A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus

In silico simulation of glycosylation and related pathways

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

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.

Use of single analytic tool to quantify both absolute N-glycosylation and glycan distribution in monoclonal antibodies

Recombinant proteins represent almost half of the top selling therapeutics-with over a hundred billion dollars in global sales-and their efficacy and safety strongly depend on glycosylation. In this study, we showcase a simple method to simultaneously analyze N-glycan micro- and macroheterogeneity of an immunoglobulin G (IgG) by quantifying glycan occupancy and distribution. Our approach is linear over a wide range of glycan and glycoprotein concentrations down to 25 ng/mL. Additionally, we present a case study demonstrating the effect of small molecule metabolic regulators on glycan heterogeneity using this approach. In particular, sodium oxamate (SOD) decreased Chinese hamster ovary (CHO) glucose metabolism and reduced IgG glycosylation by 40% through upregulating reactive oxygen species (ROS) and reducing the UDP-GlcNAc pool, while maintaining a similar glycan profile to control cultures. Here, we suggest glycan macroheterogeneity as an attribute should be included in bioprocess screening to identify process parameters that optimize culture performance without compromising antibody quality.

GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).¹.

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.

Mechanistic modeling of viral particle production

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.

Human milk oligosaccharides are fundamental to infant health. Here the authors deploy a multi-omics systems biology approach to elucidate their biosynthetic network, including the associated enzymes and likely structures of ambiguous oligosaccharides.

Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes

Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.

The interplay of protein engineering and glycoengineering to fine-tune antibody glycosylation and its impact on effector functions

The N-glycan pattern of an IgG antibody, attached at a conserved site within the fragment crystallizable (Fc) region, is a critical antibody quality attribute whose structural variability can also impact antibody function. For tailoring the Fc glycoprofile, glycoengineering in cell lines as well as Fc amino acid mutations have been applied. Multiple glycoengineered Chinese hamster ovary cell lines were generated, including defucosylated (FUT8KO), α-2,6-sialylated (ST6KI), and defucosylated α-2,6-sialylated (FUT8KOST6KI), expressing either a wild-type anti-CD20 IgG (WT) or phenylalanine to alanine (F241A) mutant. Matrix-assisted laser desorption ionization-time of flight mass spectrometry characterization of antibody N-glycans revealed that the F241A mutation significantly increased galactosylation and sialylation content and glycan branching. Furthermore, overexpression of recombinant human α-2,6-sialyltransferase resulted in a predominance of α-2,6-sialylation rather than α-2,3-sialylation for both WT and heavily sialylated F241A antibody N-glycans. Interestingly, knocking out α-1,6-fucosyltransferase (FUT8KO), which removed core fucose, lowered the content of N-glycans with terminal Gal and increased levels of terminal GlcNAc and Man5 groups on WT antibody. Further complement-dependent cytotoxicity (CDC) analysis revealed that, regardless of the production cells, WT antibody samples have higher cytotoxic CDC activity with more exposed Gal residues compared to their individual F241A mutants. However, the FUT8KO WT antibody, with a large fraction of bi-GlcNAc structures (G0), displayed the lowest CDC activity of all WT antibody samples. Furthermore, for the F241A mutants, a higher CDC activity was observed for α-2,6- compared to α-2,3-sialylation. Antibody-dependent cellular cytotoxicity (ADCC) analysis revealed that the defucosylated WT and F241A mutants showed enhanced in vitro ADCC performance compared to their fucosylated counterparts, with the defucosylated WT antibodies displaying the highest overall ADCC activity, regardless of sialic acid substitution. Moreover, the FcγRIIIA receptor binding by antibodies did not always correspond directly with ADCC result. This study demonstrates that glycoengineering and protein engineering can both promote and inhibit antibody effector functions and represent practical approaches for varying glycan composition and functionalities during antibody development.

An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells

BACKGROUND

Monoclonal antibodies (mABs) have emerged as one of the most important therapeutic recombinant proteins in the pharmaceutical industry. Their immunogenicity and therapeutic efficacy are influenced by post-translational modifications, specifically the glycosylation process. Bioprocess conditions can influence the intracellular process of glycosylation. Among all the process conditions that have been recognized to affect the mAB glycoforms, the detailed mechanism underlying how ammonium could perturb glycosylation remains to be fully understood. It was shown that ammonium induces heterogeneity in protein glycosylation by altering the sialic acid content of glycoproteins. Hence, understanding this mechanism would aid pharmaceutical manufacturers to ensure consistent protein glycosylation.

METHODS

Three different mechanisms have been proposed to explain how ammonium influences the sialylation process. In the first, the inhibition of CMP-sialic acid transporter, which transports CMP-sialic acid (sialylation substrate) into the Golgi, by an increase in UDP-GlcNAc content that is brought about by the augmented incorporation of ammonium into glucosamine formation. In the second, ammonia diffuses into the Golgi and raises its pH, thereby decreasing the sialyltransferase enzyme activity. In the third, the reduction of sialyltransferase enzyme expression level in the presence of ammonium. We employed these mechanisms in a novel integrated modular platform to link dynamic alteration in mAB sialylation process with extracellular ammonium concentration to elucidate how ammonium alters the sialic acid content of glycoproteins.

RESULTS

Our results show that the sialylation reaction rate is insensitive to the first mechanism. At low ammonium concentration, the second mechanism is the controlling mechanism in mAB sialylation and by increasing the ammonium level (< 8 mM) the third mechanism becomes the controlling mechanism. At higher ammonium concentrations (> 8 mM) the second mechanism becomes predominant again.

CONCLUSION

The presented model in this study provides a connection between extracellular ammonium and the monoclonal antibody sialylation process. This computational tool could help scientists to develop and formulate cell culture media. The model illustrated here can assist the researchers to select culture media that ensure consistent mAB sialylation.

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.

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.

Global mapping of glycosylation pathways in human-derived cells

Glycans are one of the fundamental classes of macromolecules and are involved in a broad range of biological phenomena. A large variety of glycan structures can be synthesized depending on tissue or cell types and environmental changes. Here, we developed a comprehensive glycosylation mapping tool, termed GlycoMaple, to visualize and estimate glycan structures based on gene expression. We informatically selected 950 genes involved in glycosylation and its regulation. Expression profiles of these genes were mapped onto global glycan metabolic pathways to predict glycan structures, which were confirmed using glycomic analyses. Based on the predictions of N-glycan processing, we constructed 40 knockout HEK293 cell lines and analyzed the effects of gene knockout on glycan structures. Finally, the glycan structures of 64 cell lines, 37 tissues, and primary colon tumor tissues were estimated and compared using publicly available databases. Our systematic approach can accelerate glycan analyses and engineering in mammalian cells.

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication

Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.

Simulating the enzymes of ganglioside biosynthesis with Glycologue

Gangliosides are an important class of sialylated glycosphingolipids linked to ceramide that are a component of the mammalian cell surface, especially those of the central nervous system, where they function in intercellular recognition and communication. We describe an in silico method for determining the metabolic pathways leading to the most common gangliosides, based on the known enzymes of their biosynthesis. A network of 41 glycolipids is produced by the actions of the 10 enzymes included in the model. The different ganglioside nomenclature systems in common use are compared and a systematic variant of the widely used Svennerholm nomenclature is described. Knockouts of specific enzyme activities are used to simulate congenital defects in ganglioside biosynthesis, and altered ganglioside status in cancer, and the effects on network structure are predicted. The simulator is available at the Glycologue website, https://glycologue.org/.

Fc galactosylation follows consecutive reaction kinetics and enhances immunoglobulin G hexamerization for complement activation

Fc galactosylation is a critical quality attribute for anti-tumor recombinant immunoglobulin G (IgG)-based monoclonal antibody (mAb) therapeutics with complement-dependent cytotoxicity (CDC) as the mechanism of action. Although the correlation between galactosylation and CDC has been known, the underlying structure–function relationship is unclear. Heterogeneity of the Fc N-glycosylation produced by Chinese hamster ovary (CHO) cell culture biomanufacturing process leads to variable CDC potency. Here, we derived a kinetic model of galactose transfer reaction in the Golgi apparatus and used this model to determine the correlation between differently galactosylated species from CHO cell culture process. The model was validated by a retrospective data analysis of more than 800 historical samples from small-scale and large-scale CHO cell cultures. Furthermore, using various analytical technologies, we discovered the molecular basis for Fc glycan terminal galactosylation changing the three-dimensional conformation of the Fc, which facilitates the IgG1 hexamerization, thus enhancing C1q avidity and subsequent complement activation. Our study offers insight into the formation of galactosylated species, as well as a novel three-dimensional understanding of the structure–function relationship of terminal galactose to complement activation in mAb therapeutics.

mAb Production Modeling and Design Space Evaluation Including Glycosylation Process

Due to high demand, monoclonal antibodies (mAbs) production needs to be efficient, as well as maintaining a high product quality. Quality by design (QbD) via predictive process modeling greatly facilitates process understanding and can be used to adjust process parameters to further improve the unit operations. In this work, mechanistic and dynamic kriging models are developed to capture the protein productivity and glycan fractions under different temperatures and pH levels. The design of experiments is used to generate input and output data for model training. The dynamic kriging model shows good performance in capturing the dynamic profiles of cell cultures and glycosylation using only limited input data. The developed model is further used for feasibility analysis, and successfully identifies the operating design space, maintaining high productivity and guaranteed product quality.

Understanding the effect of increased cell specific productivity on galactosylation of monoclonal antibodies produced using Chinese hamster ovary cells

Achieving optimal productivity and desired product quality of the therapeutic monoclonal antibody (mAb) is one of the primary goals of process development. Across the various mAb programs at our company, we observed that increasing the specific productivity (q_p) results in a decrease in the % galactosylation (%Gal) level on the protein. In order to gain further insight into this correlation, cells were cultured under different process conditions such as pH or media osmolality or in the presence of supplements such as sodium butyrate. A range of q_p and N-glycan profiles were obtained with the greatest changes observed under high pH (lower q_p, higher %Gal), higher osmolality (higher q_p, lower %Gal) or sodium butyrate (moderately higher q_p, moderately lower %Gal) conditions. Abundance of individual glycan species highlighted different bottlenecks in the N-glycosylation pathway depending on the treatment condition. Transcriptomics analysis was performed to identify changes in gene expression profiles that correlate with the inverse relationship between q_p and %Gal. Results showed downregulation of Beta-1,4-galactosyltransferase 1 (B4GalT1), UDP-GlcNAc and Mn²⁺ transporter (slc35a3 and slc39a8 respectively) for the high osmolality conditions. Significant downregulation of slc39a8 (Mn²⁺ transporter) was observed for the sodium butyrate condition. No significant differences were observed for any of the genes in the N-glycosylation pathway under the high pH condition even though this condition showed highest %Gal. Together, data suggests that different treatments have distinct complex mechanisms by which the overall glycan levels of a mAb are influenced. Further studies based on these results will help build the knowledge necessary to design strategies to obtain the desired productivity and product quality of mAbs.

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.

Dynamic multiscale metabolic network modeling of Chinese hamster ovary cell metabolism integrating N-linked glycosylation in industrial biopharmaceutical manufacturing

Experimental and modeling work, described in this article, is focused on the metabolic pathway of Chinese hamster ovary (CHO) cells, which are the preferred expression system for monoclonal antibody protein production. CHO cells are one of the primary hosts for monoclonal antibodies production, which have extensive applications in multiple fields like biochemistry, biology and medicine. Here, an approach to explain cellular metabolism with in silico modeling of a microkinetic reaction network is presented and validated with unique experimental results. Experimental data of 25 different fed-batch bioprocesses included the variation of multiple process parameters, such as pH, agitation speed, oxygen and CO₂ content, and dissolved oxygen. A total of 151 metabolites were involved in our proposed metabolic network, which consisted of 132 chemical reactions that describe the reaction pathways, and include 25 reactions describing N-glycosylation and additional reactions for the accumulation of the produced glycoforms. Additional eight reactions are considered for accumulation of the N-glycosylation products in the extracellular environment and one reaction to correlate cell degradation. The following pathways were considered: glycolysis, pentose phosphate pathway, nucleotide synthesis, tricarboxylic acid cycle, lipid synthesis, protein synthesis, biomass production, anaplerotic reactions, and membrane transport. With the applied modeling procedure, different operational scenarios and fed-batch techniques can be tested.

Modeling the Effect of Amino Acids and Copper on Monoclonal Antibody Productivity and Glycosylation: A Modular Approach

In manufacturing monoclonal antibodies (mAbs), it is crucial to be able to predict how process conditions and supplements affect productivity and quality attributes, especially glycosylation. Supplemental inputs, such as amino acids and trace metals in the media, are reported to affect cell metabolism and glycosylation; quantifying their effects is essential for effective process development. We aim to present and validate, through a commercially relevant cell culture process, a technique for modeling such effects efficiently. While existing models can predict mAb production or glycosylation dynamics under specific process configurations, adapting them to new processes remains challenging, because it involves modifying the model structure and often requires some mechanistic understanding. Here, a modular modeling technique for adapting an existing model for a fed-batch Chinese hamster ovary (CHO) cell culture process without structural modifications or mechanistic insight is presented. Instead, data is used, obtained from designed experimental perturbations in media supplementation, to train and validate a supplemental input effect model, which is used to "patch" the existing model. The combined model can be used for model-based process development to improve productivity and to meet product quality targets more efficiently. The methodology and analysis are generally applicable to other CHO cell lines and cell types.

N-Glycosylation of IgG and IgG-Like Recombinant Therapeutic Proteins: Why Is It Important and How Can We Control It?

Regulatory bodies worldwide consider N-glycosylation to be a critical quality attribute for immunoglobulin G (IgG) and IgG-like therapeutics. This consideration is due to the importance of posttranslational modifications in determining the efficacy, safety, and pharmacokinetic properties of biologics. Given its critical role in protein therapeutic production, we review N-glycosylation beginning with an overview of the myriad interactions of N-glycans with other biological factors. We examine the mechanism and drivers for N-glycosylation during biotherapeutic production and the several competing factors that impact glycan formation, including the abundance of precursor nucleotide sugars, transporters, glycosidases, glycosyltransferases, and process conditions. We explore the role of these factors with a focus on the analytical approaches used to characterize glycosylation and associated processes, followed by the current state of advanced glycosylation modeling techniques. This combination of disciplines allows for a deeper understanding of N-glycosylation and will lead to more rational glycan control.

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.

A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation

Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

Antibody Structure and Function: The Basis for Engineering Therapeutics

Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.

Model-Driven Engineering of N-Linked Glycosylation in Chinese Hamster Ovary Cells

Chinese hamster ovary (CHO) cells are used for industrial production of protein-based therapeutics (i.e., "biologics"). Here we describe a method for combining systems-level kinetic models with a synthetic biology platform for multigene overexpression to rationally perturb N-linked glycosylation. Specifically, we sought to increase galactose incorporation on a secreted Immunoglobulin G (IgG) protein. We rationally design, build, and test a total of 23 transgenic cell pools that express single or three-gene glycoengineering cassettes comprising a total of 100 kilobases of engineered DNA sequence. Through iterative engineering and model refinement, we rationally increase the fraction of bigalactosylated glycans five-fold from 11.9% to 61.9% and simultaneously decrease the glycan heterogeneity on the secreted IgG. Our approach allows for rapid hypothesis testing and identification of synergistic behavior from genetic perturbations by bridging systems and synthetic biology.

Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellular N-glycan processing

N-linked glycosylation plays a fundamental role in determining the thermodynamic stability of proteins and is involved in multiple key biological processes. The mechanistic understanding of the intracellular machinery responsible for the stepwise biosynthesis of N-glycans is still incomplete due to limited understanding of in vivo kinetics of N-glycan processing along the secretory pathway. We present a glycoproteomics approach to monitor the processing of site-specific N-glycans in CHO cells. On the basis of a model-based analysis of structure-specific turnover rates, we provide a kinetic description of intracellular N-glycan processing along the entire secretory pathway. This approach refines and further extends the current knowledge on N-glycans biosynthesis and provides a basis to quantify alterations in the glycoprotein processing machinery.

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

The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation patterns compared to the cultures with glucose. Furthermore, the growth rate and IgG productivity were similar in all the conditions. When the cells were cultured with galactose alone, lactate was fed as well to be used as complementary carbon source, leading to cell growth rate and IgG productivity comparable to feeding the other sugars. The data of the glycoprofiles were used for training the model, and then to simulate the glycosylation changes with varying the concentrations of mannose and galactose. In this study we showed that the GReBA model had a good predictive capacity of the N-linked glycosylation. The GReBA can be used as a guidance for development of glycoprotein cultivation processes.

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.

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.

Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses

N-linked glycosylation affects the potency, safety, immunogenicity, and pharmacokinetic clearance of several therapeutic proteins including monoclonal antibodies. A robust control strategy is needed to dial in appropriate glycosylation profile during the course of cell culture processes accurately. However, N-glycosylation dynamics remains insufficiently understood owing to the lack of integrative analyses of factors that influence the dynamics, including sugar nucleotide donors, glycosyltransferases, and glycosidases. Here, an integrative approach involving multi-dimensional omics analyses was employed to dissect the temporal dynamics of glycoforms produced during fed-batch cultures of CHO cells. Several pathways including glycolysis, tricarboxylic citric acid cycle, and nucleotide biosynthesis exhibited temporal dynamics over the cell culture period. The steps involving galactose and sialic acid addition were determined as temporal bottlenecks. Our results show that galactose, and not manganese, is able to mitigate the temporal bottleneck, despite both being known effectors of galactosylation. Furthermore, sialylation is limited by the galactosylated precursors and autoregulation of cytidine monophosphate-sialic acid biosynthesis.

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Major glycosylated species exhibit temporal dynamics during fed-batch processes

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Key metabolic pathways linked to N-glycosylation exhibit significant temporal dynamics

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Dynamics in nucleotide sugar donors (NSDs) directly influences glycoform heterogeneity

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Glycoform heterogeneity can be mitigated by supplementing NSD biosynthetic precursors

Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture

The terminal sugar molecules of the N-linked glycan attached to the fragment crystalizable (Fc) region is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) such as immunoglobulin G (IgG). There exists naturally-occurring heterogeneity in the N-linked glycan structure of mAbs, and such heterogeneity has a significant influence on the clinical safety and efficacy of mAb drugs. We previously proposed a constraint-based modeling method called glycosylation flux analysis (GFA) to characterize the rates (fluxes) of intracellular glycosylation reactions. One contribution of this work is a significant improvement in the computational efficiency of the GFA, which is beneficial for analyzing large datasets. Another contribution of our study is the analysis of IgG glycosylation in continuous perfusion Chinese Hamster Ovary (CHO) cell cultures. The GFA of the perfusion cell culture data indicated that the dynamical changes of IgG glycan heterogeneity are mostly attributed to alterations in the galactosylation flux activity. By using a random forest regression analysis of the IgG galactosylation flux activity, we were further able to link the dynamics of galactosylation with two process parameters: cell-specific productivity of IgG and extracellular ammonia concentration. The characteristics of IgG galactosylation dynamics agree well with what we previously reported for fed-batch cultivations of the same CHO cell strain.

A mechanism for bistability in glycosylation

Glycosyltransferases are a class of enzymes that catalyse the posttranslational modification of proteins to produce a large number of glycoconjugate acceptors from a limited number of nucleotide-sugar donors. The products of one glycosyltransferase can be the substrates of several other enzymes, causing a combinatorial explosion in the number of possible glycan products. The kinetic behaviour of systems where multiple acceptor substrates compete for a single enzyme is presented, and the case in which high concentrations of an acceptor substrate are inhibitory as a result of abortive complex formation, is shown to result in non-Michaelian kinetics that can lead to bistability in an open system. A kinetic mechanism is proposed that is consistent with the available experimental evidence and provides a possible explanation for conflicting observations on the β-1,4-galactosyltransferases. Abrupt switching between steady states in networks of glycosyltransferase-catalysed reactions may account for the observed changes in glycosyl-epitopes in cancer cells.

Glycoengineering in CHO Cells: Advances in Systems Biology

For several decades, glycoprotein biologics have been successfully produced from Chinese hamster ovary (CHO) cells. The therapeutic efficacy and potency of glycoprotein biologics are often dictated by their post-translational modifications, particularly glycosylation, which unlike protein synthesis, is a non-templated process. Consequently, both native and recombinant glycoprotein production generate heterogeneous mixtures containing variable amounts of different glycoforms. Stability, potency, plasma half-life, and immunogenicity of the glycoprotein biologic are directly influenced by the glycoforms. Recently, CHO cells have also been explored for production of therapeutic glycosaminoglycans (e.g., heparin), which presents similar challenges as producing glycoproteins biologics. Approaches to controlling heterogeneity in CHO cells and directing the biosynthetic process toward desired glycoforms are not well understood. A systems biology approach combining different technologies is needed for complete understanding of the molecular processes accounting for this variability and to open up new venues in cell line development. In this review, we describe several advances in genetic manipulation, modeling, and glycan and glycoprotein analysis that together will provide new strategies for glycoengineering of CHO cells with desired or enhanced glycosylation capabilities.

Controlling the Glycosylation Profile in mAbs Using Time-Dependent Media Supplementation

In order to meet desired drug product quality targets, the glycosylation profile of biotherapeutics such as monoclonal antibodies (mAbs) must be maintained consistently during manufacturing. Achieving consistent glycan distribution profiles requires identifying factors that influence glycosylation, and manipulating them appropriately via well-designed control strategies. Now, the cell culture media supplement, MnCl2, is known to alter the glycosylation profile in mAbs generally, but its effect, particularly when introduced at different stages during cell growth, has yet to be investigated and quantified. In this study, we evaluate the effect of time-dependent addition of MnCl2 on the glycan profile quantitatively, using factorial design experiments. Our results show that MnCl2 addition during the lag and exponential phases affects the glycan profile significantly more than stationary phase supplementation does. Also, using a novel computational technique, we identify various combinations of glycan species that are affected by this dynamic media supplementation scheme, and quantify the effects mathematically. Our experiments demonstrate the importance of taking into consideration the time of addition of these trace supplements, not just their concentrations, and our computational analysis provides insight into what supplements to add, when, and how much, in order to induce desired changes.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017

Glycosylation flux analysis reveals dynamic changes of intracellular glycosylation flux distribution in Chinese hamster ovary fed-batch cultures

N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this analysis to CHO fed-batch immunoglobulin G (IgG) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modeling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalysed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the IgG production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, galactosylation fluxes decreased with the cultivation time more significantly than the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.

Influence of protein/glycan interaction on site-specific glycan heterogeneity

To study how the interaction between N-linked glycans and the surrounding amino acids influences oligosaccharide processing, we used protein disulfide isomerase (PDI), a glycoprotein bearing 5 N-glycosylation sites, as a model system and expressed it transiently in a Chinese hamster ovary (CHO)-S cell line. PDI was produced as both secreted Sec-PDI and endoplasmic reticulum-retained glycoprotein (ER)-PDI, to study glycan processing by ER and Golgi resident enzymes. Quantitative site-specific glycosylation profiles were obtained, and flux analysis enabled modeling site-specific glycan processing. By altering the primary sequence of PDI, we changed the glycan/protein interaction and thus the site-specific glycoprofile because of the improved enzymatic fluxes at enzymatic bottlenecks. Our results highlight the importance of direct interactions between N-glycans and surface-exposed amino acids of glycoproteins on processing in the ER and the Golgi and the possibility of changing a site-specific N-glycan profile by modulating such interactions and thus the associated enzymatic fluxes. Altering the primary protein sequence can therefore be used to glycoengineer recombinant proteins.-Losfeld, M.-E., Scibona, E., Lin, C.-W., Villiger, T. K., Gauss, R., Morbidelli, M., Aebi, M. Influence of protein/glycan interaction on site-specific glycan heterogeneity.