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ABSTRACT Fed-batch Chinese hamster ovary (CHO) cell culture is the most commonly used process for IgG production in the biopharmaceutical industry. Amino acid and glucose consumption, cell growth, metabolism, antibody titer, and N-glycosylation patterns are always the major concerns during upstream process optimization, especially media optimization. Gaining knowledge on their interrelations could provide insight for obtaining higher immunoglobulin G (IgG) titer and better controlling glycosylation-related product quality. In this work, different fed-batch processes with two chemically defined proprietary media and feeds were studied using two IgG-producing cell lines. Our results indicate that the balance of glucose and amino acid concentration in the culture is important for cell growth, IgG titer and N-glycosylation. Accordingly, the ideal fate of glucose and amino acids in the culture could be mainly towards energy and recombinant product, respectively. Accumulation of by-products such as NH4+ and lactate as a consequence of unbalanced nutrient supply to cell activities inhibits cell growth. The levels of Leu and Arg in the culture, which relate to cell growth and IgG productivity, need to be well controlled. Amino acids with the highest consumption rates correlate with the most abundant amino acids present in the produced IgG, and thus require sufficient availability during culture. Case-by-case analysis is necessary for understanding the effect of media and process optimization on glycosylation. We found that in certain cases the presence of Man5 glycan can be linked to limitation of UDP-GlcNAc biosynthesis as a result of insufficient extracellular Gln. However, under different culture conditions, high Man5 levels can also result from low α-1,3-mannosyl-glycoprotein 2-β- N-acetylglucosaminyltransferase (GnTI) and UDP-GlcNAc transporter activities, which may be attributed to high level of [ABSTRACT FROM AUTHOR]

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 (CQAs), 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. QbD implementation will help the RNA technology reach its full potential and will be central to the development, pre-qualification, and regulatory approval of rapid response, disease-agnostic RNA platform production processes. Pfizer-BioNTech's (BNT162b2) and Moderna's (mRNA-1713) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the first RNA-based biologics to be approved for human use and mass produced. RNA technology holds great promise beyond infectious disease prophylaxis, from cancer and gene therapy to treatments against cardiovascular and autoimmune diseases. Current production processes have been developed and scaled up at an unprecedented speed, mostly under a Quality by Testing paradigm. Recent research has highlighted a strong link between product and process development. The application of advanced analytical and modeling technologies could rapidly reshape and digitize manufacturing processes. [ABSTRACT FROM AUTHOR]

The article presents a study which discusses development of quantitative and mechanistic model for monoclonal antibody glycosylation as a function of nutrient availability during cell culture. Topics discussed include role of monoclonal antibodies (mAbs) in drug manufacture, need of quality by design (QbD) principles in process development, and mathematical model consisting of modular elements such as cell culture dynamics and nucleotide sugar (NS) metabolism.

An abstract of the article "Quantification of intracellular nucleotide sugars and formulation of a mathematical model for prediction of their metabolism," by Judit M. Nagy and colleagues is presented.

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. [ABSTRACT FROM AUTHOR]

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization. • Multi-omics analysis permits synergistic evaluation of culture performance. • Overall phenotypic changes were traced back to phase-specific metabolic reprogramming. • Reprogramming nitrogen usage and metabolism was associated with increased productivity. • Amino acid usage and channeling towards desired products limited productivity. [ABSTRACT FROM AUTHOR]

The reliability and methodology of genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells have advanced. CHO-GEMs have aided in cell line and process development, thus impacting on biomanufacturing efficiency. An integrative model structure can incorporate multiple layers and capture condition-specific cell regulation. Integration of CHO-GEMs with artificial intelligence (AI) and advanced algorithms will enable autonomous bioreactor management for digital biomanufacturing. Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms. [ABSTRACT FROM AUTHOR]

• Model-based frameworks for accelerated process development. • Computer-aided resin screening. • Machine learning enhanced dataset generation. • Consideration of process flexibility in resin evaluation. The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date. Examining performance alongside flexibility is crucial for the design of processes that can handle disturbances arising from the feed stream. In this work, we present a model-based approach for the identification of design spaces, enhanced by machine learning. We demonstrate its capabilities on the design of a Protein A chromatography unit, screening five industrially relevant resins. The computational results favourably compare to experimental data and a resin performance comparison is presented. An improvement on the computational time by a factor of 300,000 is achieved using the machine learning aided methodology. This allowed for the identification of 5,120 different design spaces in only 19 h. [ABSTRACT FROM AUTHOR]

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor. • Hybrid framework improves bioprocess model predictive capabilities. • ML-modulated stoichiometric model predicts accurate instantaneous fluxes. • Prediction accuracy of cell state shifts and resource distribution affects concentration profiles. [ABSTRACT FROM AUTHOR]

Highlights • Comparison of flask and bioreactor cultures at 37 °C and 32 °C. • All four conditions produced similar HCP concentration and mAb/HCP ratio. • Bioreactor cultures had significantly higher number of HCP species than flasks. • HCP variety correlated with apoptotic cell density in bioreactors but not flasks. Abstract 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. [ABSTRACT FROM AUTHOR]

Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endoglucanase, exoglucanase, and β-glucosidase) upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model’s predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation. [ABSTRACT FROM AUTHOR]

• Bayesian optimization for sequential-modular flowsheet optimization. • Global sensitivity analysis as a dimensionality reduction technique. • Simulation-based and surrogate-based Bayesian optimization. • Two industrially relevant case studies for plasmid DNA and dimethyl ether production. This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective. [ABSTRACT FROM AUTHOR]

CE–MS glycoproteomics and glycomics. In intact methods, the glycans are identified while attached to the entire protein backbone, exposing the glycosylation profile and providing information on the macro-heterogeneity and site occupancy. In middle-up and bottom-up analysis, the protein is digested into sububits or smaller peptide fragments, revealing specific glycoforms and elucidating micro-heterogeneity in a site-specific approach. In released glycans techniques, the glycans are completely released from the protein molecule through chemical or enzymatic means. Unlike the middle-up and bottom-up techniques, released glycans do not offer site-specific information, but can achieve excellent levels of sensitivity in glycan microheterogeneity identification. [Display omitted] 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. [ABSTRACT FROM AUTHOR]

Image, graphical abstract • Constrained global sensitivity analysis proposed for design space identification. • cGSA explores range of process inputs in silico subject to output constraints. • Applied to model of antibody-producing Chinese hamster ovary (CHO) cell culture system. • Findings verified experimentally for antibody titre and glycosylation as outputs. 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 operating range 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. [ABSTRACT FROM AUTHOR]

• Stoichiometric model accurately describes CHO cell metabolism. • Artificial neural network used to model secreted antibody glycosylation. • Hybrid framework links extracellular culture conditions to recombinant protein quality. • Framework can be updated in-process with commonly monitored process variables. • Sets basis for bioprocess control. 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. [ABSTRACT FROM AUTHOR]

• Development of a hybrid model for CHO cell culture using in silico training datasets • Model development focuses on antibody product synthesis and glycosylation • Glycosylation is one of main quality indicators for therapeutic antibodies • Hybrid model used as basis for the design of dynamic optimisation studies • Hybrid model used for reactive scheduling studies that assure product quality 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. [ABSTRACT FROM AUTHOR]

• Non-perfect osmometer behavior of hMSCs from UCB described by a novel model. • A coupling of cell osmosis with mechanics and SAR is considered. • Mechanosensitive ion channels open when cell membrane stretches. • Comparison with data provided: fitting and validation through prediction. In this work, a novel mathematical model for the description of the osmotic behavior during the cryopreservation of human Mesenchymal Stem Cells (hMSCs) from Umbilical Cord Blood (UCB) is proposed. In cryopreservation, the two-parameter formalism of perfect osmometer behavior is typically adopted and preferred due to its simplicity: cell volume osmotic excursions are described as due only to the passive trans-membrane transport of water and permeant solutes such as cryoprotectant agents (CPAs); intracellular solutes, responsible of the isotonic osmolality, are assumed to be impermeant. The application of the two-parameter model fails to capture the osmotic response of hMSCs whenever a swelling phase is involved, as demonstrated by the authors. To overcome this limitation, the imperfect osmometer behavior of hMSCs is successfully modelled herein by improving the two-parameter formalism through the coupling of osmosis with cell mechanics and cell membrane Surface Area Regulation (SAR): now the transmembrane permeation of solutes (ions/salt) during the swelling phase through the temporary opening of mechanosensitive channels is allowed. This way cells can reach an equilibrium volume different from the initial isotonic one, when isotonic conditions are re-established after contact with impermeant or permeant solutes, such as sucrose or dimethyl-sulfoxide (DMSO), respectively. The sequential best-fit procedure adopted to adjust model parameters is reported herein along with model validation through full predictions. [ABSTRACT FROM AUTHOR]

Degradation tags, otherwise known as degrons, are portable sequences that can be used to alter protein stability. Here, we report that degron-tagged proteins compete for cellular degradation resources in engineered mammalian cells leading to coupling of the degradation rates of otherwise independently expressed proteins when constitutively targeted human degrons are adopted. We show the effect of this competition to be dependent on the context of the degrons. By considering different proteins, degron position and cellular hosts, we highlight how the impact of the degron on both degradation strength and resource coupling changes, with identification of orthogonal combinations. By adopting inducible bacterial and plant degrons we also highlight how controlled uncoupling of synthetic construct degradation from the native machinery can be achieved. We then build a genomically integrated capacity monitor tagged with different degrons and confirm resource competition between genomic and transiently expressed DNA constructs. This work expands the characterisation of resource competition in engineered mammalian cells to protein degradation also including integrated systems, providing a framework for the optimisation of heterologous expression systems to advance applications in fundamental and applied biological research., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings., (© 2024. The Author(s).)

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s).)

Glycosylation is a critical post-translational protein modification that affects folding, half-life and functionality. Glycosylation is a non-templated and heterogeneous process because of the promiscuity of the enzymes involved. We describe a platform for sequential glycosylation reactions for tailored sugar structures (SUGAR-TARGET) that allows bespoke, controlled N-linked glycosylation in vitro enabled by immobilized enzymes produced with a one-step immobilization/purification method. We reconstruct a reaction cascade mimicking a glycosylation pathway where promiscuity naturally exists to humanize a range of proteins derived from different cellular systems, yielding near-homogeneous glycoforms. Immobilized β-1,4-galactosyltransferase is used to enhance the galactosylation profile of three IgGs, yielding 80.2-96.3% terminal galactosylation. Enzyme recycling is demonstrated for a reaction time greater than 80 h. The platform is easy to implement, modular and reusable and can therefore produce homogeneous glycan structures derived from various hosts for functional and clinical evaluation., (© 2024. The Author(s).)

Effective management of chronic diseases and cancer can greatly benefit from disease-specific biomarkers that enable informative screening and timely diagnosis. IgG N-glycans found in human plasma have the potential to be minimally invasive disease-specific biomarkers for all stages of disease development due to their plasticity in response to various genetic and environmental stimuli. Data analysis and machine learning (ML) approaches can assist in harnessing the potential of IgG glycomics towards biomarker discovery and the development of reliable predictive tools for disease screening. This study proposes an ML-based N-glycomic analysis framework that can be employed to build, optimise, and evaluate multiple ML pipelines to stratify patients based on disease risk in an interpretable manner. To design and test this framework, a published colorectal cancer (CRC) dataset from the Study of Colorectal Cancer in Scotland (SOCCS) cohort (1999-2006) was used. In particular, among the different pipelines tested, an XGBoost-based ML pipeline, which was tuned using multi-objective optimisation, calibrated using an inductive Venn-Abers predictor (IVAP), and evaluated via a nested cross-validation (NCV) scheme, achieved a mean area under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.771 when classifying between age-, and sex-matched healthy controls and CRC patients. This performance suggests the potential of using the relative abundance of IgG N-glycans to define populations at elevated CRC risk who merit investigation or surveillance. Finally, the IgG N-glycans that highly impact CRC classification decisions were identified using a global model-agnostic interpretability technique, namely Accumulated Local Effects (ALE). We envision that open-source computational frameworks, such as the one presented herein, will be useful in supporting the translation of glycan-based biomarkers into clinical applications., Competing Interests: Gordan Lauc is the founder and CEO of Genos Ltd. Frano Vučković is employee of Genos Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

In recent years, Generalized Modules for Membrane Antigens (GMMA) have received increased attention as an innovative vaccine platform against bacterial pathogens, particularly attractive for low- and middle-income countries because of manufacturing simplicity. The assessment of critical quality attributes (CQAs), product-process interactions, identification of appropriate in process analytical methods, and process modeling is part of a robust quality by design (QbD) framework to support further development and control of manufacturing processes. QbD implementation in the context of the GMMA platform will ensure robust manufacturing of batches with desired characteristics, facilitating technical transfer to local manufacturers, regulatory approval, and commercialization of vaccines based on this technology. Here, we summarize the methodology suggested, applied to a first step of GMMA manufacturing process., (© 2024. The Author(s).)

The simultaneous administration of SARS-CoV-2 and influenza vaccines is being carried out for the first time in the UK and around the globe in order to mitigate the health, economic, and societal impacts of these respiratory tract diseases. However, a systematic approach for planning the vaccine distribution and administration aspects of the vaccination campaigns would be beneficial. This work develops a novel multi-product mixed-integer linear programming (MILP) vaccine supply chain model that can be used to plan and optimise the simultaneous distribution and administration of SARS-CoV-2 and influenza vaccines. The outcomes from this study reveal that the total budget required to successfully accomplish the SARS-CoV-2 and influenza vaccination campaigns is equivalent to USD 7.29 billion, of which the procurement costs of SARS-CoV-2 and influenza vaccines correspond to USD 2.1 billion and USD 0.83 billion, respectively. The logistics cost is equivalent to USD 3.45 billion, and the costs of vaccinating individuals, quality control checks, and vaccine shipper and dry ice correspond to USD 1.66, 0.066, and 0.014, respectively. The analysis of the results shows that the choice of rolling out the SARS-CoV-2 vaccine during the vaccination campaign can have a significant impact not only on the total vaccination cost but also on vaccine wastage rate.