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The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure–property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb‐specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split‐peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies. [ABSTRACT FROM AUTHOR]

Within the Quality by Design (QbD) framework proposed by the International Conference on Harmonisation (ICH), high-throughput process development (HTPD) and mechanistic modeling are of outstanding importance for future biopharmaceutical chromatography process development. In order to compare the data derived from different column scales or batch chromatographies, the amount of adsorber has to be quantified with the same noninvasive method. Similarly, an important requirement for the implementation of mechanistic modeling is the reliable determination of column characteristics such as the ionic capacity Λ for ion-exchange chromatography with the same method at all scales and formats. We developed a method to determine the ionic capacity in column and batch chromatography, based on the adsorption/desorption of the natural, uv-detectable amino acid histidine. In column chromatography, this method produces results comparable to those of classical acid−base titration. In contrast to acid−base titration, this method can be adapted to robotic batch chromatographic experiments. We are able to convert the adsorber volumes in batch chromatography to the equivalent volume of a compressed column. In a case study, we demonstrate that this method increases the quality of SMA parameters fitted to batch adsorption isotherms, and the capability to predict column breakthrough experiments. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:666-677, 2016 [ABSTRACT FROM AUTHOR]

UV absorbance measurements play an important role in bioprocess development. Yield and purity are often evaluated in terms of peak percentages in analytical SEC or ion-exchange chromatography. Also, industrial chromatography steps are usually controlled based on UV data with pooling decisions according to absorbance thresholds. Model-based process development would make elaborate screening experiments redundant, once the model has been calibrated to the specific process step. So far, absorbance measurements could not be used directly for modeling chromatography steps as the commonly applied models rely on mass or molar concentration. This study presents mechanistic modeling of an industrially relevant chromatography setting without any knowledge of the feed composition. The model equations were rewritten to employ boundary conditions in UV absorbance units, the absorption coefficients were shifted into the isotherm, and standard parameter estimation procedures could be applied. An anion-exchange chromatography case study of a target protein expressed in Escherichia coli and 11 lumped impurity peaks demonstrated practical applicability. The target protein concentration in the feed material was estimated from chromatograms. Using this method, initially unknown feed concentrations can be determined a posteriori for ion-exchange and multimodal chromatography from single-component absorbance curves. [ABSTRACT FROM AUTHOR]

ABSTRACT Upstream processes are rather complex to design and the productivity of cells under suitable cultivation conditions is hard to predict. The method of choice for examining the design space is to execute high-throughput cultivation screenings in micro-scale format. Various predictive in silico models have been developed for many downstream processes, leading to a reduction of time and material costs. This paper presents a combined optimization approach based on high-throughput micro-scale cultivation experiments and chromatography modeling. The overall optimized system must not necessarily be the one with highest product titers, but the one resulting in an overall superior process performance in up- and downstream. The methodology is presented in a case study for the Cherry-tagged enzyme Glutathione-S-Transferase from Escherichia coli SE1. The Cherry-Tag™ (Delphi Genetics, Belgium) which can be fused to any target protein allows for direct product analytics by simple VIS absorption measurements. High-throughput cultivations were carried out in a 48-well format in a BioLector micro-scale cultivation system (m2p-Labs, Germany). The downstream process optimization for a set of randomly picked upstream conditions producing high yields was performed in silico using a chromatography modeling software developed in-house (ChromX). The suggested in silico-optimized operational modes for product capturing were validated subsequently. The overall best system was chosen based on a combination of excellent up- and downstream performance. Biotechnol. Bioeng. 2015;112: 2123-2133. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

With an increased number of applications in the field of the avidin-biotin technology, the resulting demand for highly-purified protein avidin has drawn our attention to the purification process of avidin that naturally occurs in chicken egg white. The high-throughput process development (HTPD) methodology was exploited, in order to evaluate purification process alternatives to commonly used ion-exchange chromatography. In a high-throughput format, process parameters for aqueous two-phase extraction, selective precipitation with salts and polyethylene glycol, and hydrophobic interaction and mixed-mode column chromatography experiments were performed. The HTPD strategy was complemented by a high-throughput tandem high-performance liquid chromatography assay for protein quantification. Suitable conditions for the separation of avidin from the major impurities ovalbumin, ovomucoid, ovotransferrin, and lysozyme were identified in the screening experiments. By combination of polyethylene glycol precipitation with subsequent resolubilization and separation in a polyethylene glycol/sulfate/sodium chloride two-phase system an avidin purity of 77% was obtained with a yield >90% while at the same time achieving a significant reduction of the process volume. The two-phase extraction and precipitation results were largely confirmed in larger scale with scale-up factors of 230 and 133, respectively. Seamless processing of the avidin enriched bottom phase was found feasible by using mixed-mode chromatography. By gradient elution a final avidin purity of at least 97% and yield >90% was obtained in the elution pool. The presented identification of a new and beneficial alternative for the purification of the high value protein thus represents a successful implementation of HTPD for an industrially relevant purification task. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:957-973, 2015 [ABSTRACT FROM AUTHOR]

Abstract: 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 &y& Elsevier]

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 160 kDa 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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: 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]

• 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]

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 11.9 mg/mL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 10–100 mM. 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. [ABSTRACT FROM AUTHOR]

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 Sf 9 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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current approaches to downstream process development in the biopharmaceutical industry are commonly based on a combination of platform technology, high-throughput experimentation, and ‘rules of thumb’. These empirical strategies conflict with demands for a mechanistic process understanding and a rational definition of design space, issued by the Quality by Design approach (QbD). Model-based process simulation and optimization are options for implementation of QbD. A model-based process optimization approach has to consider the complexity of biopharmaceutical downstream processes, especially the interactions of multiple chromatographic operations. We present a case study on model-based concerted process optimization of two consecutive ion exchange chromatographies (Poros 50HS and Q Sepharose FF). Our optimization approach includes a process flowsheet optimization, the shape of the salt gradient, and the boundaries of fraction collection for both columns. The superiority of the presented concerted process optimization approach is demonstrated by comparison to a sequential approach that optimizes the two ion exchange chromatographies (IEX) consecutively. Verification is carried out with a set of three model proteins (cytochrome c , chymotrypsin, ribonuclease A). The in silico optimum is reproduced in lab experiments and the modeling tool is successfully employed for the identification and characterization of critical process parameters (CPP). [ABSTRACT FROM AUTHOR]

Highlights: [•] Multidimensional analysis to characterize complex biotechnological feedstocks. [•] pH gradient IEC is used to fractionate a lysate containing a diagnostic protein. [•] Fractions are analysed by Dot-Blot and SDS PAGE. [•] The relevant contaminants are defined and subsequently identified using LC–MS. [•] Obtained information is used to lay out purification of the target protein by IEC. [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]

• Application of in-line FTIR to on-column PEGylation of lysozyme. • Spectral data correction for cation exchange chromatography elution with EMSC-aaLS. • Near real-time estimation of the degree of PEGylation (DOP). • Estimation only based on spectral data and prior knowledge, not a statistical model. 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. [ABSTRACT FROM AUTHOR]