Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation.

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.

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

Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.

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.

Rapid Antibody Glycoengineering in Chinese Hamster Ovary Cells.

Recombinant monoclonal antibodies bind specific molecular targets and, subsequently, induce an immune response or inhibit the binding of other ligands. However, monoclonal antibody functionality and half-life may be reduced by the type and distribution of host-specific glycosylation. Attempts to produce superior antibodies have inspired the development of genetically modified producer cells that synthesize glyco-optimized antibodies. Glycoengineering typically requires the generation of a stable knockout or knockin cell line using methods such as clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9. Monoclonal antibodies produced by engineered cells are then characterized using mass spectrometric methods to determine if the desired glycoprofile has been obtained. This strategy is time-consuming, technically challenging, and requires specialists. Therefore, an alternative strategy that utilizes streamlined protocols for genetic glycoengineering and glycan detection may assist endeavors toward optimal antibodies. In this proof-of-concept study, an IgG-producing Chinese hamster ovary cell served as an ideal host to optimize glycoengineering. Short interfering RNA targeting the Fut8 gene was delivered to Chinese hamster ovary cells, and the resulting changes in FUT8 protein expression were quantified. The results indicate that knockdown by this method was efficient, leading to a ~60% reduction in FUT8. Complementary analysis of the antibody glycoprofile was performed using a rapid yet highly sensitive technique: capillary gel electrophoresis and laser-induced fluorescence detection. All knockdown experiments showed an increase in afucosylated glycans; however, the greatest shift achieved in this study was ~20%. This protocol simplifies glycoengineering efforts by harnessing in silico design tools, commercially synthesized gene targeting reagents, and rapid quantification assays that do not require extensive prior experience. As such, the time efficiencies offered by this protocol may assist investigations into new gene targets.

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.

Dynamics of immature mAb glycoform secretion during CHO cell culture: An integrated modelling framework.

Ensuring consistent glycosylation-associated quality of therapeutic monoclonal antibodies (mAbs) has become a priority in pharmaceutical bioprocessing given that the distribution and composition of the carbohydrates (glycans) bound to these molecules determines their therapeutic efficacy and immunogenicity. However, the interaction between bioprocess conditions, cellular metabolism and the intracellular process of glycosylation remains to be fully understood. To gain further insight into these interactions, we present a novel integrated modelling platform that links dynamic variations in mAb glycosylation with cellular secretory capacity. Two alternative mechanistic representations of how mAb specific productivity (qp ) influences glycosylation are compared. In the first, mAb glycosylation is modulated by the linear velocity with which secretory cargo traverses the Golgi apparatus. In the second, glycosylation is influenced by variations in Golgi volume. Within our modelling framework, both mechanisms accurately reproduce experimentally-observed dynamic changes in mAb glycosylation. In addition, an optimisation-based strategy has been developed to estimate the concentration of glycosylation enzymes required to minimise mAb glycoform variability. Our results suggest that the availability of glycosylation machinery relative to cellular secretory capacity may play a crucial role in mAb glycosylation. In the future, the modelling framework presented here may aid in selecting and engineering cell lines that ensure consistent mAb glycosylatio.

A multi-pronged investigation into the effect of glucose starvation and culture duration on fed-batch CHO cell culture.

In this study, omics-based analysis tools were used to explore the effect of glucose starvation and culture duration on monoclonal antibody (mAb) production in fed-batch CHO cell culture to gain better insight into how these parameters can be controlled to ensure optimal mAb productivity and quality. Titer and N-glycosylation of mAbs, as well as proteomic signature and metabolic status of the production cells in the culture were assessed. We found that the impact of glucose starvation on the titer and N-glycosylation of mAbs was dependent on the degree of starvation during early stationary phase of the fed-batch culture. Higher degree of glucose starvation reduced intracellular concentrations of UDP-GlcNAc and UDP-GalNAc, but increased the levels of UDP-Glc and UDP-Gal. Increased GlcNAc and Gal occupancy correlated well with increased degree of glucose starvation, which can be attributed to the interplay between the dilution effect associated with change in specific productivity of mAbs and the changed nucleotide sugar metabolism. Herein, we also show and discuss that increased cell culture duration negatively affect the maturation of glycans. In addition, comparative proteomics analysis of cells was conducted to observe differences in protein abundance between early growth and early stationary phases. Generally higher expression of proteins involved in regulating cellular metabolism, extracellular matrix, apoptosis, protein secretion and glycosylation was found in early stationary phase. These analyses offered a systematic view of the intrinsic properties of these cells and allowed us to explore the root causes correlating culture duration with variations in the productivity and glycosylation quality of monoclonal antibodies produced with CHO cells.

Amino acid and glucose metabolism in fed-batch CHO cell culture affects antibody production and glycosylation.

Fed-batch Chinese hamster ovary (CHO) cell culture is the most commonly used process for IgG production in the biopharmaceutical industry. Amino acid and glucose consumption, cell growth, metabolism, antibody titer, and N-glycosylation patterns are always the major concerns during upstream process optimization, especially media optimization. Gaining knowledge on their interrelations could provide insight for obtaining higher immunoglobulin G (IgG) titer and better controlling glycosylation-related product quality. In this work, different fed-batch processes with two chemically defined proprietary media and feeds were studied using two IgG-producing cell lines. Our results indicate that the balance of glucose and amino acid concentration in the culture is important for cell growth, IgG titer and N-glycosylation. Accordingly, the ideal fate of glucose and amino acids in the culture could be mainly towards energy and recombinant product, respectively. Accumulation of by-products such as NH4(+) and lactate as a consequence of unbalanced nutrient supply to cell activities inhibits cell growth. The levels of Leu and Arg in the culture, which relate to cell growth and IgG productivity, need to be well controlled. Amino acids with the highest consumption rates correlate with the most abundant amino acids present in the produced IgG, and thus require sufficient availability during culture. Case-by-case analysis is necessary for understanding the effect of media and process optimization on glycosylation. We found that in certain cases the presence of Man5 glycan can be linked to limitation of UDP-GlcNAc biosynthesis as a result of insufficient extracellular Gln. However, under different culture conditions, high Man5 levels can also result from low α-1,3-mannosyl-glycoprotein 2-ß-N-acetylglucosaminyltransferase (GnTI) and UDP-GlcNAc transporter activities, which may be attributed to high level of NH4+ in the cell culture. Furthermore, galactosylation of the mAb Fc glycans was found to be limited by UDP-Gal biosynthesis, which was observed to be both cell line and cultivation condition-dependent. Extracellular glucose and glutamine concentrations and uptake rates were positively correlated with intracellular UDP-Gal availability. All these findings are important for optimization of fed-batch culture for improving IgG production and directing glycosylation quality.

Integration of models and experimentation to optimise the production of potential biotherapeutics.

Despite decades of clinical and commercial success, the current paradigm for drug discovery and development is still empirical and costly. The many hundreds of therapeutic proteins (TPs) in the development pipeline and the FDA-led quality-by-design initiative represent opportunities to address this issue. Advances in our understanding of cellular mechanisms as well as the physicochemical and biological characteristics of TPs have enabled researchers to develop computational models that analyse or even predict molecular and cellular behaviour under different conditions. Coupled with new analytical tools, these models are increasingly used to systemise and expedite the design and optimisation of protein production processes throughout the discovery and development stages.

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

Monoclonal antibodies (mAbs) are one of the most important products of the biopharmaceutical industry. Their therapeutic efficacy depends on the post-translational process of glycosylation, which is influenced by manufacturing process conditions. Herein, we present a dynamic mathematical model for mAb glycosylation that considers cisternal maturation by approximating the Golgi apparatus to a plug flow reactor and by including recycling of Golgi-resident proteins (glycosylation enzymes and transport proteins [TPs]). The glycosylation reaction rate expressions were derived based on the reported kinetic mechanisms for each enzyme, and transport of nucleotide sugar donors [NSDs] from the cytosol to the Golgi lumen was modeled to serve as a link between glycosylation and cellular metabolism. Optimization-based methodologies were developed for estimating unknown enzyme and TP concentration profile parameters. The resulting model is capable of reproducing glycosylation profiles of commercial mAbs. It can further reproduce the effect gene silencing of the FucT glycosylation enzyme and cytosolic NSD depletion have on the mAb oligosaccharide profile. All novel elements of our model are based on biological evidence and generate more accurate results than previous reports. We therefore believe that the improvements contribute to a more detailed representation of the N-linked glycosylation process. The overall results show the potential of our model toward evaluating cell engineering strategies that yield desired glycosylation profiles. Additionally, when coupled to cellular metabolism, this model could be used to assess the effect of process conditions on glycosylation and aid in the design, control, and optimization of biopharmaceutical manufacturing processes.