123 results found
Strain B, Morrissey R, Antonakoudis A, et al., 2023, How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?, Biotechnology and Bioengineering, Pages: 1-19, ISSN: 0006-3592
Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.
Kotidis P, Donini R, Arnsdorf J, et al., 2023, CHOGlycoNET: comprehensive glycosylation reaction network for CHO cells, Metabolic Engineering, Vol: 76, Pages: 87-96, ISSN: 1096-7176
Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.
Strain B, Morrissey R, Antonakoudis A, et al., 2023, Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems, Computational and Structural Biotechnology Journal, Vol: 21, Pages: 1543-1549, ISSN: 2001-0370
With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.
Gopalakrishnan S, Joshi CJ, Valderrama-Gomez MA, et al., 2022, Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data, Metabolic Engineering, Vol: 75, Pages: 181-191, ISSN: 1096-7176
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
Daniel S, Kis Z, Kontoravdi K, et al., 2022, Quality by design for enabling RNA platform production processes, Trends in Biotechnology, Vol: 40, Pages: 1213-1228, ISSN: 0167-7799
RNA-based products have emerged as one of the most promising and strategic technologies for global vaccination, infectious disease control and future therapy development. The assessment of critical quality attributes, product-process interactions, relevant process analytical technologies, and process modeling capabilities can feed into a robust Quality by Design (QbD) framework for future development, design and control of manufacturing processes. Its implementation will help the RNA technology to reach its full potential and will be central in the development, pre-qualification and regulatory approval of rapid response, disease-agnostic RNA platform production processes.
Clarke C, Kontoravdi C, 2022, Editorial overview: Mechanistic and data-driven modelling of biopharmaceutical manufacturing processes, Current Opinion in Chemical Engineering, Vol: 37, Pages: 1-3, ISSN: 2211-3398
Palmieri E, Kis Z, Ozanne J, et al., 2022, GMMA as an alternative carrier for a glycoconjugate vaccine against Group A streptococcus, Vaccines, Vol: 10, Pages: 1-17, ISSN: 2076-393X
Group A Streptococcus (GAS) causes about 500,000 annual deaths globally, and no vaccines are currently available. The Group A Carbohydrate (GAC), conserved across all GAS serotypes, conjugated to an appropriate carrier protein, represents a promising vaccine candidate. Here, we explored the possibility to use Generalized Modules for Membrane Antigens (GMMA) as an alternative carrier system for GAC, exploiting their intrinsic adjuvant properties. Immunogenicity of GAC-GMMA conjugate was evaluated in different animal species in comparison to GAC-CRM197; and the two conjugates were also compared from a techno-economic point of view. GMMA proved to be a good alternative carrier for GAC, resulting in a higher immune response compared to CRM197 in different mice strains, as verified by ELISA and FACS analyses. Differently from CRM197, GMMA induced significant levels of anti-GAC IgG titers in mice also in the absence of Alhydrogel. In rabbits, a difference in the immune response could not be appreciated; however, antibodies from GAC-GMMA-immunized animals showed higher affinity toward purified GAC antigen compared to those elicited by GAC-CRM197. In addition, the GAC-GMMA production process proved to be more cost-effective, making this conjugate particularly attractive for low- and middle-income countries, where this pathogen has a huge burden.
Marbiah M, Kotidis P, Donini R, et al., 2022, Rapid antibody glycoengineering in Chinese hamster ovary cells., Journal of Visualized Experiments, Vol: 184, Pages: 1-19, ISSN: 1940-087X
Recombinant monoclonal antibodies bind specific molecular targets and, subsequently, induce an immune response or inhibit the binding of other ligands. However, monoclonal antibody functionality and half-life may be reduced by the type and distribution of host-specific glycosylation. Attempts to produce superior antibodies have inspired the development of genetically modified producer cells that synthesize glyco-optimized antibodies. Glycoengineering typically requires the generation of a stable knockout or knockin cell line using methods such as clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9. Monoclonal antibodies produced by engineered cells are then characterized using mass spectrometric methods to determine if the desired glycoprofile has been obtained. This strategy is time-consuming, technically challenging, and requires specialists. Therefore, an alternative strategy that utilizes streamlined protocols for genetic glycoengineering and glycan detection may assist endeavors toward optimal antibodies. In this proof-of-concept study, an IgG-producing Chinese hamster ovary cell served as an ideal host to optimize glycoengineering. Short interfering RNA targeting the Fut8 gene was delivered to Chinese hamster ovary cells, and the resulting changes in FUT8 protein expression were quantified. The results indicate that knockdown by this method was efficient, leading to a ~60% reduction in FUT8. Complementary analysis of the antibody glycoprofile was performed using a rapid yet highly sensitive technique: capillary gel electrophoresis and laser-induced fluorescence detection. All knockdown experiments showed an increase in afucosylated glycans; however, the greatest shift achieved in this study was ~20%. This protocol simplifies glycoengineering efforts by harnessing in silico design tools, commercially synthesized gene targeting reagents, and rapid quantification assays that do not require extensive prior experience. As such, t
Flevaris K, Kontoravdi K, 2022, Immunoglobulin G N-glycan biomarkers for autoimmune diseases: Current state and a glycoinformatics perspective, International Journal of Molecular Sciences, Vol: 23, Pages: 1-23, ISSN: 1422-0067
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.
Makrydaki E, Donini R, Krueger A, et al., 2022, Immobilised enzyme cascade for targeted glycosylation
<jats:title>Abstract</jats:title><jats:p>Glycosylation is a critical post-translational modification of proteins, improving properties such as folding, half-life and functionality. However, glycosylation is a non-templated and heterogeneous process because of the promiscuity of the enzymes involved. Here we describe a platform for <jats:underline>s</jats:underline>eq<jats:underline>u</jats:underline>ential <jats:underline>g</jats:underline>lycosyl<jats:underline>a</jats:underline>tion <jats:underline>r</jats:underline>eactions for <jats:underline>ta</jats:underline>ilo<jats:underline>r</jats:underline>ed su<jats:underline>g</jats:underline>ar s<jats:underline>t</jats:underline>ructures (SUGAR-TARGET) that allows bespoke, controlled N-linked glycosylation <jats:italic>in vitro</jats:italic>. This novel proof-of-concept system is enabled by immobilised enzymes produced with a “one-step immobilisation/purification” method to express, biotinylate <jats:italic>in vivo</jats:italic> and immobilise glycosyltransferases. The immobilised enzymes are used in a reaction cascade mimicking a human-like N-linked glycosylation pathway where promiscuity naturally exists. The enzyme cascade is applied to free glycans, and a monomeric Fc domain expressed in glycoengineered <jats:italic>Pichia pastoris</jats:italic>, yielding near homogeneous glycoforms (>95% conversion). Finally, immobilised β-1,4 galactosyltransferase is used to enhance the galactosylation profile of three different IgGs yielding 80.2 – 96.3 % terminal galactosylation. Enzyme recycling was further demonstrated for 7 cycles, with a combined reaction time greater than 140 hours. The novel SUGAR-TARGET platform is easy to implement, modular and reusable, and therefore can lead to the development of homogeneous glycan structures fo
Alhuthali S, Kontoravdi C, 2022, Population balance modelling captures host cell protein dynamics in CHO cell cultures, PLoS One, Vol: 17, ISSN: 1932-6203
Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels.
Moya-Ramirez I, Kotidis P, Marbiah M, et al., 2022, Polymer encapsulation of bacterial biosensors enables co-culture with mammalian cells, ACS Synthetic Biology, Vol: 11, ISSN: 2161-5063
Coexistence of different populations of cells and isolation of tasks can provide enhanced robustness and adaptability or impart new functionalities to a culture. However, generating stable cocultures involving cells with vastly different growth rates can be challenging. To address this, we developed living analytics in a multilayer polymer shell (LAMPS), an encapsulation method that facilitates the coculture of mammalian and bacterial cells. We leverage LAMPS to preprogram a separation of tasks within the coculture: growth and therapeutic protein production by the mammalian cells and l-lactate biosensing by Escherichia coli encapsulated within LAMPS. LAMPS enable the formation of a synthetic bacterial–mammalian cell interaction that enables a living biosensor to be integrated into a biomanufacturing process. Our work serves as a proof-of-concept for further applications in bioprocessing since LAMPS combine the simplicity and flexibility of a bacterial biosensor with a viable method to prevent runaway growth that would disturb mammalian cell physiology.
Kis Z, Tak K, Ibrahim D, et al., 2022, Pandemic-response adenoviral vector and RNA vaccine manufacturing, npj Vaccines, Vol: 7, ISSN: 2059-0105
Rapid global COVID-19 pandemic response by mass vaccination is currently limited by the rate of vaccine manufacturing. This study presents a techno-economic feasibility assessment and comparison of three vaccine production platform technologies deployed during the COVID-19 pandemic: (1) adenovirus-vectored (AVV) vaccines, (2) messenger RNA (mRNA) vaccines, and (3) the newer self-amplifying RNA (saRNA) vaccines. Besides assessing the baseline performance of the production process, impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using variance-based global sensitivity analysis. Cost and resource requirement projections are computed for manufacturing multi-billion vaccine doses for covering the current global demand shortage and for providing annual booster immunisations. The model-based assessment provides key insights to policymakers and vaccine manufacturers for risk analysis, asset utilisation, directions for future technology improvements and future pidemic/pandemic preparedness, given the disease-agnostic nature of these vaccine production platforms.
Ibrahim D, Kis Z, Tak K, et al., 2022, Optimal design and planning of supply chains for viral vectors and RNA vaccines, Computer Aided Chemical Engineering, Pages: 1633-1638
This work develops a multi-product MILP vaccine supply chain model that supports planning, distribution, and administration of viral vectors and RNA-based vaccines. The capability of the proposed vaccine supply chain model is illustrated using a real-world case study on vaccination against SARS-CoV-2 in the UK that concerns both viral vectors (e.g., AZD1222 developed by Oxford-AstraZeneca) and RNA-based vaccine (e.g., BNT162b2 developed by Pfizer-BioNTech). A comparison is made between the resources required and logistics costs when viral vectors and RNA vaccines are used during the SARS-CoV-2 vaccination campaign. Analysis of results shows that the logistics cost of RNA vaccines is 85% greater than that of viral vectors, and that transportation cost dominates logistics cost of RNA vaccines compared to viral vectors.
Sachio S, Kontoravdi C, Papathanasiou MM, 2022, Model-Based Design Space for Protein A Chromatography Resin Selection, Computer Aided Chemical Engineering, Pages: 733-738
As demand for biopharmaceuticals rises, manufacturers are required to meet multiple competing key performance indicators (KPIs) such as process sustainability, efficiency and product efficacy and quality. Advanced process optimisation and control in biopharmaceutical manufacturing is challenged by the lack of online Process Analytical Technologies (PAT). This results in processes relying heavily on wet-lab experimentation, which may be costly and inefficient. In this work, a novel methodology for evaluating process robustness and alternative operating strategies using design space identification is proposed to accelerate process design and optimisation. The focus in this work is on the initial separation step for the purification of monoclonal antibodies (mAbs) separating the majority of process impurities generated upstream using affinity (protein A) chromatography. A high fidelity process model is used to computationally explore the multidimensional design space. The performance and robustness of the process under three different resin properties and a variety of input conditions are evaluated using the framework. Three scenarios for each of the resins are considered resulting in a total of nine design spaces. The results indicate that using a higher column protein A density resin can increase operational flexibility.
Kis Z, Tak K, Ibrahim D, et al., 2022, Quality by design and techno-economic modelling of RNA vaccine production for pandemic-response, Computer Aided Chemical Engineering, Pages: 2167-2172
Vaccine production platform technologies have played a crucial role in rapidly developing and manufacturing vaccines during the COVID-19 pandemic. The role of disease agnostic platform technologies, such as the adenovirus-vectored (AVV), messenger RNA (mRNA), and the newer self-amplifying RNA (saRNA) vaccine platforms is expected to further increase in the future. Here we present modelling tools that can be used to aid the rapid development and mass-production of vaccines produced with these platform technologies. The impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using techno-economic modelling and variance-based global sensitivity analysis. Furthermore, the use of the quality by digital design framework and techno-economic modelling for supporting the rapid development and improving the performance of these vaccine production technologies is also illustrated.
Alifia KCH, Kontoravdi C, Kis Z, et al., 2022, Techno-Economic Evaluation of Novel SARS-CoV-2 Vaccine Manufacturing in the Insect Cell Baculovirus Platform, International Journal of Technology, Vol: 13, Pages: 1630-1639, ISSN: 2086-9614
The need to increase the COVID-19 vaccine manufacturing capacity at low to middle-income countries (LMIC) led to a growing focus on Novavax (NVX-CoV2373), a thermostable protein subunit vaccine manufactured using a baculovirus and insect cell system (BICS) platform. This study aimed to conduct a techno-economic analysis to assess the BICS platform of vaccine manufacturing and compare it to the mRNA and the saRNA platform. The data from the Novavax patent for the COVID-19 vaccine formulation and the manufacturing steps were used to simulate the BICS vaccine production in SuperPro Designer. From the techno-economic analysis, the productivity of all platforms was compared in terms of doses/day per L production scale. The saRNA platform’s productivity is about 1,000-fold of the BICS platform and 20-fold of the mRNA platform. BICS is a feasible option for LMIC to produce vaccines because the cost per dose is like the saRNA platform, while the mRNA platform’s cost per dose is 7 times higher than the BICS and saRNA platforms. However, further optimization is necessary to improve the productivity of the BICS platform to match saRNA’s platform.
Kotidis P, Marbiah M, Donini R, et al., 2022, Rapid Antibody Glycoengineering in CHO Cells Via RNA Interference and CGE-LIF N-Glycomics, GLYCOSYLATION, Pages: 147-167, ISSN: 1064-3745
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Ibrahim D, Kis Z, Tak K, et al., 2021, Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK, Vaccines, Vol: 9, Pages: 1-19, ISSN: 2076-393X
Vaccination plays a key role in reducing morbidity and mortality caused by infectious diseases, including the recent COVID-19 pandemic. However, a comprehensive approach that allows the planning of vaccination campaigns and the estimation of the resources required to deliver and administer COVID-19 vaccines is lacking. This work implements a new framework that supports the planning and delivery of vaccination campaigns. Firstly, the framework segments and priorities target populations, then estimates vaccination timeframe and workforce requirements, and lastly predicts logistics costs and facilitates the distribution of vaccines from manufacturing plants to vaccination centres. The outcomes from this study reveal the necessary resources required and their associated costs ahead of a vaccination campaign. Analysis of results shows that by integrating demand stratification, administration, and the supply chain, the synergy amongst these activities can be exploited to allow planning and cost-effective delivery of a vaccination campaign against COVID-19 and demonstrates how to sustain high rates of vaccination in a resource-efficient fashion.
Stefani I, Blaudin de The F-X, Kontoravdi K, et al., 2021, Model identifies genetic predisposition of Alzheimer’s disease as key decider in cell susceptibility to stress, International Journal of Molecular Sciences, Vol: 22, Pages: 1-15, ISSN: 1422-0067
Accumulation of unfolded/misfolded proteins in neuronal cells perturbs endoplasmic reticulum homeostasis, triggering a stress cascade called unfolded protein response (UPR), markers of which are upregulated in Alzheimer’s disease (AD) brain specimens. We measured the UPR dynamic response in three human neuroblastoma cell lines overexpressing the wild-type and two familial AD (FAD)-associated mutant forms of amyloid precursor protein (APP), the Swedish and Swedish-Indiana mutations, using gene expression analysis. The results reveal a differential response to subsequent environmental stress depending on the genetic background, with cells overexpressing the Swedish variant of APP exhibiting the highest global response. We further developed a dynamic mathematical model of the UPR that describes the activation of the three branches of this stress response in response to unfolded protein accumulation. Model-based analysis of the experimental data suggests that the mutant cell lines experienced a higher protein load and subsequent magnitude of transcriptional activation compared to the cells overexpressing wild-type APP, pointing to higher susceptibility of mutation-carrying cells to stress. The model was then used to understand the effect of therapeutic agents salubrinal, lithium, and valproate on signalling through different UPR branches. This study proposes a novel integrated platform to support the development of therapeutics for AD.
Kotidis P, Pappas I, Avraamidou S, et al., 2021, DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems, Computers and Chemical Engineering, Vol: 154, Pages: 1-7, ISSN: 0098-1354
Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies.
Antonakoudis A, Strain B, Barbosa R, et al., 2021, Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells, Computers and Chemical Engineering, Vol: 154, Pages: 1-11, ISSN: 0098-1354
In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications.
Makrydaki E, Kotidis P, Polizzi KM, et al., 2021, Hitting the sweet spot with capillary electrophoresis: advances in N-glycomics and glycoproteomics, CURRENT OPINION IN BIOTECHNOLOGY, Vol: 71, Pages: 182-190, ISSN: 0958-1669
Makrydaki E, Marshall O, Heide C, et al., 2021, Cell-free protein synthesis using Chinese hamster ovary cells, Methods in Enzymology, ISSN: 0076-6879
Cell-Free Protein Synthesis (CFPS) platforms can be used for rapid and flexible expression or proteins. The use CFPS platforms from mammalian, specifically Chinese Hamster Ovary (CHO) cells, offers the possibility of a rapid prototyping platform for recombinant protein production with the capabilities of post-translational modifications (PTMs). In this chapter, we discuss a refined CFPS system based on CHO cells, including: extract preparation, reaction mix composition, and accessory protein supplementation to enhance expression. Specifically, when the CHO cell extract is combined with a truncated version of GADD34 and K3L, stress-induced eIF2 phosphorylation is reduced and inhibition of translation initiation is relieved, increasing yields. A brief summary of the protocol for running the CFPS reactions is also described. Overall, the method is reliable and leads to a highly reproducible expression system. Finally, the advantages and disadvantages of the platform, in addition to expected outcomes, are also discussed.
Jiménez del Val I, Kyriakopoulos S, Albrecht S, et al., 2021, CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability
<jats:title>Abstract</jats:title><jats:p>Metabolic modelling has emerged as a key tool for the characterisation of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimise the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are, therefore, difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy multi-objective optimisation and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and, with further development, will serve as a platform to develop dynamic metabolic models for the control and optimisation of biopharmaceutical cell culture processes.</jats:p>
Shmool TA, Martin LK, Bui-Le L, et al., 2021, An experimental approach probing the conformational transitions and energy landscape of antibodies: a glimmer of hope for reviving lost therapeutic candidates using ionic liquid, Chemical Science, Vol: 12, Pages: 9528-9545, ISSN: 2041-6520
Understanding protein folding in different environmental conditions is fundamentally important for predicting protein structures and developing innovative antibody formulations. While the thermodynamics and kinetics of folding and unfolding have been extensively studied by computational methods, experimental methods for determining antibody conformational transition pathways are lacking. Motivated to fill this gap, we prepared a series of unique formulations containing a high concentration of a chimeric immunoglobin G4 (IgG4) antibody with different excipients in the presence and absence of the ionic liquid (IL) choline dihydrogen phosphate. We determined the effects of different excipients and IL on protein thermal and structural stability by performing variable temperature circular dichroism and bio-layer interferometry analyses. To further rationalise the observations of conformational changes with temperature, we carried out molecular dynamics simulations on a single antibody binding fragment from IgG4 in the different formulations, at low and high temperatures. We developed a methodology to study the conformational transitions and associated thermodynamics of biomolecules, and we showed IL-induced conformational transitions. We showed that the increased propensity for conformational change was driven by preferential binding of the dihydrogen phosphate anion to the antibody fragment. Finally, we found that a formulation containing IL with sugar, amino acids and surfactant is a promising candidate for stabilising proteins against conformational destabilisation and aggregation. We hope that ultimately, we can help in the quest to understand the molecular basis of the stability of antibodies and protein misfolding phenomena and offer new candidate formulations with the potential to revive lost therapeutic candidates.
van de Berg D, Kis Z, Behmer CF, et al., 2021, Quality by design modelling to support rapid RNA vaccine production against emerging infectious diseases, npj Vaccines, Vol: 6, ISSN: 2059-0105
Rapid-response vaccine production platform technologies, including RNA vaccines, are being developed to combat viral epidemics and pandemics. A key enabler of rapid response is having quality-oriented disease-agnostic manufacturing protocols ready ahead of outbreaks. We are the first to apply the Quality by Design (QbD) framework to enhance rapid-response RNA vaccine manufacturing against known and future viral pathogens. This QbD framework aims to support the development and consistent production of safe and efficacious RNA vaccines, integrating a novel qualitative methodology and a quantitative bioprocess model. The qualitative methodology identifies and assesses the direction, magnitude and shape of the impact of critical process parameters (CPPs) on critical quality attributes (CQAs). The mechanistic bioprocess model quantifies and maps the effect of four CPPs on the CQA of effective yield of RNA drug substance. Consequently, the first design space of an RNA vaccine synthesis bioreactor is obtained. The cost-yield optimization together with the probabilistic design space contribute towards automation of rapid-response, high-quality RNA vaccine production.
Donini R, Haslam S, Kontoravdi K, 2021, Glycoengineering Chinese hamster ovary cells: a short history, Biochemical Society Transactions, Vol: 49, Pages: 915-931, ISSN: 0300-5127
Biotherapeutic glycoproteins have revolutionised the field of pharmaceuticals, with new discoveries and continuous improvements underpinning the rapid growth of this industry. N-glycosylation is a critical quality attribute of biotherapeutic glycoproteins that influences the efficacy, half-life and immunogenicity of these drugs. This review will focus on the advances and future directions of remodelling N-glycosylation in Chinese hamster ovary (CHO) cells, which are the workhorse of recombinant biotherapeutic production, with particular emphasis on antibody products, using strategies such as cell line and protein backbone engineering.
Alhuthali S, Kotidis P, Kontoravdi K, 2021, Osmolality effects on CHO cell growth, cell volume and antibody productivity and glycosylation, International Journal of Molecular Sciences, Vol: 22, ISSN: 1422-0067
The addition of nutrients and accumulation of metabolites in a fed-batch culture of Chinese hamster ovary (CHO) cells leads to an increase in extracellular osmolality in late stage culture. Herein, we explore the effect of osmolality on CHO cell growth, specific monoclonal antibody (mAb) productivity and glycosylation achieved with the addition of NaCl or the supplementation of a commercial feed. Although both methods lead to an increase in specific antibody productivity, they have different effects on cell growth and antibody production. Osmolality modulation using NaCl up to 470 mOsm kg−1 had a consistently positive effect on specific antibody productivity and titre. The addition of the commercial feed achieved variable results: specific mAb productivity was increased, yet cell growth rate was significantly compromised at high osmolality values. As a result, Feed C addition to 410 mOsm kg−1 was the only condition that achieved a significantly higher mAb titre compared to the control. Additionally, Feed C supplementation resulted in a significant reduction in galactosylated antibody structures. Cell volume was found to be positively correlated to osmolality; however, osmolality alone could not account for observed changes in average cell diameter without considering cell cycle variations. These results help delineate the overall effect of osmolality on titre and highlight the potentially negative effect of overfeeding on cell growth.
Kis Z, Papathanasiou M, Kotidis P, et al., 2021, Stability modelling for biopharmaceutical process intermediates, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc, Pages: 200-225, ISBN: 978-1-945584-22-0
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