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

Application of mechanistic modelling in membrane and fiber chromatography for purification of biotherapeutics - A review

Mechanistic modelling is a simulation tool which has been effectively applied in downstream bioprocessing to model resin chromatography. Membrane and fiber chromatography are newer approaches that offer higher rates of mass transfer and consequently higher flow rates and reduced processing times. This review describes the key considerations in the development of mechanistic models for these unit operations. Mass transfer is less complex than in resin columns, but internal housing volumes can make modelling difficult, particularly for laboratory-scale devices. Flow paths are often non-linear and the dead volume is often a larger fraction of the overall volume, which may require more complex hydrodynamic models to capture residence time distributions accurately. In this respect, the combination of computational fluid dynamics with appropriate protein binding models is emerging as an ideal approach.

Removal of plant endogenous proteins from tobacco leaf extract by freeze-thaw treatment for purification of recombinant proteins

Plants are useful as a low-cost source for producing biopharmaceutical proteins. A significant hurdle in the production of recombinant proteins in plants, however, is the complicated process of removing plant-derived components. Removing endogenous plant proteins, including ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), a major photosynthetic plant enzyme that catalyzes photosynthesis through carboxylation and oxygenation, is important for the purification of recombinant plant proteins. In particular, RuBisCO accounts for 50% of the soluble leaf protein; thus, the removal of RuBisCO is critical for the purification of recombinant proteins from plant materials. An effective conventional method, known as freeze-thaw treatment, was developed for the removal of RuBisCO from Nicotiana benthamiana, which expresses recombinant green fluorescent protein (GFP). Crude extracts or supernatants were frozen at - 30 °C. Upon thawing, most of the RuBisCO was precipitated by centrifugation without significant inactivation and/or yield reduction of GFP. Based on the proteomics analysis, using this method, RuBisCO large and small subunits were reduced to approximately 10% and 20% of those of the unfrozen supernatant solutions, respectively, without the need for specific reagents or equipment. The proteomic analysis also revealed that many ribosomal proteins were removed from the extracts. This method improves the purification process of recombinant proteins from plant materials. Prolonged freezing damaged recombinant β-glucuronidase (GUS), suggesting that the applicability of this treatment should be carefully considered for each recombinant protein.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling

Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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.

A combined pH and temperature precipitation step facilitates the purification of tobacco-derived recombinant proteins that are sensitive to extremes of either parameter

Incubation at pH 4.0 or blanching at ∼65°C facilitates the purification of biopharmaceutical proteins from plants by precipitating most of the host cell proteins (HCPs) before chromatography. However, both methods are compatible only with pH or thermostable target proteins whereas many target proteins may irreversibly denature, e.g., at pH < 4.0. Here, we developed a combined pH/temperature treatment for clarified tobacco extracts and intact leaves. The latter were subjected to a blanching procedure, i.e., the submersion into a hot buffer. Using a design of experiments approach we identified conditions that remove ∼70% of HCPs at ∼55°C, using the thermosensitive antibody 2G12 and the pH-sensitive DsRed as model proteins. We found that pH and temperature exerted a combined effect during the precipitation of HCPs in the pH range 5.0-7.0 at 35°C-60°C. For clarified extracts, the temperature required to achieve a DsRed purity threshold of 20% total soluble protein (TSP) increased from 54°C to 63°C when the pH was increased from 6.4 to 7.3. The pH-stable antibody 2G12 was less responsive to the combined treatment, but the purity of 1% TSP was achieved at 35°C instead of 44°C when the pH was reduced from 6.3 to 5.8. When blanching intact leaves, product losses were not exacerbated at pH 4.0. Indeed, the highest DsRed purity (58% TSP) was achieved at this pH, combined with a temperature of 60°C and an incubation time of 30 min. In contrast, the highest 2G12 purity (0.7% TSP) was achieved at pH 5.1 and 40°C with an incubation time of 20 min. Our data suggest that a combined pH/temperature regime can avoid extreme values of either parameter; therefore, broadening the applicability of these simple purification techniques to other recombinant proteins.

Will plant-made biopharmaceuticals play a role in the fight against COVID-19?

Given the dramatic impact of the COVID-19 pandemic, it is imperative to divulge all the available technologies with the potential to fight against this virus. Plant biotechnology offers potential solutions to this pandemic through the development of low-cost vaccines and antibodies useful for therapy, prophylaxis, and diagnosis. The technology to produce plant-made biopharmaceuticals is already established; two examples of these are: a therapeutic enzyme that has entered the market and the influenza vaccines that are currently under clinical trials with encouraging results. Thus far, some companies have started developing anti-COVID-19 antibodies and vaccines. In particular, plant-made antibodies might be timely produced and approved for human use in the short term, while the development of vaccines will take longer time (clinical evaluations could be concluded by the end of 2021); nonetheless, the candidates obtained will be valuable tools for future outbreaks. The key aspects that will define the exploitation of this technology in the fight against COVID-19 are discussed.

Molecular farming - The slope of enlightenment

Molecular farming can be defined as the use of plants to produce recombinant protein products. The technology is now >30 years old. The early promise of molecular farming was based on three perceived advantages: the low costs of growing plants, the immense scalability of agricultural production, and the inherent safety of plants as hosts for the production of pharmaceuticals. This resulted in a glut of research publications in which diverse proteins were expressed in equally diverse plant-based systems, and numerous companies were founded hoping to commercialize the new technology. There was a moderate degree of success for companies producing non-pharmaceutical proteins, but in the pharmaceutical sector the anticipation raised by promising early research was soon met by the cold hard reality of industrial pragmatism. Plants did not have a track record of success in pharmaceutical protein manufacturing, lacked a regulatory framework, and did not perform as well as established industry platforms. Negative attitudes towards genetically modified plants added to the mix. By the early 2000s, major industry players started to lose interest and pharmaceutical molecular farming fell from a peak of expectation into a trough of disillusionment, just as predicted by the Gartner hype cycle. But many of the pioneers of molecular farming have refocused their activities and have worked to address the limitations that hampered the first generation of technologies. The field has now consolidated around a smaller number of better-characterized platforms and has started to develop standardized methods and best practices, mirroring the evolution of more mature industry sectors. Likewise, attention has turned from proof-of-principle studies to realistic techno-economic modeling to capture significant niche markets, replicating the success of the industrial molecular farming sector. Here we argue that these recent developments signify that pharmaceutical molecular farming is now climbing the slope of enlightenment and will soon emerge as a mature technology.

The Emergency Response Capacity of Plant-Based Biopharmaceutical Manufacturing-What It Is and What It Could Be

Several epidemic and pandemic diseases have emerged over the last 20 years with increasing reach and severity. The current COVID-19 pandemic has affected most of the world's population, causing millions of infections, hundreds of thousands of deaths, and economic disruption on a vast scale. The increasing number of casualties underlines an urgent need for the rapid delivery of therapeutics, prophylactics such as vaccines, and diagnostic reagents. Here, we review the potential of molecular farming in plants from a manufacturing perspective, focusing on the speed, capacity, safety, and potential costs of transient expression systems. We highlight current limitations in terms of the regulatory framework, as well as future opportunities to establish plant molecular farming as a global, de-centralized emergency response platform for the rapid production of biopharmaceuticals. The implications of public health emergencies on process design and costs, regulatory approval, and production speed and scale compared to conventional manufacturing platforms based on mammalian cell culture are discussed as a forward-looking strategy for future pandemic responses.

Downstream Processing Technologies/Capturing and Final Purification : Opportunities for Innovation, Change, and Improvement. A Review of Downstream Processing Developments in Protein Purification

Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

Plant Molecular Farming - Integration and Exploitation of Side Streams to Achieve Sustainable Biomanufacturing

Plants have unique advantages over other systems such as mammalian cells for the production of valuable small molecules and proteins. The benefits cited most often include safety due to the absence of replicating human pathogens, simplicity because sterility is not required during production, scalability due to the potential for open-field cultivation with transgenic plants, and the speed of transient expression potentially providing gram quantities of product in less than 4 weeks. Initially there were also significant drawbacks, such as the need to clarify feed streams with a high particle burden and the large quantities of host cell proteins, but efficient clarification is now readily achieved. Several additional advantages have also emerged reflecting the fact that plants are essentially biodegradable, single-use bioreactors. This article will focus on the exploitation of this concept for the production of biopharmaceutical proteins, thus improving overall process economics. Specifically, we will discuss the single-use properties of plants, the sustainability of the production platform, and the commercial potential of different biomass side streams. We find that incorporating these side streams through rational process integration has the potential to more than double the revenue that can currently be achieved using plant-based production systems.

A Combined Ultrafiltration/Diafiltration Step Facilitates the Purification of Cyanovirin-N From Transgenic Tobacco Extracts

The production of biopharmaceutical proteins in plants offers many advantages over traditional expression platforms, including improved safety, greater scalability and lower upstream production costs. However, most products are retained within plant cells or the apoplastic space instead of being secreted into a liquid medium, so downstream processing necessarily involves tissue and cell disruption followed by the removal of abundant particles and host cell proteins (HCPs). We investigated whether ultrafiltration/diafiltration (UF/DF) can simplify the purification of the model recombinant protein cyanovirin-N (CVN), an ~ 11 kDa HIV-neutralizing lectin, from tobacco extracts prior to chromatography. We compared different membrane types and process conditions, and found that at pH 8.0 and 50 mS cm^-1 an UF step using a 100 kDa regenerated cellulose membrane removed more than 80% of the ~ 0.75 mg mL^-1 total soluble protein present in the clarified plant extract. We recovered ~ 70% of the CVN and the product purity increased ~ 3-fold in the permeate. The underlying effects of tobacco HCP retention during the UF/DF step were investigated by measuring the zeta potential and particle size distribution in the 2-10,000 nm range. Combined with a subsequent 10 kDa DF step, this approach simultaneously reduced the process volume, conditioned the process intermediate, and facilitated early, chromatography-free purification. Due to the generic, size-based nature of the method, it is likely to be compatible with most products smaller than ~50 kDa.

Expression and purification of human phosphatase and actin regulator 1 (PHACTR1) in plant-based systems

Cardiovascular diseases are a prevalent cause of morbidity and mortality especially in industrialized countries. The human phosphatase and actin regulator 1 (PHACTR1) may be involved in such diseases, but its precise regulatory function remains unclear due to the large number of potential interaction partners. The same phenomenon makes this protein difficult to express in mammalian cells, but it is also an intrinsically disordered protein that likely aggregates when expressed in bacteria due to the absence of chaperones. We therefore used a design of experiments approach to test the suitability of three plant-based systems for the expression of satisfactory quantities of recombinant PHACTR1, namely transient expression in tobacco (Nicotiana tabacum) BY-2 plant cell packs (PCPs), whole N. benthamiana leaves and BY-2 cell lysate (BYL). The highest yield was achieved using the BYL: up to 120 mg product kg-1 biomass equivalent within 48 h of translation. This was 1.3-fold higher than transient expression in N. benthamiana together with the silencing inhibitor p19, and 6-fold higher than the PCP system. The presence of Triton X-100 in the extraction buffer increased the recovery of PHACTR1 by 2-200-fold depending on the conditions. PHACTR1 was incompatible with biomass blanching and was stable for less than 16 h in raw plant extracts. Purification using a DDK-tag proved inefficient whereas 15% purity was achieved by immobilized metal affinity chromatography.

Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients

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 B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc.

Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure-activity relationship models

Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure-activity relationships (QSARs) to model two parameters, the discontinuity point m* and the β-value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m* is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the β-value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m* and the β-value exhibit a good correlation between observed and predicted data with a coefficient of determination of R²≥0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved.

Comparison of Tobacco Host Cell Protein Removal Methods by Blanching Intact Plants or by Heat Treatment of Extracts

Plants not only provide food, feed and raw materials for humans, but have also been developed as an economical production system for biopharmaceutical proteins, such as antibodies, vaccine candidates and enzymes. These must be purified from the plant biomass but chromatography steps are hindered by the high concentrations of host cell proteins (HCPs) in plant extracts. However, most HCPs irreversibly aggregate at temperatures above 60 °C facilitating subsequent purification of the target protein. Here, three methods are presented to achieve the heat precipitation of tobacco HCPs in either intact leaves or extracts. The blanching of intact leaves can easily be incorporated into existing processes but may have a negative impact on subsequent filtration steps. The opposite is true for heat precipitation of leaf extracts in a stirred vessel, which can improve the performance of downstream operations albeit with major changes in process equipment design, such as homogenizer geometry. Finally, a heat exchanger setup is well characterized in terms of heat transfer conditions and easy to scale, but cleaning can be difficult and there may be a negative impact on filter capacity. The design-of-experiments approach can be used to identify the most relevant process parameters affecting HCP removal and product recovery. This facilitates the application of each method in other expression platforms and the identification of the most suitable method for a given purification strategy.

Polyclonal antibodies for specific detection of tobacco host cell proteins can be efficiently generated following RuBisCO depletion and the removal of endotoxins

The production of biopharmaceutical proteins in plants requires efficient downstream processing steps that remove impurities such as host cell proteins (HCPs) and adventitious endotoxins produced by bacteria during transient expression. We therefore strived to develop effective routines for endotoxin removal from plant extracts and the subsequent use of the extracts to generate antibodies detecting a broad set of HCPs. At first, we depleted the superabundant protein ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) for which PEG precipitation achieved the best results, preventing a dominant immune reaction against this protein. We found that a mixture of sera from rabbits immunized with pre-depleted or post-depleted extracts detected more HCPs than the individual sera used alone. We also developed a powerful endotoxin removal procedure using Polymyxin B for extracts from wild type plants or a combination of fiber-flow filtration and EndoTrap Blue for tobacco plants infiltrated with Agrobacterium tumefaciens. The antibodies we generated will be useful for quality and performance assessment in future process development and the methods we present can easily be transferred to other expression systems rendering them useful in the field of plant molecular farming.

Leaf proteome rebalancing in Nicotiana benthamiana for upstream enrichment of a transiently expressed recombinant protein

A key factor influencing the yield of biopharmaceuticals in plants is the ratio of recombinant to host proteins in crude extracts. Postextraction procedures have been devised to enrich recombinant proteins before purification. Here, we assessed the potential of methyl jasmonate (MeJA) as a generic trigger of recombinant protein enrichment in Nicotiana benthamiana leaves before harvesting. Previous studies have reported a significant rebalancing of the leaf proteome via the jasmonate signalling pathway, associated with ribulose 1,5-bisphosphate carboxylase oxygenase (RuBisCO) depletion and the up-regulation of stress-related proteins. As expected, leaf proteome alterations were observed 7 days post-MeJA treatment, associated with lowered RuBisCO pools and the induction of stress-inducible proteins such as protease inhibitors, thionins and chitinases. Leaf infiltration with the Agrobacterium tumefaciens bacterial vector 24 h post-MeJA treatment induced a strong accumulation of pathogenesis-related proteins after 6 days, along with a near-complete reversal of MeJA-mediated stress protein up-regulation. RuBisCO pools were partly restored upon infiltration, but most of the depletion effect observed in noninfiltrated plants was maintained over six more days, to give crude protein samples with 50% less RuBisCO than untreated tissue. These changes were associated with net levels reaching 425 μg/g leaf tissue for the blood-typing monoclonal antibody C5-1 expressed in MeJA-treated leaves, compared to less than 200 μg/g in untreated leaves. Our data confirm overall the ability of MeJA to trigger RuBisCO depletion and recombinant protein enrichment in N. benthamiana leaves, estimated here for C5-1 at more than 2-fold relative to host proteins.

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.

High-value products from plants: the challenges of process optimization

Plants can be used to produce a diverse repertoire of complex small-molecule compounds and recombinant proteins that are valuable as industrial and pharmaceutical products. But as we move from proof-of-principle experiments and begin to consider the realistic prospects of commercial production, the focus must shift from the achievement of target molecule production and move towards quality, purity and yield aspects that determine commercial feasibility. This review describes some of the recent advances that have been implemented to improve the development of integrated production processes for high-value molecules expressed in plants, including the introduction of novel procedures to increase the likelihood of regulatory acceptance.

Trends in Upstream and Downstream Process Development for Antibody Manufacturing

A steady increase of product titers and the corresponding change in impurity composition represent a challenge for development and optimization of antibody production processes. Additionally, increasing demands on product quality result in higher complexity of processes and analytics, thereby increasing the costs for product work-up. Concentration and composition of impurities are critical for efficient process development. These impurities can show significant variations, which primarily depend on culture conditions. They have a major impact on the work-up strategy and costs. The resulting "bottleneck" in downstream processing requires new optimization, technology and development approaches. These include the optimization and adaptation of existing unit operations respective to the new separation task, the assessment of alternative separation technologies and the search for new methods in process development. This review presents an overview of existing methods for process optimization and integration and indicates new approaches for future developments.

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.