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High‐throughput screening (HTS) approaches are commonly used to accelerate downstream process development. Although most HTS approaches use batch isothermal data (KP 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH‐dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well‐established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate‐enriched load material led to a significant reduction of model uncertainty for the estimates of this low‐concentrated product‐related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late‐stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided. [ABSTRACT FROM AUTHOR]

• First mechanistic model for preparative separation of antibody-drug conjugates. • Chromatography model allows to react to variations in preceding process step. • Linkage study of chromatography model and kinetic reaction model. • Model linkage creates 'digital twin' of conjugation and subsequent purification. • 'Digital twin' can facilitate efficient characterization of extended design space. Antibody-drug conjugates (ADCs) are hybrid molecules based on monoclonal antibodies (mAbs) with covalently attached cytotoxic small-molecule drugs. Due to their potential for targeted cancer therapy, they form part of the diversifying pipeline of various biopharmaceutical companies, in addition to currently seven commercial ADCs. With other new modalities, ADCs contribute to the increasing complexity of biopharmaceutical development in times of growing costs and competition. Another challenge is the implementation of quality by design (QbD), which receives a lot of attention. In order to answer these challenges, mechanistic models are gaining interest as tools for enhanced process understanding and efficient process development. The drug-to-antibody ratio (DAR) is a critical quality attribute (CQA) of ADCs. After the conjugation reaction, the DAR can still be adjusted by including a hydrophobic interaction chromatography (HIC) step. In this work, we developed a mechanistic model for the preparative separation of cysteine-engineered mAbs with different degrees of conjugation with a non-toxic surrogate drug. The model was successfully validated for varying load compositions with linear and optimized step gradient runs, applying conditions differing from the calibration runs. In two in silico studies, we then present scenarios for how the model can be applied profitably to ensure a more robust achievement of the target DAR and for the efficient characterization of the design space. For this, we also used the model in a linkage study with a kinetic reaction model developed by us previously. The combination of the two models effectively widens system boundaries over two adjacent process steps. We believe this work has great potential to help advance the incorporation of digital tools based on mechanistic models in ADC process development by illustrating their capabilities for efficient process development and increased robustness. Mechanistic models can support the implementation of QbD and eventually might be the basis for digital process twins able to represent multiple unit operations. [ABSTRACT FROM AUTHOR]

The protein A capture step is the main cost-driver in downstream processing, with high attrition costs especially when using protein A resin not until end of resin lifetime. Here we describe a feasibility study, transferring a batch downstream process to a hybrid process, aimed at replacing batch protein A capture chromatography with a continuous capture step, while leaving the polishing steps unchanged to minimize required process adaptations compared to a batch process. 35 g of antibody were purified using the hybrid approach, resulting in comparable product quality and step yield compared to the batch process. Productivity for the protein A step could be increased up to 420%, reducing buffer amounts by 30–40% and showing robustness for at least 48 h continuous run time. Additionally, to enable its potential application in a clinical trial manufacturing environment cost of goods were compared for the protein A step between hybrid process and batch process, showing a 300% cost reduction, depending on processed volumes and batch cycles. [ABSTRACT FROM AUTHOR]

Chromatography is an essential tool for purifying biopharmaceutical products. Many processes are still developed based on traditional routines and empirical procedures. Product losses are mostly due to insufficient optimization of purification setups and product sensitivity to process conditions. In order to eliminate these shortcomings, a systematic strategy for the setup of ion-exchange chromatography is presented, which considers both product stability as well as operational conditions. The stages-a hybrid approach combining high-throughput screening and analytical small-scale chromatography-are as follows: (1) pH stability (short-term); (2) pH stability (long-term), followed by a screening of additives to enhance protein stability, if required; (3) analytical pH gradient chromatography for evaluation of the operational pH window; and (4) salt stability (long-term) in the operational pH window determined. The efficiency and straightforwardness of the strategy were shown in a case study on capturing the human α-galactosidase A enzyme. Following the above procedure, the enzyme was found to be salt-unstable; a purification factor of 13.2, a concentration factor of 4, and an overall yield of 84.3% were achieved. The applied strategy allowed for a quick establishment of a dedicated capture step at low salt concentrations under stable conditions by well-chosen prior screening experiments. [ABSTRACT FROM AUTHOR]

Nanostructured non-spherical (named nanocaps) and spherical porous particles (named mosaic nanoparticles) are generated by aerosol-photopolymerization. Nanocaps exhibit well-defined shapes independent of their diameter and are formed by employing a softening agent and a volatile non-solvent in the formulation, combining non-solvent evaporation and retarded gelation. Mosaic nanoparticles are produced by the addition of a non-volatile non-solvent into the monomer formulation, provoking phase separation. Both particle structures are generated in situ in the presence of zinc oxide nanoparticles. Similarly, they are loaded with caffeine for release experiments and feature potential for applications in emerging technologies such as optics, functionalized coatings and nanomedicine. [ABSTRACT FROM AUTHOR]

In the last decade, high-throughput downstream process development techniques have entered the biopharmaceutical industry. As chromatography is the standard downstream purification method, several high-throughput chromatographic methods have been developed and applied including miniaturized chromatographic columns for utilization on liquid handling stations. These columns were used to setup a complete downstream process on a liquid handling station for the first time. In this article, a monoclonal antibody process was established in lab-scale and miniaturized afterwards. The scale-down methodology is presented and discussed. Liquid handling in miniaturized single and multicolumn processes was improved and applicability was demonstrated by volume balances. The challenges of absorption measurement are discussed and strategies were shown to improve volume balances and mass balances in 96-well microtiter plates. The feasibility of miniaturizing a complete downstream process was shown. In the future, analytical bottlenecks should be addressed to gain the full benefit from miniaturized complete process development. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development and manufacturing of safe and effective vaccines relies essentially on the availability of robust and precise analytical techniques. Virus-like particles (VLPs) have emerged as an important and valuable class of vaccines for the containment of infectious diseases. VLPs are produced by recombinant protein expression followed by purification procedures to minimize the levels of process- and product-related impurities. The control of these impurities is necessary during process development and manufacturing. Especially monitoring of the VLP size distribution is important for the characterization of the final vaccine product. Currently used methods require long analysis times and tailor-made assays. In this work, we present a size-exclusion ultra-high performance liquid chromatography (SE-UHPLC) method to characterize VLPs and quantify aggregates within 3.1 min per sample applying interlaced injections. Four analytical SEC columns were evaluated for the analysis of human B19 parvo-VLPs and murine polyoma-VLPs. The optimized method was successfully used for the characterization of five recombinant protein-based VLPs including human papillomavirus (HPV) VLPs, human enterovirus 71 (EV71) VLPs, and chimeric hepatitis B core antigen (HBcAg) VLPs pointing out the generic applicability of the assay. Measurements were supported by transmission electron microscopy and dynamic light scattering. It was demonstrated that the iSE-UHPLC method provides a rapid, precise and robust tool for the characterization of VLPs. Two case studies on purification tools for VLP aggregates and storage conditions of HPV VLPs highlight the relevance of the analytical method for high-throughput process development and process monitoring of virus-like particles. [ABSTRACT FROM AUTHOR]

Highlights: [•] Using high throughput screening to develop a centrifugal partitioning chromatography process step. [•] Using high throughput screening to develop a precipitation and resolubilization step. [•] Combining aqueous two-phase extraction and precipitation to purify a monoclonal antibody. [•] Using centrifugal partitioning chromatography with citrate-PEG ATPS. [•] Integration into a traditional protein purification process. [Copyright &y& Elsevier]

Highlights: [•] Characterization of complex protein mixtures. [•] Multidimensional analysis applying high-throughput techniques. [•] Estimation of single protein chromatograms from multidimensional analysis. [•] Determination of linear steric-mass action parameters for the single proteins. [•] Retention volume prediction and chromatogram simulation using obtained parameters. [Copyright &y& Elsevier]

Abstract: 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 &y& Elsevier]

Abstract: 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 &y& Elsevier]

Abstract: A trend to scientifically sound, robust and especially accelerated downstream process development in the biopharmaceutical industry is encouraged by the FDA . The underlying methodology – high throughput process development – is an interplay of high throughput experimentation, usage of adequate analytics and model based experimentation or process development. In this study, high-throughput batch chromatographic methods were combined with design of experiments, genetic algorithm and response surface analysis to optimize a cation exchange step. The optimization was successful in batch mode and validated in packed bed column mode. Full automation was achieved by establishing a method to automate pH and salt concentration adjustment on liquid handling stations. Several process optima were identified by the genetic algorithm. It was demonstrated that the initial population design influenced the number of optima found during a genetic algorithm optimization procedure. The mere application of response surface analysis on the experimental results showed that for systems with several optima no distinct statements on parameter dependency are achieved. A combination of genetic algorithm, design of experiments and response surface analysis showed to be the most efficient data usage during process optimization if no information on the process landscape investigated is available. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Based on Lambert–Beer law and the light attenuation model, a new method will be introduced in this work in order to eliminate the disturbances caused by signal attenuation in the experimental data measured by confocal laser scanning microscopy (CLSM). This new method considers the attenuation effects which depend on concentration of fluorophore-labelled protein as well as attenuation effects which are independent from protein concentration. Furthermore, no solvent additive is required in order to match the refraction index of solvent to bead material. The determination of correction factors is, thus, easily done using the currently investigated chromatographic phase system, so that the validity of the correction factors in the current system can be guaranteed. The introduced correction method has been applied for the investigation of intraparticle protein distribution inside an HIC (hydrophobic interaction chromatography) particle. [Copyright &y& Elsevier]

• Multimodal chromatography for standard and multispecific antibody formats. • Mechanistic chromatography model calibrated for 59 full-length IgG derivatives. • Thermodynamic parameters predicted from antibody-specific descriptors. • Multiscale models validated at pH 5.0 to 7.0 for a priori process development. 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. [ABSTRACT FROM AUTHOR]

• Calibration-free mathematical approach for analyzing preparative protein chromatograms. • Application to multivariate UV/Vis chromatograms of a mixture of model proteins. • Application to chromatograms from off-line reference analytics for a mAb and its impurities. • Based on a state-of-the-art multivariate curve resolution technique with unimodality constraint. • The influence of solution ambiguities of the multivariate curve resolution approach is analyzed. 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. [ABSTRACT FROM AUTHOR]

Highlights • A Bayesian approach can be used for model calibration and quality assessment. • The approach enables a prediction-based model uncertainty assessment. • Mechanistic models can perform reliable extrapolations despite substantial parameter uncertainty. • A sole parameter uncertainty assessment is insufficient for evaluating model quality. Abstract 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. [ABSTRACT FROM AUTHOR]

Highlights • Calibration-free factorization of multivariate UV–vis protein chromatograms. • Successful factorization of binary and tertiary protein chromatograms. • Close match of factorized chromatograms to off-line analytics. • The factorized spectra allowed identification of proteins from a spectral library. Abstract 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. [ABSTRACT FROM AUTHOR]

• Connected process modeling. • Extrapolation from lab to manufacturing scale. • Prediction of manufacturing variability. Mechanistic modeling has shown to contribute greatly to the process understanding of chromatography and filtration processes. However, these are mostly considered individually and not connected for an entire downstream process. In this study, mechanistic models were connected to describe an entire downstream process of a Fab fragment. For the capture step, a transport-dispersion model (TDM) combined with an extended Langmuir isotherm was applied. Depth filtration was modeled with a combined pore blocking model. The polishing ion exchange chromatography steps were described by a TDM combined with the colloidal particle adsorption model. The tangential flow filtration model accounts for both the Donnan effects and flow limitations. The presented downstream process model could predict online and offline data recorded at 12,000 L manufacturing scale. Process variations of 23 manufacturing batches were adequately reproduced by the model based on the consideration of input process parameter variations. [ABSTRACT FROM AUTHOR]

Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure–activity relationships (QSARs) to model two parameters, the discontinuity point m * and the β -value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m * is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the β -value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m * and the β -value exhibit a good correlation between observed and predicted data with a coefficient of determination of R 2 ≥ 0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved. [ABSTRACT FROM AUTHOR]

• Hydrophobic interaction chromatography (HIC) isotherms employed for anionic multimodal modeling. • Model parameters analytically determined using negative linear gradient elution (LGE). • Standardized modeling workflow validated for four different antibody formats. 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. [ABSTRACT FROM AUTHOR]

• Steric exclusion chromatography (SXC) of lentiviral vectors yields a recovery of 86%. • High impurity removal of 80% is achieved by SXC. • SXC is a gentle purification method for fragile enveloped viral vectors. • Lentiviral vector infectivity is preserved. • Loading strategy and pressure are the critical process parameters discussed. 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 × 1012 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. [ABSTRACT FROM AUTHOR]

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 Sf 9 insect cells. A solubility-guided approach enabled the design of polyethylene (PEG) salt aqueous two-phase systems with a high capacity of up to 4.1 mg/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 500 mL 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%. [ABSTRACT FROM AUTHOR]

Abstract: 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 5kDa and 10kDa 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 &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

• A non-stoichiometric adsorption model for IEC chromatography is derived. • Adsorption is governed by electrostatic interactions inside an interaction force boundary layer. • The model accounts explicitly for ionic strength and pH based on the protein primary structure. • Model parameters are theoretically valid on different stationary phases. • Effect of post-translational modifications on protein adsorption can be simulated. 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. [ABSTRACT FROM AUTHOR]