Pascal Baumann

Influence of binding pH and protein solubility on the dynamic binding capacity in hydrophobic interaction chromatography.

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 proteins 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.

An iterative modelling approach for improving the performance of a pulsed electric field (PEF) treatment chamber

Abstract An important component of the pulsed electric field (PEF) technology is the treatment chamber in which the food is exposed to high voltage pulses. Non-uniformity of the treatment, particularly with respect to the electric field distribution, is a common problem in continuous PEF treatment chambers. A previously developed and validated Multiphysics model of a pilot-scale PEF system with co-linear electrode configuration was simplified and embedded into an iterative algorithm, automatically modifying the models treatment chamber geometry and dimensions, and evaluating the simulated process with respect to a set of performance indicators. The algorithm was capable of identifying configurations that were superior to the standard co-linear treatment chamber configuration. A 3D Multiphysics model with the identified geometrical properties was developed and a corresponding treatment chamber manufactured. The Multiphysics model was validated by comparing measured and predicted temperatures at various process conditions induced in NaCl solution and apple juice.

UV absorption-based inverse modeling of protein chromatography

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.

Integrated development of up- and downstream processes supported by the Cherry-Tag™ for real-time tracking of stability and solubility of proteins

Product analytics is the bottleneck of most processes in bioprocess engineering, as it is rather time-consuming. Real-time and in-line product tracing without sample pre-treatment is only possible for few products. The Cherry-Tag™ (Delphi Genetics, Belgium) which can be fused to any target protein allows for straightforward product analytics by VIS absorption measurements. When the fused protein becomes unstable or insoluble, the chromophore function of the group is lost, which makes this technology an ideal screening tool for solubility and stability in up- and downstream process development. The Cherry-Tag™ technology will be presented for the tagged enzyme glutathione-S-transferase (GST) from Escherichia coli in a combined up- and downstream process development study. High-throughput cultivations were carried out in a 48-well format in a BioLector system (m2p-Labs, Germany). The best cultivation setup of highest product titer was scaled up to a 2.5L shake flask culture, followed by a selective affinity chromatography product capturing step. In upstream applications the tag was capable of identifying conditions where insoluble and non-native inclusion bodies were formed. In downstream applications the red-colored product was found to be bound effectively to a GST affinity column. Thus, it was identified to be a native and active protein, as the binding mechanism relies on catalytic activity of the enzyme. The Cherry-Tag™ was found to be a reliable and quantitative tool for real-time tracking of stable and soluble proteins in up- and downstream processing applications. Denaturation and aggregation of the product can be detected in-line at any stage of the process. Critical stages can be identified and subsequently changed or replaced.

High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development.

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.

High-throughput process development of an alternative platform for the production of virus-like particles in Escherichia coli.

The production of safe vaccines against untreatable or new diseases has pushed the research in the field of virus-like particles (VLPs). Currently, a large number of commercial VLP-based human vaccines and vaccine candidates are available or under development. A promising VLP production route is the controlled in vitro assembly of virus proteins into capsids. In the study reported here, a high-throughput screening (HTS) procedure was implemented for the upstream process development of a VLP platform in bacterial cell systems. Miniaturized cultivations were carried out in 48-well format in the BioLector system (m2p-Labs, Germany) using an Escherichia coli strain with a tac promoter producing the murine polyomavirus capsid protein (VP1). The screening procedure incorporated micro-scale cultivations, HTS cell disruption by sonication and HTS-compatible analytics by capillary gel electrophoresis. Cultivation temperatures, shaking speeds, induction and medium conditions were varied to optimize the product expression in E. coli. The most efficient system was selected based on an evaluation of soluble and insoluble product concentrations as well as on the percentage of product in the total soluble protein fraction. The optimized system was scaled up to cultivation 2.5L shaker flask scale and purified using an anion exchange chromatography membrane adsorber, followed by a size exclusion chromatography polishing procedure. For proof of concept, purified VP1 capsomeres were assembled under defined buffer conditions into empty capsids and characterized using transmission electron microscopy (TEM). The presented HTS procedure allowed for a fast development of an efficient production process of VLPs in E. coli. Under optimized cultivation conditions, the VP1 product totalled up to 43% of the total soluble protein fraction, yielding 1.63 mg VP1 per mL of applied cultivation medium. The developed production process strongly promotes the murine polyoma-VLP platform, moving towards an industrially feasible technology for new chimeric vaccines.

Effect of PEG molecular weight and PEGylation degree on the physical stability of PEGylated lysozyme

During production, purification, formulation, and storage proteins for pharmaceutical or biotechnological applications face solution conditions that are unfavorable for their stability. Such harmful conditions include extreme pH changes, high ionic strengths or elevated temperatures. The characterization of the main influencing factors promoting undesired changes of protein conformation and aggregation, as well as the manipulation and selective control of protein stabilities are crucially important to biopharmaceutical research and process development. In this context PEGylation, i.e. the covalent attachment of polyethylene glycol (PEG) to proteins, represents a valuable strategy to improve the physico-chemical properties of proteins. In this work, the influence of PEG molecular weight and PEGylation degree on the physical stability of PEGylated lysozyme is investigated. Specifically, conformational and colloidal properties were studied by means of high-throughput melting point determination and automated generation of protein phase diagrams, respectively. Lysozyme from chicken egg-white as a model protein was randomly conjugated to 2kDa, 5kDa and 10kDa mPEG-aldehyde and resulting PEGamer species were purified by chromatographic separation. Besides protein stability assessment, residual enzyme activities were evaluated employing a Micrococcus lysodeikticus based activity assay. PEG molecules with lower molecular weights and lower PEGylation degrees resulted in higher residual activities. Changes in enzyme activities upon PEGylation have shown to result from a combination of steric hindrance and molecular flexibility. In contrast, higher PEG molecular weights and PEGylation degrees enhanced conformational and colloidal stability. By PEGylating lysozyme an increase of the protein solubility by more than 11-fold was achieved.

Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches

The biopharmaceutical industry is at a turning point moving toward a more customized and patient‐oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high‐throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry.

Quantification of PEGylated proteases with varying degree of conjugation in mixtures: An analytical protocol combining protein precipitation and capillary gel electrophoresis.

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

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks

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