An investigation of nucleotide sugar dynamics under the galactose supplementation in CHO cell culture

Temporal Galactose-Manganese Feeding in Fed-Batch and Perfusion Bioreactors Modulates UDP-Galactose Pools for Enhanced mAb Glycosylation Homogeneity

Monoclonal antibodies (mAbs) represent a majority of biotherapeutics in the market today. These glycoproteins undergo posttranslational modifications, such as N-linked glycosylation, that influence the structural & functional characteristics of the antibody. Glycosylation is a heterogenous posttranslational modification that may influence therapeutic glycoprotein stability and clinical efficacy, which is why it is often considered a critical quality attribute (CQA) of the mAb product. While much is known about the glycosylation pathways of Chinese Hamster Ovary (CHO) cells and how cell culture chemical modifiers may influence the N-glycosylation profile of the final product, this knowledge is often based on the final cumulative glycan profile at the end of the batch process. Building a temporal understanding of N-glycosylation and how mAb glycoform composition responds to real-time changes in the biomanufacturing process will help build integrated process models that may allow for glycosylation control to produce a more homogenous product. Here, we look at the effect of specific nutrient feed media additives (e.g., galactose, manganese) and feeding times on the N-glycosylation pathway to modulate N-glycosylation of a Herceptin biosimilar mAb (i.e., Trastuzumab). We deploy the N-GLYcanyzer process analytical technology (PAT) to monitor glycoforms in near real-time for bench-scale bioprocesses operated in both fed-batch and perfusion modes to build an understanding of how temporal changes in mAb N-glycosylation are dependent on specific media additives. We find that Trastuzumab terminal galactosylation is sensitive to media feeding times and intracellular nucleotide sugar pools. Temporal analysis reveals an increased desirable production of single and double galactose-occupied glycoforms over time under glucose-starved fed-batch cultures. Comparable galactosylation profiles were also observed between fed-batch (nutrient-limited) and perfusion (non-nutrient-limited) bioprocess conditions. In summary, our results demonstrate the utility of real-time monitoring of mAb glycoforms and feeding critical cell culture nutrients under fed-batch and perfusion bioprocessing conditions to produce higher-quality biologics.

A Novel, Site-Specific N-Linked Glycosylation Model Provides Mechanistic Insights Into the Process-Condition Dependent Distinct Fab and Fc Glycosylation of an IgG1 Monoclonal Antibody Produced by CHO VRC01 Cells

The CHO VRC01 cell line produces an anti-HIV IgG1 monoclonal antibody containing N-linked glycans on both the Fab (variable) and Fc (constant) regions. Site-specific glycan analysis was used to measure the complex effects of cell culture process conditions on Fab and Fc glycosylation. Experimental data revealed major differences in glycan fractions across the two sites. Bioreactor pH was found to influence fucosylation, galactosylation, and sialylation in the Fab region and galactosylation in the Fc region. To understand the complex effects of process conditions on site-specific N-linked glycosylation, a kinetic model of site-specific N-linked glycosylation was developed. The model parameters provided mechanistic insights into the differences in glycan fractions observed in the Fc and Fab regions. Enzyme activities calculated from the model provided insights into the effect of bioreactor pH on site-specific N-linked glycosylation. Model predictions were experimentally tested by measuring glycosyltransferase-enzyme mRNA-levels and intracellular nucleotide sugar concentrations. The model was used to demonstrate the effect of increasing galactosyltransferase activity on site-specific N-linked glycan fractions. Experiments involving galactose and MnCl2 supplementation were used to test model predictions. The model is capable of providing insights into experimentally measured data and also of making predictions that can be used to design media supplementation strategies.

Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models.

Developing microRNAs as engineering tools to modulate monoclonal antibody galactosylation

N-linked glycosylation is one of the most important post-translational modifications of monoclonal antibodies (mAbs) and is considered to be a critical quality attribute (CQA), as the glycan composition often has immunomodulatory effects. Since terminal galactose residues of mAbs can affect antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytolysis (CDC) activation, serum half-life, and antiviral activity it has to be monitored, controlled and modulated to ensure therapeutic effects. The ability of small noncoding microRNAs (miRNAs) to modulate glycosylation in Chinese hamster ovary (CHO) production cells was recently reported establishing miRNAs as engineering tools for modulation of protein glycosylation. In this study, we report the characterization and validation of miRNAs as engineering tools for increased (mmu-miR-452-5p, mmu-miR-193b-3p) or decreased (mmu-miR-7646-5p, mmu-miR-7243-3p, mmu-miR-1668, mmu-let-7c-1-3p, mmu-miR-7665-3p, mmu-miR-6403) degree of galactosylation. Furthermore, the biological mode of action regulating gene expression of the galactosylation pathway was characterized as well as their influence on bioprocess-related parameters. Most important, stable plasmid-based overexpression of these miRNAs represents a versatile tool for engineering N-linked galactosylation to achieve favorable phenotypes in cell lines for biopharmaceutical production.

Effects of process intensification on homogeneity of an IgG1:κ monoclonal antibody during perfusion culture

The pharmaceutical industry employs various strategies to improve cell productivity. These strategies include process intensification, culture media improvement, clonal selection, media supplementation and genetic engineering of cells. However, improved cell productivity has inherent risk of impacting product quality attributes (PQA). PQAs may affect the products' efficacy via stability, bioavailability, or in vivo bioactivity. Variations in manufacturing process may introduce heterogeneity in the products by altering the type and extent of N-glycosylation, which is a PQA of therapeutic proteins. We investigated the effect of different cell densities representing increasing process intensification in a perfusion cell culture on the production of an IgG1-κ monoclonal antibody from a CHO-K1 cell line. This antibody is glycosylated both on light chain and heavy chain. Our results showed that the contents of glycosylation of IgG1-κ mAb increased in G0F and fucosylated type glycans as a group, whereas sialylated type glycans decreased, for the mAb whole protein. Overall, significant differences were observed in amounts of G0F, G1F, G0, G2FS1, and G2FS2 type glycans across all process intensification levels. G2FS2 and G2 type N-glycans were predominantly quantifiable from light chain rather than heavy chain. It may be concluded that there is a potential impact to product quality attributes of therapeutic proteins during process intensification via perfusion cell culture that needs to be assessed. Since during perfusion cell culture the product is collected throughout the duration of the process, lot allocation needs careful attention to process parameters, as PQAs are affected by the critical process parameters (CPPs). KEY POINTS: • Molecular integrity may suffer with increasing process intensity. • Galactosylated and sialylated N-glycans may decrease. • Perfusion culture appears to maintain protein charge structure.

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.

Progress toward rapid, at-line N-glycosylation detection and control for recombinant protein expression

Proteins continue to represent a large fraction of the therapeutics market, reaching over a hundred billion dollars in market size globally. One key feature of protein modification that can affect both structure and function is the addition of glycosylation following protein folding, leading to regulatory requirements for the accurate assessment of protein attributes, including glycan structures. The non-template-driven, innately heterogeneous N-glycosylation process thus requires accurate detection to robustly generate protein therapies. A challenge exists in the timely detection of protein glycosylation without labor-intensive manipulation. In this article, we discuss progress toward N-glycoprotein control, focusing on novel control strategies and the advancement of rapid, high-throughput analysis methods.

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.

Metabolic engineering challenges of extending N-glycan pathways in Chinese hamster ovary cells

In mammalian cells, N-glycans may include multiple N-acetyllactosamine (poly-LacNAc) units that can play roles in various cellular functions and properties of therapeutic recombinant proteins. Previous studies indicated that β-1,3-N-acetylglucosaminyltransferase 2 (B3GNT2) and β-1,4-galactotransferase 1 (B4GALT1) are two of the primary glycosyltransferases involved in generating LacNAc units. In the current study, knocking out sialyltransferase genes slightly enhanced the LacNAc content (≥4 repeats per glycan) on recombinant EPO protein. Next, the role of single and dual-overexpression of B3GNT2 and B4GALT1 was explored in recombinant EPO-expressing Chinese hamster ovary (CHO) cells. While overexpression of B4GALT1 slightly enhanced the levels of large glycans on recombinant EPO, overexpression of B3GNT2 in EPO-expressing CHO cells significantly decreased the recombinant EPO LacNAc content, resulting in N-glycans terminating primarily with GlcNAc structures, a limited number of Gals, and nearly undetectable sialylation, which was also observed in sialyltransferases knock-out-B3GNT2 overexpression cell lines. Considering the nature of the binding domain motifs present on B3GNT2, which evolved from β1,3-galactosyltransferases, its overexpression may have competed and inhibited endogenous β1,4-galactosyltransferases for exposed GlcNAc residues on the N-glycans, resulting in premature termination of many N-glycans at GlcNAc. Furthermore, B3GNT2 overexpression enhanced intracellular UDP-GlcNAc and CMP-Neu5Ac content while slightly lowering UDP-Gal content. The presence of a sink for UDP-GlcNAc in the form of B3GNT2 with no disposition may have also elevated the intracellular levels of this nucleotide as well as its downstream product, CMP-Neu5Ac. Furthermore, we were unable to overexpress B4GALT1 at either the transcriptional or translational levels following initial B3GNT2 expression. Expression of B3GNT2 following initial expression of B4GALT1 was also problematic in that transcriptional and translational analysis indicated the accumulation of truncated B3GNT2 missing a section of the B3GNT2 trans-Golgi lumen domain while transmembrane and cytoplasmic domains were present. Given that glycosylation is a very complex intra-network process, the addition of one or more recombinant glycosyltransferases may have an unexpected influence on the expression and activities of glycosyltransferases, which can disrupt the nucleotide sugar levels and lead to unexpected modifications of the resulting N-glycan patterns.

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.

A high-resolution measurement of nucleotide sugars by using ion-pair reverse chromatography and tandem columns

N-Linked glycosylation is a cellular process transferring sugars from glycosyl donors to proteins or lipids. Biopharmaceutical products widely produced by culturing mammalian cells such as Chinese hamster ovary (CHO) cells are typically glycosylated during biosynthesis. For some biologics, the N-linked glycan is a critical quality attribute of the drugs. Nucleotide sugars are the glycan donors and impact the intracellular glycosylation process. In current analytical methods, robust separation of nucleotide sugar isomers such as UDP glucose and UDP galactose remains a challenge because of their structural similarity. In this study, we developed a strategy to resolve the separation of major nucleotide sugars including challenging isomers based on the use of ion-pair reverse phase (IP-RP) chromatography. The strategy applies core-shell columns and connects multiple columns in tandem to increase separation power and ultimately enables high-resolution detection of nucleotide sugars from cell extracts. The key parameters in the IP-RP method, including temperature, mobile phase, and flow rates, have been systematically evaluated in this work and the theoretical mechanisms of the chromatographic behavior were proposed. Graphical abstract.

Prediction of N-linked Glycoform Profiles of Monoclonal Antibody with Extracellular Metabolites and Two-Step Intracellular Models

Monoclonal antibodies (mAbs) are commonly glycosylated and show varying levels of galactose attachment. A set of experiments in our work showed that the galactosylation level of mAbs was altered by the culture conditions of hybridoma cells. The uridine diphosphate galactose (UDP-Gal) is one of the substrates of galactosylation. Based on that, we proposed a two-step model to predict N-linked glycoform profiles by solely using extracellular metabolites from cell culture. At the first step, the flux level of UDP-Gal in each culture was estimated based on a computational flux balance analysis (FBA); its level was found to be linear with the galactosylation degree on mAbs. At the second step, the glycoform profiles especially for G0F (agalactosylated), G1F (monogalactosylated) and G2F (digalactosylated) were predicted by a kinetic model. The model outputs well matched with the experimental data. Our study demonstrated that the integrated mathematical approach combining FBA and kinetic model is a promising strategy to predict glycoform profiles for mAbs during cell culture processes.