CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

Fed-batch performance profiles for mAb production using different intensified N - 1 seed strategies are CHO cell-line dependent

Recent optimizations of cell culture processes have focused on the final seed scale-up step (N - 1 stage) used to inoculate the production bioreactor (N-stage bioreactor) to enable higher inoculation cell densities (2-20 × 106 cells/mL), which could shorten the production culture duration and/or increase the volumetric productivity. N - 1 seed process intensification can be achieved by either non-perfusion (enriched-batch or fed-batch) or perfusion culture to reach those higher final N - 1 viable cell densities (VCD). In this study, we evaluated how different N - 1 intensification strategies, specifically enriched-batch (EB) N - 1 versus perfusion N - 1, affect cell growth profiles and monoclonal antibody (mAb) productivity in the final N-stage production bioreactor operated in fed-batch mode. Three representative Chinese Hamster Ovary (CHO) cell lines producing different mAbs were cultured using either EB or perfusion N - 1 seeds and found that the N-stage cell growth and mAb productivities were comparable between EB N - 1 and perfusion N - 1 conditions for two of the cell lines but were very different for the third. In addition, within the two similar cell growth cell lines, differences in cell-specific productivity were observed. This suggests that the impact of the N - 1 intensification process on production was cell-line dependent. This study revealed that the N - 1 intensification strategy and the state of seeds from the different N - 1 conditions may affect the outcome of the N production stage, and thus, the choice of N - 1 intensification strategy could be a new target for future upstream optimization of mAb production.

Debottlenecking and reformulating feed media for improved CHO cell growth and titer by data-driven and model-guided analyses

Designing and selecting cell culture media along with their feeding are a key strategy to maximize culture performance in biopharmaceutical processes. However, the sensitivity of mammalian cells to their culture environment necessitates specific nutritional requirements for their growth and the production of high-quality proteins such as antibodies, depending on the cell lines and operational conditions employed. In this regard, previously we developed a data-driven and in-silico model-guided systematic framework to investigate the effect of growth media on Chinese hamster ovary (CHO) cell culture performance, allowing us to design and reformulate basal media. To expand our exploration for media development research, we evaluated two chemically defined feed media, A and B, using a monoclonal antibody-producing CHO-K1 cell line in ambr15 bioreactor runs. We observed a significant impact of the feed media on various aspects of cell culture, including growth, longevity, viability, productivity, and the production of toxic metabolites. Specifically, the concentrated feed A was inadequate in sustaining prolonged cell culture and achieving high titers when compared to feed B. Within our framework, we systematically investigated the major metabolic bottlenecks in the tricarboxylic acid cycle and relevant amino acid transferase reactions. This analysis identified target components that play a crucial role in alleviating bottlenecks and designing highly productive cell cultures, specifically the addition of glutamate to feed A and asparagine to feed B. Based on our findings, we reformulated the feeds by adjusting the amounts of the targeted amino acids and successfully validated the effectiveness of the strategy in promoting cell growth, life span, and/or titer.

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.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.

Understanding the effect of temperature downshift on CHO cell growth, antibody titer and product quality by intracellular metabolite profiling and in vivo monitoring of redox state

The strategy of temperature downshift has been widely used in the biopharmaceutical industry to improve antibody production and cell-specific production rate (qp ) with Chinese hamster ovary cells (CHO). However, the mechanism of temperature-induced metabolic rearrangement, especially important intracellular metabolic events, remains poorly understood. In this work, in order to explore the mechanisms of temperature-induced cell metabolism, we systematically assessed the differences in cell growth, antibody expression, and antibody quality between high-producing (HP) and low-producing (LP) CHO cell lines under both constant temperature (37°C) and temperature downshift (37°C→33°C) settings during fed-batch culture. Although the results showed that low-temperature culture during the late phase of exponential cell growth significantly reduced the maximum viable cell density (p < 0.05) and induced cell cycle arrest in the G0/G1 phase, this temperature downshift led to a higher cellular viability and increased antibody titer by 48% and 28% in HP and LP CHO cell cultures, respectively (p < 0.001), and favored antibody quality reflected in reduced charge heterogeneity and molecular size heterogeneity. Combined extra- and intra-cellular metabolomics analyses revealed that temperature downshift significantly downregulated intracellular glycolytic and lipid metabolic pathways while upregulated tricarboxylic acid (TCA) cycle, and particularly featured upregulated glutathione metabolic pathways. Interestingly, all these metabolic pathways were closely associated with the maintenance of intracellular redox state and oxidative stress-alleviating strategies. To experimentally address this, we developed two high-performance fluorescent biosensors, denoted SoNar and iNap1, for real-time monitoring of intracellular nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide + hydrogen (NAD+ /NADH) ratio and nicotinamide adenine dinucleotide phosphate (NADPH) amount, respectively. Consistent with such metabolic rearrangements, the results showed that temperature downshift decreased the intracellular NAD+ /NADH ratio, which might be ascribed to the re-consumption of lactate, and increased the intracellular NADPH amount (p < 0.01) to scavenge intracellular reactive oxygen species (ROS) induced by the increased metabolic requirements for high-level expression of antibody. Collectively, this study provides a metabolic map of cellular metabolic rearrangement induced by temperature downshift and demonstrates the feasibility of real-time fluorescent biosensors for biological processes, thus potentially providing a new strategy for dynamic optimization of antibody production processes.

Experimental studies from shake flasks to 3 L stirred tank bioreactor of nutrients and oxygen supply conditions to improve the growth of the avian cell line DuckCelt®-T17

BACKGROUND

To produce viral vaccines, avian cell lines are interesting alternatives to replace the egg-derived processes for viruses that do not grow well on mammalian cells. The avian suspension cell line DuckCelt^®-T17 was previously studied and investigated to produce a live attenuated metapneumovirus (hMPV)/respiratory syncytial virus (RSV) and influenza virus vaccines. However, a better understanding of its culture process is necessary for an efficient production of viral particles in bioreactors.

RESULTS

The growth and metabolic requirements of the avian cell line DuckCelt^®-T17 were investigated to improve its cultivation parameters. Several nutrient supplementation strategies were studied in shake flasks highlighting the interest of (i) replacing L-glutamine by glutamax as main nutrient or (ii) adding these two nutrients in the serum-free growth medium in a fed-batch strategy. The scale-up in a 3 L bioreactor was successful for these types of strategies confirming their efficiencies in improving the cells' growth and viability. Moreover, a perfusion feasibility test allowed to achieve up to ~ 3 times the maximum number of viable cells obtained with the batch or fed-batch strategies. Finally, a strong oxygen supply - 50% dO₂ - had a deleterious effect on DuckCelt^®-T17 viability, certainly because of the greater hydrodynamic stress imposed.

CONCLUSIONS

The culture process using glutamax supplementation with a batch or a fed-batch strategy was successfully scaled-up to 3 L bioreactor. In addition, perfusion appeared as a very promising culture process for subsequent continuous virus harvesting.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Kinetic modeling: A tool for temperature shift and feeding optimization in cell culture process development

Mammalian cells have dominated the biopharmaceutical industry for biotherapeutic protein production and tremendous efforts have been devoted to enhancing productivity during the cell culture process development. However, determining the optimal process conditions is still a huge challenge. Constrained by the limited resources and timeline, usually it is impossible to fully explore the optimal range of all process parameters (temperature, pH, dissolved oxygen, basal and feeding medium, additives, etc.). Kinetic modeling, which finds out the global optimum by systematically screening all potential conditions for cell culture process, provides a solution to this dilemma. However, studies on optimizing temperature shift and feeding strategies simultaneously using this approach have not been reported. In this study, we built up a kinetic model of fed-batch culture process for simultaneous optimization of temperature shift and feeding strategies. The fitting results showed high accuracy and demonstrated that the kinetic model can be used to describe the mammalian cell culture performance. In addition, five more fed-batch experiments were conducted to test this model's predicting power on different temperature shift and feeding strategies. It turned out that the predicted data matched well with experimental ones on viable cell density (VCD), metabolites, and titer for the entire culture duration and allowed selecting the same best condition with the experimental results. Therefore, adopting this approach can potentially reduce the number of experiments required for culture process optimization.

Antibody charge variant modulation by in vitro enzymatic treatment in different CHO cell cultures

Charge variants represent a critical quality attribute that must be controlled during the development and manufacturing of monoclonal antibodies (mAb). Previously, we reported the development of a cost-effective enzymatic treatment capable of removing the C-terminal lysine from a mAb produced by a Chinese hamster ovary (CHO) GS cell line. This treatment resulted in a significant decrease in basic charge variants and a corresponding improvement in the main peak, enabling a longer cell culture production duration for titer improvement. Here, we describe this enzymatic treatment protocol in detail and demonstrate its applicability to two additional mAbs produced by distinct industrial cell lines. The simple addition of carboxypeptidase B (CpB) at a ratio of 1:10,000 (w/w) to whole cell cultures significantly improved the main peaks for both mAbs without affecting other critical quality attributes, including size exclusion chromatography impurities and N-glycans. Our results demonstrate that this in vitro CpB treatment protocol can be used as a platform strategy to improve main peak for mAbs that exhibit high levels of basic variants attributable to C-terminal lysines. An in vitro enzymatic treatment in general may be another good addition to existing in vivo CHO cell culture strategies for titer improvement and control of critical quality attributes. This article is protected by copyright. All rights reserved.

Insights into the Impact of Rosmarinic Acid on CHO Cell Culture Improvement through Transcriptomics Analysis

The use of antioxidants in Chinese hamster ovary (CHO) cell cultures to improve monoclonal antibody production has been a topic of great interest. Nevertheless, the antioxidants do not have consistent benefits of production improvement, which might be cell line specific and/or process specific. In this work, we investigated how treatment with the antioxidant rosmarinic acid (RA) improved cell growth and titer in CHO cell cultures using transcriptomics. In particular, transcriptomics analysis indicated that RA treatment modified gene expression and strongly affected the MAPK and PI3K/Akt signaling pathways, which regulate cell survival and cell death. Moreover, it was observed that these signaling pathways, which had been identified to be up-regulated on day 2 and day 6 by RA, were also up-regulated over time (from initial growth phase day 2 to slow growth or protein production phase day 6) in both conditions. In summary, this transcriptomics analysis provides insights into the role of the antioxidant RA in industrial cell culture processes. The current study also represents an example in the industry of how omics can be applied to gain an in-depth understanding of CHO cell biology and to identify critical pathways that can contribute to cell culture process improvement and cell line engineering.

Valine feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of CHO cells

Ammonia is a toxic byproduct of CHO cell metabolism, which inhibits cell growth, reduces cell viability, alters glycosylation, and decreases recombinant protein productivity. In an attempt to minimize the ammonium accumulation in cell culture media, different amino acids were added individually to the culture medium before the production phase to alleviate the negative effects of ammonium on cell culture performance. Among all the amino acids examined in this study, valine showed the most positive impact on CHO cell culture performance. When the cultured CHO cells were fed with 5 mM valine, EPO titer was increased by 25% compared to the control medium, and ammonium and lactate production were decreased by 23 and 26%, respectively, relative to the control culture. Moreover, the sialic acid content of the EPO protein in valine-fed culture was higher than in the control culture, most likely because of the lower ammonium concentration. Flux balance analysis (FBA) results demonstrated that the citric acid cycle was enriched by valine feeding. The measurement of TCA cycle activity supported this finding. The analysis revealed that there might be a link between promoting tricarboxylic acid (TCA) cycle metabolism in valine-fed culture and reduction in lactate and ammonia accumulation. Furthermore, in valine-fed culture, FBA outcomes showed that alanine was excreted into the medium as the primary mechanism for reducing ammonium concentration. It was predicted that the elevated TCA cycle metabolism was concurrent with an increment in recombinant protein production. Taken together, our data demonstrate that valine addition could be an effective strategy for mitigating the negative impacts of ammonium and enhancing glycoprotein production in both quality and quantity. KEY POINTS: • Valine feeding can mitigate the negative impacts of ammonia on CHO cell growth. • Valine addition assists the ammonia removal mechanism by enriching the TCA cycle. • Ammonia is removed from the media through alanine excretion in valine-fed culture.

A Metabolomics Approach to Increasing Chinese Hamster Ovary (CHO) Cell Productivity

Much progress has been made in improving the viable cell density of bioreactor cultures in monoclonal antibody production from Chinese hamster ovary (CHO) cells; however, specific productivity (qP) has not been increased to the same degree. In this work, we analyzed a library of 24 antibody-expressing CHO cell clones to identify metabolites that positively associate with qP and could be used for clone selection or medium supplementation. An initial library of 12 clones, each producing one of two antibodies, was analyzed using untargeted LC-MS experiments. Metabolic model-based annotation followed by correlation analysis detected 73 metabolites that significantly correlated with growth, qP, or both. Of these, metabolites in the alanine, aspartate, and glutamate metabolism pathway, and the TCA cycle showed the strongest association with qP. To evaluate whether these metabolites could be used as indicators to identify clones with potential for high productivity, we performed targeted LC-MS experiments on a second library of 12 clones expressing a third antibody. These experiments found that aspartate and cystine were positively correlated with qP, confirming the results from untargeted analysis. To investigate whether qP correlated metabolites reflected endogenous metabolic activity beneficial for productivity, several of these metabolites were tested as medium additives during cell culture. Medium supplementation with citrate improved qP by up to 490% and more than doubled the titer. Together, these studies demonstrate the potential for using metabolomics to discover novel metabolite additives that yield higher volumetric productivity in biologics production processes.

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.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.