Determination of parameters for the steric mass action model—A comparison between two approaches

From protein structure to an optimized chromatographic capture step using multiscale modeling

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

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.

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.

Competitive protein adsorption in isocratic anion exchange chromatography

Protein chromatography is the dominant method of purification of biopharmaceuticals. Although all practical chromatography involves competitive absorption and separation of M. species, competitive protein absorption has remained inadequately understood. We previously introduced the measurement of equilibrium protein adsorption isotherms with all intensive variables held constant, including competitor concentration. In this work, we introduce isocratic chromatographic retention measurements of dynamic protein adsorption in the presence of a constant concentration of a competitor protein. These measurements are achieved by establishing a dynamic equilibrium with a constant concentration of competitor (insulin) in the mobile phase flowing through an ion exchange adsorbent column and following the behavior of a test protein (α-lactalbumin) injected into this environment. We observed decreased retention times for α-lactalbumin in presence of the competitor. The presence of competitor also reduces the heterogeneity of the sites available for adsorption of the test protein. This investigation provides an approach to fundamental understanding of competitive dynamics of multicomponent protein chromatography.

Rational downstream development for adeno-associated virus full/empty capsid separation - A streamlined methodology based on high-throughput screening and mechanistic modeling

Recombinant adeno-associated virus (AAV) has emerged as one of the most promising systems for therapeutic gene delivery and has demonstrated clinical success in a wide range of genetic disorders. However, manufacturing of high-quality AAV in large amounts still remains a challenge. A significant difficulty for downstream processing is the need to remove empty capsids that are generated in all currently utilized expression systems and that represent product-related impurities that adversely affect safety and efficacy of AAV vectors. Empty and full capsids exhibit only subtle differences in surface charge and size, making chromatography-based separations highly challenging. Here, we present a rapid methodology for the systematic process development of the crucial AAV full/empty capsid separation on ion-exchange media based on high-throughput screening and mechanistic modeling. Two of the most commonly employed serotypes, AAV8 and AAV9, are used as case studies. First, high-throughput studies in filter-plate format are performed that allow the rapid and comprehensive study of binding and elution behavior of AAV on different resins, using different buffer systems, pH, salt conditions, and solution additives. Small amounts of separated empty and full AAV capsids are generated by iodixanol gradient centrifugation that allow studying the binding and elution behavior of the two vector species separately in miniaturized format. Process conditions that result in maximum differences in elution behavior between empty and full capsids are then transferred to benchtop chromatography systems that are used to generate calibration data for the estimation of steric mass-action isotherm and mass transport parameters for process simulation. The resulting column models are employed for in-silico process development that serves to enhance understanding of separation constraints and to identify optimized conditions for the removal of empty particles. Finally, optimized separation conditions are verified experimentally. The methodology presented in this work provides a systematic framework that affords mechanistic understanding of the crucial empty/full capsid separation and accelerates the development of a scalable AAV downstream process.

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.

Influence of Mixed Salts on Retention Behavior of Model Proteins in Cation Exchange Chromatography

Mobile phase composition is an important factor for a further improvement of ion exchange chromatography steps of proteins. In this work, the effects of mixed salts on the retention factors of the two model proteins lysozyme (LYZ) and bovine serum albumin (BSA) in cation exchange chromatography (CEC) were investigated and compared to effects previously observed in hydrophobic interaction chromatography (HIC). The model equation describing the effects in HIC was adjusted for linear gradient elution experiments in CEC. The investigated salts were sodium chloride, sodium sulfate, ammonium chloride and ammonium sulfate. By varying binary salt mixtures as well as using pure salts, model parameters were determined. The normalized root mean square error (NRMSE) of the predicted retention factors for the calibration runs was 4.1% for BSA and 3.1% for LYZ. Additional validation experiments proved the ability of the model to describe and predict retention behavior of the proteins for further salt compositions. Hereby, the NRMSE values for BSA and LYZ were 2.0% and 1.5%, respectively. While the retention factors of LYZ changed linearly with the salt composition, non-linearities in the impact of the anion composition were found for BSA. This was contributed to an overlay of a synergetic salt effect on a protein-specific effect by sulfate on BSA with non-specific effects of the ions for CEC. However, the impact of the synergetic effects on protein separation is lower for CEC than for HIC, as mixed salts do not increase the separation of these proteins. The best salt composition for separating BSA and LYZ is pure ammonium sulfate. Thus, synergetic salt effects can also occur in CEC, but they have a lower impact than in HIC.

Mechanistic modeling of cation exchange chromatography scale-up considering packing inhomogeneities

In process development and characterization, the scale-up of chromatographic steps is a crucial part and brings a number of challenges. Usually, scale-down models are used to represent the process step, and constant column properties are assumed. The scaling is then typically based on the concept of linear scale-up. In this work, a mechanistic model describing an anti-Langmuirian to Langmuirian elution behavior of a polypeptide, calibrated with a pre-packed 1 ml column, is applied to demonstrate the scalability to larger column volumes up to 28.2 ml. Using individual column parameters for each column size, scaling to similar eluting salt concentrations, peak heights, and shapes is experimentally demonstrated by considering the model's relationship between the normalized gradient slope and the eluting salt concentration. Further scale-up simulations show improved model predictions when radial inhomogeneities in packing quality are considered.

Protein association on multimodal chromatography media

The phenomenon of protein-protein association on multimodal chromatography resins was described for two different case study examples. The adsorption pattern of single-component solutions of calcium-rich alpha-lactalbumin (aLaCa) and calcium-depleted alpha-lactalbumin (aLa) and their mixtures with bovine serum albumin was determined on a multimodal anion-exchange chromatography medium. In single-component solutions, both aLaCa and aLa exhibited identical adsorption behavior at low resin loadings, whereas at high loadings the adsorption strength of aLa markedly exceeded that of alaCa. In binary mixtures, the adsorption of BSA enhanced at high concentrations of aLa or aLaCa in the adsorbed phase. The unusual adsorption patterns observed were attributed to the tendency of the proteins for molecular association in the adsorbed phase in single and binary solutions. The phenomena was examined for different pH of the solution: pH 6, 7, 8, and different solvent environments: phosphate buffer (PB), bis tris buffer (BT), 100 mM NaCl in BT and bis tris propane buffer (BTP). The strongest effect was observed for PB and for 100 mM NaCl in BT. Its occurrence was also evidenced for other case study example, i.e., adsorption of single-component solutions and binary mixtures of a monoclonal antibody (mAb) and lysozyme (LYZ) on a multimodal cation-exchange chromatography medium. The enhancement of adsorption of mAb was observed at high concentrations of LYZ in the adsorbed phase. To quantify the underlying effects, a mechanistic model was used, which accounted for both protein association and exclusion resulting from attractive and repulsive protein-protein iterations in the adsorbed phase.

Standardized method for mechanistic modeling of multimodal anion exchange chromatography in flow through operation

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics.

Industrial ion-exchange chromatography development using discontinuous Galerkin methods coupled with forward sensitivity analysis

In this work, a discontinuous Galerkin method coupled with forward sensitivity analysis (DG-FSA) is presented. The DG-FSA method is used to reduce computational cost required for model-based ion-exchange chromatography development using industrial load samples. As an example, the design of an anion-exchange chromatography step is considered. This step is used to purify an experimental peptide product called Protein G from Novo Nordisk A/S (Bagsværd, Denmark). The results demonstrate, that a fourth order DG-FSA method can reduce computational cost of inverse problems by a factor ×16 compared to a second (low) order DG-FSA method. Furthermore, the fourth-order DG-FSA method enable the computation of probability distributions of optimized processing conditions given uncertainty in model parameters or inputs. This analysis is not possible within a reasonable timeframe when applying the second (low) order DG-FSA method. The design procedure facilitates the optimization of the Protein G purification step. In an experimental validation run, the productivity is increased by 70% while sacrificing 4% yield at a similar purity constraint compared to an experiment with baseline performance.

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.

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.

Automation of Modeling and Calibration of Integrated Preparative Protein Chromatography Systems

With the increasing global demand for precise and efficient pharmaceuticals and the biopharma industry moving towards Industry 4.0, the need for advanced process integration, automation, and modeling has increased as well. In this work, a method for automatic modeling and calibration of an integrated preparative chromatographic system for pharmaceutical development and production is presented. Based on a user-defined system description, a system model was automatically generated and then calibrated using a sequence of experiments. The system description and model was implemented in the Python-based preparative chromatography control software Orbit.

Mechanistic modeling and simulation of a complex low and high loading elution behavior of a polypeptide in cation exchange chromatography

The mechanistic modeling of preparative liquid chromatography is still a challenging task. Non-ideal thermodynamic conditions may require activity coefficients for the mechanistic description of preparative chromatography. In this work, a chromatographic cation exchange step with a polypeptide having a complex elution behavior in low and high loading situations is modeled. Model calibration in the linear range of the isotherm is done by applying counterion-induced linear gradient elution experiments between pH 3.3 and pH 4.3. Inverse fitting with column loads up to 25 mg/mL_CV is performed for parameter estimation in the non-linear range. The polypeptide elution peak shows an anti-Langmuirian behavior with fronting under low loading conditions and a switch to a Langmuirian behavior with increasing load. This unusual elution behavior could be described with an extended version of the sigmoidal Self-Association isotherm, including two activity coefficients for the polypeptide and counterion in solution. The activity coefficient of the solute polypeptide shows a strong influence on the model parameters and is crucial in the linear and non-linear range of the isotherm. The modeling procedure results in a unique and robust model parameter set that is sufficient to describe the complex elution behavior and allows modeling over the full isotherm range. This article is protected by copyright. All rights reserved.

Robust mechanistic modeling of protein ion-exchange chromatography

Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO₃^-, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.

Understanding the effects of system differences for parameter estimation and scale-up of high throughput chromatographic data

In this paper, we evaluate how employing fraction collection and multistep gradients with RoboColumns® (Repligen, formally Atoll) affects both comparison to benchtop experimental data and column simulation parameter estimation. These operational differences arise from the RoboColumn® system (operated on an automated liquid handling device) requiring offline analysis for determination of elution profiles rather than the continuous in-line UV curves obtained with larger scale systems. In addition, multistep gradients are used to model the smooth linear gradients of larger scale systems because sequential injections are used to provide liquid flow. Comparisons of two sets of column simulations was first carried out to demonstrate that fraction collection reduced the first moments of the elution peaks by 1/2 of the fraction volumes. Additional column simulations determined that the effect of a multistep gradient approximation on retention volume was dependent upon the gradient step length. An empirical transformation was then developed to correct the first moments obtained from gradient experimental data using the RoboColumn® system. These corrected values provided a more direct comparison of the experimental data at different scales and resulted in a significant improvement in agreement with results obtained using a 20 mL benchtop column. Linear steric mass-action (SMA) parameters were then estimated using the corrected values and employed to successfully predict the performance of the benchtop system data. Finally, these parameters were demonstrated to be well suited for modeling the RoboColumn® gradient data when properly accounting for multistep gradients and fraction collection. This work continues previous investigations into understanding system differences associated with robotic liquid handling devices and proposes a methodology for properly accounting for operational differences to predict operation at larger scales using conventional chromatography systems.

Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling

Least squares estimation of unknown parameters from measurement data is a well-established standard method in chromatography modeling but can suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. We have therefore developed a new score system that separately accounts for the shape, position and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. The proposed method is demonstrated with synthetic and experimental case studies of increasing complexity. All software is freely available as open source code (https://github.com/modsim/CADET-Match).

Precision analysis for the determination of steric mass action parameters using eight tobacco host cell proteins

Plants are advantageous as biopharmaceutical manufacturing platforms because they allow the economical and scalable upstream production of proteins, including those requiring post-translational modifications, but do not support the replication of human viruses. However, downstream processing can be more labor-intensive compared to fermenter-based systems because the product is often mixed with abundant host cell proteins (HCPs). Modeling chromatographic separation can minimize the number of process development experiments and thus reduce costs. An important part of such modeling is the sorption isotherm, such as the steric mass action (SMA) model, which describes the multicomponent protein-salt equilibria established in ion-exchange systems. Here we purified ten HCPs, including 2-Cys-peroxiredoxin, from tobacco (Nicotiana tabacum and N. benthamiana). For eight of these HCPs, we obtained sufficient quantities to determine the SMA binding parameters (KSMA and ν) under different production-relevant conditions. We studied the parameters for 2-Cys-peroxiredoxin on Q-Sepharose HP in detail, revealing that pH, resin batch and buffer batch had little influence on KSMA and ν, with coefficients of variation (COVs) less than 0.05 and 0.21, respectively. In contrast, the anion-exchange resins SuperQ-650S, Q-Sepharose FF and QAE-550C led to COVs of 0.69 for KSMA and 0.05 for ν, despite using the same quaternary amine functional group as Q-Sepharose HP. Plant cultivation in summer vs winter resulted in COVs of 0.09 for KSMA and 0.02 for ν, revealing a small impact compared to COVs of 17.15 for KSMA and 0.20 for ν when plants were grown in different settings (climate-controlled phytotron vs greenhouse). We conclude that plant cultivation can substantially affect protein properties and the resulting SMA parameters. Accordingly, plant growth but also protein purification and characterization for chromatography model building should be tightly controlled and well documented.

Process development exploiting competitive adsorption-based displacement effects in monoclonal antibody aggregate removal-A new high-throughput screening procedure for membrane chromatography

High-throughput screening (HTS) approaches are commonly used to accelerate downstream process development. Although most HTS approaches use batch isothermal data (K_P screen) or bind and elute mode as screening procedure, different or new process designs are rarely investigated. In this paper, a mechanistic model case study for the separation of two different two-component solutions was conducted and confirmed prior evidence. With these outcomes, a novel HTS screening procedure was developed including the determination of competitive adsorption-based displacement effects and key parameter identification. The screening procedure employing an overload bind and elute (OBE) mode is presented in a case study dealing with IgG aggregate removal in a typical monoclonal antibody purification step, applying a Sartobind® S membrane adsorber (MA). Based on a MA scale down device, the OBE mode allows the determination of classical process parameters and dynamic effects, such as displacement effects. Competitive adsorption-based displacement effects are visualized by introducing a displacement identifier leading to a displacement process map. Based on this map, the approach is transferred to and confirmed by the OBE recycle experiments with 4.6 and 8.2 ml benchtop scsale devices resulting in 45% reduced IgG monomer and 88% increased higher molecular weight species binding capacities.

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

Using multimodal chromatography for post-conjugation antibody-drug conjugate purification: A methodology from high throughput screening to in-silico process development

In this paper, a methodology for the development of a multimodal chromatography process is presented that is aimed at removal of under-conjugated antibody-drug conjugate (ADC) species. Two ADCs are used as case studies: One ADC results from site-directed conjugation to inserted cysteine residues and has a drug-to-antibody ratio (DAR) of two, the other is the product of conjugation to interchain disulfide bonds with a DAR of eight. First, filter plate screening studies are designed for the unconjugated antibody and the ADCs. Different metrics for the analysis of these data sets are presented and discussed. From this analysis, the selected process conditions are then carried out using a benchtop chromatography system to confirm the separations observed in the filter plate studies while simultaneously generating data to estimate steric mass-action isotherm and mass transport parameters for process simulation. This column model is then employed to develop separation processes in-silico for the removal of the unconjugated parent antibody and under-conjugated product variants. The optimized process conditions identified using the model are then verified experimentally. The methodology presented in this work utilizes multimodal chromatography for ADC purification and provides the framework for a streamlined systematic approach to process development.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Cross-scale quality assessment of a mechanistic cation exchange chromatography model

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

Downstream Processing Technologies/Capturing and Final Purification : Opportunities for Innovation, Change, and Improvement. A Review of Downstream Processing Developments in Protein Purification

Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

Chromatographic parameter determination for complex biological feedstocks

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018

Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles

Recombinant protein-based virus-like particles (VLPs) are steadily gaining in importance as innovative vaccines against cancer and infectious diseases. Multiple VLPs are currently evaluated in clinical phases requiring a straightforward and rational process design. To date, there is no generic platform process available for the purification of VLPs. In order to accelerate and simplify VLP downstream processing, there is a demand for novel development approaches, technologies, and purification tools. Membrane adsorbers have been identified as promising stationary phases for the processing of bionanoparticles due to their large pore sizes. In this work, we present the potential of two strategies for designing VLP processes following the basic tenet of 'quality by design': High-throughput experimentation and process modeling of an anion-exchange membrane capture step. Automated membrane screenings allowed the identification of optimal VLP binding conditions yielding a dynamic binding capacity of 5.7 mg/mL for human B19 parvovirus-like particles derived from Spodoptera frugiperda Sf9 insect cells. A mechanistic approach was implemented for radial ion-exchange membrane chromatography using the lumped-rate model and stoichiometric displacement model for the in silico optimization of a VLP capture step. For the first time, process modeling enabled the in silico design of a selective, robust and scalable process with minimal experimental effort for a complex VLP feedstock. The optimized anion-exchange membrane chromatography process resulted in a protein purity of 81.5%, a DNA clearance of 99.2%, and a VLP recovery of 59%.