102 results found
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, 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, 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.
Makrydaki E, Kotidis P, Polizzi KM, et al., 2021, Hitting the sweet spot with capillary electrophoresis: advances in N-glycomics and glycoproteomics., Curr Opin Biotechnol, Vol: 71, Pages: 182-190
N-glycosylation is of paramount importance for understanding the mechanisms of various human diseases and ensuring the safety and efficacy of biotherapeutics. Traditional glycan analysis techniques include LC-based separations and MALDI-TOF-MS identification. However, the current state-of-the-art methods lack throughput and structural information, include laborious sample preparation procedures and require large sample volumes. Capillary electrophoresis (CE) has long been used for the screening and reliable quantitation of glycans, but its applications have been limited. Because of its speed, sensitivity and complementarity with standard glycan analysis techniques, CE is currently emerging as one of the most versatile and adaptable methods for glycan analysis in both academia and industry. Herein, we review the latest advancements in CE-based applications to glycomics and glycoproteomics within both the biopharmaceutical and clinical sectors.
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.
Antonakoudis A, Kis Z, Kontoravdi K, et al., 2021, Accelerating product and process development through a model centric approach, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc., Pages: 285-338, ISBN: 978-1-945584-22-0
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
Kis Z, Kontoravdi C, Shattock R, et al., 2021, Correction: Kis, Z. et al. Resources, Production Scales and Time Required for Producing RNA Vaccines for the Global Pandemic Demand. Vaccines 2021, 9, 3, Vaccines, Vol: 9, Pages: 1-1, ISSN: 2076-393X
Heide C, Buldum G, Moya-Ramirez I, et al., 2021, Design, development and optimisation of a functional mammalian cell-free protein synthesis platform, Frontiers in Bioengineering and Biotechnology, Vol: 8, ISSN: 2296-4185
In this paper, we describe the stepwise development of a cell-free protein synthesis (CFPS) platform derived from cultured Chinese hamster ovary (CHO) cells. We provide a retrospective summary of the design challenges we faced, and the optimized methods developed for the cultivation of cells and the preparation of translationally active lysates. To overcome low yields, we developed procedures to supplement two accessory proteins, GADD34 and K3L, into the reaction to prevent deactivation of the translational machinery by phosphorylation. We compared different strategies for implementing these accessory proteins including two variants of the GADD34 protein to understand the potential trade-offs between yield and ease of implementation. Addition of the accessory proteins increased yield of turbo Green Fluorescent Protein (tGFP) by up to 100-fold depending on which workflow was used. Using our optimized protocols as a guideline, users can successfully develop their own functional CHO CFPS system, allowing for broader application of mammalian CFPS.
Kis Z, Kontoravdi C, Shattock R, et al., 2020, Resources, production scales and time required for producing RNA vaccines for the global pandemic demand., Vaccines (Basel), Vol: 9, Pages: 1-14, ISSN: 2076-393X
To overcome pandemics, such as COVID-19, vaccines are urgently needed at very high volumes. Here we assess the techno-economic feasibility of producing RNA vaccines for the demand associated with a global vaccination campaign. Production process performance is assessed for three messenger RNA (mRNA) and one self-amplifying RNA (saRNA) vaccines, all currently under clinical development, as well as for a hypothetical next-generation saRNA vaccine. The impact of key process design and operation uncertainties on the performance of the production process was assessed. The RNA vaccine drug substance (DS) production rates, volumes and costs are mostly impacted by the RNA amount per vaccine dose and to a lesser extent by the scale and titre in the production process. The resources, production scale and speed required to meet global demand vary substantially in function of the RNA amount per dose. For lower dose saRNA vaccines, global demand can be met using a production process at a scale of below 10 L bioreactor working volume. Consequently, these small-scale processes require a low amount of resources to set up and operate. RNA DS production can be faster than fill-to-finish into multidose vials; hence the latter may constitute a bottleneck.
Moya-Ramirez I, Bouton C, Kontoravdi C, et al., 2020, High resolution biosensor to test the capping level and integrity of mRNAs, Nucleic Acids Research, Vol: 48, Pages: 1-11, ISSN: 0305-1048
5 Cap structures are ubiquitous on eukaryotic mRNAs, essential for post-transcriptional processing,translation initiation and stability. Here we describea biosensor designed to detect the presence of capstructures on mRNAs that is also sensitive to mRNAdegradation, so uncapped or degraded mRNAs canbe detected in a single step. The biosensor is basedon a chimeric protein that combines the recognitionand transduction roles in a single molecule. The mainfeature of this sensor is its simplicity, enabling semiquantitative analyses of capping levels with minimalinstrumentation. The biosensor was demonstratedto detect the capping level on several in vitro transcribed mRNAs. Its sensitivity and dynamic rangeremained constant with RNAs ranging in size from250 nt to approximately 2700 nt and the biosensorwas able to detect variations in the capping level inincrements of at least 20%, with a limit of detection of2.4 pmol. Remarkably, it also can be applied to morecomplex analytes, such mRNA vaccines and mRNAstranscribed in vivo. This biosensor is an innovativeexample of a technology able to detect analyticallychallenging structures such as mRNA caps. It couldfind application in a variety of scenarios, from qualityanalysis of mRNA-based products such as vaccinesto optimization of in vitro capping reactions.
Antonakoudis A, Barbosa R, Kotidis P, et al., 2020, The era of big data: Genome-scale modelling meets machine learning, Computational and Structural Biotechnology Journal, Vol: 18, Pages: 3287-3300, ISSN: 2001-0370
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
Kis Z, Kontoravdi K, Dey AK, et al., 2020, Rapid development and deployment of high-volumevaccines for pandemic response, Journal of Advanced Manufacturing and Processing, Vol: 2, Pages: 1-10, ISSN: 2637-403X
Overcoming pandemics, such as the current Covid‐19 outbreak, requires the manufacture of several billion doses of vaccines within months. This is an extremely challenging task given the constraints in small‐scale manufacturing for clinical trials, clinical testing timelines involving multiple phases and large‐scale drug substance and drug product manufacturing. To tackle these challenges, regulatory processes are fast‐tracked, and rapid‐response manufacturing platform technologies are used. Here, we evaluate the current progress, challenges ahead and potential solutions for providing vaccines for pandemic response at an unprecedented scale and rate. Emerging rapid‐response vaccine platform technologies, especially RNA platforms, offer a high productivity estimated at over 1 billion doses per year with a small manufacturing footprint and low capital cost facilities. The self‐amplifying RNA (saRNA) drug product cost is estimated at below 1 USD/dose. These manufacturing processes and facilities can be decentralized to facilitate production, distribution, but also raw material supply. The RNA platform technology can be complemented by an a priori Quality by Design analysis aided by computational modeling in order to assure product quality and further speed up the regulatory approval processes when these platforms are used for epidemic or pandemic response in the future.
Kotidis P, Kontoravdi K, 2020, Harnessing the potential of artificial neural networks for predicting protein glycosylation, Metabolic Engineering Communications, Vol: 10, ISSN: 2214-0301
Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
Thaore V, Tsourapas D, Shah N, et al., 2020, Techno-Economic Assessment of Cell-Free Synthesis of Monoclonal Antibodies Using CHO Cell Extracts, PROCESSES, Vol: 8
Papathanasiou MM, Kontoravdi C, 2020, Engineering challenges in therapeutic protein product and process design, CURRENT OPINION IN CHEMICAL ENGINEERING, Vol: 27, Pages: 81-88, ISSN: 2211-3398
Casula E, Traversari G, Fadda S, et al., 2019, Modelling the osmotic behaviour of human mesenchymal stem cells, BIOCHEMICAL ENGINEERING JOURNAL, Vol: 151, ISSN: 1369-703X
Kotidis P, Jedrzejewski P, Sou SN, et al., 2019, Model-based optimization of antibody galactosylation in CHO cell culture, Biotechnology and Bioengineering, Vol: 116, Pages: 1612-1626, ISSN: 0006-3592
Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses.
Moya-Ramirez I, Kontoravdi K, Polizzi K, 2019, Low-cost and user-friendly biosensor to test the integrity of mRNA molecules suitable for field applications, Biosensors and Bioelectronics, Vol: 137, Pages: 199-206, ISSN: 0956-5663
The use of mRNA in biotechnology has expanded with novel applications such as vaccines and therapeutic mRNA delivery recently demonstrated. For mRNA to be used in patients, quality control assays will need to be routinely established. Currently, there is a gap between the highly sophisticated RNA integrity tests available and broader application of mRNA-based products by non-specialist users, e.g. in mass vaccination campaigns. Therefore, the aim of this work was to develop a low-cost biosensor able to test the integrity of a mRNA molecule with low technological requirements and easy end-user application. The biosensor is based on a bi-functional fusion protein, composed by the λN peptide that recognizes its cognate aptamer encoded on the 5’ end of the RNA under study and β-lactamase, which is able to produce a colorimetric response through a simple test. We propose two different mechanisms for signal processing adapted to two levels of technological sophistication, one based on spectrophotometric measurements and other on visual inspection. We show that the proposed λN-βLac chimeric protein specifically targets its cognate RNA aptamer, boxB, using both gel shift and biolayer interferometry assays. More importantly, the results presented confirm the biosensor performs reliably, with a wide dynamic range and a proportional response at different percentages of full-length RNA, even when gene-sized mRNAs were used. Thus, the features of the proposed biosensor would allow to end-users of products such as mRNA vaccines to test the integrity of the product before its application in a low-cost fashion, enabling a more reliable application of these products.
Kis Z, Shattock R, Shah N, et al., 2019, Correction: Emerging technologies for low‐cost, rapid vaccine manufacture, Biotechnology Journal, Vol: 14, Pages: 1-2, ISSN: 1860-6768
Kis Z, Papathanasiou M, CalvoSerrano R, et al., 2019, A model‐based quantification of the impact of new manufacturing technologies on developing country vaccine supply chain performance: A Kenyan case study, Journal of Advanced Manufacturing and Processing, Vol: 1, ISSN: 2637-403X
Kotidis P, Demis P, Goey C, et al., 2019, Constrained global sensitivity analysis for bioprocess design space identification, Computers and Chemical Engineering, Vol: 125, Pages: 558-568, ISSN: 1873-4375
The manufacture of protein-based therapeutics presents unique challenges due to limited control over the biotic phase. This typically gives rise to a wide range of protein structures of varying safety and in vivo efficacy. Herein we propose a computational methodology, enabled by the application of constrained Global Sensitivity Analysis, for efficiently exploring the operatingrange of process inputs in silico and identifying a design space that meets output constraints. The methodology was applied to an antibody-producing Chinese hamster ovary (CHO) cell culture system: we explored >8000 feeding strategies to identify a subset of manufacturing conditions that meet constraints on antibody titre and glycan distribution as an attribute of product quality. Our computational findings were then verified experimentally, confirming the applicability of this approach to a challenging production system. We envisage that this methodology can significantly expedite bioprocess development and increase operational flexibility.
Goey CH, Bell D, Kontoravdi C, 2019, CHO cell cultures in shake flasks and bioreactors present different host cell protein profiles in the supernatant, Biochemical Engineering Journal, Vol: 144, Pages: 185-192, ISSN: 1369-703X
Several studies on the impact of cell culture parameters on the profile of host cell protein (HCP) impurities have been carried out in shake flasks. Herein, we explore how transferable the findings and conclusions of such investigations are to lab-scale bioreactors. Experiments were performed in both systems in fed-batch mode under physiological temperature and with a shift to mild hypothermia and the impact on key upstream performance indicators was quantified. Under both temperatures, bioreactors produced a richer HCP pool despite the overall concentration being similar at both scales and temperatures. The number of different HCPs detected in bioreactor supernatants was four times higher than that in flasks under physiological temperature and more than eight times higher under mild hypothermia. The origin of HCPs was also altered from mostly naturally secreted proteins in flasks to mainly intracellular proteins in bioreactors at the lower temperature. Although the number of species correlated with apoptotic cell density in bioreactors, this was not the case in flasks. Even though the level of HCP impurities and mAb/HCP concentration ratio were similar under all four conditions with an average of approximately 330 μg HCP/mL culture and 0.3 mg HCP/mg IgG4, respectively, the fact that culture method significantly affects the number of species present in the supernatant can have implications for downstream processing steps.
Kotidis P, Kontoravdi C, 2019, Strategic framework for parameterization of cell culture models, Processes, Vol: 7, ISSN: 2227-9717
Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development.
Kis Z, Shattock R, Shah N, et al., 2019, Emerging technologies for low-cost, rapid vaccine manufacture, Biotechnology Journal, Vol: 14, ISSN: 1860-6768
To stop the spread of future epidemics and meet infant vaccination demands in low‐ and middle‐income countries, flexible, rapid and low‐cost vaccine development and manufacturing technologies are required. Vaccine development platform technologies that can produce a wide range of vaccines are emerging, including: a) humanized, high‐yield yeast recombinant protein vaccines; b) insect cell‐baculovirus ADDomer vaccines; c) Generalized Modules for Membrane Antigens (GMMA) vaccines; d) RNA vaccines. Herein, existing and future platforms are assessed in terms of addressing challenges of scale, cost, and responsiveness. To assess the risk and feasibility of the four emerging platforms, the following six metrics are applied: 1) technology readiness; 2) technological complexity; 3) ease of scale‐up; 4) flexibility for the manufacturing of a wide range of vaccines; 5) thermostability of the vaccine product at tropical ambient temperatures; and 6) speed of response from threat identification to vaccine deployment. The assessment indicated that technologies in the order of increasing feasibility and decreasing risk are the yeast platform, ADDomer platform, followed by RNA and GMMA platforms. The comparative strengths and weaknesses of each technology are discussed in detail, illustrating the associated development and manufacturing needs and priorities.
Heide C, Buldum G, Ces P, et al., 2019, Boosting the activity of CHO-based cell-free protein synthesis factories for high-throughput in vitro production of functional antibodies
Kontoravdi K, Jimenez Del Val I, 2018, Computational tools for predicting and controlling the glycosylation of biopharmaceuticals, Current Opinion in Chemical Engineering, Vol: 22, Pages: 89-97, ISSN: 2211-3398
Glycosylation is a critical quality attribute of biopharmaceuticals because it is a major source of structural variability that influences the in vivo safety and therapeutic efficacy of these products. Manufacturing process conditions are known to influence the monosaccharide composition and relative abundance of the complex carbohydrates bound to therapeutic proteins. Multiple computational tools have been developed to describe these process/product quality relationships in order to control and optimise the glycosylation of biopharmaceuticals. This review will provide a summary highlighting the strengths and weaknesses of each modelling strategy in their application towards cellular glycoengineering or bioprocess design and control. To conclude, potential unified glycosylation modelling approaches for biopharmaceutical quality assurance are proposed.
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