A theoretical estimate for nucleotide sugar demand towards Chinese Hamster Ovary cellular glycosylation

Glycosylation greatly influences the safety and efficacy of many of the highest-selling recombinant therapeutic proteins (rTPs). In order to define optimal cell culture feeding strategies that control rTP glycosylation, it is necessary to know how nucleotide sugars (NSs) are consumed towards host cell and rTP glycosylation. Here, we present a theoretical framework that integrates the reported glycoproteome of CHO cells, the number of N-linked and O-GalNAc glycosylation sites on individual host cell proteins (HCPs), and the carbohydrate content of CHO glycosphingolipids to estimate the demand of NSs towards CHO cell glycosylation. We have identified the most abundant N-linked and O-GalNAc CHO glycoproteins, obtained the weighted frequency of N-linked and O-GalNAc glycosites across the CHO cell proteome, and have derived stoichiometric coefficients for NS consumption towards CHO cell glycosylation. By combining the obtained stoichiometric coefficients with previously reported data for specific growth and productivity of CHO cells, we observe that the demand of NSs towards glycosylation is significant and, thus, is required to better understand the burden of glycosylation on cellular metabolism. The estimated demand of NSs towards CHO cell glycosylation can be used to rationally design feeding strategies that ensure optimal and consistent rTP glycosylation

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

Host cell protein removal from biopharmaceutical preparations: toward the implementation of quality by design

Downstream processing of protein products of mammalian cell culture currently accounts for the largest fraction of the total production cost. A major challenge is the removal of host cell proteins, which are cell-derived impurities. Host cell proteins are potentially immunogenic and can compromise product integrity during processing and hold-up steps. There is an increasing body of evidence that the type of host cell proteins present in recombinant protein preparations is a function of cell culture conditions and handling of the harvest cell culture fluid. This, in turn, can affect the performance of downstream purification steps as certain species are difficult to remove and may require bespoke process solutions. Herein, we review recent research on the interplay between upstream process conditions, host cell protein composition and their downstream removal in antibody production processes, identifying opportunities for increasing process understanding and control. We further highlight advances in analytical and computational techniques that can enable the application of quality by design

Osmolality effects on CHO cell growth, cell volume and antibody productivity and glycosylation

The addition of nutrients and accumulation of metabolites in a fed-batch culture of Chinese hamster ovary (CHO) cells leads to an increase in extracellular osmolality in late stage culture. Herein, we explore the effect of osmolality on CHO cell growth, specific monoclonal antibody (mAb) productivity and glycosylation achieved with the addition of NaCl or the supplementation of a commercial feed. Although both methods lead to an increase in specific antibody productivity, they have different effects on cell growth and antibody production. Osmolality modulation using NaCl up to 470 mOsm kg−1 had a consistently positive effect on specific antibody productivity and titre. The addition of the commercial feed achieved variable results: specific mAb productivity was increased, yet cell growth rate was significantly compromised at high osmolality values. As a result, Feed C addition to 410 mOsm kg−1 was the only condition that achieved a significantly higher mAb titre compared to the control. Additionally, Feed C supplementation resulted in a significant reduction in galactosylated antibody structures. Cell volume was found to be positively correlated to osmolality; however, osmolality alone could not account for observed changes in average cell diameter without considering cell cycle variations. These results help delineate the overall effect of osmolality on titre and highlight the potentially negative effect of overfeeding on cell growth

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

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

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 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

Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK

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

Model-based optimization of antibody galactosylation in CHO cell culture

Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses

Quality by design modelling to support rapid RNA vaccine production against emerging infectious diseases

Rapid-response vaccine production platform technologies, including RNA vaccines, are being developed to combat viral epidemics and pandemics. A key enabler of rapid response is having quality-oriented disease-agnostic manufacturing protocols ready ahead of outbreaks. We are the first to apply the Quality by Design (QbD) framework to enhance rapid-response RNA vaccine manufacturing against known and future viral pathogens. This QbD framework aims to support the development and consistent production of safe and efficacious RNA vaccines, integrating a novel qualitative methodology and a quantitative bioprocess model. The qualitative methodology identifies and assesses the direction, magnitude and shape of the impact of critical process parameters (CPPs) on critical quality attributes (CQAs). The mechanistic bioprocess model quantifies and maps the effect of four CPPs on the CQA of effective yield of RNA drug substance. Consequently, the first design space of an RNA vaccine synthesis bioreactor is obtained. The cost-yield optimization together with the probabilistic design space contribute towards automation of rapid-response, high-quality RNA vaccine production