Multi-dimensional fractionation and characterization of crude protein mixtures: Toward establishment of a database of protein purification process development parameters

From protein structure to an optimized chromatographic capture step using multiscale modeling

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Experimental characterization and prediction of Escherichia coli host cell proteome retention during preparative chromatography

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure-property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.

Enhancing thermodynamic consistency: Clarification on the application of asymmetric activity model in multi-component chromatographic separation

The single-component Mollerup model, with over 40 direct applications and 442 citations, is the most widely used activity model for chromatographic mechanistic modeling. Many researchers have extended this formula to multi-component systems by directly adding subscripts, a modification deemed thermodynamically inconsistent (referred to as the reference model). In this work, we rederived the asymmetric activity model for multi-component systems, using the van der Waals equation of state, and termed it the multi-component Mollerup model. In contrast to the reference model, our proposed model accounts for the contributions of all components to the activity. Three numerical experiments were performed to investigate the impact of the three different activity models on the chromatographic modeling. The results indicate that our proposed model represents a thermodynamically consistent generalization of the single-component Mollerup model to multi-component systems. This communication advocates adopting of the multi-component Mollerup model for activity modeling in multi-component chromatographic separation to enhance thermodynamic consistency.

Using artificial neural networks to accelerate flowsheet optimization for downstream process development

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or heuristic-based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model-based approaches, can be speed-limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.

Proteomics-based method to comprehensively model the removal of host cell protein impurities

Mechanistic models mostly focus on the target protein and some selected process- or product-related impurities. For a better process understanding, however, it is advantageous to describe also reoccurring host cell protein impurities. Within the purification of biopharmaceuticals, the binding of host cell proteins to a chromatographic resin is far from being described comprehensively. For a broader coverage of the binding characteristics, large-scale proteomic data and systems level knowledge on protein interactions are key. However, a method for determining binding parameters of the entire host cell proteome to selected chromatography resins is still lacking. In this work, we have developed a method to determine binding parameters of all detected individual host cell proteins in an Escherichia coli harvest sample from large-scale proteomics experiments. The developed method was demonstrated to model abundant and problematic proteins, which are crucial impurities to be removed. For these 15 proteins covering varying concentration ranges, the model predicts the independently measured retention time during the validation gradient well. Finally, we optimized the anion exchange chromatography capture step in silico using the determined isotherm parameters of the persistent host cell protein contaminants. From these results, strategies can be developed to separate abundant and problematic impurities from the target antigen.

Characterisation of the E. coli HMS174 and BLR host cell proteome to guide purification process development

Mass-spectrometry-based proteomics is increasingly employed to monitor purification processes or to detect critical host cell proteins in the final drug substance. This approach is inherently unbiased and can be used to identify individual host cell proteins without prior knowledge. In process development for the purification of new biopharmaceuticals, such as protein subunit vaccines, a broader knowledge of the host cell proteome could promote a more rational process design. Proteomics can establish qualitative and quantitative information on the complete host cell proteome before purification (i.e., protein abundances and physicochemical properties). Such information allows for a more rational design of the purification strategy and accelerates purification process development. In this study, we present an extensive proteomic characterisation of two E. coli host cell strains widely employed in academia and industry to produce therapeutic proteins, BLR and HMS174. The established database contains the observed abundance of each identified protein, information relating to their hydrophobicity, the isoelectric point, molecular weight, and toxicity. These physicochemical properties were plotted on proteome property maps to showcase the selection of suitable purification strategies. Furthermore, sequence alignment allowed integration of subunit information and occurrences of post-translational modifications from the well-studied E. coli K12 strain.

Precision analysis for the determination of steric mass action parameters using eight tobacco host cell proteins

Plants are advantageous as biopharmaceutical manufacturing platforms because they allow the economical and scalable upstream production of proteins, including those requiring post-translational modifications, but do not support the replication of human viruses. However, downstream processing can be more labor-intensive compared to fermenter-based systems because the product is often mixed with abundant host cell proteins (HCPs). Modeling chromatographic separation can minimize the number of process development experiments and thus reduce costs. An important part of such modeling is the sorption isotherm, such as the steric mass action (SMA) model, which describes the multicomponent protein-salt equilibria established in ion-exchange systems. Here we purified ten HCPs, including 2-Cys-peroxiredoxin, from tobacco (Nicotiana tabacum and N. benthamiana). For eight of these HCPs, we obtained sufficient quantities to determine the SMA binding parameters (KSMA and ν) under different production-relevant conditions. We studied the parameters for 2-Cys-peroxiredoxin on Q-Sepharose HP in detail, revealing that pH, resin batch and buffer batch had little influence on KSMA and ν, with coefficients of variation (COVs) less than 0.05 and 0.21, respectively. In contrast, the anion-exchange resins SuperQ-650S, Q-Sepharose FF and QAE-550C led to COVs of 0.69 for KSMA and 0.05 for ν, despite using the same quaternary amine functional group as Q-Sepharose HP. Plant cultivation in summer vs winter resulted in COVs of 0.09 for KSMA and 0.02 for ν, revealing a small impact compared to COVs of 17.15 for KSMA and 0.20 for ν when plants were grown in different settings (climate-controlled phytotron vs greenhouse). We conclude that plant cultivation can substantially affect protein properties and the resulting SMA parameters. Accordingly, plant growth but also protein purification and characterization for chromatography model building should be tightly controlled and well documented.

Mechanistic Modeling of Preparative Column Chromatography for Biotherapeutics

Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.

Chromatographic parameter determination for complex biological feedstocks

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018

Accounting for host cell protein behavior in anion-exchange chromatography

Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1453-1463, 2016.

Process development of a New Haemophilus influenzae type b conjugate vaccine and the use of mathematical modeling to identify process optimization possibilities

Vaccination is one of the most successful public health interventions being a cost-effective tool in preventing deaths among young children. The earliest vaccines were developed following empirical methods, creating vaccines by trial and error. New process development tools, for example mathematical modeling, as well as new regulatory initiatives requiring better understanding of both the product and the process are being applied to well-characterized biopharmaceuticals (for example recombinant proteins). The vaccine industry is still running behind in comparison to these industries. A production process for a new Haemophilus influenzae type b (Hib) conjugate vaccine, including related quality control (QC) tests, was developed and transferred to a number of emerging vaccine manufacturers. This contributed to a sustainable global supply of affordable Hib conjugate vaccines, as illustrated by the market launch of the first Hib vaccine based on this technology in 2007 and concomitant price reduction of Hib vaccines. This paper describes the development approach followed for this Hib conjugate vaccine as well as the mathematical modeling tool applied recently in order to indicate options for further improvements of the initial Hib process. The strategy followed during the process development of this Hib conjugate vaccine was a targeted and integrated approach based on prior knowledge and experience with similar products using multi-disciplinary expertise. Mathematical modeling was used to develop a predictive model for the initial Hib process (the 'baseline' model) as well as an 'optimized' model, by proposing a number of process changes which could lead to further reduction in price. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:568-580, 2016.

Extraction and downstream processing of plant-derived recombinant proteins

Plants offer the tantalizing prospect of low-cost automated manufacturing processes for biopharmaceutical proteins, but several challenges must be addressed before such goals are realized and the most significant hurdles are found during downstream processing (DSP). In contrast to the standardized microbial and mammalian cell platforms embraced by the biopharmaceutical industry, there are many different plant-based expression systems vying for attention, and those with the greatest potential to provide inexpensive biopharmaceuticals are also the ones with the most significant drawbacks in terms of DSP. This is because the most scalable plant systems are based on the expression of intracellular proteins in whole plants. The plant tissue must therefore be disrupted to extract the product, challenging the initial DSP steps with an unusually high load of both particulate and soluble contaminants. DSP platform technologies can accelerate and simplify process development, including centrifugation, filtration, flocculation, and integrated methods that combine solid-liquid separation, purification and concentration, such as aqueous two-phase separation systems. Protein tags can also facilitate these DSP steps, but they are difficult to transfer to a commercial environment and more generic, flexible and scalable strategies to separate target and host cell proteins are preferable, such as membrane technologies and heat/pH precipitation. In this context, clarified plant extracts behave similarly to the feed stream from microbes or mammalian cells and the corresponding purification methods can be applied, as long as they are adapted for plant-specific soluble contaminants such as the superabundant protein RuBisCO. Plant-derived pharmaceutical proteins cannot yet compete directly with established platforms but they are beginning to penetrate niche markets that allow the beneficial properties of plants to be exploited, such as the ability to produce 'biobetters' with tailored glycans, the ability to scale up production rapidly for emergency responses and the ability to produce commodity recombinant proteins on an agricultural scale.

Improved virus purification processes for vaccines and gene therapy

The downstream processing of virus particles for vaccination or gene therapy is becoming a critical bottleneck as upstream titers keep improving. Moreover, the growing pressure to develop cost-efficient processes has brought forward new downstream trains. This review aims at analyzing the state-of-the-art in viral downstream purification processes, encompassing the classical unit operations and their recent developments. Emphasis is given to novel strategies for process intensification, such as continuous or semi-continuous systems based on multicolumn technology, opening up process efficiency. Process understanding in the light of the pharmaceutical quality by design (QbD) initiative is also discussed. Finally, an outlook of the upcoming breakthrough technologies is presented.

Generic chromatography-based purification strategies accelerate the development of downstream processes for biopharmaceutical proteins produced in plants

Plants offer a valuable alternative to cultured mammalian cells for the production of recombinant biopharmaceutical proteins. However, the target protein typically represents only a minor fraction of the total protein in the initial plant extract, which means that the development of product-specific chromatography-based purification strategies is often laborious and expensive. To address this challenge, we designed a generic downstream process that is suitable for the purification of recombinant proteins with diverse properties from plant production platforms. This was achieved by focusing on the binding behavior of tobacco host cell proteins (HCPs) to a broad set of chromatography resins under different pH and conductivity conditions. Strong cation exchanger and salt-tolerant anion exchanger resins exhibited the best resolution of tobacco HCPs among the 13 tested resins, and their selectivity was easy to manipulate through the adjustment of pH and conductivity. The advantages, such as direct capture of a target protein from leaf extract, and limitations, such as low binding capacity, of various chromatography ligands and resins are discussed. We also address the most useful applications of the chromatography ligands, namely recovery of proteins with a certain pI, in a downstream process that aims to purify diverse plant-derived biopharmaceutical proteins. Based on these results, we describe generic purification schemes that are suitable for acidic, neutral, and basic target proteins, as a first step toward the development of industrial platform processes.

The use of quantitative structure-activity relationship models to develop optimized processes for the removal of tobacco host cell proteins during biopharmaceutical production

The production of recombinant pharmaceutical proteins in plants benefits from the low cost of upstream production and the greater scalability of plants compared to fermenter-based systems. Now that manufacturing processes that comply with current good manufacturing practices have been developed, plants can compete with established platforms on equal terms. However, the costs of downstream processing remain high, in part because of the dedicated process steps required to remove plant-specific process-related impurities. We therefore investigated whether the ideal strategy for the chromatographic removal of tobacco host cell proteins can be predicted by quantitative structure-activity relationship (QSAR) modeling to reduce the process development time and overall costs. We identified more than 100 tobacco proteins by mass spectrometry and their structures were reconstructed from X-ray crystallography, nuclear magnetic resonance spectroscopy and/or homology modeling data. The resulting three-dimensional models were used to calculate protein descriptors, and significant descriptors were selected based on recently-published retention data for model proteins to develop QSAR models for protein retention on anion, cation and mixed-mode resins. The predicted protein retention profiles were compared with experimental results using crude tobacco protein extracts. Because of the generic nature of the method, it can easily be transferred to other expression systems such as mammalian cells. The quality of the models and potential improvements are discussed.

Identification and characterization of host cell protein product-associated impurities in monoclonal antibody bioprocessing

Downstream processing of monoclonal antibodies (mAbs) has evolved to allow the specific process for a new product to be developed largely by empirical specialization of a platform process that enables removal of impurities of different kinds. A more complete characterization of impurities and the product itself would provide insights into the rational design of efficient downstream processes. This work identifies and characterizes host cell protein (HCP) product-associated impurities, that is, HCP species carried through the downstream processes via direct interactions with the mAb. Interactions between HCPs and mAbs are characterized using cross-interaction chromatography under solution conditions typical of those used in downstream processing. The interacting species are then identified by two-dimensional gel electrophoresis and mass spectrometry. This methodology has been applied to identify product-associated impurities in one particular purification step, namely protein A affinity chromatography, for four therapeutic mAbs as well as the Fab and Fc domains of one of these mAbs. The results show both the differences in HCP-mAb interactions among different mAbs, and the relative importance of product association compared to co-elution in protein A affinity chromatography.

Analytical characterization of complex, biotechnological feedstocks by pH gradient ion exchange chromatography for purification process development

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