A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

Bioprocess biomarker identification and diagnosis for industrial mAb production based on metabolic profiling and multivariate data analysis

Monoclonal antibody (mAb) production is a complex bioprocess influenced by various cellular and metabolic factors. Understanding these interactions is critical for optimizing manufacturing and improving yields. In this study, we proposed a diagnostic and identification strategy using quantitative proton nuclear magnetic resonance (1H qNMR) technology-based pharmaceutical process-omics to analyze bioprocess variability and unveil significant metabolites affecting cell growth and yield during industrial mAb manufacturing. First, batch level model (BLM) and orthogonal partial least squares-discriminant analysis (OPLS-DA) identified glucose and lactate as primary contributors to culture run variability. Maintaining an optimal glucose set point was crucial for high-yield runs. Second, a partial least squares (PLS) regression model was established, which revealed viable cell density (VCD), along with glutamine, maltose, tyrosine, citrate, methionine, and lactate, as critical variables impacting mAb yield. Finally, hierarchical clustering analysis (HCA) highlighted one-carbon metabolism metabolites, such as choline, pyroglutamate, and formate, as closely associated with VCD. These findings provide a foundation for future bioprocess optimization through cell line engineering and media formulation adjustments, ultimately enhancing mAb production efficiency.

Flux Sampling Suggests Metabolic Signatures of High Antibody-Producing CHO Cells

Chinese hamster ovary (CHO) cells remain the industry standard for producing numerous therapeutic proteins, particularly monoclonal antibodies (mAbs). However, achieving higher recombinant protein titers remains an ongoing challenge and a fundamental understanding of the cellular mechanism driving improved bioprocess performance remains elusive. To directly address these challenges and achieve substantial improvements, a more in-depth understanding of cellular function within a bioprocess environment may be required. Over the past decade, significant advancements have been made in the building of genome-scale metabolic models (GEMs) for CHO cells, bridging the gap between high information content 'omics data and the ability to perform in silico phenotypic predictions. Here, time-course transcriptomics has been employed to constrain culture phase-specific GEMs, representing the early exponential, late exponential, and stationary/death phases of CHO cell fed-batch bioreactor culture. Temporal bioprocess data, including metabolite uptake and secretion rates, as well as growth and productivity, has been used to validate flux sampling results. Additionally, high mAb-producing solutions have been identified and the metabolic signatures associated with improved mAb production have been hypothesized. Finally, constraint-based modeling has been utilized to infer specific amino acids, cysteine, histidine, leucine, isoleucine, asparagine, and serine, which could drive increased mAb production and guide optimal media and feed formulations.

Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches

The production of monoclonal antibodies (mAbs) using Chinese Hamster Ovary (CHO) cells has revolutionized the treatment of numerous diseases, solidifying their position as a cornerstone of the biopharmaceutical industry. However, achieving maximum mAb production while upholding strict product quality standards remains a significant hurdle. Optimizing cell culture media emerges as a critical factor in this endeavor, requiring a nuanced understanding of the complex interplay of nutrients, growth factors, and other components that profoundly influence cellular growth, productivity, and product quality. Significant strides have been made in media optimization, including techniques such as media blending, one factor at a time, and statistical design of experiments approaches. The present review provides a comprehensive analysis of the recent advancements in culture media design strategies, focusing on the comparative application of systems biology (SB) and machine learning (ML) approaches. The applications of SB and ML in optimizing CHO cell culture medium and successful examples of their use are summarized. Finally, we highlight the immense potential of integrating SB and ML, emphasizing the development of hybrid models that leverage the strengths of both approaches for robust, efficient, and scalable optimization of mAb production in CHO cells. This review provides a roadmap for researchers and industry professionals to navigate the complex landscape of mAb production optimization, paving the way for developing next-generation CHO cell culture media that drive significant improvements in yield and productivity.

Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis

There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.

Driving towards digital biomanufacturing by CHO genome-scale models

Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.

A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process

The monoclonal antibody (mAb) manufacturing process comes with high profits and high costs, and thus mAb productivity is of vital importance. However, many factors can impact the cell culture process, and lead to mAb productivity reduction. Nowadays, the biopharma industry is actively employing manufacturing information systems, which enable the integration of both online data and offline data. Although the volume of data is large, related data mining studies for mAb productivity improvement are rare. Therefore, a data-driven approach is proposed in this study to leverage both the inline and offline data of the cell culture process to discover the causes of mAb productivity reduction. The approach consists of four steps, namely data preprocessing, phase division, feature extraction and fusion, and cluster comparing. First, data quality issues are solved during the data preprocessing step. Next, the inline data are divided into several phases based on the moving window k-nearest neighbor method. Then, the inline data features are extracted via functional data analysis and combined with the offline data features. Finally, the causes of mAb productivity reduction are identified using the contrasting clusters via the principal component analysis method. A commercial-scale cell culture process case study is provided in this research to verify the effectiveness of the approach. Data from 35 batches were collected, and each batch contained nine inline variables and seven offline variables. The causes of mAb productivity reduction were identified to be the lack of nutrients, and recommended actions were taken according to the result, which was subsequently proven by six validation batches.

Enhancing recombinant antibody yield in Chinese hamster ovary cells

A range of recombinant monoclonal antibodies (rMAbs) have found application in treating diverse diseases, spanning various cancers and immune system disorders. Chinese hamster ovary (CHO) cells have emerged as the predominant choice for producing these rMAbs due to their robustness, ease of transfection, and capacity for posttranslational modifications akin to those in human cells. Transient transfection and/or stable expression could be conducted to express rMAbs in CHO cells. To bolster the yield of rMAbs in CHO cells, a multitude of approaches have been developed, encompassing vector optimization, medium formulation, cultivation parameters, and cell engineering. This review succinctly outlines these methodologies when also addressing challenges encountered in the production process, such as issues with aggregation and fucosylation.

Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways

Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.

Flux Balance Analysis of Mammalian Cell Systems

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

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.

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13 C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.

A genome-scale metabolic model of the effect of dissolved oxygen on 1,3-propanediol fermentation by Klebsiella pneumoniae

Although 1,3-propanediol (1,3-PD) is usually considered an anaerobic fermentation product from glycerol by Klebsiella pneumoniae, microaerobic conditions proved to be more conducive to 1,3-PD production. In this study, a genome-scale metabolic model (GSMM) specific to K. pneumoniae KG2, a high 1.3-PD producer, was constructed. The iZY1242 model contains 2090 reactions, 1242 genes and 1433 metabolites. The model was not only able to accurately characterise cell growth, but also accurately simulate the fed-batch 1,3-PD fermentation process. Flux balance analyses by iZY1242 was performed to dissect the mechanism of stimulated 1,3-PD production under microaerobic conditions, and the maximum yield of 1,3-PD on glycerol was 0.83 mol/mol under optimal microaerobic conditions. Combined with experimental data, the iZY1242 model is a useful tool for establishing the best conditions for microaeration fermentation to produce 1,3-PD from glycerol in K. pneumoniae.

A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models

The optimization of animal feeds and cell culture media are problems of interest to a wide range of industries and scientific disciplines. Both problems are dictated by the properties of an organism's metabolism. However, due to the tremendous complexity of metabolic systems, it can be difficult to predict how metabolism will respond to changes in nutrient availability. A common tool used to capture the complexity of metabolism in a computational framework is a genome-scale metabolic model (GEM). GEMs are useful for predicting the fluxes of reactions within an organism's metabolism. To optimize feed or media, in silico experiments can be performed with GEMs by systematically varying nutritional constraints and predicting metabolic activity. In this way, the influence of various nutritional changes on metabolic outcomes can be evaluated. However, this methodology does not guarantee an optimal solution. Here, we develop a nutrition algorithm that utilizes linear programming to search the entire flux solution space of possible dietary intervention strategies to identify the most efficient changes to nutrition for a desirable metabolic outcome. We illustrate the utility of the nutrition algorithm on GEMs of Atlantic salmon (Salmo salar) and Chinese hamster ovary (CHO) cell metabolism and find that the nutrition algorithm makes predictions that not only align with experimental findings but reveal new insights into promising feeding strategies. We show that the nutrition algorithm is highly versatile and customizable to meet the user's needs. For instance, we demonstrate that the nutrition algorithm can be used to predict feed/media compositions that maximize profit margins. While the nutrition algorithm can be used to define an optimal feed/medium ab initio, it can also identify minimal changes to be made to an existing feed/medium to drive the largest metabolic shift. Moreover, the nutrition algorithm can target multiple metabolic pathways simultaneously with only a marginal increase in computational expense. While the nutrition algorithm has its limitations, we believe that this tool can be leveraged in a broad range of biotechnological applications to enhance the feed/medium optimization process.

Enhancing recombinant protein and viral vector production in mammalian cells by targeting the YTHDF readers of N6 -methyladenosine in mRNA

N⁶ -methyladenosine (m6A) is the most abundant internal modification on eukaryotic mRNA and has been implicated in a wide range of fundamental cellular processes. This modification is regulated and interpreted by a set of writer, eraser, and reader proteins. To date, there have been no reports on the potential of mRNA epigenetic regulators to influence recombinant protein expression in mammalian cells. In this study, the potential of manipulating the expression of the m6A YTH domain-containing readers, YTHDF1, 2 and 3 to improve recombinant protein yield based on their role in regulating mRNA stability and promoting translation were evaluated. Using siRNA-mediated gene depletion, cDNA over-expression, and methylation-specific RNA immunoprecipitation, it is demonstrated that (i) knock-down of YTHDF2 enhances (~2-fold) the levels of recombinant protein derived from GFP and EPO transgenes in CHO cells; (ii) the effects of YTHDF2 depletion on transgene expression is m6A-mediated; and (iii) YTHDF2 depletion, or over-expression of YTHDF1 increases viral protein expression and yield of infectious lentiviral (LV) particles (~2-3-fold) in HEK293 cells. We conclude that various transgenes can be subjected to regulation by m6A regulators in mammalian cell lines and that these findings demonstrate the utility of epitranscriptomic-based approaches to host cell line engineering for improved recombinant protein and viral vector production.

Progress in fed-batch culture for recombinant protein production in CHO cells

Nearly 80% of the approved human therapeutic antibodies are produced by Chinese Hamster Ovary (CHO) cells. To achieve better cell growth and high-yield recombinant protein, fed-batch culture is typically used for recombinant protein production in CHO cells. According to the demand of nutrients consumption, feed medium containing multiple components in cell culture can affect the characteristics of cell growth and improve the yield and quality of recombinant protein. Fed-batch optimization should have a connection with comprehensive factors such as culture environmental parameters, feed composition, and feeding strategy. At present, process intensification (PI) is explored to maintain production flexible and meet forthcoming demands of biotherapeutics process. Here, CHO cell culture, feed composition in fed-batch culture, fed-batch culture environmental parameters, feeding strategies, metabolic byproducts in fed-batch culture, chemostat cultivation, and the intensified fed-batch are reviewed. KEY POINTS: • Fed-batch culture in CHO cells is reviewed. • Fed-batch has become a common technology for recombinant protein production. • Fed batch culture promotes recombinant protein production in CHO cells.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

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.

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production

PURPOSE

Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain. In the present study, a genome-scale metabolic model (GEM)-guided engineering strategy is proposed to identify gene targets for improved antiEpEX-scFv production in E. coli.

METHODS

In this study, a genome-scale metabolic model of E. coli (iJO1366) and a metabolic modeling tool (FVSEOF) were employed to find appropriate genes to be amplified in order to improve the strain for incresed production of antiEpEX-scFv. To validate the model predictions, one target gene was overexpressed in the parent strain Escherichia coli BW25113 (DE3).

RESULTS

For improving scFv production, we applied the FVSEOF method to identify a number of potential genetic engineering targets. These targets were found to be localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, the glk gene encoding glucokinase was chosen to be overexpressed in the parent strain Escherichia coli BW25113 (DE3). By overexpressing glk, the growth capacity of the recombinant E. coli strain was recovered. Moreover, the engineered strain with glk overexpression successfully led to increased scFv production.

CONCLUSION

The genome-scale metabolic modeling can be considered for the improvement of the production of other recombinant proteins.

Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells

Recombinant protein production can cause severe stress on cellular metabolism, resulting in limited titer and product quality. To investigate cellular and metabolic characteristics associated with these limitations, we compare HEK293 clones producing either erythropoietin (EPO) (secretory) or GFP (non-secretory) protein at different rates. Transcriptomic and functional analyses indicate significantly higher metabolism and oxidative phosphorylation in EPO producers compared with parental and GFP cells. In addition, ribosomal genes exhibit specific expression patterns depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased EPO secretion, we detect higher gene expression related to negative regulation of endoplasmic reticulum (ER) stress, including upregulation of ATF6B, which aids EPO production in a subset of clones by overexpression or small interfering RNA (siRNA) knockdown. Our results offer potential target pathways and genes for further development of the secretory power in mammalian cell factories.

Strategies and Considerations for Improving Recombinant Antibody Production and Quality in Chinese Hamster Ovary Cells

Recombinant antibodies are rapidly developing therapeutic agents; approximately 40 novel antibody molecules enter clinical trials each year, most of which are produced from Chinese hamster ovary (CHO) cells. However, one of the major bottlenecks restricting the development of antibody drugs is how to perform high-level expression and production of recombinant antibodies. The high-efficiency expression and quality of recombinant antibodies in CHO cells is determined by multiple factors. This review provides a comprehensive overview of several state-of-the-art approaches, such as optimization of gene sequence of antibody, construction and optimization of high-efficiency expression vector, using antibody expression system, transformation of host cell lines, and glycosylation modification. Finally, the authors discuss the potential of large-scale production of recombinant antibodies and development of culture processes for biopharmaceutical manufacturing in the future.

Towards Autonomous Process Control—Digital Twin for CHO Cell-Based Antibody Manufacturing Using a Dynamic Metabolic Model

The development of new biologics is becoming more challenging due to global competition and increased requirements for process understanding and assured quality in regulatory approval. As a result, there is a need for predictive, mechanistic process models. These reduce the resources and time required in process development, generating understanding, expanding the possible operating space, and providing the basis for a digital twin for automated process control. Monoclonal antibodies are an important representative of industrially produced biologics that can be used for a wide range of applications. In this work, the validation of a mechanistic process model with respect to sensitivity, as well as accuracy and precision, is presented. For the investigated process conditions, the concentration of glycine, phenylalanine, tyrosine, and glutamine have been identified as significant influencing factors for product formation via statistical evaluation. Cell growth is, under the investigated process conditions, significantly dependent on the concentration of glucose within the investigated design space. Other significant amino acids were identified. A Monte Carlo simulation was used to simulate the cultivation run with an optimized medium resulting from the sensitivity analysis. The precision of the model was shown to have a 95% confidence interval. The model shown here includes the implementation of cell death in addition to models described in the literature.

Advances of Glycometabolism Engineering in Chinese Hamster Ovary Cells

As the most widely used mammalian cell line, Chinese hamster ovary (CHO) cells can express various recombinant proteins with a post translational modification pattern similar to that of the proteins from human cells. During industrial production, cells need large amounts of ATP to support growth and protein expression, and since glycometabolism is the main source of ATP for cells, protein production partly depends on the efficiency of glycometabolism. And efficient glycometabolism allows less glucose uptake by cells, reducing production costs, and providing a better mammalian production platform for recombinant protein expression. In the present study, a series of progresses on the comprehensive optimization in CHO cells by glycometabolism strategy were reviewed, including carbohydrate intake, pyruvate metabolism and mitochondrial metabolism. We analyzed the effects of gene regulation in the upstream and downstream of the glucose metabolism pathway on cell’s growth and protein expression. And we also pointed out the latest metabolic studies that are potentially applicable on CHO cells. In the end, we elaborated the application of metabolic models in the study of CHO cell metabolism.

Clever Experimental Designs: Shortcuts for Better iPSC Differentiation

For practical use of pluripotent stem cells (PSCs) for disease modelling, drug screening, and regenerative medicine, the cell differentiation process needs to be properly refined to generate end products with consistent and high quality. To construct and optimize a robust cell-induction process, a myriad of cell culture conditions should be considered. In contrast to inefficient brute-force screening, statistical design of experiments (DOE) approaches, such as factorial design, orthogonal array design, response surface methodology (RSM), definitive screening design (DSD), and mixture design, enable efficient and strategic screening of conditions in smaller experimental runs through multifactorial screening and/or quantitative modeling. Although DOE has become routinely utilized in the bioengineering and pharmaceutical fields, the imminent need of more detailed cell-lineage specification, complex organoid construction, and a stable supply of qualified cell-derived material requires expedition of DOE utilization in stem cell bioprocessing. This review summarizes DOE-based cell culture optimizations of PSCs, mesenchymal stem cells (MSCs), hematopoietic stem cells (HSCs), and Chinese hamster ovary (CHO) cells, which guide effective research and development of PSC-derived materials for academic and industrial applications.

A Beginner's Guide to the COBRA Toolbox

COBRA toolbox is one of the most popular tools for systems biology analyses using genome-scale metabolic reconstructions. The toolbox permits the use of many constraint-based analytical methods for examining characteristics of metabolism in the biosystems ranging in complexity from single cells to microbial communities and ultimately multicellular organisms. The toolbox has a number of different variants that can be used depending on a user's choice of programming language. Here, I provide a basic tutorial for beginners that plan to use the original MATLAB version of the toolbox.

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.

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 emerging role of cellular post-translational modifications in modulating growth and productivity of recombinant Chinese hamster ovary cells

Chinese hamster ovary (CHO) cells are one of the most commonly used host cell lines used for the production human therapeutic proteins. Much research over the past two decades has focussed on improving the growth, titre and cell specific productivity of CHO cells and in turn lowering the costs associated with production of recombinant proteins. CHO cell engineering has become of particular interest in recent years following the publication of the CHO cell genome and the availability of data relating to the proteome, transcriptome and metabolome of CHO cells. However, data relating to the cellular post-translational modification (PTMs) which can affect the functionality of CHO cellular proteins has only begun to be presented in recent years. PTMs are important to many cellular processes and can further alter proteins by increasing the complexity of proteins and their interactions. In this review, we describe the research presented from CHO cells to date related on three of the most important PTMs; glycosylation, phosphorylation and ubiquitination.

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

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.

Systematically gap-filling the genome-scale metabolic model of CHO cells

OBJECTIVE

Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.

RESULTS

We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.

CONCLUSIONS

The present CHO model is an important step towards more complete metabolic models of CHO cells.

A Systems-Based Approach for Cyanide Overproduction by Bacillus megaterium for Gold Bioleaching Enhancement

With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable development. Bacillus megaterium is currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering of B. megaterium to overproduce cyanide, and thus ameliorate the bioleaching process. For this, we employed constraint-based modeling, running in silico simulations on iJA1121, the genome-scale metabolic model of B. megaterium DSM319. Flux balance analysis (FBA) was initially used to identify amino acids to be added to the culture medium. Considering cyanide as the desired product, we used growth-coupled methods, constrained minimal cut sets (cMCSs) and OptKnock to identify gene inactivation targets. To identify gene overexpression targets, flux scanning based on enforced objective flux (FSEOF) was performed. Further analysis was carried out on the identified targets to determine compounds with beneficial regulatory effects. We have proposed a chemical-defined medium for accelerating cyanide production on the basis of microplate assays to evaluate the components with the greatest improving effects. Accordingly, the cultivation of B. megaterium DSM319 in a chemically-defined medium with 5.56 mM glucose as the carbon source, and supplemented with 413 μM cysteine, led to the production of considerably increased amounts of cyanide. Bioleaching experiments were successfully performed in this medium to recover gold and copper from telecommunication printed circuit boards. The results of inductively coupled plasma (ICP) analysis confirmed that gold recovery peaked out at around 55% after 4 days, whereas copper recovery continued to increase for several more days, peaking out at around 85%. To further validate the bioleaching results, FESEM, XRD, FTIR, and EDAX mapping analyses were performed. We concluded that the proposed strategy represents a viable route for improving the performance of the bioleaching processes.