Computer-aided design space identification for screening of protein A affinity chromatography resins.

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.

Driving towards digital biomanufacturing by CHO genome-scale models.

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.

Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification.

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.

Quality by Design Framework Applied to GMMA Purification.

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.

Flux Balance Analysis of Mammalian Cell Systems.

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

High-Throughput Spectroscopic Analysis of mRNA Capping Level.

Eukaryotic mRNAs are characterized by terminal 5' cap structures and 3' polyadenylation sites, which are essential for posttranscriptional processing, translation initiation, and stability. Here, we describe a novel biosensor method designed to detect the presence of both cap structures and polyadenylation sites on mRNA molecules. This novel biosensor is sensitive to mRNA degradation and can quantitatively determine capping levels of mRNA molecules within a mixture of capped and uncapped mRNA molecules. The biosensor displays a constant dynamic range between 254 nt and 6507 nt with reproducible sensitivity to increases in capping level of at least 20% and a limit of detection of 2.4 pmol of mRNA. Overall, the biosensor can provide key information about mRNA quality before mammalian cell transfection.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

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.

Strategic Planning of a Joint SARS-CoV-2 and Influenza Vaccination Campaign in the UK.

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.

Immobilized enzyme cascade for targeted glycosylation.

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 ß-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.

Uncertainty quantification for gene delivery methods: A roadmap for pDNA manufacturing from phase I clinical trials to commercialization.

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.

Towards advanced bioprocess optimization: A multiscale modelling approach.

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.

CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability.

Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize 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 difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the 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 robust, nonlinear optimization 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 will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.

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.

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

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 13 C 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.

CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells.

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.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

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.

GMMA as an Alternative Carrier for a Glycoconjugate Vaccine against Group A Streptococcus.

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.

Rapid Antibody Glycoengineering in Chinese Hamster Ovary Cells.

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, the time efficiencies offered by this protocol may assist investigations into new gene targets.

Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective.

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.

Quality by Design for enabling RNA platform production processes.

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.