated and compared to traditional macroporous cation-exchange and tentacle cation-exchange resins. Linear salt gradient experiments were conducted to study the separation efficiency of scFv-Fc-Ab variants using different resins. A classical thermodynamic model was used to develop a mechanistic understanding of the differences in charge variant retention behaviour of different resins. High selectivity in separation of scFv-Fc-Ab charge variants is obtained in the Poly CSX resin.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Artificial Neural Network for Fast and Versatile Model Parameter Adjustment Utilizing PAT Signals of Chromatography Processes for Process Control under Production Conditions

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for advanced process control (APC), the model parameters have to be updated with the aid of inline Process Analytical Technology (PAT) data to describe the actual operational status. This updating process also includes any operational change phenomena that occur, and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium-related separation performance decrease due to adsorbent aging or feed and buffer composition changes. In order to track these changes, an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step, based on the simulation results of a distinct and previously experimentally validated process model. The model is implemented in the open source tool CasADi for Python. This allows the implementation of interfaces to process control systems, among others, with relatively low effort. Therefore, PAT signals can easily be incorporated for sufficient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on PAT and ANN predictions to derive optimal operation points with the model.

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture

The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics.

Oncolytic virus purification with periodic counter-current chromatography

Virus-based biologicals are one of the most promising biopharmaceuticals of the 21st century medicine and play a significant role in the development of innovative therapeutic, prophylactic, and clinical applications. Oncolytic virus manufacturing scale can range from 5 L in research and development up to 50 L for clinical studies and reach hundreds of liters for commercial scale. The inherent productivity and high integration potential of periodic counter-current chromatography (PCC) offer a transversal solution to decrease equipment footprint and the reduction of several non-value-added unit operations. We report on the design of an efficient PCC process applied to the intermediate purification of oncolytic adenovirus. The developed ion-exchange chromatographic purification method was carried out using a four-column setup for three different scenarios: (i) variation in the feedstock, (ii) potential use of a post-load washing step to improve virus recovery, and (iii) stability during extended operation. Obtained virus recoveries (57%-86%) and impurity reductions (>80% DNA, and >70% total protein) match or overcome batch purification. Regarding process stability and automation, our results show that not only the dynamic control strategy used is able to suppress perturbations in the sample inlet but also allows for unattended operation in the case of ion exchange capture.

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

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market.

Protein Purification Technologies

Protein Biotechnology is an exciting and fast- growing area of research, with numerous industrial applications. The growing demand for developing efficient and rapid protein purification methods is driving research and growth in this area. Advances and progress in the techniques and methods of protein purification have been such that one can reasonably expect that any protein of a given order of stability may be purified to currently acceptable standards of homogeneity. However, protein manufacturing cost remains extremely high, with downstream processing constituting a substantial proportion of the overall cost. Understanding of the methods and optimization of the experimental conditions have become critical to the manufacturing industry in order to minimize production costs while satisfying the quality as well as all regulatory requirements. New purification processes exploiting specific, effective and robust methods and chromatographic materials are expected to guide the future of the protein purification market.

Recent Developments in Bioprocessing of Recombinant Proteins: Expression Hosts and Process Development

Infectious diseases, along with cancers, are among the main causes of death among humans worldwide. The production of therapeutic proteins for treating diseases at large scale for millions of individuals is one of the essential needs of mankind. Recent progress in the area of recombinant DNA technologies has paved the way to producing recombinant proteins that can be used as therapeutics, vaccines, and diagnostic reagents. Recombinant proteins for these applications are mainly produced using prokaryotic and eukaryotic expression host systems such as mammalian cells, bacteria, yeast, insect cells, and transgenic plants at laboratory scale as well as in large-scale settings. The development of efficient bioprocessing strategies is crucial for industrial production of recombinant proteins of therapeutic and prophylactic importance. Recently, advances have been made in the various areas of bioprocessing and are being utilized to develop effective processes for producing recombinant proteins. These include the use of high-throughput devices for effective bioprocess optimization and of disposable systems, continuous upstream processing, continuous chromatography, integrated continuous bioprocessing, Quality by Design, and process analytical technologies to achieve quality product with higher yield. This review summarizes recent developments in the bioprocessing of recombinant proteins, including in various expression systems, bioprocess development, and the upstream and downstream processing of recombinant proteins.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.