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In the production of antibody‐drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real‐time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so‐called soft‐sensor to fuse sensor predictions with long‐horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft‐sensors is challenging. In this study, we demonstrate the combination of an uncertainty‐aware sensor model with a kinetic reaction model using an EKF to monitor a site‐directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time‐variant determination of the sensor uncertainty. The EKF fuses the time‐discrete predictions of the amount of conjugated drug from the sensor model with the time‐continuous predictions from the kinetic model. While the ADC species are not distinguishable by on‐line recorded UV/Vis spectra, the developed soft‐sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time‐variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed‐batch mode. In future, this framework could be implemented for model‐based process control and be adopted for other ADC conjugation reaction types. [ABSTRACT FROM AUTHOR]

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]

Virus‐like particles (VLPs) are an emerging biopharmaceutical modality with great potential as a platform technology. VLPs can be applied as gene therapy vectors and prophylactic or therapeutic vaccines. For non‐enveloped VLPs, recombinant production of the protein subunits leads to intracellular self‐assembly. The subsequent purification process includes VLP dis‐ and reassembly which aim at removing encapsulated impurities and improving particle properties. Filtration‐based separation and processing has proven successful for VLPs but requires large product quantities and laborious experiments in early development stages. Both challenges can be tackled by implementation of process analytical technology (PAT) to efficiently obtain extensive process information. In this study, an existing PAT setup was extended to comprehensively monitor the diafiltration‐based disassembly of hepatitis B core antigen (HBcAg) VLPs. Process‐related signals were monitored in‐line, while product‐related signals, such as ultraviolet light (UV) spectra as well as static and dynamic light scattering (SLS and DLS), were monitored on‐line. The applicability of the sensors for disassembly monitoring was evaluated under varying processing conditions. HBcAg VLP subunit concentrations were accurately predicted based on UV data using ordinary and partial least squares regression models (Q2 from 0.909 to 0.976). DLS data were used for aggregation monitoring while the SLS intensity qualitatively reflected the disassembly progress. [ABSTRACT FROM AUTHOR]

Freezing processes are a well‐established unit operation in the biopharmaceutical industry to increase the shelf‐life of protein‐based drugs. While freezing reduces degradation reaction rates, it may also exert stresses such as freeze concentration. Macroscopic freeze concentration in large‐scale freezing processes has been described thoroughly by examination of frozen bulk material, but the transient process leading to such freeze concentration profiles has not been monitored yet for biopharmaceutical solutions. In this study, Raman spectroscopy as a process analytical technology is demonstrated for model formulations containing monoclonal antibodies (mAbs) or bovine serum albumin (BSA) in varying concentrations of sucrose and buffer salts. Therefore, a Raman probe was immersed into a bulk volume at different heights, monitoring the freeze concentration in the liquid phase during the freezing processes. Partial least square regression models were used to quantitatively discriminate between the protein and excipients simultaneously. The freeze concentration profiles were dependend on freezing temperature and formulation with freeze concentrations up to 2.4‐fold. Convection currents at the bottom of the freezing container were observed with a maximum height of 1 mm. Furthermore, freeze concentration was correlated with the sucrose concentration in a formulation. Analysis of the freeze concentration slope indicated diffusion from the bottom to the top of the container. In summary, Raman spectroscopy is a valuable tool for process validation of freeze concentration simulations and to overcome scale‐dependent challenges. [ABSTRACT FROM AUTHOR]

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in‐line setup and directly compared. The data of each sensor were analyzed independently with Partial‐Least‐Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L−1 was reached with the UV‐based PLS model. The Raman‐based PLS model reached an RMSEP of 0.232 g L−1. The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV‐based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real‐time monitoring of the Protein A load phase. [ABSTRACT FROM AUTHOR]

Virus‐like particles (VLPs) are particulate structures, which are applied as vaccines or delivery vehicles. VLPs assemble from subunits, named capsomeres, composed of recombinantly expressed viral structural proteins. During downstream processing, in vivo‐assembled VLPs are typically dis‐ and reassembled to remove encapsulated impurities and to improve particle morphology. Disassembly is achieved in a high‐pH solution and by the addition of a denaturant or reducing agent. The optimal disassembly conditions depend on the VLP amino acid sequence and structure, thus requiring material‐consuming disassembly experiments. To this end, we developed a low‐volume and high‐resolution disassembly screening that provides time‐resolved insight into the VLP disassembly progress. In this study, two variants of C‐terminally truncated hepatitis B core antigen were investigated showing different disassembly behaviors. For both VLPs, the best capsomere yield was achieved at moderately high urea concentration and pH. Nonetheless, their disassembly behaviors differed particularly with respect to disassembly rate and aggregation. Based on the high‐throughput screening results, a diafiltration‐based disassembly process step was developed. Compared with mixing‐based disassembly, it resulted in higher yields of up to 0.84 and allowed for integrated purification. This process step was embedded in a filtration‐based process sequence of disassembly, capsomere separation, and reassembly, considerably reducing high‐molecular‐weight species. [ABSTRACT FROM AUTHOR]

Freezing of cell culture supernatant (CCS) is a standard procedure in process development of monoclonal antibody (mAb) platform processes as up‐ and downstream development are usually separated. In the manufacturing process of mAb, however, freezing is avoided, which poses the question of comparability and transferability from process development to manufacturing. In this case study, mAb CCS from Chinese hamster ovary (CHO) cells is frozen and thawed in a novel active freezing device and subsequently captured by protein A chromatography. Critical quality attributes such as host cell protein (HCP) concentration and soluble mAb dimer shares have been monitored throughout the case study. Furthermore, cryo‐concentration of individual proteins was investigated. The main factors that drive cryo‐concentration are diffusion and natural convection. Natural convection in freezing processes was found to increase at warmer freezing temperatures and thus slower freezing, leading to higher concentration gradients from top to bottom of a freezing chamber. The freeze concentration was dependent on protein size and correlated to diffusivity, where smaller proteins are exposed to higher cryo‐concentration. Our results suggest that as a result of freezing processes, large particles based on mAb and specific host cell proteins (HCPs) expressing a certain affinity to mAbs are formed that have to be removed before purification. This leads to a significant improvement in HCP reduction by the protein A step, when compared with reference samples, where twice as much HCP remained in the eluate. Furthermore, HCP and mAb dimer concentrations in protein A eluate were dependent on the freezing temperature. As a conclusion, CCS should be frozen as rapidly as possible during process development to minimize issues of transferability from process development to manufacturing. [ABSTRACT FROM AUTHOR]

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

Real‐time monitoring and control of protein A capture steps by process analytical technologies (PATs) promises significant economic benefits due to the improved usage of the column's binding capacity, by eliminating time‐consuming off‐line analytics and costly resin lifetime studies, and enabling continuous production. The PAT method proposed in this study relies on ultraviolet (UV) spectroscopy with a dynamic background subtraction based on the leveling out of the conductivity signal. This point in time can be used to collect a reference spectrum for removing the majority of spectral contributions by process‐related contaminants. The removal of the background spectrum facilitates chemometric model building and model accuracy. To demonstrate the benefits of this method, five different feedstocks from our industry partner were used to mix the load material for a case study. To our knowledge, such a large design space, which covers possible variations in upstream condition besides the product concentration, has not been disclosed yet. By applying the conductivity‐based background subtraction, the root mean square error of prediction (RMSEP) of the partial least squares (PLS) model improved from 0.2080 to 0.0131 g L−1. Finally, the potential of the background subtraction method was further evaluated for single wavelength‐based predictions to facilitate implementation in production processes. An RMSEP of 0.0890 g L−1 with univariate linear regression was achieved, showing that by subtraction of the background better prediction accuracy is achieved then without subtraction and a PLS model. In summary, the developed background subtraction method is versatile, enables accurate prediction results, and is easily implemented into existing chromatography setups with typically already integrated sensors. [ABSTRACT FROM AUTHOR]

Virus‐like particles (VLPs) have shown great potential as biopharmaceuticals in the market and in clinics. Nonenveloped, in vivo assembled VLPs are typically disassembled and reassembled in vitro to improve particle stability, homogeneity, and immunogenicity. At the industrial scale, cross‐flow filtration (CFF) is the method of choice for performing reassembly by diafiltration. Here, we developed an experimental CFF setup with an on‐line measurement loop for the implementation of process analytical technology (PAT). The measurement loop included an ultraviolet and visible (UV/Vis) spectrometer as well as a light scattering photometer. These sensors allowed for monitoring protein concentration, protein tertiary structure, and protein quaternary structure. The experimental setup was tested with three Hepatitis B core Antigen (HBcAg) variants. With each variant, three reassembly processes were performed at different transmembrane pressures (TMPs). While light scattering provided information on the assembly progress, UV/Vis allowed for monitoring the protein concentration and the rate of VLP assembly based on the microenvironment of Tyrosine‐132. VLP formation was verified by off‐line dynamic light scattering (DLS) and transmission electron microscopy (TEM). Furthermore, the experimental results provided evidence of aggregate‐related assembly inhibition and showed that off‐line size‐exclusion chromatography does not provide a complete picture of the particle content. Finally, a Partial‐Least Squares (PLS) model was calibrated to predict VLP concentrations in the process solution. Q2 values of 0.947–0.984 were reached for the three HBcAg variants. In summary, the proposed experimental setup provides a powerful platform for developing and monitoring VLP reassembly steps by CFF. [ABSTRACT FROM AUTHOR]

ABSTRACT The aggregation of proteins became one of the major challenges in the development of biopharmaceu­ticals since the formation of aggregates can affect drug quality and immunogenicity. However, aggregation mechanisms are highly complex and the investigation requires cost, time, and material intensive experi­mental effort. In the present work, the predictive power of protein characteristics for the phase behavior of three different proteins which are very similar in size and structure was studied. In particular, the surface hydrophobicity, zeta potential, and conformational flexibility of human lysozyme, lysozyme from chicken egg white, and α-lactalbumin at pH 3, 5, 7, and 9 were assessed and examined for correlation with experimental stability studies focusing on protein phase behavior induced by sodium chloride and ammonium sulfate. The molecular dynamics (MD) simulation based study of the conformational flexibility without precipitants was able to identify highly flexible protein regions which could be associated to the less regular secondary structure elements and random coiled and terminal regions in particular. Conformational flex­ibility of the entire protein structure and protein surface hydrophobicity could be correlated to differing aggregation propensities among the studied proteins and could be identified to be applicable for predic­tion of protein phase behavior in aqueous solution without precipitants. For prediction of protein phase behavior and aggregation propensity in aqueous solution with precipitants, protein flexibility was further studied in dependency of salt concentration and species by means of human lysozyme. Even though the results of the salt dependent MD simulations could not be shown to be sufficient for prediction of salt depending phase behavior, this study revealed a more pronounced destabilizing effect of ammonium sulfate in comparison to sodium chloride and thus, was found to be in good agreement with theoretical considerations along the Hofmeister series as well as experimentally evaluated phase behavior. Biotechnol. Bioeng. 2017;114: 1170-1183. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient [ABSTRACT FROM AUTHOR]

ABSTRACT The load phase in preparative Protein A capture steps is commonly not controlled in real-time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin-life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real-time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real-time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time-consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368-373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT This study aims at defining rheological parameters for the characterization of highly concentrated protein solutions. As a basis for comparing rheological behavior with protein solution characteristics the protein phase behavior of Lysozyme from chicken egg white with concentrations up to 225 mg/mL, changing pH values and additive concentrations was studied in a microbatch scale format. The prepared phase diagrams, scored after 40 days ( [ABSTRACT FROM AUTHOR]

ABSTRACT The surface hydrophobicity of a protein is an important factor for its interactions in solution and thus the outcome of its production process. Yet most of the methods are not able to evaluate the influence of these hydrophobic interactions under natural conditions. In the present work we have established a high resolution stalagmometric method for surface tension determination on a liquid handling station, which can cope with accuracy as well as high throughput requirements. Surface tensions could be derived with a low sample consumption (800 μL) and a high reproducibility (<0.1‰ for water) within a reasonable time (3.5 min per sample). This method was used as a non-invasive HTP compatible approach to determine surface tensions of protein solutions dependent on protein content. The protein influence on the solutions' surface tension was correlated to the hydrophobicity of lysozyme, human lysozyme, BSA, and α-lactalbumin. Differences in proteins' hydrophobic character depending on pH and species could be resolved. Within this work we have developed a pH dependent hydrophobicity ranking, which was found to be in good agreement with literature. For the studied pH range of 3-9 lysozyme from chicken egg white was identified to be the most hydrophilic. α-lactalbumin at pH 3 exhibited the most pronounced hydrophobic character. The stalagmometric method occurred to outclass the widely used spectrophotometric method with bromophenol blue sodium salt as it gave reasonable results without restrictions on pH and protein species. Biotechnol. Bioeng. 2015;112: 2485-2494. © 2015 Wiley Periodicals, Inc. [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]

ABSTRACT Pooling decisions in preparative liquid chromatography for protein purification are usually based on univariate UV absorption measurements that are not able to differentiate between product and co-eluting contaminants. This can result in inconsistent pool purities or yields, if there is a batch-to-batch variability of the feedstock. To overcome this analytical bottleneck, a tool for selective inline quantification of co-eluting model proteins using mid-UV absorption spectra and Partial Least Squares Regression (PLS) was presented in a previous study and applied for real-time pooling decisions. In this paper, a process-data-based method for the PLS model calibration will be introduced that allows the application of the tool towards chromatography steps of real-life processes. The process-data-based calibration method uses recorded inline mid-UV absorption spectra that are correlated with offline fraction analytics to calibrate PLS models. In order to generate average spectra from the inline data, a Visual Basic for Application macro was successfully developed. The process-data-based model calibration was established using a ternary model protein system. Afterwards, it was successfully demonstrated in two case studies that the calibration method is applicable towards real-life separation issues. The calibrated PLS models allowed a successful quantification of the co-eluting species in a cation-exchange-based aggregate and fraction removal during the purification of monoclonal antibodies and of co-eluting serum proteins in an anion-exchange-based purification of Cohn supernatant I. Consequently, the presented process-data-based PLS model calibration in combination with the tool for selective inline quantification has a great potential for the monitoring of future chromatography steps and may contribute to manage batch-to-batch variability by real-time pooling decisions. Biotechnol. Bioeng. 2015;112: 1406-1416. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Selective quantification of co-eluting proteins in chromatography is usually performed by offline analytics. This is time-consuming and can lead to late detection of irregularities in chromatography processes. To overcome this analytical bottleneck, a methodology for selective protein quantification in multicomponent mixtures by means of spectral data and partial least squares regression was presented in two previous studies. In this paper, a powerful integration of software and chromatography hardware will be introduced that enables the applicability of this methodology for a selective inline quantification of co-eluting proteins in chromatography. A specific setup consisting of a conventional liquid chromatography system, a diode array detector, and a software interface to Matlab® was developed. The established tool for selective inline quantification was successfully applied for a peak deconvolution of a co-eluting ternary protein mixture consisting of lysozyme, ribonuclease A, and cytochrome c on SP Sepharose FF. Compared to common offline analytics based on collected fractions, no loss of information regarding the retention volumes and peak flanks was observed. A comparison between the mass balances of both analytical methods showed, that the inline quantification tool can be applied for a rapid determination of pool yields. Finally, the achieved inline peak deconvolution was successfully applied to make product purity-based real-time pooling decisions. This makes the established tool for selective inline quantification a valuable approach for inline monitoring and control of chromatographic purification steps and just in time reaction on process irregularities. Biotechnol. Bioeng. 2014;111: 1365-1373. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Since the first FDA approval of a PEGylated product in 1990, so called random PEGylation reactions are still used to increase the efficacy of biopharmaceuticals and represent the major technology of all approved PEG-modified drugs. However, the great influence of process parameters on PEGylation degree and the PEG-binding site results in a lack of reaction specificity which can have severe impact on the product profile. Consequently, reproducible and well characterized processes are essential to meet increasing regulative requirements resulting from the quality-by-design (QbD) initiative, especially for this kind of modification type. In this study we present a general approach which combines the simple chemistry of random PEGylation reactions with high throughput experimentation (HTE) to achieve a well-defined process. Robotic based batch experiments have been established in a 96-well plate format and were analyzed to investigate the influence of different PEGylation conditions for lysozyme as model protein. With common SEC analytics highly reproducible reaction kinetics were measured and a significant influence of PEG-excess, buffer pH, and reaction time could be investigated. Additional mono-PEG-lysozyme analytics showed the impact of varying buffer pH on the isoform distribution, which allowed us to identify optimal process parameters to get a maximum concentration of each isoform. Employing Micrococcus lysodeikticus based activity assays, PEG-lysozyme33 was identified to be the isoform with the highest residual activity, followed by PEG-lysozyme1. Based on these results, a control space for a PEGylation reaction was defined with respect to an optimal overall volumetric activity of mono-PEG-lysozyme isoform mixtures. Biotechnol. Bioeng. 2014;111: 104-114. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Chromatography is the method of choice for the separation of proteins, at both analytical and preparative scale. Orthogonal purification strategies for industrial use can easily be implemented by combining different modes of adsorption. Nevertheless, with flexibility comes the freedom of choice and optimal conditions for consecutive steps need to be identified in a robust and reproducible fashion. One way to address this issue is the use of mathematical models that allow for an in silico process optimization. Although this has been shown to work, model parameter estimation for complex feedstocks becomes the bottleneck in process development. An integral part of parameter assessment is the accurate measurement of retention times in a series of isocratic or gradient elution experiments. As high-resolution analytics that can differentiate between proteins are often not readily available, pure protein is mandatory for parameter determination. In this work, we present an approach that has the potential to solve this problem. Based on the uniqueness of UV absorption spectra of proteins, we were able to accurately measure retention times in systems of up to four co-eluting compounds. The presented approach is calibration-free, meaning that prior knowledge of pure component absorption spectra is not required. Actually, pure protein spectra can be determined from co-eluting proteins as part of the methodology. The approach was tested for size-exclusion chromatograms of 38 mixtures of co-eluting proteins. Retention times were determined with an average error of 0.6 s (1.6% of average peak width), approximated and measured pure component spectra showed an average coefficient of correlation of 0.992. Biotechnol. Bioeng. 2013; 110: 683-693. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The application of high throughput experimentation (HTE) in protein purification process development has created an analytical bottleneck. Recently, a new label-free and non-invasive methodology for analyzing multicomponent protein mixtures by means of spectral measurements was presented. Analytics based on the methodology was shown to increase analytical throughput for selective protein quantification significantly, however this was only demonstrated for one particular protein combination. In this work, the possibilities and limitations of the analytical method are investigated further. Principal component analysis (PCA) was performed on a broad range of absorption spectra to investigate their common characteristics and differences. The PCA was used both for cluster analysis and to define a measure for spectral similarity. For binary protein combinations, the calibration precision was shown to decrease exponentially with the defined spectral similarity factor. Knowledge of this correlation can be used to determine a priori whether a calibration will be successful or not. Calibration robustness was investigated by applying the analytics to liquid chromatography performed in HTE mode. Further it was shown, that a spectral difference of 0.6% was sufficient to sucessfully preform a spectral based calibration of two IgG1 monoclonals. Biotechnol. Bioeng. 2013; 110: 448-460. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The application of high throughput experimentation (HTE) in protein purification process development has created an analytical bottleneck. Using a new label-free and non-invasive methodology for analyzing multicomponent protein mixtures by means of spectral measurements, we show that the analytical throughput for selective protein quantification can be increased significantly. An analytical assay based on this new methodology was shown to generate very precise results. Further, the assay was successfully applied as analytics for a resin screening performed in HTE mode. The increase in analytical throughput was obtained without decreasing the level of information when compared to analytical chromatography. This proves its potential as a valuable analytical tool in conjugation with high throughput process development (HTPD). Further, fast selective protein quantification can enhance process control in a commercial production environment and, hence, minimize the need for off-line release analysis. Biotechnol. Bioeng. 2011;108: 2661-2669. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]