How does mild hypothermia affect monoclonal antibody glycosylation?

Temperature-dependent intracellular crystallization of firefly luciferase in mammalian cells is suppressed by D-luciferin and stabilizing inhibitors

Firefly luciferase (Fluc) from Photinus pyralis is one of the most widely used reporter proteins in biomedical research. Despite its widespread use, Fluc's protein phase transition behaviors and phase separation characteristics have not received much attention. Current research uncovers Fluc's intrinsic property to phase separate in mammalian cells upon a simple cell culture temperature change. Specifically, Fluc spontaneously produced needle-shaped crystal-like inclusion bodies upon temperature shift to the hypothermic temperatures ranging from 25 °C to 31 °C. The crystal-like inclusion bodies were not associated with or surrounded by membranous organelles and were likely built from the cytosolic pool of Fluc. Furthermore, the crystal-like inclusion formation was suppressed when cells were cultured in the presence of D-luciferin and its synthetic analog, as well as the benzothiazole family of so-called stabilizing inhibitors. These two classes of compounds inhibited intracellular Fluc crystallization by different modes of action as they had contrasting effects on steady-state luciferase protein accumulation levels. This study suggests that, under substrate insufficient conditions, the excess Fluc phase separates into a crystal-like state that can modulate intracellular soluble enzyme availability and protein turnover rate.

The Protective Effect and Mechanism of Mild Hypothermia on Lung Injury after Cardiopulmonary Resuscitation in Pigs

To explore the protective effect and mechanism of mild hypothermia on lung tissue damage after cardiopulmonary resuscitation in pigs. In this experiment, we electrically stimulated 16 pigs (30 ± 2 kg) for 10 min to cause ventricular fibrillation. The successfully resuscitated animals were randomly divided into two groups, a mild hypothermia group and a control group. We took arterial blood 0.5, 1, 3, and 6 h after ROSC recovery in the two groups of animals for blood gas analysis. We observed the structural changes of lung tissue under an electron microscope and calculate the wet weight/dry weight (W/D) ratio. We quantitatively analyzed the expression differences of representative inflammatory factors [interleukin-6 (IL-6) and tumor necrosis factor-alpha TNF-α)] through the ELISA test. We detected the expression levels of Bax, Bcl-2, and Caspase-3 proteins in lung tissues by Western blot. After 3 h and 6 h of spontaneous circulation was restored, compared with the control group, PaO2/FiO2 decreased significantly (P < 0.05). In addition, the pathological changes, lung W/D and lung MDA of the mild hypothermia group were better than those of the control group. The levels of IL-6 and TNF-α in the lung tissue of the mild hypothermia group were significantly lower than those of the control group (P < 0.05). The content of Caspase-3 and Bax in the mild hypothermia group was significantly lower than that of the control group. Our experiments have shown that mild hypothermia can reduce lung tissue damage after cardiopulmonary resuscitation.

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.

Engineering of Chinese hamster ovary cells for co-overexpressing MYC and XBP1s increased cell proliferation and recombinant EPO production

Improving the cellular capacity of Chinese hamster ovary (CHO) cells to produce large amounts of therapeutic proteins remains a major challenge for the biopharmaceutical industry. In previous studies, we observed strong correlations between the performance of CHO cells and expression of two transcription factors (TFs), MYC and XBP1s. Here, we have evaluated the effective of overexpression of these two TFs on CHO cell productivity. To address this goal, we generated an EPO-producing cell line (CHO_EPO) using a targeted integration approach, and subsequently engineered it to co-overexpress MYC and XBP1s (a cell line referred to as CHOCX_EPO). Cells overexpressing MYC and XBP1s increased simultaneously viable cell densities and EPO production, leading to an enhanced overall performance in cultures. These improvements resulted from the individual effect of each TF in the cell behaviour (i.e., MYC-growth and XBP1s-productivity). An evaluation of the CHOCX_EPO cells under different environmental conditions (temperature and media glucose concentration) indicated that CHOCX_EPO cells increased cell productivity in high glucose concentration. This study showed the potential of combining TF-based cell engineering and process optimisation for increasing CHO cell productivity.

Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics

Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for the production of monoclonal antibodies (mAbs). However, a challenge of analyzing large cell culture data is the high correlation between regressors (particularly media composition), which makes traditional analyses, such as analysis of variance and multivariate linear regression, inappropriate. Instead, partial least-squares regression (PLSR) models, in combination with machine learning techniques such as variable importance metrics, are an orthogonal or alternative approach to identifying important regressors and overcoming the challenge of a highly covariant data structure. A specific workflow for the retrospective analysis of cell culture data is proposed that covers data curation, PLS regression, model analysis, and further steps. In this study, the proposed workflow was applied to data from four mAb products in an industrial cell culture process to identify significant process parameters that influence the afucosylation levels. The PLSR workflow successfully identified several significant parameters, such as temperature and media composition, to enhance process understanding of the relationship between cell culture processes and afucosylation levels.

Increase recombinant antibody yields through optimizing vector design and production process in CHO cells

Chinese hamster ovary (CHO) cells are the most commonly used host cells for the production of recombinant monoclonal antibodies (mAbs) due to their several advantages. Although the yields of recombinant mAbs can be greatly increased by some strategies, such as medium formulation, culture conditions, and cell engineering, most studies focused on either upstream design or downstream processes. In the present study, we first expressed recombinant adalimumab through combination of the vector design and production process optimization in CHO cells. Bicistronic vector, monocistronic vector, and dual promoter vector were constructed, and the production process was optimized using low-temperature and fed-batch culture. The results showed that the dual promoter vector exhibited the highest yield under the transient and stable transfected cells among three different vector systems in CHO cells. In addition, low-temperature and fed-batch culture could further improve the yields of adalimumab. The purified antibody displayed tumor necrosis factor-α (TNF-α) binding activity. In conclusion, combination of expression vector design and production process optimization can achieve higher expression of recombinant mAbs in CHO cells. KEY POINTS: • The dual promoter vector is more effective for expressing recombinant antibodies. • The yields of antibodies are related to the LC chain expression level. • Low-temperature and feed addition can promote antibody production.

Factors Affecting the Expression of Recombinant Protein and Improvement Strategies in Chinese Hamster Ovary Cells

Recombinant therapeutic proteins (RTPs) are important parts of biopharmaceuticals. Chinese hamster ovary cells (CHO) have become the main cell hosts for the production of most RTPs approved for marketing because of their high-density suspension growth characteristics, and similar human post-translational modification patterns et al. In recent years, many studies have been performed on CHO cell expression systems, and the yields and quality of recombinant protein expression have been greatly improved. However, the expression levels of some proteins are still low or even difficult-to express in CHO cells. It is urgent further to increase the yields and to express successfully the “difficult-to express” protein in CHO cells. The process of recombinant protein expression of is a complex, involving multiple steps such as transcription, translation, folding processing and secretion. In addition, the inherent characteristics of molecular will also affect the production of protein. Here, we reviewed the factors affecting the expression of recombinant protein and improvement strategies in CHO cells.

Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward Industry 4.0

Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multi-variate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization have made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing. This article is protected by copyright. All rights reserved.

Characterization of Monoclonal Antibody Glycan Heterogeneity Using Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry

Glycosylation is a critical quality attribute of monoclonal antibody (mAb) therapeutics. Hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) is an invaluable technology for the characterization of protein glycosylation. HILIC/MS-based glycan analysis relies on the library search using Glucose Units (GU) and accurate mass (AM) as the primary search parameters for identification. However, GU-based identifications are gradient-dependent and are not suitable for applications where separation gradients need to be optimized to analyze complex samples or achieve higher throughput. Additionally, the workflow requires calibration curves (using dextran ladder) to be generated for each analysis campaign, which in turn, are used to derive the GU values of the separated glycan species. To overcome this limitation, we employed a two-step strategy for targeted glycan analysis of a mAb expressed in Chinese Hamster Ovary (CHO) cells. The first step is to create a custom library of the glycans of interest independent of GU values (thereby eliminating the need for a calibration curve) and instead uses AM and retention time (RT) as the primary search variables. The second step is to perform targeted glycan screening using the custom-built library. The developed workflow was applied for targeted glycan analysis of a mAb expressed in CHO for 1) cell line selection 2) characterizing the day-wise glycan evolution in a model mAb during a fed-batch culture, 3) assessing the impact of different media conditions on glycosylation, and 4) evaluating the impact of two different process conditions on glycosylation changes in a model mAb grown in a bioreactor. Taken together, the data presented in this study provides insights into the sources of glycan heterogeneity in a model mAb that are seen during its commercial manufacturing.

Combined gene and environmental engineering offers a synergetic strategy to enhance r-protein production in Chinese hamster ovary cells

Environmental growth-inhibition conditions (GICs) have been used extensively for increasing cell-specific productivity (q_P ) of Chinese hamster ovary (CHO) cells, with the most common being temperature downshift and sodium butyrate (NaBu) treatment. B lymphocyte-induced maturation protein-1 (BLIMP1) overexpression in CHO cells can also inhibit cell growth and increase product titers and q_P . Given the similar responses, this study evaluated the individual and combined effects of BLIMP1 expression, low temperature, and NaBu treatment on culture performance, cell metabolism, and recombinant protein production of CHO cells. As expected, all three interventions decreased cell growth, arrested cells in G1/G0 cell cycle phase, and increased q_P . However, CHO cells presented different responses when considering cell viability, recombinant gene expression, and cell metabolism that indicated differences in the molecular loci by which BLIMP1 and GICs generated higher productivities. Combinations of BLIMP1 expression and GICs acted synergistically to inhibit cell growth and maximize r-protein production, with the BLIMP1/NaBu condition leading to the most significant improvements in product titers and q_P . This latter condition also proved to substantially increase product yields (up to 9.8 g immunoglobulin G1 [IgG1]/L and 2.2 g erythropoietin-Fc [EPO-Fc]/L) and q_P (up to 179 pg/cell/day [pcd] for IgG1 and 30 pcd for EPO-Fc) in high-density perfusion cultures. These findings offered mechanistic insights about the productivity-enhancing effects of BLIMP1 and GICs, as well as their complementarity for generating highly productive processes.

Factors affecting the quality of therapeutic proteins in recombinant Chinese hamster ovary cell culture

Chinese hamster ovary (CHO) cells are the most widely used mammalian host cells for the commercial production of therapeutic proteins. Fed-batch culture is widely used to produce therapeutic proteins, including monoclonal antibodies, because of its operational simplicity and high product titer. Despite technical advances in the development of culture media and cell cultures, it is still challenging to maintain high productivity in fed-batch cultures while also ensuring good product quality. In this review, factors that affect the quality attributes of therapeutic proteins in recombinant CHO (rCHO) cell culture, such as glycosylation, charge variation, aggregation, and degradation, are summarized and categorized into three groups: culture environments, chemical additives, and host cell proteins accumulated in culture supernatants. Understanding the factors that influence the therapeutic protein quality in rCHO cell culture will facilitate the development of large-scale, high-yield fed-batch culture processes for the production of high-quality therapeutic proteins.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Continuous optical in-line glucose monitoring and control in CHO cultures contributes to enhanced metabolic efficiency while maintaining darbepoetin alfa product quality

Great efforts are directed towards improving productivity, consistency and quality of biopharmaceutical processes and products. One particular area is the development of new sensors for continuous monitoring of critical bioprocess parameters by using online or in-line monitoring systems. Recently, we developed a glucose biosensor applicable in single-use, in-line and long-term glucose monitoring in mammalian cell bioreactors. Now, we integrated this sensor in an automated glucose monitoring and feeding system capable of maintaining stable glucose levels, even at very low concentrations. We compared this fed-batch feedback system at both low (< 1 mM) and high (40 mM) glucose levels with traditional batch culture methods, focusing on glycosylation and glycation of the recombinant protein darbepoetin alfa (DPO) produced by a CHO cell line. We evaluated cell growth, metabolite and product concentration under different glucose feeding strategies and show that continuous feeding, even at low glucose levels, has no harmful effects on DPO quantity and quality. We conclude that our system is capable of tight glucose level control throughout extended bioprocesses and has the potential to improve performance where constant maintenance of glucose levels is critical.

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.

Osmolality Effects on CHO Cell Growth, Cell Volume, Antibody Productivity and Glycosylation

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

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.

Characterization of glycosylation in monoclonal antibodies and its importance in therapeutic antibody development

Glycosylation is one of the structurally diverse and complex forms of post translational modifications observed in proteins which influence the effector functions of IgG-Fc. Although the glycosylation constitutes 2-3% of the total mass of the IgG antibody, a thorough assessment of glycoform distribution present on the antibody is a critical quality attribute (cQA) for the majority of novel and biosimilar monoclonal antibody (mAb) development. This review paper will highlight the impact of different glycoforms such as galactose, fucose, high mannose, NANA (N-acetylneuraminic acid), and NGNA (N-glycoylneuraminic acid) on the safety/immunogeneicity, efficacy/biological activity and clearance (pharmacodynamics/pharmacokinetic property (PD/PK)) of biological molecules. In addition, this paper will summarize routinely employed reliable analytical techniques such as hydrophilic interaction chromatography (HILIC), high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and mass spectrometry (MS) for characterizing and monitoring glycosylation in monoclonal antibodies (mAbs). The advantages and disadvantages of each of the methods are addressed. The scope of this review paper is limited to only N-linked and O-linked glycosylation.

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.

Development, characterization, and application of a 2-Compartment system to investigate the impact of pH inhomogeneities in large-scale CHO-based processes

Large-scale bioreactors for the production of monoclonal antibodies reach volumes of up to 25 000 L. With increasing bioreactor size, mixing is however affected negatively, resulting in the formation of gradients throughout the reactor. These gradients can adversely affect process performance at large scale. Since mammalian cells are sensitive to changes in pH, this study investigated the effects of pH gradients on process performance. A 2-Compartment System was established for this purpose to expose only a fraction of the cell population to pH excursions and thereby mimicking a large-scale bioreactor. Cells were exposed to repeated pH amplitudes of 0.4 units (pH 7.3), which resulted in decreased viable cell counts, as well as the inhibition of the lactate metabolic shift. These effects were furthermore accompanied by increased absolute lactate levels. Continuous assessment of molecular attributes of the expressed target protein revealed that subunit assembly or N-glycosylation patterns were only slightly influenced by the pH excursions. The exposure of more cells to the same pH amplitudes further impaired process performance, indicating this is an important factor, which influences the impact of pH inhomogeneity. This knowledge can aid in the design of pH control strategies to minimize the effects of pH inhomogeneity in large-scale bioreactors.

Media supplementation for targeted manipulation of monoclonal antibody galactosylation and fucosylation

Monoclonal antibodies are critically important biologics as the largest class of molecules used to treat cancers, rheumatoid arthritis, and other chronic diseases. Antibody glycosylation is a critical quality attribute that has ramifications for patient safety and physiological efficacy-one that can be modified by such factors as media formulation and process conditions during production. Using a design-of-experiments approach, we examined the effect of 2-F-peracetyl fucose (2FP), uridine, and galactose on cell growth and metabolism, titer, and gene expression of key glycosylation-related proteins, and report how the glycoform distribution changed from Days 4 to 7 in a batch process used for IgG1 production from Chinese hamster ovary cells. We observed major glycosylation changes upon supplement addition, where the addition of 2FP decreased antibody fucosylation by up to 48%, galactose addition increased galactosylation by up to 21%, and uridine addition decreased fucosylation and increased galactosylation by 6% and 2%, respectively. Despite having major effects on glycosylation, neither galactose nor 2FP significantly affected cell culture growth, metabolism, or titer. Uridine improved peak cell densities by 23% but also reduced titer by ∼30%. The supplements caused significant changes in gene expression by Day 4 of the cultures where 2FP addition significantly reduced fucosyltransferase 8 and nucleotide sugar transporter gene expression (by ∼2-fold), and uridine addition significantly increased expression of UDP-GlcNAcT (SLC35A3) and B4GALT1-6 genes (by 1.5-3-fold). These gene expression data alongside glycosylation, metabolic, and growth data improve our understanding of the cellular mechanisms affected by media supplementation and suggest approaches for modifying antibody glycosylation in antibody production processes.

Highly sensitive HPLC analysis and biophysical characterization of N-glycans of IgG-Fc domain in comparison between CHO and 293 cells using FcγRIIIa ligand

Quality control of monoclonal antibodies is challenging due in part to the diversity of post‐translational modifications present. The regulation of the N‐glycans of IgG‐Fc domain is one of the key factors to maintain the safety and efficacy of antibody drugs. The FcγRIIIa affinity column is an attractive tool for the precise analysis of the N‐glycans in IgG‐Fc domain. We used the mutant FcγRIIIa, which is produced in Escherichia coli and is therefore not glycosylated, as an affinity reagent to analyze the N‐glycans of monoclonal antibodies expressed in Expi293 and ExpiCHO cells. The monoclonal antibodies expressed in these cells showed very different chromatograms, because of differences in terminal galactose residues on the IgG‐Fc domains. We also carried out kinetic and thermodynamic analyses to understand the interaction between monoclonal antibodies and the mutant FcγRIIIa. Expi293 cell‐derived monoclonal antibodies had higher affinity for the mutant FcγRIIIa than those derived from ExpiCHO cells, due to slower off rates and lower binding entropy loss. Collectively, our results suggest that the FcγRIIIa column can be used to analyze the glycosylation of antibodies rapidly and specifically.

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.

Temperature Down-Shift Modifies Expression of UPR-/ERAD-Related Genes and Enhances Production of a Chimeric Fusion Protein in CHO Cells

Low culture temperature enhances the cell-specific productivity of Chinese hamster ovary (CHO) cells expressing varied recombinant (r-) proteins, but the mechanisms remain unclear. Regulation of unfolded protein response (UPR) pathway genes, such as transcriptional regulatory factors and endoplasmic reticulum (ER)-resident proteins, appear to be involved in the improvements of r-protein production under low temperature conditions. The transcriptional regulation of UPR-specific targets is studied in response to decreased culture temperature in relation to production of a difficult-to-express protein. A clonally-derived CHO cell line expressing a chimeric fusion protein (human erythropoietin [hEPO] linked to a murine Fc region, hEPO-Fc) is evaluated in terms of growth, metabolism, r-protein production and UPR-/ER associated degradation (ERAD)-specific gene expression at standard (37 °C) and low (32 °C) temperature in batch and fed-batch systems. Low temperature decreased peak cell density, improved viability, generated cell cycle arrest in the G1 phase and enhanced hEPO-Fc expression in both batch and fed-batch cultures. A low culture temperature significantly upregulated genes encoding UPR-specific transcriptional activators (xbp1s, ddit3, and atf5) and ER-resident proteins (grp78, grp94, trib3, and ero1α), that are associated with folding and processing of proteins within the ER. Further, low culture temperature decreased expression of genes involved in ERAD (edem3, sels, herpud1, and syvn1) indicating a decreased potential for protein degradation.

Reduction of charge variants by CHO cell culture process optimization

Over the past decade, global interest in the development of therapeutic monoclonal antibodies (mAbs) has risen rapidly. As therapeutic agents, antibodies have shown marked efficacy in combatting a range of cancers and immune diseases with high target specificity and low toxicity (Carla Lucia et al. in PLoS ONE 6:e24071, 2011; Donaghy in MAbs 8:659-671, 2016; Nasiri et al. in J Cell Physiol 9:6441-6457, 2018; Teo et al. in Cancer Immunol Immunother 61:2295-2309, 2012). Recent advances in cell culture technology, such as high-throughput clone screening, have facilitated antibody production at concentrations exceeding 10 g/L (Chen et al. in BMC Immunol 19:35, 2018; Huang et al. in Biotechnol Prog 26:1400-1410, 2010; Lu et al. in Biotechnol Bioeng 110:191-205, 2013; Singh et al. in Biotechnol Bioeng 113:698-716, 2016). As titers have improved, the industry has begun to focus on the adjustment of target antibody quality profiles to improve efficacy. Cell lines, culture media, and culture conditions impact protein quality (Van Beers and Bardor in Biotechnol J 7:1473-1484, 2012). Optimization of critical quality attributes (CQAs), such as charge variants, can be achieved through bioprocess development and is the preferred approach as changes to the cell line or growth media used is considered unfavorable by regulatory bodies (Gawlitzek et al. in Biotechnol Bioeng 103:1164-1175, 2009; Jordan et al. in Cytotechnology 65:31-40, 2013; Pan et al. in Cytotechnology 69:39-56, 2016). In this study, the effect of process control and ion supplementation on charge variants of mAbs produced by Chinese hamster ovary (CHO) cells was investigated. Results of this study demonstrated that the concentration of Zn²⁺, duration of culturing, and temperature affect charge variants of a given mAb. Under the optimum conditions of 3L bioreactors, the most significant was that Zn^{2 +} and temperature shift could further improve the quality of antibody. The main peak increased by 12%, and the acid peak decreased by 16%. At the same time, there was no significant loss of titer. This study provided supporting evidence for methods to improve charge variants arising during mAb production.

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

mRNA Transfection into CHO-Cells Reveals Production Bottlenecks

Obtaining highly productive Chinese hamster ovary (CHO)-cell clones for the production of therapeutic proteins relies on multiple time-consuming selection steps. Several CHO-cell strains with high degrees of genomic and epigenetic variation are available. Each harbor potential advantages and disadvantages for any given product, particularly those considered difficult to express. A simple test system to quickly assess compatibility of cell line and product may therefore prove useful. Transient plasmid transfection falls short of the specific productivities of stable producer cells, making it unsuitable for the elucidation of high specific productivity bottlenecks. The aim of the study is to reach specific productivities approaching those of industrial production cell lines by transfection of in vitro transcribed mRNA. The system is characterized with respect to transfection efficacy (by quantitative PCR) and protein production (by flow cytometry and biolayer interferometry). Fluorescence of intracellular eGFP saturates at higher amounts of mRNA per cell, while the amount of secreted and intracellular EPO-Fc remain linearly correlated to the amount of mRNA taken up. Nevertheless, MS shows a severe reduction in N-glycosylation quality. This method allows for rapid elucidation of bottlenecks that would otherwise remain undetected until later during cell line development, giving insight into suitable strategies for preemptive targeted metabolic engineering and host cell line optimization.

Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction

The cell culture medium is an intricate mixture of components which has a tremendous effect on cell growth and recombinant protein production. Regular cell culture medium includes various components, and the decision about which component should be included in the formulation and its optimum amount is an underlying issue in biotechnology industries. Applying conventional techniques to design an optimal medium for the production of a recombinant protein requires meticulous and immense research. Moreover, since the medium formulation for the production of one protein could not be the best choice for another protein, hence, the most suitable media should be determined for each recombinant cell line. Accordingly, medium formulation becomes a laborious, time-consuming, and costly process in biomanufacturing of recombinant protein, and finding alternative strategies for medium development seems to be crucial. In silico modeling is an attractive concept to be adapted for medium formulation due to its high potential to supersede laboratory examinations. By emerging the high-throughput datasets, scientists can disclose the knowledge about the effect of medium components on cell growth and metabolism, and via applying this information through systems biology approach, medium formulation optimization could be accomplished in silico with no need of significant amount of experimentation. This review demonstrates some of the applications of systems biology as a powerful tool for medium development and illustrates the effect of medium optimization with system-level analysis on the production of recombinant proteins in different host cells.

Metabolomic profiling of CHO fed-batch growth phases at 10, 100, and 1,000 L

Established bioprocess monitoring is based on quick and reliable methods, including cell count and viability measurement, extracellular metabolite measurement, and the measurement of physicochemical qualities of the cultivation medium. These methods are sufficient for monitoring of process performance, but rarely give insight into the actual physiological states of the cell culture. However, understanding of the latter is essential for optimization of bioprocess development. Our study used LC-MS metabolomics as a tool for additional resolution of bioprocess monitoring and was designed at three bioreactors scales (10 L, 100 L, and 1,000 L) to gain insight into the basal metabolic states of the Chinese hamster ovary (CHO) cell culture during fed-batch. Metabolites characteristics of the four growth stages (early and late exponential phase, stationary phase, and the phase of decline) were identified by multivariate analysis. Enriched metabolic pathways were then established for each growth phase using the CHO metabolic network model. Biomass generation and nucleotide synthesis were enriched in early exponential phase, followed by increased protein production and imbalanced glutathione metabolism in late exponential phase. Glycolysis became downregulated in stationary phase and amino-acid metabolism increased. Phase of culture decline resulted in rise of oxidized glutathione and fatty acid concentrations. Intracellular metabolic profiles of the CHO fed-batch culture were also shown to be consistent with scale and thus demonstrate metabolomic profiling as an informative method to gain physiological insight into the cell culture states during bioprocess regardless of scale.

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.

The Less the Better: How Suppressed Base Addition Boosts Production of Monoclonal Antibodies With Chinese Hamster Ovary Cells

Biopharmaceutical production processes strive for the optimization of economic efficiency. Among others, the maximization of volumetric productivity is a key criterion. Typical parameters such as partial pressure of CO2 (pCO2) and pH are known to influence the performance although reasons are not yet fully elucidated. In this study the effects of pCO2 and pH shifts on the phenotypic performance were linked to metabolic and energetic changes. Short peak performance of qmAb (23 pg/cell/day) was achieved by early pCO2 shifts up to 200 mbar but followed by declining intracellular ATP levels to 2.5 fmol/cell and 80% increase of qLac. On the contrary, steadily rising qmAb could be installed by slight pH down-shifts ensuring constant cell specific ATP production (qATP) of 27 pmol/cell/day and high intracellular ATP levels of about 4 fmol/cell. As a result, maximum productivity was achieved combining highest qmAb (20 pg/cell/day) with maximum cell density and no lactate formation. Our results indicate that the energy availability in form of intracellular ATP is crucial for maintaining antibody synthesis and reacts sensitive to pCO2 and pH-process parameters typically responsible for inhomogeneities after scaling up.

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.

Perfusion cultures require optimum respiratory ATP supply to maximize cell-specific and volumetric productivities

Perfusion processes are an emerging alternative to common fed-batch processes in the growing biopharmaceutical industry. However, the challenge of maintaining high cell-specific productivities remains. In this study, glucose limitation was applied to two perfusion steady states and compared with a third steady state without any detectable limitation. The metabolic phenotype was enhanced under glucose limitation with a decrease of 30% in glucose uptake and 75% in lactate formation. Cell-specific productivities were substantially improved by 50%. Remarkably, the productivities showed a strong correlation to respiratory adenosine triphosphate (ATP) supply. As less reduced nicotinamide adenine dinucleotide (NADH) remained in the cytosol, the ATP generation from oxidative phosphorylation was increased by almost 30%. Consequently, the efficiency of carbon metabolism and the resulting respiratory ATP supply was crucial for maintaining the highly productive cellular state. This study highlights that glucose limitation can be used for process intensification in perfusion cultures as ATP generation via respiration is significantly increased, leading to elevated productivities.

Impact of cell culture media additives on IgG glycosylation produced in Chinese hamster ovary cells

Glycosylation is a key critical quality attribute for monoclonal antibodies and other recombinant proteins because of its impact on effector mechanisms and half‐life. In this study, a variety of compounds were evaluated for their ability to modulate glycosylation profiles of recombinant monoclonal antibodies produced in Chinese hamster ovary cells. Compounds were supplemented into the cell culture feed of fed‐batch experiments performed with a CHO K1 and a CHO DG44 cell line expressing a recombinant immunoglobulin G1 (IgG1). Experiments were performed in spin tubes or the ambr®15 controlled bioreactor system, and the impact of the compounds at various concentrations was determined by monitoring the glycosylation profile of the IgG and cell culture parameters, such as viable cell density, viability, and titer. Results indicate that the highest impact on mannosylation was achieved through 15 µM kifunensine supplementation leading to an 85.8% increase in high‐mannose containing species. Fucosylation was reduced by 76.1% through addition of 800 µM 2‐F‐peracetyl fucose. An increase of 40.9% in galactosylated species was achieved through the addition of 120 mM galactose in combination with 48 µM manganese and 24 µM uridine. Furthermore, 6.9% increased sialylation was detected through the addition of 30 µM dexamethasone in combination with the same manganese, uridine, and galactose mixture used to increase total galactosylation. Further compounds or combinations of additives were also efficient at achieving a smaller overall glycosylation modulation, required, for instance, during the development of biosimilars. To the best of our knowledge, no evaluation of the efficacy of such a variety of compounds in the same cell culture system has been described. The studied cell culture media additives are efficient modulators of glycosylation and are thus a valuable tool to produce recombinant glycoproteins.

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

Impact of temperature and pH on recombinant human IgM quality attributes and productivity

IgM antibodies are arousing considerable interest as biopharmaceuticals. Despite their immunotherapeutic potential, little is known about the impact of environmental conditions on product quantity and quality of these complex molecules. Process conditions influence the critical quality attributes (CQAs) of therapeutic proteins and thus are important parameters for biological safety and efficacy. Here, the results of a systematic study are presented that characterized the influence of temperature and pH on cell-specific productivity and IgM quality attributes. Biphasic temperature and pH shift experiments were performed as batch cultures in DASGIP^® bioreactors under controlled conditions and defined by a specific design of experiment (DOE) approach. An internally-developed recombinant IgM producing CHO cell line was used. With respect to product quality, after an initial purification step efforts were focused on pentamer content, nucleic acid (NA) impurities and the glycosylation profile after an initial purification step. All quality attributes were evaluated by densitometric and chromatographic methods. The reduction of cultivation temperature severely reduced IgM titers, while pH variation had no impact. In contrast, IgM quality was not significantly influenced by bioprocessing parameters. Data revealed that an additional purification step is required to reduce the presence of NAs for in vivo applications. In conclusion, the results showed that for the chosen IgM model, IgM012_GL, variation in quality attributes is not caused by the environmental conditions of temperature and pH.

Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling

Temperature shift (TS) to a hypothermic condition has been widely used during protein production processes that use Chinese hamster ovary (CHO) cells. The effect of temperature on cell growth, metabolites, protein titer and quality depends on cell line, product, and other bioreactor conditions. Due to the large numbers of experiments, which typically last 2-3 weeks each, limited systematic TS studies have been reported with multiple shift temperatures and steps at different times. Here, we systematically studied the effect of temperature on cell culture performance for the production of two monoclonal antibodies by industrial GS and DG44 CHO cell lines. Three 2-8 day short-duration methods were developed and validated for researching the effect of many different temperatures on CHO cell culture and quality attributes. We found that minor temperature differences (1-1.5 °C) affected cell culture performance. The kinetic parameters extracted from the short duration data were subsequently used to compute and predict cell culture performance in extended duration of 10-14 days with multiple TS conditions for both CHO cell lines. These short-duration culture methods with kinetic modeling tools may be used for effective TS optimization to achieve the best profiles for cell growth, metabolites, titer and quality attributes. Although only three short-duration methods were developed with two CHO cell lines, similar short-duration methods with kinetic modeling may be applied for different hosts, including both microbial and other mammalian cells.