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Multimodal chromatography has emerged as a powerful method for the purification of therapeutic antibodies. However, process development of this separation technique remains challenging because of an intricate and molecule-specific interaction towards multimodal ligands, leading to time-consuming and costly experimental optimization. This study presents a multiscale modeling approach to predict the multimodal chromatographic behavior of therapeutic antibodies based on their sequence information. Linear gradient elution (LGE) experiments were performed on an anionic multimodal resin for 59 full-length antibodies, including five different antibody formats at pH 5.0, 6.0, and 7.0 that were used for parameter determination of a linear adsorption model at low loading density conditions. Quantitative structure-property relationship (QSPR) modeling was utilized to correlate the adsorption parameters with up to 1374 global and local physicochemical descriptors calculated from antibody homology models. The final QSPR models employed less than eight descriptors per model and demonstrated high training accuracy (R² > 0.93) and reasonable test set prediction accuracy (Q² > 0.83) for the adsorption parameters. Model evaluation revealed the significance of electrostatic interaction and hydrophobicity in determining the chromatographic behavior of antibodies, as well as the importance of the HFR3 region in antibody binding to the multimodal resin. Chromatographic simulations using the predicted adsorption parameters showed good agreement with the experimental data for the vast majority of antibodies not employed during the model training. The results of this study demonstrate the potential of sequence-based prediction for determining chromatographic behavior in therapeutic antibody purification. This approach leads to more efficient and cost-effective process development, providing a valuable tool for the biopharmaceutical industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2022. Published by Elsevier B.V.)

Enveloped viral vectors like lentiviral vectors pose purification challenges due to their low stability. A gentle purification method is considered one of the major bottlenecks for lentiviral vector bioprocessing. To overcome these challenges, a promising method is steric exclusion chromatography which has been used to purify a variety of target molecules. In this study, we successfully identified optimal process parameters for steric exclusion chromatography to purify lentiviral vectors. Lentiviral vector particle recoveries and infectious recoveries of 86% and 88%, respectively, were achieved. The process parameters optimal for steric exclusion chromatography were determined as follows: polyethylene glycol with a molecular weight of 4000 Da, a polyethylene glycol concentration of 12.5%, and a flow rate of 7 mL⋅min -1 using 5 layers of stabilized cellulose membranes as a stationary phase. High protein and dsDNA removal of approximately 80% were obtained. The remaining polyethylene glycol concentration in the eluate was determined. We defined the maximum loading capacity as 7.5 × 10 12 lentiviral particles for the lab device used and provide deeper insights into loading strategies. Furthermore, we determined critical process parameters like pressure. We demonstrated in our experiments that steric exclusion chromatography is a gentle purification method with high potential for fragile enveloped viral vectors as it yields high recoveries while efficiently removing impurities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution., Competing Interests: Declaration of Competing Interest The authors affiliated to GoSilico declare that the software ChromX used in this study is a commercial product of GoSilico. The presented research does not depend on the usage of ChromX, the model can be implemented in any kind of modeling software., (Copyright © 2021. Published by Elsevier B.V.)

Cation exchange chromatography (CEX) is a widely used technique for the removal of monoclonal antibody (mAb) aggregates. At present, resins are mainly used for this purpose, as convective types of adsorbents such as membrane adsorbers (MAs) have often not demonstrated overall comparable performance for this particular application. Fiber-based adsorbents can overcome the current limitations of MAs with respect to permeability, binding capacity, and adsorbent cost, and could therefore be a viable alternative to resins for the removal of mAb aggregates. It has not been evaluated, however, whether and under which conditions the use of such adsorbents is feasible for this purpose. In the present study, the use of fiber-based CEX adsorbents for mAb aggregate removal was examined. Two types of fiber-based adsorbents, an uncontrolled grafted and a controlled grafted fiber-based adsorbent, were evaluated with respect to permeability, dynamic mAb binding capacity (DBC), resolution capabilities, and the performance in bind and elute (B/E) and frontal chromatography mode with respect to typical performance indicators. The permeabilities of the fiber-based adsorbents ranged from 200 to 1700 mD, making it possible to use the fiber-based adsorbents at larger bed heights than membrane adsorbers with fast mobile phase velocities. Antibody DBCs ranged from 20 to 41 g/L at 150 cm/h, and at higher mobile phase velocities exceeded the DBC of an existing resin material, Poros 50 HS, which has frequently been used for aggregate removal. Both fiber types showed good resolution capabilities of monomer and aggregates, and provided better resolution per column length than Poros 50 HS. Typical purity and yield constraints were fulfilled for both fiber types in both B/E and frontal chromatography mode for mobile phase velocities ranging up to 480 cm/h and 1060 cm/h. The overall performance of the controlled grafted fibers was found to be superior to the performance of uncontrolled grafted fiber-based adsorbents due to higher productivity and lower buffer consumption. The overall performance of the fiber-based adsorbents was found to be comparable to the performance of Poros 50 HS at typical operating conditions. The results in this study indicate that the use of fiber-based adsorbents for mAb aggregate removal is feasible with a performance that is comparable to the performance of an existing resin material. Depending on the cost of the adsorbents and the use scenario, the usage of such adsorbents could be beneficial., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

There has been a growing interest in fibers and fiber-based adsorbents as alternative adsorbents for preparative chromatography. While the benefits of fiber-based adsorbents in terms of productivity have been highlighted in several recent studies, microscale tools that enable a fast characterization of these novel adsorbents, and an easy integration into process development workflows, are still lacking. In the present study an automated high-throughput screening (HTS) for fiber-based adsorbents was established on a robotic liquid handling station in 96 well filter plates. Two techniques - punching and weighing - were identified as techniques that enabled accurate and reproducible portioning of short-cut fiber-based adsorbents. The impact of several screening parameters such as phase ratio, shaking frequency, and incubation time were investigated and optimized for different types of fiber-based adsorbents. The data from the developed HTS correlated with data from packed fiber columns, and binding capacities from both scales matched closely. Subsequently, the developed HTS was utilized to optimize the hydrogel architecture of anion exchange (AEX) fiber-based adsorbent prototypes. A novel AEX fiber-based adsorbent was developed that compared favorably with existing resin and membrane adsorbents in terms of productivity and DNA binding capacity. In addition, the developed HTS was also successfully employed in order to identify step elution conditions for the purification of a monoclonal antibody from product- and process-related impurities with a cation exchange (CEX) fiber-based adsorbent. Trends from the HTS were found to be in good agreement with trends from lab scale column runs. The tool developed in this paper will enable a faster and more complete characterization of fiber-based adsorbents, easier tailoring of such adsorbents towards specific process applications, and an easier integration of such materials into processes. In comparison to previous lab scale experiments, material requirements are reduced by a factor of 3-40 and time requirements are reduced by a factor of 2-5., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

For mechanistic modeling of ion exchange (IEX) processes, a profound understanding of the adsorption mechanism is important. While the description of protein adsorption in IEX processes has been dominated by stoichiometric models like the steric mass action (SMA) model, discrepancies between experimental data and model results suggest that the conceptually simple stoichiometric description of protein adsorption provides not always an accurate representation of nonlinear adsorption behavior. In this work an alternative colloidal particle adsorption (CPA) model is introduced. Based on the colloidal nature of proteins, the CPA model provides a non-stoichiometric description of electrostatic interactions within IEX columns. Steric hindrance at the adsorber surface is considered by hard-body interactions between proteins using the scaled-particle theory. The model's capability of describing nonlinear protein adsorption is demonstrated by simulating adsorption isotherms of a monoclonal antibody (mAb) over a wide range of ionic strength and pH. A comparison of the CPA model with the SMA model shows comparable model results in the linear adsorption range, but significant differences in the nonlinear adsorption range due to the different mechanistic interpretation of steric hindrance in both models. The results suggest that nonlinear adsorption effects can be overestimated by the stoichiometric formalism of the SMA model and are generally better reproduced by the CPA model., Competing Interests: Declaration of Competing Interest The authors affiliated to GoSilico declare that the software ChromX used in this study is a commercial product of GoSilico. The presented research does not depend on the usage of ChromX, the model can be implemented in any kind of modeling software., (Copyright © 2021. Published by Elsevier B.V.)

Monitoring preparative protein chromatographic steps by in-line spectroscopic tools or fraction analytics results in medium or large sized data matrices. Multivariate Curve Resolution (MCR) serve to compute or to estimate the concentration values of the pure components only from these data matrices. However, MCR methods often suffer from an inherent solution ambiguity which underlies the factorization problem. The typical unimodality of the chromatographic profiles of pure components can support the chemometric analysis. Here we present the pure components estimation process within the framework of the area of feasible solutions, which is a systematic approach to represent the range of all possible solutions. The unimodality constraint in combination with Pareto optimization is shown to be an effective method for the pure component calculation. Applications are presented for chromatograms on a model protein mixture containing ribonuclease A, cytochrome c and lysozyme and on a two-dimensional chromatographic separation of a monoclonal antibody from its aggregate species. The root mean squared errors of the first case study are 0.0373, 0.0529 and 0.0380 g/L compared to traditional off-line analytics. The second case study illustrates the potential of recovering hidden components with MCR from off-line reference analytics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Mechanistic modeling of protein adsorption has gained increasing importance in the development of ion-exchange (IEX) chromatography processes. The most common adsorption models use a stoichiometric representation of the adsorption process based on the law of mass action. Despite the importance of these models in model-based development, the stoichiometric representation of the adsorption process is not accurate for the description of long-range electrostatic interactions in IEX chromatography, limiting the application and mechanistic extension of these models. In this work an adsorption model is introduced describing the non-stoichiometric electrostatic interaction in IEX chromatography based on the linear Poisson-Boltzmann equation and a simplified colloidal representation of the protein. In contrast to most recent non-stoichiometric models, the introduced model accounts for charge regulation during the adsorption process. Its capability of describing the adsorption equilibrium is demonstrated by simulating partitioning coefficients of multiple proteins on different adsorber systems as a function of ionic strength and pH. Despite model simplifications the physical meaning and predictive value of the model could be preserved. By transferring model parameters of a monoclonal antibody (mAb) from one adsorber system to another, it could be demonstrated that protein parameters are theoretically not only valid on a specific adsorber system but freely transferable to other adsorbers. The predictive value of the mechanistic model on the new adsorber system was highlighted by predicting the elution behavior of charge variants of the mAb., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious time-consuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence., (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

PEGylation of biological macromolecules is a well-established strategy to increase circulation half-life, decrease renal clearance and improve biocompatibility. PEGylation is a process in which polyethylene glycol (PEG) is covalently attached to a target molecule. The production of PEGylated biopharmaceuticals is usually executed by first producing and purifying the base molecule followed by the PEGylation reaction and purification of the modified molecule. Most PEGylated pharmaceuticals are produced by random PEGylation in batch mode and need to be purified as mainly the mono-PEGylated form is the desired drug product. In this work we propose a method to estimate the degree of PEGylation (DOP) of modified protein eluting from a chromatography column in near real-time. extended multiplicative signal correction (EMSC) is used in conjunction with asymmetric least squares (aaLS) to alleviate the influence of a salt gradient during ion exchange chromatography (IEX) on the spectral data. To convert the raw data obtained from spectral data to the actual DOP additional information obtained from off-line measurements is utilized. Once the signal correction is applied to in-line spectral data the DOP can be estimated without further use of off-line analytics. As the prerequisites for the application of this method are relatively easy to obtain it may also find use to speed up process development., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Mechanistic modeling of chromatography has been around in academia for decades and has gained increased support in pharmaceutical companies in recent years. Despite the large number of published successful applications, process development in the pharmaceutical industry today still does not fully benefit from a systematic mechanistic model-based approach. The hesitation on the part of industry to systematically apply mechanistic models can often be attributed to the absence of a general approach for determining if a model is qualified to support decision making in process development. In this work a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Current biopharmaceutical production heavily relies on chromatography for protein purification. Recently, research has intensified towards finding suitable solutions to monitoring the chromatographic steps by multivariate spectroscopic sensors. Here, hard-constraint multivariate curve resolution (MCR) was investigated as a calibration-free method for factorizing bilinear preparative protein chromatograms into concentrations and spectra. Protein elutions were assumed to follow exponentially modified Gaussian (EMG) curves. In three case studies, MCR was applied to chromatograms of second-derivative ultraviolet and visible (UV-vis) spectra. The three case studies consisted of the separation of a ternary mixture (ribonuclease A, cytochrome c, and lysozyme), multiple binary chromatography runs of cytochrome c and lysozyme, and the separation of an antibody-drug conjugate (ADC) from unconjugated immunoglobulin G (IgG). In all case studies, good estimates of the elution curves were obtained. R 2 values compared to off-line analytics exceeded 0.90. The estimated spectra allowed for protein identification based on a protein spectral library. In summary, MCR was shown to be well able to factorize protein chromatograms without prior calibration. The method may thus substantially simplify analysis of multivariate protein chromatograms with multiple co-eluting species. It may be especially useful in process development., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Protein phase diagrams are a tool to investigate the cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphologic features, such as crystal size, as well as kinetic features, such as crystal growth time. Commonly used data visualization techniques include individual line graphs or phase diagrams based on symbols. These techniques have limitations in terms of handling large data sets, comprehensiveness or completeness. To eliminate these limitations, morphologic and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram method. Morphologic features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength, and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the empirical phase diagram method can support high-throughput crystallization experiments in its data amount as well as its data complexity., (Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Polymer fibers have been identified as a promising alternative support material for liquid chromatography. Area enhanced fibers may overcome the shortcomings of conventional fiber supports with respect to binding capacity and packing efficiency. One type of area enhanced fiber supports are winged shaped microfibers, which have a more than tenfold higher surface area than round fibers, and can be manufactured via inexpensive, conventional extrusion techniques. In the present study, the packing characteristics of native and grafted winged shaped fiber supports have been investigated. A suspension based packing technique was used to pack short winged shaped polyamide 6 (PA6) fibers into small laboratory scale columns. Low column-to-column variabilities in porosities, plate heights, axial dispersion coefficients, and peak asymmetries were observed. Peak asymmetries were within typical ranges of preparative columns, and plate heights were at the lower end of those reported for other fiber supports. Packing density was found to be the main parameter that affected column performance. Lower packing densities were associated with lower plate heights, while increases in bed height resulted in more symmetric peak shapes. Packing density was also found to have a strong impact on the performance of poly (glycidyl methacrylate) (PGMA) grafted and sulfonated (SO 3 - ) winged shaped PA6 fibers. Higher packing densities resulted in higher dynamic binding capacities (DBCs), but led to a decrease in capacity utilization and resolution. A comparison to conventional perfusive and diffusive adsorbents revealed that under optimized packing conditions such adsorbents can achieve a better resolution than conventional adsorbents at high mobile phase velocities. Overall, these results suggest, that winged shaped fibers have strong potential as supports for preparative chromatography. Further improvements may be possible via adjustments in the fiber dimensions., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Fourier-transform infrared spectroscopy (FTIR) is a well-established spectroscopic method in the analysis of small molecules and protein secondary structure. However, FTIR is not commonly applied for in-line monitoring of protein chromatography. Here, the potential of in-line FTIR as a process analytical technology (PAT) in downstream processing was investigated in three case studies addressing the limits of currently applied spectroscopic PAT methods. A first case study exploited the secondary structural differences of monoclonal antibodies (mAbs) and lysozyme to selectively quantify the two proteins with partial least squares regression (PLS) giving root mean square errors of cross validation (RMSECV) of 2.42 g/l and 1.67 g/l, respectively. The corresponding Q 2 values are 0.92 and, respectively, 0.99, indicating robust models in the calibration range. Second, a process separating lysozyme and PEGylated lysozyme species was monitored giving an estimate of the PEGylation degree of currently eluting species with RMSECV of 2.35 g/l for lysozyme and 1.24 g/l for PEG with Q 2 of 0.96 and 0.94, respectively. Finally, Triton X-100 was added to a feed of lysozyme as a typical process-related impurity. It was shown that the species could be selectively quantified from the FTIR 3D field without PLS calibration. In summary, the proposed PAT tool has the potential to be used as a versatile option for monitoring protein chromatography. It may help to achieve a more complete implementation of the PAT initiative by mitigating limitations of currently used techniques., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

In the past decades, research was carried out to find cost-efficient alternatives to Protein A chromatography as a capture step in monoclonal antibody (mAb) purification processes. In this work, polyethylene glycol (PEG) precipitation has shown promising results in the case of mAb yield and purity. Especially with respect to continuous processing, PEG precipitation has many advantages, like low cost of goods, simple setup, easy scalability, and the option to handle perfusion reactors. Nevertheless, replacing Protein A has the disadvantage of renouncing a platform unit operation as well. Furthermore, PEG precipitation is not capable of reducing high molecular weight impurities (HMW) like aggregates or DNA. To overcome these challenges, an integrated process strategy combining PEG precipitation with cation-exchange chromatography (CEX) for purification of a mAb is presented. This work discusses the process strategy as well as the associated fast, easy, and material-saving process development platform. These were implemented through the combination of high-throughput methods with empirical and mechanistic modeling. The strategy allows the development of a common batch process. Additionally, it is feasible to develop a continuous process. In the presented case study, a mAb provided from cell culture fluid (HCCF) was purified. The precipitation and resolubilization conditions as well as the chromatography method were optimized, and the mutual influence of all steps was investigated. A mAb yield of over 95.0% and a host cell protein (HCP) reduction of over 99.0% could be shown. At the same time, the aggregate level was reduced from 3.12% to 1.20% and the DNA level was reduced by five orders of magnitude. Furthermore, the mAb was concentrated three times to a final concentration of 11.9mg/mL., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. Based on visual inspection of the UV signal, it is hard to identify the source of the error and almost unfeasible to determine the quantity of deviation. The problem becomes even more pronounced, if multiple root causes of the deviation are interconnected and lead to an observable deviation. In the presented work, a novel method based on the combination of mechanistic chromatography models and the artificial neural networks is suggested to solve this problem. In a case study using a model protein mixture, the determination of deviations in column capacity and elution gradient length was shown. Maximal errors of 1.5% and 4.90% for the prediction of deviation in column capacity and elution gradient length respectively demonstrated the capability of this method for root cause investigation., (Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.)

Chromatographic separation of biopharmaceuticals in general and monoclonal antibodies (mAbs) specifically is the bottleneck in terms of cost and throughput in preparative purification. Still, generalized platform processes are used, neglecting molecule specific characteristics, defining protein-resin interaction terms. Currently used in silico modeling approaches do not consider the orientation of the molecule towards the chromatographic resins as a result of the structural features on an atomic level. This paper describes a quantitative structure-activity relationship (QSAR) approach to model the orientation of mAbs on ion exchange chromatographic matrices as a function of property distribution and mobile phase characteristics. 6 mAbs were used to build a predictive QSAR model and to investigate the preferred binding orientations and resulting surface shielding on resins. Thereby different dominating orientations, caused by composition of F ab fragments of the mAbs, could be identified. The presented methodology is suitable to gain extended insight in molecule orientation on chromatographic resins and to tailor purification strategies based on molecule structure., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The antibody species that patrol in a patient's blood are an invaluable part of the immune system. While most of them shield us from life-threatening infections, some of them do harm in autoimmune diseases. If we knew exactly all the antigens that elicited all the antibody species within a group of patients, we could learn which ones correlate with immune protection, are irrelevant, or do harm. Here, we demonstrate an approach to this question: First, we use a plethora of phage-displayed peptides to identify many different serum antibody binding peptides. Next, we synthesize identified peptides in the array format and rescreen the serum used for phage panning to validate antibody binding peptides. Finally, we systematically vary the sequence of validated antibody binding peptides to identify those amino acids within the peptides that are crucial for binding "their" antibody species. The resulting immune fingerprints can then be used to trace them back to potential antigens. We investigated the serum of an individual in this pipeline, which led to the identification of 73 antibody fingerprints. Some fingerprints could be traced back to their most likely antigen, for example the immunodominant capsid protein VP1 of enteroviruses, most likely elicited by the ubiquitous poliovirus vaccination. Thus, with our approach, it is possible, to pinpoint those antibody species that correlate with a certain antigen, without any pre-information. This can help to unravel hitherto enigmatic diseases., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Process analytical technologies (PAT) for the manufacturing of biologics have drawn increased interest in the last decade. Besides being encouraged by the Food and Drug Administration's (FDA's) PAT initiative, PAT promises to improve process understanding, reduce overall production costs and help to implement continuous manufacturing. This article focuses on spectroscopic tools for PAT in downstream processing (DSP). Recent advances and future perspectives will be reviewed. In order to exploit the full potential of gathered data, chemometric tools are widely used for the evaluation of complex spectroscopic information. Thus, an introduction into the field will be given., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture., (Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.)

Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Fibers are prominent among novel stationary phase supports for preparative chromatography. Several recent studies have highlighted the potential of fiber-based adsorbents for high productivity downstream processing in both batch and continuous mode, but so far the development of these materials and of processes employing these materials has solely been based on experimental data. In this study we assessed whether mechanistic modeling can be performed on fiber-based adsorbents. With a column randomly filled with short cut hydrogel grafted anion exchange fibers, we tested whether tracer, linear gradient elution, and breakthrough data could be reproduced by mechanistic models. Successful modeling was achieved for all of the considered experiments, for both non-retained and retained molecules. For the fibers used in this study the best results were obtained with a transport-dispersive model in combination with a steric mass action isotherm. This approach accurately accounted for the convection and dispersion of non-retained tracers, and the breakthrough and elution behaviors of three different proteins with sizes ranging from 6 to 160kDa were accurately modeled, with simulation results closely resembling the experimental data. The estimated model parameters were plausible both from their physical meaning, and from an analysis of the underlying model assumptions. Parameters were determined within good confidence levels; the average confidence estimate was below 7% for confidence levels of 95%. This shows that fiber-based adsorbents can be modeled mechanistically, which will be valuable for the future design and evaluation of these novel materials and for the development of processes employing such materials., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Mechanistic models are successfully used for protein purification process development as shown for ion-exchange column chromatography (IEX). Modeling and simulation of hydrophobic interaction chromatography (HIC) in the column mode has been seldom reported. As a combination of these two techniques is often encountered in biopharmaceutical purification steps, accurate modeling of protein adsorption in HIC is a core issue for applying holistic model-based process development, especially in the light of the Quality by Design (QbD) approach. In this work, a new mechanistic isotherm model for HIC is derived by consideration of an equilibrium between well-ordered water molecules and bulk-like ordered water molecules on the hydrophobic surfaces of protein and ligand. The model's capability of describing column chromatography experiments is demonstrated with glucose oxidase, bovine serum albumin (BSA), and lysozyme on Capto™ Phenyl (high sub) as model system. After model calibration from chromatograms of bind-and-elute experiments, results were validated with batch isotherms and prediction of further gradient elution chromatograms., (Copyright © 2016 Elsevier B.V. All rights reserved.)

As the clinical development of cell-based therapeutics has evolved immensely within the past years, downstream processing strategies become more relevant than ever. Aqueous two-phase systems (ATPS) enable the label-free, scalable, and cost-effective separation of cells, making them a promising tool for downstream processing of cell-based therapeutics. Here, we report the development of an automated robotic screening that enables high-throughput cell partitioning analysis in ATPS. We demonstrate that this setup enables fast and systematic investigation of factors influencing cell partitioning. Moreover, we examined and optimized separation conditions for the differentiable promyelocytic cell line HL-60 and used a counter-current distribution-model to investigate optimal separation conditions for a multi-stage purification process. Finally, we show that the separation of CD11b-positive and CD11b-negative HL-60 cells is possible after partial DMSO-mediated differentiation towards the granulocytic lineage. The modeling data indicate that complete peak separation is possible with 30 transfers, and >93% of CD11b-positive HL-60 cells can be recovered with >99% purity. The here described screening platform facilitates faster, cheaper, and more directed downstream process development for cell-based therapeutics and presents a powerful tool for translational research., (Copyright © 2016 Elsevier B.V. All rights reserved.)

PEGylation, i.e. the covalent attachment of chemically activated polyethylene glycol (PEG) to proteins, is a technique commonly used in biopharmaceutical industry to improve protein stability, pharmacokinetics and resistance to proteolytic degradation. Therefore, PEGylation represents a valuable strategy to reduce autocatalysis of biopharmaceutical relevant proteases during production, purification and storage. In case of non-specific random conjugation the existence of more than one accessible binding site results in conjugates which vary in position and number of attached PEG molecules. These conjugates may differ considerably in their physicochemical properties. Optimizing the reaction conditions with respect to the degree of PEGylation (number of linked PEG molecules) using high-throughput screening (HTS) technologies requires a fast and reliable analytical method which allows stopping the reaction at defined times. In this study an analytical protocol for PEGylated proteases is proposed combining preservation of sample composition by trichloroacetic acid (TCA) precipitation with high-throughput capillary gel electrophoresis (HT-CGE). The well-studied protein hen egg-white lysozyme served as a model system for validating the newly developed analytical protocol for 10kDa mPEG-aldehyde conjugates. PEGamer species were purified by chromatographic separation for calibrating the HT-CGE system. In a case study, the serine protease Savinase(®) which is highly sensitive to autocatalysis was randomly modified with 5kDa and 10kDa mPEG-aldehyde and analyzed. Using the presented TCA protocol baseline separation between PEGamer species was achieved allowing for the analysis of heterogeneous PEGamer mixtures while preventing protease autocatalysis., (Copyright © 2016 Elsevier B.V. All rights reserved.)

In chromatographic protein purification, process variations, aging of columns, or processing errors can lead to deviations of the expected elution behavior of product and contaminants and can result in a decreased pool purity or yield. A different elution behavior of all or several involved species leads to a deviating chromatogram. The causes for deviations are however hard to identify by visual inspection and complicate the correction of a problem in the next cycle or batch. To overcome this issue, a tool for root cause investigation in protein chromatography was developed. The tool combines a spectral deconvolution with inverse mechanistic modelling. Mid-UV spectral data and Partial Least Squares Regression were first applied to deconvolute peaks to obtain the individual elution profiles of co-eluting proteins. The individual elution profiles were subsequently used to identify errors in process parameters by curve fitting to a mechanistic chromatography model. The functionality of the tool for root cause investigation was successfully demonstrated in a model protein study with lysozyme, cytochrome c, and ribonuclease A. Deviating chromatograms were generated by deliberately caused errors in the process parameters flow rate and sodium-ion concentration in loading and elution buffer according to a design of experiments. The actual values of the three process parameters and, thus, the causes of the deviations were estimated with errors of less than 4.4%. Consequently, the established tool for root cause investigation is a valuable approach to rapidly identify process variations, aging of columns, or processing errors. This might help to minimize batch rejections or contribute to an increased productivity., (Copyright © 2016 Elsevier B.V. All rights reserved.)

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%., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The combination of PEG-protein conjugation and chromatographic separation is generally known as solid-phase or on-column PEGylation and can provide advantages compared to commonly applied batch PEGylation. Even though the concept was already applied by several authors, changes in the isoform distribution compared to liquid-phase PEGylation due to sterically hindered PEGylation sites could not be confirmed. In this manuscript, a method for solid-phase PEGylation experiments in a 96-well plate format, using an automated liquid handling station is described. Applying size exclusion chromatography (SEC) and highly sensitive isoform analytics for mono-PEGylated lysozyme, we were able to investigate the differences in reaction kinetics and isoform distribution between adsorber-based PEGylation and modifications in free solution. Accordingly, solid-phase PEGylation with SP Sepharose FF and XL generally showed a reduced PEGylation reaction. In contrast to the predominant N-terminal PEGylation of lysozyme in liquid phase, a main modification of lys 97 and lys 116 was found for solid-phase experiments, which could be explained by binding orientations on corresponding adsorbent materials. Further experiments with varying amounts of bound protein additionally showed an influence on the isoform distribution of mono-PEGylated lysozyme., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The performance of functionalized materials, e.g., ion exchange resins, depends on multiple resin characteristics, such as type of ligand, ligand density, the pore accessibility for a molecule, and backbone characteristics. Therefore, the screening and identification process for optimal resin characteristics for separation is very time and material consuming. Previous studies on the influence of resin characteristics have focused on an experimental approach and to a lesser extent on the mechanistic understanding of the adsorption mechanism. In this in silico study, a previously developed molecular dynamics (MD) tool is used, which simulates any given biomolecule on resins with varying ligand densities. We describe a set of simulations and experiments with four proteins and six resins varying in ligand density, and show that simulations and experiments correlate well in a wide range of ligand density. With this new approach simulations can be used as pre-experimental screening for optimal adsorber characteristics, reducing the actual number of screening experiments, which results in a faster and more knowledge-based development of custom-tailored adsorbers., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Optimization of chromatographic processes by high-throughput screening (HTS) methodologies have become a critical part of downstream process development. Nevertheless there are still no non-invasive optical methods to characterize resin as well as protein-resin interaction on liquid-handling platforms available. Several approaches to automated resin screening in microplates are described in literature, yet all those methods involve indirect measurements by removal of, and sample quantification within, supernatant volumes. In this work, we introduce light extinction by light scattering to directly assess resin volume and bead density within microplates. Methods for this novel resin characterization are described for 96 and 384-well microplates. An example application demonstrates ligand concentration measurement in microplates with four commercial SP Sepharose™ Fast Flow batches. Further, direct quantification of adsorbent bound biomolecules is shown in an example with kinetic protein-resin interaction measurement in a batch screening process. This new approach is expected to promote batch-based resin characterization and monitoring on HTS platforms and further miniaturization and increase in throughput of chromatographic HTS processes., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Hydrophobic interaction chromatography (HIC) is one of the most frequently used purification methods in biopharmaceutical industry. A major drawback of HIC, however, is the rather low dynamic binding capacity (DBC) obtained when compared to e.g. ion exchange chromatography (IEX). The typical purification procedure for HIC includes binding at neutral pH, independently of the proteins nature and isoelectric point. Most approaches to process intensification are based on resin and salt screenings. In this paper a combination of protein solubility data and varying binding pH leads to a clear enhancement of dynamic binding capacity. This is shown for three proteins of acidic, neutral, and alkaline isoelectric points. High-throughput solubility screenings as well as miniaturized and parallelized breakthrough curves on Media Scout RoboColumns (Atoll, Germany) were conducted at pH 3-10 on a fully automated robotic workstation. The screening results show a correlation between the DBC and the operational pH, the protein's isoelectric point and the overall solubility. Also, an inverse relationship of DBC in HIC and the binding kinetics was observed. By changing the operational pH, the DBC could be increased up to 30% compared to the standard purification procedure performed at neutral pH. As structural changes of the protein are reported during HIC processes, the applied samples and the elution fractions were proven not to be irreversibly unfolded., (Copyright © 2015 Elsevier B.V. All rights reserved.)

High-throughput batch screening technologies have become an important tool in downstream process development. Although continuative miniaturization saves time and sample consumption, there is yet no screening process described in the 384-well microplate format. Several processes are established in the 96-well dimension to investigate protein-adsorbent interactions, utilizing between 6.8 and 50 μL resin per well. However, as sample consumption scales with resin volumes and throughput scales with experiments per microplate, they are limited in costs and saved time. In this work, a new method for in-well resin quantification by optical means, applicable in the 384-well format, and resin volumes as small as 0.1 μL is introduced. A HTS batch isotherm process is described, utilizing this new method in combination with optical sample volume quantification for screening of isotherm parameters in 384-well microplates. Results are qualified by confidence bounds determined by bootstrap analysis and a comprehensive Monte Carlo study of error propagation. This new approach opens the door to a variety of screening processes in the 384-well format on HTS stations, higher quality screening data and an increase in throughput., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The demand for vaccines against untreated diseases has enforced the research and development of virus-like particle (VLP) based vaccine candidates in recent years. Significant progress has been made in increasing VLP titres during upstream processing in bacteria, yeast and insect cells. Considering downstream processing, the separation of host cell impurities is predominantly achieved by time-intensive ultracentrifugation processes or numerous chromatography and filtration steps. In this work, we evaluate the potential of an alternative separation technology for VLPs: aqueous two-phase extraction (ATPE). The benefits of ATPE have been demonstrated for various biomolecules, but capacity and separation efficiency were observed to be low for large biomolecules such as VLPs or viruses. Both performance parameters were examined in detail in a case study on human B19 parvovirus-like particles derived from Spodoptera frugiperda Sf9 insect cells. A solubility-guided approach enabled the design of polyethylene (PEG) salt aqueous two-phase systems with a high capacity of up to 4.1mg/mL VLPs. Unique separation efficiencies were obtained by varying the molecular weight of PEG, the pH value and by using neutral salt additives. Further improvement of the separation of host cell impurities was achieved by multi-stage ATPE on a centrifugal partition chromatography (CPC) device in 500mL scale. While single-stage ATPE enabled a DNA clearance of 99.6%, multi-stage ATPE improved the separation of host cell proteins (HCPs). The HPLC purity ranged from 16.8% (100% VLP recovery) for the single-stage ATPE to 69.1% (40.1% VLP recovery) for the multi-stage ATPE. An alternative two-step downstream process is presented removing the ATPS forming polymer, cell debris and 99.77% DNA with a HPLC purity of 90.6% and a VLP recovery of 63.9%., (Copyright © 2015 Elsevier B.V. All rights reserved.)

In downstream processing, the underlying adsorption mechanism of biomolecules to adsorbent material are still subject of extensive research. One approach to more mechanistic understanding is simulating this adsorption process and hereby the possibility to identify the parameters with strongest impact. So far this method was applied with all-atom molecular dynamics simulations of two model proteins on one cation exchanger. In this work we developed a molecular dynamics tool to simulate protein-adsorber interaction for various proteins on an anion exchanger and ran gradient elution experiments to relate the simulation results to experimental data. We were able to show that simulation results yield similar results as experimental data regarding retention behavior as well as binding orientation. We could identify arginines in case of cation exchangers and aspartic acids in case of anion exchangers as major contributors to binding., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Over the last decade, the use of design of experiment approaches in combination with fully automated high throughput (HTP) compatible screenings supported by robotic liquid handling stations (LHS), adequate fast analytics and data processing has been developed in the biopharmaceutical industry into a strategy of high throughput process development (HTPD) resulting in lower experimental effort, sample reduction and an overall higher degree of process optimization. Apart from HTP technologies, lab-on-a-chip technology has experienced an enormous growth in the last years and allows further reduction of sample consumption. A combination of LHS and lab-on-a-chip technology is highly desirable and realized in the present work to characterize aqueous two phase systems with respect to tie lines. In particular, a new high throughput compatible approach for the characterization of aqueous two phase systems regarding tie lines by exploiting differences in phase densities is presented. Densities were measured by a standalone micro fluidic liquid density sensor, which was integrated into a liquid handling station by means of a developed generic Tip2World interface. This combination of liquid handling stations and lab-on-a-chip technology enables fast, fully automated, and highly accurate density measurements. The presented approach was used to determine the phase diagram of ATPSs composed of potassium phosphate (pH 7) and polyethylene glycol (PEG) with a molecular weight of 300, 400, 600 and 1000 Da respectively in the presence and in the absence of 3% (w/w) sodium chloride. Considering the whole ATPS characterization process, two complete ATPSs could be characterized within 24h, including four runs per ATPS for binodal curve determination (less than 45 min/run), and tie line determination (less than 45 min/run for ATPS preparation and 8h for density determination), which can be performed fully automated over night without requiring man power. The presented methodology provides a cost, time and material effective approach for characterization of ATPS phase diagram on base on highly accurate and comprehensive data. By this means the derived data opens the door for a more detailed description of ATPS towards generating mechanistic based models, since molecular approaches such as MD simulations or molecular descriptions along the line of QSAR heavily rely on accurate and comprehensive data., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Protein drugs continue to grow both in medicinal importance as in scale of their production. This furthers the interest in separation technologies that have the potential to replace chromatographic steps in a protein purification process. Two such unit operations that are employed in large scale in the chemical industry are extraction and precipitation. Their usefulness for the purification of proteins has been demonstrated, but the integration of such unit operations in a way that generate an output stream of high protein concentration and low process related impurities was missing. In this work, we employ centrifugal partitioning chromatography ('CPC') in combination with precipitation of the protein of interest to purify a cell culture supernatant of a monoclonal antibody producing cell line. Centrifugal partitioning chromatography was used as means of multi-step extraction using aqueous two-phase systems and was able to remove up to 88.2% of host cell protein ('HCP'). The following PEG driven precipitation and resolubilization of the protein of interest was use to condition the CPC output stream to suit subsequent chromatographic steps, to increase mAb concentration, remove the phase forming polymer, further improve HCP clearance, and integrate a low pH hold step for viral clearance. The entire process reduced HCP content by 99.4% while recovering 93% of the protein of interest. High throughput screening techniques were extensively employed during the development of the process., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The accelerating growth of the market for biopharmaceutical proteins, the market entry of biosimilars and the growing interest in new, more complex molecules constantly pose new challenges for bioseparation process development. In the presented work we demonstrate the application of a multidimensional, analytical separation approach to obtain the relevant physicochemical parameters of single proteins in a complex mixture for in silico chromatographic process development. A complete cell lysate containing a low titre target protein was first fractionated by multiple linear salt gradient anion exchange chromatography (AEC) with varying gradient length. The collected fractions were subsequently analysed by high-throughput capillary gel electrophoresis (HT-CGE) after being desalted and concentrated. From the obtained data of the 2D-separation the retention-volumes and the concentration of the single proteins were determined. The retention-volumes of the single proteins were used to calculate the related steric-mass action model parameters. In a final evaluation experiment the received parameters were successfully applied to predict the retention behaviour of the single proteins in salt gradient AEC., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The accelerating growth of the market for proteins and the growing interest in new, more complex molecules are bringing new challenges to the downstream process development of these proteins. This results in a demand for faster, more cost efficient, and highly understood downstream processes. Screening procedures based on high-throughput methods are widely applied nowadays to develop purification processes for proteins. However, screening highly complex biotechnological feedstocks, such as complete cell lysates containing target proteins often expressed with a low titre, is still very challenging. In this work we demonstrate a multidimensional, analytical screening approach based on pH gradient ion exchange chromatography (IEC), gel electrophoresis and protein identification via mass spectrometry to rationally characterize a biotechnological feedstock for the purpose of purification process development. With this very simple characterization strategy a two-step purification based on consecutive IEC operations was rapidly laid out for the purification of a diagnostic protein from a cell lysate reaching a purity of ∼80%. The target protein was recombinantly produced using an insect cell expression system., (Copyright © 2013 Elsevier B.V. All rights reserved.)

pH gradient protein separations are widely used techniques in the field of protein analytics, of which isoelectric focusing is the most well known application. The chromatographic variant, based on the formation of pH gradients in ion exchange columns is only rarely applied due to the difficulties to form controllable, linear pH gradients over a broad pH range. This work describes a method for the systematic generation of buffer compositions with linear titration curves, resulting in well controllable pH gradients. To generate buffer compositions with linear titration curves an in silico method was successfully developed. With this tool, buffer compositions for pH gradient ion exchange chromatography with pH ranges spanning up to 7.5 pH units were established and successfully validated. Subsequently, the buffer systems were used to characterize the elution behavior of 22 different model proteins in cation and anion exchange pH gradient chromatography. The results of both chromatographic modes as well as isoelectric focusing were compared to describe differences in between the methods., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Covalent attachment of PEG to proteins, known as PEGylation, is currently one of the main approaches for improving the pharmacokinetics of biopharmaceuticals. However, the separation and characterization especially of positional isoforms of PEGylated proteins are still challenging tasks. A common purification strategy uses ion exchange chromatography with increasing ionic strength by shallow salt gradients. This paper presents a method which applies a linear pH gradient chromatography to separate five of six possible isoforms of mono-PEGylated lysozyme, modified with 5 kDa and 10 kDa mPEG-aldehyde. To identify the corresponding PEGylation sites a comparison of elution pH values and calculated isoelectric points of each isoform, was used. The resulting correlation showed an R(2)>0.99. Fractionation, tryptic digestion and subsequent MALDI-MS analysis of each peak, verified the predicted elution order. Based on UV areas the N-terminal amine at lysine 1 exhibited the highest reactivity, followed by the lysine 33 residue., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Size exclusion chromatography (SEC) is often applied for characterization of protein-polyethylene glycol (PEG) conjugates regarding the number of attached PEG chains (PEGamers). SEC analysis is advantageous as it is precise, robust, and straightforward to establish. However, most SEC based assays have a maximal throughput of a few samples per hour. We present a strategy to increase analytical throughput based on combining a short column with a fast flow rate, and finally multivariate calibration in order to compensate for the resolution lost in the trade off for speed. Different multivariate approaches were compared and multilinear regression was shown to result in the most precise calibrations. Further, a dynamic calibration approach was developed in order to account for changes in column performance over time. In this way, it was possible to establish a highly precise assay for protein PEGamer quantification with a throughput of 30 samples per hour., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Aqueous two-phase systems have been demonstrated to be a possible alternative to chromatographic separations during the industrial purification of proteins. While convenient high throughput screening methods were shown to drastically reduce experimental effort for the evaluations of ATPS as a unit operation, the selection of which phases to investigate is currently guided largely by prior knowledge. Correlations between protein descriptors and distribution were found, but the general applicability of such correlations especially under conditions of high protein load, is questionable, as currently no correlations take the saturation of the phases with protein into account. In this manuscript, we demonstrate how precipitation experiments using the phase forming components can guide the selection of both system type and tieline length for the purification of monoclonal antibodies. Phase selection and process development time can thus be significantly reduced, as all the necessary precipitation, binodal, and tieline experiments can be conducted within one day. Good qualitative correlations between precipitation data and both distribution and recovery of the target molecule were found. Most promising systems were selected for upscale to a 500mL CPC. Influence of operation condition on the column and on HCP clearance was investigated. An increase in HCP clearance of more than threefold compared to batch extractions was observed. The importance of load protein concentration underlined the value of using a screening approach that incorporated target protein solubility data., (Copyright © 2012 Elsevier B.V. All rights reserved.)

In process development and during commercial production of monoclonal antibodies (mAb) the monitoring of aggregate levels is obligatory. The standard assay for mAb aggregate quantification is based on size exclusion chromatography (SEC) performed on a HPLC system. Advantages hereof are high precision and simplicity, however, standard SEC methodology is very time consuming. With an average throughput of usually two samples per hour, it neither fits to high throughput process development (HTPD), nor is it applicable for purification process monitoring. We present a comparison of three different SEC columns for mAb-aggregate quantification addressing throughput, resolution, and reproducibility. A short column (150 mm) with sub-two micron particles was shown to generate high resolution (~1.5) and precision (coefficient of variation (cv)<1) with an assay time below 6 min. This column type was then used to combine interlaced sample injections with parallelization of two columns aiming for an absolute minimal assay time. By doing so, both lag times before and after the peaks of interest were successfully eliminated resulting in an assay time below 2 min. It was demonstrated that determined aggregate levels and precision of the throughput optimized SEC assay were equal to those of a single injection based assay. Hence, the presented methodology of parallel interlaced SEC (PI-SEC) represents a valuable tool addressing HTPD and process monitoring., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Chromatography is the most widely used technique for the purification of biopharmaceuticals in the biotech industry. Surprisingly, process development is often still based on empirical studies or experience; recently high-throughput screening stations are employed to minimize development time and to improve screening quality. Still, experimental effort remains high and a more detailed understanding of adsorption mechanisms on a molecular level underlying chromatographic separation could help in the future to select and design chromatography steps in silico. In this study, we focused on the elucidation of protein orientation upon adsorption onto a chromatographic resin. We identified two characteristic binding sites of lysozyme on SP Sepharose Fast Flow and one multipoint interaction of lysozyme with SP Sepharose XL. Increasing ionic strength did not significantly influence the binding, whereas changes in the mobile phase pH led to a re-orientation on SP Sepharose FF. This phenomenon agrees well with theoretical considerations, including a detailed description of the surface charge distribution with changing pH and linear elution experiments, giving an idea why proteins are often retained on ion-exchange materials beyond their isoelectric point.

A comparative study on weak anion exchangers was performed to investigate the pH dependence, binding strength, particle size distribution, and static and dynamic capacity of the chromatographic resins. The resins tested included: DEAE Sepharose FF, Poros 50 D, Fractogel EMD DEAE (M), MacroPrep DEAE Support, DEAE Ceramic HyperD 20, and Toyopearl DEAE 650 M. Testing was performed with five different model proteins: Anti-FVII mAb (immunoglobulin G), aprotinin, bovine serum albumin (BSA), Lipolase (Novozymes), and myoglobin. Retention showed an expected increasing trend as a function of pH for proteins with low pI. A decrease in retention was observed for some resins at pH 9 likely due to initiation of deprotonation of the weak anion-exchange ligands. Expected particle size distribution was obtained for all resins compared to previous studies. Binding strength to weak anion-exchange resins as a function of ionic strength depends on the specific protein. Binding and elution at low salt concentration may be performed with Toyopearl DEAE 650 M, while binding and elution at high salt concentration may be performed with MacroPrep DEAE Support. Highest binding capacities were generally obtained with Poros 50 D followed by DEAE Ceramic HyperD 20. A general good agreement was obtained between this study and data obtained by the suppliers. Verification of binding strength trends with model proteins was achieved with human growth hormone (hGH) and a hGH variant on the same resins with different elution salts, sodium chloride, sodium hydrogenphosphate, sodium sulphate, and sodium acetate. Static capacity measurements obtained in the traditional experimental set-up were compared with high-throughput screening (HTS) technique experiments with reasonable agreement. Isotherm data obtained from HTS techniques and pulse experiments were successfully combined with mathematical modelling to simulate, develop and optimise the separation process of two model proteins, Lipolase and BSA. The data presented in this paper may be used for selection of resins for testing in process development.

A novel two-zone model (TZM) is presented to describe the rate of solute uptake by the stationary phase of a sorption-type chromatography column. The TZM divides the porous stationary-phase particle into an inner protein-free core and an outer protein-containing zone where intraparticle transport is limited by pore diffusion and binding follows Langmuir theory. The TZM and the classic pore-diffusion model (PDM) of chromatography are applied to the prediction of stationary-phase uptake and elution bands within a cellulose-based affinity chromatography column designed to selectively purify proteins genetically labelled with a CBM9 (family 9 cellulose binding module) affinity tag. Under both linear and nonlinear loading conditions, the TZM closely matches rates of protein uptake within the stationary phase particles as measured by confocal laser scanning microscopy, while the PDM deviates from experiment in the linear-binding region. As a result, the TZM is shown to provide improved predictions of product breakthrough, including elution behavior from a bacterial lysate feed.