Publications
143 results found
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.
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.
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.
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.
Kotidis P, Marbiah M, Donini R, et al., 2022, Rapid Antibody Glycoengineering in CHO Cells Via RNA Interference and CGE-LIF <i>N</i>-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
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, 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
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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
Biologics represent the fastest growing sector of the pharmaceutical industry, yet their manufacture lags significantly behind that of small molecule drugs. This paper discusses the main product-related and process-related challenges during the development and production of therapeutic proteins, with particular focus on product heterogeneity and process monitoring and analytics. Emphasis is placed on novel contributions from the field of computational research that aim to enable the application of model-based process control strategies or are working towards the development of a digital twin of bioprocesses. Lastly, we review promising developments in the paradigm shift from batch to continuous processing.
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