Glycan Residues Balance Analysis - GReBA: A novel model for the N-linked glycosylation of IgG produced by CHO cells

A hierarchical structure in the N-glycosylation process governs the N-glycosylation output: prolonged cultivation induces glycoenzymes expression variations that are reflected in the cellular N-glycome but not in the protein and site-specific glycoprofile of CHO cells

N-glycosylation is a central component in the modification of secretory proteins. One characteristic of this process is a heterogeneous output. The heterogeneity is the result of both structural constraints of the glycoprotein as well as the composition of the cellular glycosylation machinery. Empirical data addressing correlations between glycosylation output and glycosylation machinery composition are seldom due to the low abundance of glycoenzymes. We assessed how differences in the glycoenzyme expression affected the N-glycosylation output at a cellular as well as at a protein-specific level. Our results showed that cellular N-glycome changes could be correlated with the variation of glycoenzyme expression, whereas at the protein level differential responses to glycoenzymes alterations were observed. We therefore identified a hierarchical structure in the N-glycosylation process: the enzyme levels in this complex pathway determine its capacity (reflected in the N-glycome), while protein-specific parameters determine the glycosite-specificity. What emerges is a highly variable and adaptable protein modification system that represents a hallmark of eukaryotic cells.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Optimization of medium with perfusion microbioreactors for high density CHO cell cultures at very low renewal rate aided by design of experiments

A novel approach of design of experiment (DoE) is developed for the optimization of key substrates of the culture medium, amino acids, and sugars, by utilizing perfusion microbioreactors with 2 mL working volume, operated in high cell density continuous mode, to explore the design space. A mixture DoE based on a simplex-centroid is proposed to test multiple medium blends in parallel perfusion runs, where the amino acids concentrations are selected based on the culture behavior in presence of different amino acid mixtures, and using targeted specific consumption rates. An optimized medium is identified with models predicting the culture parameters and product quality attributes (G0 and G1 level N-glycans) as a function of the medium composition. It is then validated in runs performed in perfusion microbioreactor in comparison with stirred-tank bioreactors equipped with alternating tangential flow filtration (ATF) or with tangential flow filtration (TFF) for cell separation, showing overall a similar process performance and N-glycosylation profile of the produced antibody. These results demonstrate that the present development strategy generates a perfusion medium with optimized performance for stable Chinese hamster ovary (CHO) cell cultures operated with very high cell densities of 60 × 106 and 120 × 106  cells/mL and a low cell-specific perfusion rate of 17 pL/cell/day, which is among the lowest reported and is in line with the framework recently published by the industry.

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications

Much of the biopharmaceutical industry's success over the past 30 years has relied on products derived from Chinese Hamster Ovary (CHO) cell lines. During this time, improvements in mammalian cell cultures have come from cell line development and process optimization suited for large-scale fed-batch processes. Originally developed for high cell densities and sensitive products, perfusion processes have a long history. Driven by high volumetric titers and a small footprint, perfusion-based bioprocess research has regained an interest from academia and industry. The recent pandemic has further highlighted the need for such intensified biomanufacturing options. In this review, we outline the technical history of research in this field as it applies to biologics production in CHO cells. We demonstrate a number of emerging trends in the literature and corroborate these with underlying drivers in the commercial space. From these trends, we speculate that the future of perfusion bioprocesses is bright and that the fields of media optimization, continuous processing, and cell line engineering hold the greatest potential. Aligning in its continuous setup with the demands for Industry 4.0, perfusion biomanufacturing is likely to be a hot topic in the years to come.

On the Use of Surface Plasmon Resonance Biosensing to Understand IgG-FcγR Interactions

Surface plasmon resonance (SPR)-based optical biosensors offer real-time and label-free analysis of protein interactions, which has extensively contributed to the discovery and development of therapeutic monoclonal antibodies (mAbs). As the biopharmaceutical market for these biologics and their biosimilars is rapidly growing, the role of SPR biosensors in drug discovery and quality assessment is becoming increasingly prominent. One of the critical quality attributes of mAbs is the N-glycosylation of their Fc region. Other than providing stability to the antibody, the Fc N-glycosylation influences immunoglobulin G (IgG) interactions with the Fcγ receptors (FcγRs), modulating the immune response. Over the past two decades, several studies have relied on SPR-based assays to characterize the influence of N-glycosylation upon the IgG-FcγR interactions. While these studies have unveiled key information, many conclusions are still debated in the literature. These discrepancies can be, in part, attributed to the design of the reported SPR-based assays as well as the methodology applied to SPR data analysis. In fact, the SPR biosensor best practices have evolved over the years, and several biases have been pointed out in the development of experimental SPR protocols. In parallel, newly developed algorithms and data analysis methods now allow taking into consideration complex biomolecular kinetics. In this review, we detail the use of different SPR biosensing approaches for characterizing the IgG-FcγR interactions, highlighting their merit and inherent experimental complexity. Furthermore, we review the latest SPR-derived conclusions on the influence of the N-glycosylation upon the IgG-FcγR interactions and underline the differences and similarities across the literature. Finally, we explore new avenues taking advantage of novel computational analysis of SPR results as well as the latest strategies to control the glycoprofile of mAbs during production, which could lead to a better understanding and modelling of the IgG-FcγRs interactions.

Towards rational glyco-engineering in CHO: from data to predictive models

Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.

Modulation of high mannose levels in N-linked glycosylation through cell culture process conditions to increase antibody-dependent cell-mediated cytotoxicity activity for an antibody biosimilar

The regulatory approval of a biosimilar product is contingent on the favorable comparability of its safety and efficacy to that of the innovator product. As such, it is important to match the critical quality attributes of the biosimilar product to that of the innovator product. The N-glycosylation profile of a monoclonal antibody (mAb) can influence effector function activities such as antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity. In this study, we describe efforts to modulate the high-mannose (HM) levels of a biosimilar mAb produced in a Chinese hamster ovary cell fed-batch process. Because the HM level of the mAb was observed to impact ADCC activity, it was desirable to match it to the innovator mAb's levels. Several cell culture process related factors known to modulate the HM content of N-glycosylation were investigated, including osmolality, ammonium chloride (NH₄ Cl) addition, glutamine concentration, monensin addition, and the addition of alternate sugars and amino sugars to the feed medium. The process conditions evaluated varied in impact on HM levels, process performance and product quality. One condition, the addition of alternate sugars and amino sugars to feed medium, was identified as the preferred method for increasing HM levels with minimal disruptions to process performance or other product quality attributes. Interestingly, a secondary interaction between sugar and amino sugar supplemented feeds and osmolality was observed during process scale-up. These studies demonstrate sugar and amino sugar concentrations and osmolality are critical variables to evaluate to match HM content in biosimilar and their innovator mAbs.

Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures

Glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs). The glycan pattern can have a large impact on the immunological functions, serum half-life and stability. The medium components and cultivation parameters are known to potentially influence the glycosylation profile. Mathematical modelling provides a strategy for rational design and control of the upstream bioprocess. However, the kinetic models usually contain a very large number of unknown parameters, which limit their practical applications. In this article, we consider the metabolic network of N-linked glycosylation as a Bayesian network (BN) and calculate the fluxes of the glycosylation process as joint probability using the culture parameters as inputs. The modelling approach is validated with data of different Chinese hamster ovary cell cultures in pseudo perfusion, perfusion, and fed batch cultures, all showing very good predictive capacities. In cases where a large number of cultivation parameters is available, it is shown here that principal components analysis can efficiently be employed for a dimension reduction of the inputs compared to Pearson correlation analysis and feature importance by decision tree. The present study demonstrates that BN model can be a powerful tool in upstream process and medium development for glycoprotein productions.

Control of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE

The N-linked glycosylation pattern is an important quality attribute of therapeutic glycoproteins. It has been reported by our group and by others that different carbon sources, such as glucose, mannose and galactose, can differently impact the glycosylation profile of glycoproteins in mammalian cell culture. Acting on the sugar feeding is thus an attractive strategy to tune the glycan pattern. However, in case of feeding of more than one carbon source simultaneously, the cells give priority to the one with the highest uptake rate, which limits the usage of this tuning, e.g. the cells favor consuming glucose in comparison to galactose. We present here a new feeding strategy (named 'TAFE' for targeted feeding) for perfusion culture to adjust the concentrations of fed sugars influencing the glycosylation. The strategy consists in setting the sugar feeding such that the cells are forced to consume these substrates at a target cell specific consumption rate decided by the operator and taking into account the cell specific perfusion rate (CSPR). This strategy is applied in perfusion cultures of Chinese hamster ovary (CHO) cells, illustrated by ten different regimes of sugar feeding, including glucose, galactose and mannose. Applying the TAFE strategy, different glycan profiles were obtained using the different feeding regimes. Furthermore, we successfully forced the cells to consume higher proportions of non-glucose sugars, which have lower transport rates than glucose in presence of this latter, in a controlled way. In previous work, a mathematical model named Glycan Residues Balance Analysis (GReBA) was developed to model the glycosylation profile based on the fed carbon sources. The present data were applied to the GReBA to design a feeding regime targeting a given glycosylation profile. The ability of the model to achieve this objective was confirmed by a multi-round of leave-one-out cross-validation (LOOCV), leading to the conclusion that the GReBA model can be used to design the feeding regime of a perfusion cell culture to obtain a desired glycosylation profile.

Harnessing the potential of artificial neural networks for predicting protein glycosylation

Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.