133 results found
Triantafyllou N, Sarkis M, Krassakopoulou A, et al., 2023, Uncertainty quantification for gene delivery methods: A roadmap for pDNA manufacturing from phase I clinical trials to commercialization., Biotechnol J
The fast-growing interest in cell and gene therapy (C>) products has led to a growing demand for the production of plasmid DNA (pDNA) and viral vectors for clinical and commercial use. Manufacturers, regulators, and suppliers need to develop strategies for establishing robust and agile supply chains in the otherwise empirical field of C>. A model-based methodology that has great potential to support the wider adoption of C> is presented, by ensuring efficient timelines, scalability, and cost-effectiveness in the production of key raw materials. Specifically, key process and economic parameters are identified for (1) the production of pDNA for the forward-looking scenario of non-viral-based Chimeric Antigen Receptor (CAR) T-cell therapies from clinical (200 doses) to commercial (40,000 doses) scale and (2) the commercial (40,000 doses) production of pDNA and lentiviral vectors for the current state-of-the-art viral vector-based CAR T-cell therapies. By applying a systematic global sensitivity analysis, we quantify uncertainty in the manufacturing process and apportion it to key process and economic parameters, highlighting cost drivers and limitations that steer decision-making. The results underline the cost-efficiency and operational flexibility of non-viral-based therapies in the overall C> supply chain, as well as the importance of economies-of-scale in the production of pDNA.
Sachio S, Kontoravdi C, Papathanasiou MM, 2023, A model-based approach towards accelerated process development: A case study on chromatography, Chemical Engineering Research and Design, Vol: 197, Pages: 800-820, ISSN: 0263-8762
Process development is typically associated with lengthy wet-lab experiments for the identification of good candidate setups and operating conditions. In this paper, we present the key features of a model-based approach for the identification and assessment of process design space (DSp), integrating the analysis of process performance and flexibility. The presented approach comprises three main steps: (1) model development & problem formulation, (2) DSp identification, and (3) DSp analysis. We demonstrate how such an approach can be used for the identification of acceptable operating spaces that enable the assessment of different operating points and quantification of process flexibility. The above steps are demonstrated on Protein A chromatographic purification of antibody-based therapeutics used in biopharmaceutical manufacturing.
Strain B, Morrissey R, Antonakoudis A, et al., 2023, How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?, Biotechnology and Bioengineering, Vol: 120, Pages: 2460-2478, 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.
del Val IJ, Kyriakopoulos S, Albrecht S, et al., 2023, CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability, BIOTECHNOLOGY AND BIOENGINEERING, ISSN: 0006-3592
Kontoravdi C, 2023, Editorial, Journal of Advanced Manufacturing and Processing, Vol: 5, ISSN: 2637-403X
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.
Monteiro M, Kontoravdi C, 2023, Hybrid dynamic model of monoclonal antibody production using CHO cells, Computer Aided Chemical Engineering, Pages: 375-380
Mammalian cells are used to produce up to 80% of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the main production host. Antibody production occurs in a train of bioreactors typically operated in fed-batch, i.e., semi-continuous, mode. Real-time monitoring of antibody concentration is costly and involves sampling and offline analytics. Current reactor models lack extrapolation power while being highly reliant on extensive amounts of data for parameterization. This work aims to leverage knowledge of the cell's metabolism and incorporate it in the production reactor model, thus creating a hybrid formulation with increased predictive capability and generality. This novel formulation can make predictions without requiring kinetic parameter estimations. The applicability of the proposed formulation is demonstrated with a soft sensor for antibody production.
Triantafyllou N, Shah N, Papathanasiou MM, et al., 2023, Combined Bayesian optimization and global sensitivity analysis for the optimization of simulation-based pharmaceutical processes, Computer Aided Chemical Engineering, Pages: 381-386
We propose an efficient framework that employs Bayesian optimization and global sensitivity analysis for the optimization of detailed pharmaceutical flowsheets. Global sensitivity analysis based on quasi-random sampling is utilized to reduce the dimensionality of the problem by identifying critical process and economic parameters that contribute significantly to the variability of Key Performance Indicators (KPIs) such as batch size and OpEx. Then, Bayesian optimization is performed in the previously identified critical input space based on gaussian process surrogate models and a number of different acquisition functions to find the optimal critical operating conditions that minimize the aforementioned KPIs. We apply this framework to the manufacture of plasmid DNA (pDNA), which is a critical raw material for advanced therapeutics, leading to a surge in demand for pDNA for clinical or commercial use. Optimized manufacturing recipes identified with the proposed framework are projected to achieve an up to 170% increase in the batch size and a 34.7% decrease in the OpEx per batch.
Yu L, del Rio Chanona EA, Kontoravdi C, 2023, Ensemble Kalman Filter for estimation of intracellular nucleotide sugars from extracellular metabolites in monoclonal antibodies, Computer Aided Chemical Engineering, Pages: 2661-2666
The emergence of Quality by Design (QbD) and Process Analytical Technology (PAT) paradigm supported by the FDA imposes a strong motivation for digital transformation in biopharmaceutical industry. The inherent complexity of bioprocess dynamics, batch-to-batch variability resulting from raw materials and process operations, as well as the need for accelerating product manufacturing, makes dynamic soft sensors such as Kalman Filters highly desirable for process development, monitoring, and control. In this work, we develop an Ensemble Kalman Filter framework in the context of monoclonal antibody bioprocessing, where the noise on physical sensors is mitigated for extracellular metabolite states by integrating the process’ dynamic mechanistic model and sensor measurements. More importantly, the framework accurately estimates the nucleotide sugar concentrations, an intracellular state of the cell that is not routinely measured in industry due to experimental complexity. The proposed EnKF soft sensor retrieves this knowledge through state inference, providing valuable insights for monitoring and control of key quality attributes such as glycan distribution.
Sachio S, Kontoravdi C, Papathanasiou MM, 2023, Embedding Operating Flexibility in Process Design, Computer Aided Chemical Engineering, Pages: 1999-2004
Inherently flexible processes are able to handle disturbances better than point-specific designed processes. However, there is a lack of systematic framework to embed operating flexibility in process design. In this work, we propose a model-based framework for integrated analysis of process flexibility and performance based on the identification and assessment of process design spaces. The proposed framework enables the identification of operating regions, quantification, within which the product and process meet the specifications. The operational flexibility and sensitivity of the process to design and operating parameters is also quantified. We demonstrate the capabilities of the framework on a Protein A chromatographic separation, used in biopharmaceutical manufacturing. We identify the feed flow rate as the most influential process parameter, while we quantify an acceptable range for the feed stream variability (concentration: 0.37 – 0.43 mg ml-1 and flowrate: 0.72 – 0.88 ml min-1) for a fixed process design.
Wang H, Kontoravdi C, del Rio Chanona EA, 2023, A Hybrid Modelling Framework for Dynamic Modelling of Bioprocesses, Computer Aided Chemical Engineering, Pages: 469-474
One of the main hurdles in the computer-aided design and optimization of industrial bioprocesses is the limited capability of models to accurately represent biosystems. On one hand, mechanistic models have drawbacks in terms of expressing all potential biomechanisms. On the other hand, data-driven models have limited extrapolation ability. Thus, hybrid models may represent the best of both worlds. However, it is challenging to build a hybrid model that can accurately balance its data-driven and first principles components, hence allowing for extrapolation ability and accuracy. In this work, we propose a framework for the development of hybrid models that includes a mechanistic backbone for its construction and judiciously adds the data-driven components. Statistical methods e.g., Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC) and Hannan Quinn Criterion (HQC), are employed to choose the statistically best hybrid model structure. The proposed framework is tested on a simplified microalgae cultivation case study and shows good prediction capabilities under different noise levels, especially by applying BIC for hybrid model selection.
Monteiro M, Fadda S, Kontoravdi K, 2023, Towards advanced bioprocess optimization: a multiscale modelling approach, Computational and Structural Biotechnology Journal, Vol: 21, Pages: 3639-3655, ISSN: 2001-0370
Mammalian cells produce up to 80% of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.
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.
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
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.
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
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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.