Impact of CHO Metabolism on Cell Growth and Protein Production: An Overview of Toxic and Inhibiting Metabolites and Nutrients

Quantitative 1H NMR optimization for high-throughput metabolite analysis in industrial bioprocess monitoring

Quantitative 1H NMR (1H qNMR) is an ideal tool for bioprocess monitoring because it can comprehensively detect and quantify diverse metabolites that significantly influence bioprocess performance. However, the long experiment time associated with the 1H qNMR, due to the long longitudinal relaxation time (T1) of some metabolites, does not meet the requirements for high-throughput analysis. We developed a high-throughput 1H qNMR method for bioprocess analysis using a short relaxation delay (D1) to reduce analytical time and a correction factor (k) to compensate for incomplete relaxation. A total of 27 metabolites were quantified using spectral deconvolution via a peak fitting algorithm and MCR-ALS. Methodological validation results indicated that the precision and accuracy of the developed qNMR method were consistently high across different D1 values, with LOQs ranging from 0.008 to 0.13 mM and LODs ranging from 0.024 to 0.38 mM. Notably, a longer D1 value generally resulted in lower LODs and LOQs for most metabolites. A D1 value of 4 s was optimal for balancing analysis time and performance. The method is broadly applicable for bioprocess monitoring and control, offering valuable guidance for optimizing CHO cell culture processes and improving yield.

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.

Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models

Background: Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. Methods: Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. Results: PLS models exhibited high predictive accuracy (R2 > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. Conclusions: This study confirms Raman spectroscopy's potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production.

NEXT-FBA: A hybrid stoichiometric/data-driven approach to improve intracellular flux predictions

Genome-scale metabolic models (GEMs) have been widely utilized to understand cellular metabolism. The application of GEMs has been advanced by computational methods that enable the prediction and analysis of intracellular metabolic states. However, the accuracy and biological relevance of these predictions often suffer from the many degrees of freedom and scarcity of available data to constrain the models adequately. Here, we introduce Neural-net EXtracellular Trained Flux Balance Analysis, (NEXT-FBA), a novel computational methodology that addresses these limitations by utilizing exometabolomic data to derive biologically relevant constraints for intracellular fluxes in GEMs. We achieve this by training artificial neural networks (ANNs) with exometabolomic data from Chinese hamster ovary (CHO) cells and correlating it with 13C-labeled intracellular fluxomic data. By capturing the underlying relationships between exometabolomics and cell metabolism, NEXT-FBA predicts upper and lower bounds for intracellular reaction fluxes to constrain GEMs. We demonstrate the efficacy of NEXT-FBA across several validation experiments, where it outperforms existing methods in predicting intracellular flux distributions that align closely with experimental observations. Furthermore, a case study demonstrates how NEXT-FBA can guide bioprocess optimization by identifying key metabolic shifts and refining flux predictions to yield actionable process and metabolic engineering targets. Overall, NEXT-FBA aims to improve the accuracy and biological relevance of intracellular flux predictions in metabolic modelling, with minimal input data requirements for pre-trained models.

Investigating the metabolic load of monoclonal antibody production conveyed to an inducible CHO cell line using a transfer-rate online monitoring system

Shake flasks are a foundational tool in early process development by allowing high throughput exploration of the design space. However, lack of online data at this scale can hamper rapid decision making. Oxygen transfer rate (OTR) monitoring has been readily applied as an online process characterization tool at the benchtop bioreactor scale. Recent advances in modern sensing technology have allowed OTR monitoring to be available at the shake flask level. It is now possible to multiplex time-of-action (e.g., Induction, temperature shift, pH shift, feeding initiation, point of harvest) characterization studies by relying on careful analysis of OTR profile kinetics. As a result, there is potential to save time and capital expenditures while exploring process intensification studies though accurate and physiologically relevant online data. In this article, we detail the application of OTR monitoring to characterize the impact that recombinant protein production has on an inducible CHO cell line expressing Palivizumab. We then test out time-of-action studies to intensify protein production outcomes. We observe that recombinant protein expression causes a metabolic load that diminishes potential biomass growth. As a result, when compared to a control standard process, delaying induction and temperature shift has the potential to improve viable cell densities (VCD) by 2-fold thus increasing recombinant protein yield by over 30 %. The study also demonstrates that OTR can serve as a useful tool to detect cessation of exponential growth. Consequently, time-of-action points that are characteristic of inducible systems can be formulated accurately and reliably to maximize production performance.

HEK-omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, robust, scalable, industrialized production is required to meet demand and provide affordable patient access, which has not yet materialized. Closing the chasm between demand and supply requires innovation in biomanufacturing to achieve the essential step change in rAAV product yield and quality. Omics provides a rich source of mechanistic knowledge that can be applied to HEK293, the most commonly used cell line for rAAV production. In this review, the findings from a growing number of diverse studies that apply genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293 bioproduction are explored. Learnings from CHO-omics, application of omics approaches to improve CHO bioproduction, provide a framework to explore the potential of "HEK-omics" as a multi-omics-informed approach providing actionable mechanistic insights for improved transient and stable production of rAAV and other recombinant products in HEK293.

Time-Resolved Hierarchical Modeling Highlights Metabolites Influencing Productivity and Cell Death in Chinese Hamster Ovary Cells

Biopharmaceuticals are medical compounds derived from biological sources and are often manufactured by living cells, primarily Chinese hamster ovary (CHO) cells. CHO cells display variation among cell clones, leading to growth and productivity differences that influence the product's quantity and quality. The biological and environmental factors behind these differences are not fully understood. To identify metabolites with a consistent relationship to productivity or cell death over time, we analyzed the extracellular metabolome of 11 CHO clones with different growth and productivity characteristics over 14 days. However, in bioreactor processes, metabolic profiles and process variables are both strongly time-dependent, confounding the metabolite-process variable relationship. To address this, we customized an existing hierarchical approach for handling time dependency to highlight metabolites with a consistent correlation to a process variable over a selected timeframe. We benchmarked this new method against conventional orthogonal partial least squares (OPLS) models. Our hierarchical method highlighted several metabolites consistently related to productivity or cell death that the conventional method missed. These metabolites were biologically relevant; most were known already, but some that had not been reported in CHO literature before, such as 3-methoxytyrosine and succinyladenosine, had ties to cell death in studies with other cell types. The metabolites showed an inverse relationship with the response variables: those positively correlated with productivity were typically negatively correlated with the death rate, or vice versa. For both productivity and cell death, the citrate cycle and adjacent pathways (pyruvate, glyoxylate, pantothenate) were among the most important. In summary, we have proposed a new method to analyze time-dependent omics data in bioprocess production. This approach allowed us to identify metabolites tied to cell death and productivity that were not detected with traditional models.

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

Evaluating the impact of media and feed combinations on CHO cell culture performance and monoclonal antibody (trastuzumab) production

The choice of media and feeds significantly influences the performance of Chinese Hamster Ovary (CHO) mammalian cell cultures in producing desired biologics like monoclonal antibodies (mAb). Sub-optimal nutrient feed/media composition can severely impact cell proliferation and the quality of the final mAb product. For instance, proper protein glycosylation, crucial for mAb stability, safety, and efficacy, heavily relies on cell culture conditions. Currently, starter CHO culture media and daily supplemental feeds used in industrial manufacturing consist of proprietary composition of nutrients critical for mAb production. Standardized optimal media/feed combinations necessary for different cell lines are often lacking, necessitating individualized optimization for each cell line and mAb product. Here, we focused on a CHO-K1 cell line engineered to produce a Trastuzumab biosimilar and evaluated the effects of fourteen commercially relevant basal media and seven feeds on cell culture parameters such as viable cell density, viability, nutrient consumption, metabolite production, mAb titer, and mAb N-glycosylation. Our findings demonstrate clearly that the compositions of the basal medium and feed play a pivotal role in enhancing cell growth and mAb production. This work offers valuable insights into strategies for optimizing feed/media composition for glycosylated monoclonal antibody production using CHO cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-024-00690-7.

Enhancing monoclonal antibody production efficiency using CHO-MK cells and specific media in a conventional fed-batch culture

Chinese hamster ovary (CHO) cell lines, derived as subclones from the original CHO cell line, are widely used hosts for current biopharmaceutical productions. Recently, a highly proliferative host cell line, CHO-MK, was established from the Chinese hamster ovary tissue. In this study, we assessed the fundamental culture characteristics and capabilities of CHO-MK cells for monoclonal antibody (mAb) production using specified chemically defined media. To achieve this, we established fed-batch cultures of model CHO-MK cells in shake flasks and ambr15 and 2 L bioreactors under various conditions. The mAb-producing CHO-MK cell line A produced 12.6 g/L of antibody within 7 days in the fed-batch culture using a 2 L bioreactor, with a seeding density of 1 × 106 cells/mL. This performance corresponded to a space-time yield of 1.80 g/L/day, representing a productivity level that could be challengingly attained in fed-batch cultures using conventional CHO cells. In addition, when we subjected six different mAb-producing CHO-MK cell lines to fed-batch culture in the ambr15 bioreactor for 7 days, the antibody production ranged between 5.1 and 10.8 g/L, confirming that combining CHO-MK cells and specified media leads to enhanced versatility. These discoveries underscore that CHO-MK cells combined with specified media might represent a next-generation production platform, which could potentially respond to an increasing demand for antibody drugs, reducing production costs, and shortening antibody drug development times. This study is expected to serve as a benchmark for future production process development using CHO-MK cells.

Cell clone selection-impact of operation modes and medium exchange strategies on clone ranking

Bioprocessing has been transitioning from batch to continuous processes. As a result, a considerable amount of resource was dedicated to optimising strategies for continuous production. However, the focus has been on developing a suitable and scalable perfusion strategy with little attention given to the selection of optimal cell clones. Cell line development and lead clone selection are critical to bioprocess development. The screening and selection process is typically performed in stages. Microwell plates (MWP) are used to narrow down the number of clone candidates, which will undergo further selective screening in progressively larger small-scale bioreactors (12 mL-3 L) to identify the top clone for GMP production. Perfusion mode is typically applied at bench-scale for optimisation purposes, while process development and cell clone screening studies at mL-scale still commonly use fed-batch methods. The change of operation mode from bolus feeding to perfusion with a regular exchange of medium, leads to questions regarding the reliability and fit of initial clone selection. Is the early-stage clone ranking impacted by the discrepancy in the operation mode, and does this potentially result in the exclusion of cell clones suitable for perfusion processes? To address this question, we evaluated various CHO cell clones expressing two antibody products using MWP methodologies in fed-batch and semi-perfusion mode. We assessed growth, metabolic, and productivity performance, and ranked cell clones using two different strategies. The first strategy evaluated clones based on a single parameter: the cell-specific productivity (qP). The second considered a collection of multiple parameters using the metric of the Manufacturability index (MICL). Both ranking strategies showed an impact of operation mode and perfusion rate on the clone ranking. Notably, depending on the chosen operation mode, different sets of candidate clones might have been selected for further, more extensive screening. Additionally, we evaluated the reproducibility of our results demonstrating consistency in cell clone growth performance and ranking.

CHO stable pool fed-batch process development of SARS-CoV-2 spike protein production: Impact of aeration conditions and feeding strategies

Technology scale-up and transfer are a fundamental and critical part of process development in biomanufacturing. Important bioreactor hydrodynamic characteristics such as working volume, overhead gas flow rate, volumetric power input (P/V), impeller type, agitation regimen, sparging aeration strategy, sparger type, and kLa must be selected based on key performance indicators (KPI) to ensure a smooth and seamless process scale-up and transfer. Finding suitable operational setpoints and developing an efficient feeding regimen to ensure process efficacy and consistency are instrumental. In this investigation, process development of a cumate inducible Chinese hamster ovary (CHO) stable pool expressing trimeric SARS-CoV-2 spike protein in 1.8 L benchtop stirred-tank bioreactors is detailed. Various dissolved oxygen levels and aeration air caps were studied to determine their impact on cell growth and metabolism, culture longevity, and endpoint product titers. Once hydrodynamic conditions were tuned to an optimal zone, various feeding strategies were explored to increase culture performance. Dynamic feedings such as feeding based on current culture volume, viable cell density (VCD), oxygen uptake rate (OUR), and bio-capacitance signals were tested and compared to standard bolus addition. Increases in integral of viable cell concentration (IVCC) (1.25-fold) and protein yield (2.52-fold), as well as greater culture longevity (extension of 5 days) were observed in dynamic feeding strategies when compared to periodic bolus feeding. Our study emphasizes the benefits of designing feeding strategies around metabolically relevant signals such as OUR and bio-capacitance signals.

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.

A user-friendly fluorescent biosensor for precise lactate detection and quantification in vitro

As a critical metabolite, the standardization of lactate quantification is increasingly crucial. Therefore, we developed LaconicSF, a lactate-responsive biosensor exhibiting exceptional specificity in lactate detection. LaconicSF enables efficient lactate quantification in CHO cell culture medium and holds potential as a user-friendly detection tool for lactate quantification in vitro.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Metabolomic characterization of monoclonal antibody-producing Chinese hamster lung (CHL)-YN cells in glucose-controlled serum-free fed-batch operation

The fast-growing Chinese hamster lung (CHL)-YN cell line was recently developed for monoclonal antibody production. In this study, we applied a serum-free fed-batch cultivation process to immunoglobulin (Ig)G1-producing CHL-YN cells, which were then used to design a dynamic glucose supply system to stabilize the extracellular glucose concentration based on glucose consumption. Glucose consumption of the cultures rapidly oscillated following three phases of glutamine metabolism: consumption, production, and re-consumption. Use of the dynamic glucose supply prolonged the viability of the CHL-YN-IgG1 cell cultures and increased IgG1 production. Liquid chromatography with tandem mass spectrometry-based target metabolomics analysis of the extracellular metabolites during the first glutamine shift was conducted to search for depleted compounds. The results suggest that the levels of four amino acids, namely arginine, aspartate, methionine, and serine, were sharply decreased in CHL-YN cells during glutamine production. Supporting evidence from metabolic and gene expression analyses also suggest that CHL-YN cells acquired ornithine- and cystathionine-production abilities that differed from those in Chinese hamster ovary-K1 cells, potentially leading to proline and cysteine biosynthesis.

Machine learning: an advancement in biochemical engineering

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

More Than One Enzyme: Exploring Alternative FMN-Dependent L-Lactate Oxidases for Biosensor Development

The α-hydroxy acid oxidoreductase (HAOx) family contains a diverse group of enzymes that can be applied in biosensors for L-lactate detection, most prominently lactate oxidase (LOx). The limited availability and a lack of diversity of L-lactate-oxidizing enzymes have currently hindered advancements in L-lactate biosensor development. Until now, the field has mostly relied on a single, commercially available enzyme, namely Aerococcus viridans L-lactate oxidase (AvLOx). In this study, we present newly discovered alternative L-lactate oxidases that exhibit a narrow substrate specificity and varied kinetic efficiencies toward L-lactate, making them suitable for integration into existing biosensor configurations. Some of these FMN-dependent L-lactate oxidases could be obtained in substantial amounts from routine E. coli expression, potentially facilitating commercial production. Using electrochemical characterization with a mediated biosensor setup, we present 7 enzymes that perform comparable or even better than commercial AvLOx. Finally, we show that their electrochemical performance is not directly correlating with their biochemical performance, making predictions of the suitability of enzymes for biosensor applications extremely difficult. Our research emphasizes the significance of expanding the enzyme toolbox of L-lactate oxidases for the development of improved L-lactate biosensors.

Metabolic engineering of CHO cells towards cysteine prototrophy and systems analysis of the ensuing phenotype

Chinese hamster ovary (CHO) cells require cysteine for growth and productivity in fed-batch cultures. In intensified processes, supplementation of cysteine at high concentrations is a challenge due to its limited solubility and instability in solution. Methionine can be converted to cysteine (CYS) but key enzymes, cystathionine beta-synthase (Cbs) and cystathionine gamma-lyase (Cth), are not active in CHO cells resulting in accumulation of an intermediate, homocysteine (HCY), in cell culture milieu. In this study, Cbs and Cth were overexpressed in CHO cells to confer cysteine prototrophy, i.e., the ability to grow in a cysteine free environment. These pools (CbCt) needed homocysteine and beta-mercaptoethanol (βME) to grow in CYS-free medium. To increase intracellular homocysteine levels, Gnmt was overexpressed in CbCt pools. The resultant cell pools (GnCbCt), post adaptation in CYS-free medium with decreasing residual HCY and βME levels, were able to proliferate in the HCY-free, βME-free and CYS-free environment. Interestingly, CbCt pools were also able to be adapted to grow in HCY-free and CYS-free conditions, albeit at significantly higher doubling times than GnCbCt cells, but couldn't completely adapt to βME-free conditions. Further, single cell clones derived from the GnCbCt cell pool had a wide range in expression levels of Cbs, Cth and Gnmt and, when cultivated in CYS-free fed-batch conditions, performed similarly to the wild type (WT) cell line cultivated in CYS supplemented fed-batch culture. Intracellular metabolomic analysis showed that HCY and glutathione (GSH) levels were lower in the CbCt pool in CYS-free conditions but were restored closer to WT levels in the GnCbCt cells cultivated in CYS-free conditions. Transcriptomic analysis showed that GnCbCt cells upregulated several genes encoding transporters as well as methionine catabolism and transsulfuration pathway enzymes that support these cells to biosynthesize cysteine effectively. Further, 'omics analysis suggested CbCt pool was under ferroptotic stress in CYS-free conditions, which, when inhibited, enhanced the growth and viability of these cells in CYS-free conditions.

Advances in serum-free media for CHO cells: From traditional serum substitutes to microbial-derived substances

The Chinese hamster ovary (CHO) cell is an epithelial-like cell that produces proteins with post-translational modifications similar to human glycosylation. It is widely used in the production of recombinant therapeutic proteins and monoclonal antibodies. Culturing CHO cells typically requires the addition of a certain proportion of fetal bovine serum (FBS) to maintain cell proliferation and passaging. However, serum is characterized by its complex composition, batch-to-batch variability, high cost, and potential risk of exogenous contaminants such as mycoplasma and viruses, which impact the purity and safety of the synthesized proteins. Therefore, search for serum alternatives and development of serum-free media for CHO-based protein biomanufacturing are of great significance. This review systematically summarizes the application advantages of CHO cells and strategies for high-density expression. It highlights the developmental trends of serum substitutes from human platelet lysates to animal-free extracts and microbial-derived substances and elucidates the mechanisms by which these substitutes enhance CHO cell culture performance and recombinant protein production, aiming to provide theoretical guidance for exploring novel serum alternatives and developing serum-free media for CHO cells.

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Harnessing metabolic plasticity in CHO cells for enhanced perfusion cultivation

Chinese Hamster Ovary (CHO) cells have rapidly become a cornerstone in biopharmaceutical production. Recently, a reinvigoration of perfusion culture mode in CHO cell cultivation has been observed. However, most cell lines currently in use have been engineered and adapted for fed-batch culture methods, and may not perform optimally under perfusion conditions. To improve the cell's resilience and viability during perfusion culture, we cultured a triple knockout CHO cell line, deficient in three apoptosis related genes BAX, BAK, and BOK in a perfusion system. After 20 days of culture, the cells exhibited a halt in cell proliferation. Interestingly, following this phase of growth arrest, the cells entered a second growth phase. During this phase, the cell numbers nearly doubled, but cell specific productivity decreased. We performed a proteomics investigation, elucidating a distinct correlation between growth arrest and cell cycle arrest and showing an upregulation of the central carbon metabolism and oxidative phosphorylation. The upregulation was partially reverted during the second growth phase, likely caused by intragenerational adaptations to stresses encountered. A phase-dependent response to oxidative stress was noted, indicating glutathione has only a secondary role during cell cycle arrest. Our data provides evidence of metabolic regulation under high cell density culturing conditions and demonstrates that cell growth arrest can be overcome. The acquired insights have the potential to not only enhance our understanding of cellular metabolism but also contribute to the development of superior cell lines for perfusion cultivation.

Metabolomic changes in culture media with varying passage numbers of pig muscle stem cell culture for cultured meat production

Selecting the primary cells in an optimal state for cultured meat production is a crucial challenge in commercializing cultured meat. We investigated the metabolomic changes in culture media according to passage numbers for indirectly assessing the state of primary cells. Pig skeletal muscle stem cells (PSCs) harvested from the biceps femoris muscles of 7-d-old crossbred pigs (Landrace × Yorkshire × Duroc, LYD) were used for cell characterization. Fresh media (FM) and spent media (SM) of PSCs during passages 1 to 3 in vitro culture were prepared for metabolomics analysis. SM was collected on the third day of proliferation for each passage of PSCs. Cell characterization analysis revealed that the proliferation rate was highest at passage 2; however, a significant loss of expression of myogenic marker genes was observed at passage 3. Based on metabolomic profiles of culture media, FM and SM groups (SM1, SM2, and SM3) were clearly separated by partial least squares-discriminant analysis. A total of seven differentially abundant metabolites (DAMs) were identified from FM and SM for each passage, based on the following criteria: P < 0.05, fold change > 1.5 or < 0.66, and a variable importance in projection score > 1.5. All seven DAMs and their interconnected metabolites might be primarily used as substrates for energy production and most of them were relatively abundant in SM3. Among the seven DAMs, the three potential biomarkers (γ-glutamyl-L-leucine, cytosine, and ketoleucine), which showed significant changes exclusively in SM3, each had an area under the curve value of 1. Therefore, monitoring the levels of these key metabolites in culture media could serve as a quality control measure for cultured meat production by enabling the indirect detection of suboptimal PSCs based on their proliferation ability.

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.

Development of a novel tyrosine-based selection system for generation of recombinant Chinese hamster ovary cells

Efficiently expanding Chinese hamster ovary (CHO) cells, which serve as the primary host cells for recombinant protein production, have gained increasing industrial significance. A significant hurdle in stable cell line development is the low efficiency of the target gene integrated into the host genome, implying the necessity for an effective screening and selection procedure to separate these stable cells. In this study, the genes of phenylalanine hydroxylase (PAH) and pterin 4 alpha carbinolamine dehydratase 1 (PCBD1), which are key enzymes in the tyrosine synthesis pathway, were utilized as selection markers and transduced into host cells together with the target genes. This research investigated the enrichment effect of this system and advanced further in understanding its benefits for cell line development and rCHO cell culture. A novel tyrosine-based selection system that only used PCBD1 as a selection marker was designed to promote the enrichment effect. Post 9 days of starvation, positive transductants in the cell pool approached 100%. Applied the novel tyrosine-based selection system, rCHO cells expressing E2 protein were generated and named CHO TS cells. It could continue to grow, and the yield of E2 achieved 95.95 mg/L in a tyrosine-free and chemically-defined (CD) medium. Herein, we introduced an alternative to antibiotic-based selections for the establishment of CHO cell lines and provided useful insights for the design and development of CD medium.

Advances and Challenges in Cell Biology for Cultured Meat

Cultured meat is an emerging biotechnology that aims to produce meat from animal cell culture, rather than from the raising and slaughtering of livestock, on environmental and animal welfare grounds. The detailed understanding and accurate manipulation of cell biology are critical to the design of cultured meat bioprocesses. Recent years have seen significant interest in this field, with numerous scientific and commercial breakthroughs. Nevertheless, these technologies remain at a nascent stage, and myriad challenges remain, spanning the entire bioprocess. From a cell biological perspective, these include the identification of suitable starting cell types, tuning of proliferation and differentiation conditions, and optimization of cell-biomaterial interactions to create nutritious, enticing foods. Here, we discuss the key advances and outstanding challenges in cultured meat, with a particular focus on cell biology, and argue that solving the remaining bottlenecks in a cost-effective, scalable fashion will require coordinated, concerted scientific efforts. Success will also require solutions to nonscientific challenges, including regulatory approval, consumer acceptance, and market feasibility. However, if these can be overcome, cultured meat technologies can revolutionize our approach to food.

Exploring metabolic responses and pathway changes in CHO-K1 cells under varied aeration conditions and copper supplementations using 1 H NMR-based metabolomics

The optimization of bioprocess for CHO cell culture involves careful consideration of factors such as nutrient consumption, metabolic byproduct accumulation, cell growth, and monoclonal antibody (mAb) production. Valuable insights can be obtained by understanding cellular physiology to ensure robust and efficient bioprocess. This study aims to improve our understanding of the CHO-K1 cell metabolism using 1 H NMR-based metabolomics. Initially, the variations in culture performance and metabolic profiles under varied aeration conditions and copper supplementations were thoroughly examined. Furthermore, a comprehensive metabolic pathway analysis was performed to assess the impact of these conditions on the implicated pathways. The results revealed substantial alterations in the pyruvate metabolism, histidine metabolism, as well as phenylalanine, tyrosine and tryptophan biosynthesis, which were especially evident in cultures subjected to copper deficiency conditions. Conclusively, significant metabolites governing cell growth and mAb titer were identified through orthogonal partial least square-discriminant analysis (OPLS-DA). Metabolites, including glycerol, alanine, formate, glutamate, phenylalanine, and valine, exhibited strong associations with distinct cell growth phases. Additionally, glycerol, acetate, lactate, formate, glycine, histidine, and aspartate emerged as metabolites influencing cell productivity. This study demonstrates the potential of employing 1 H NMR-based metabolomics technology in bioprocess research. It provides valuable guidance for feed medium development, feeding strategy design, bioprocess parameter adjustments, and ultimately the enhancement of cell proliferation and mAb yield.

Developing an ultra-intensified fed-batch cell culture process with greatly improved performance and productivity

Intensified fed-batch (IFB), a popular cell culture intensification strategy, has been widely used for productivity improvement through high density inoculation followed by fed-batch cultivation. However, such an intensification strategy may counterproductively induce rapidly progressing cell apoptosis and difficult-to-sustain productivity. To improve culture performance, we developed a novel cell culture process intermittent-perfusion fed-batch (IPFB) which incorporates one single or multiple cycles of intermittent perfusion during an IFB process for better sustained cellular and metabolic behaviors and notably improved productivity. Unlike continuous perfusion or other semi-continuous processes such as hybrid perfusion fed-batch with only early-stage perfusion, IPFB applies limited times of intermittent perfusion in the mid-to-late stage of production and still inherits bolus feedings on nonperfusion days as in a fed-batch culture. Compared to IFB, an average titer increase of ~45% was obtained in eight recombinant CHO cell lines studied. Beyond IPFB, ultra-intensified IPFB (UI-IPFB) was designed with a markedly elevated seeding density of 20-80 × 106 cell/mL, achieved through the conventional alternating tangential flow filtration (ATF) perfusion expansion followed with a cell culture concentration step using the same ATF system. With UI-IPFB, up to ~6 folds of traditional fed-batch and ~3 folds of IFB productivity were achieved. Furthermore, the application grounded in these two novel processes showed broad-based feasibility in multiple cell lines and products of interest, and was proven to be effective in cost of goods reduction and readily scalable to a larger scale in existing facilities.

Recombinant therapeutic proteins degradation and overcoming strategies in CHO cells

Mammalian cell lines are frequently used as the preferred host cells for producing recombinant therapeutic proteins (RTPs) having post-translational modified modification similar to those observed in proteins produced by human cells. Nowadays, most RTPs approved for marketing are produced in Chinese hamster ovary (CHO) cells. Recombinant therapeutic antibodies are among the most important and promising RTPs for biomedical applications. One of the issues that occurs during development of RTPs is their degradation, which caused by a variety of factors and reducing quality of RTPs. RTP degradation is especially concerning as they could result in reduced biological functions (antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity) and generate potentially immunogenic species. Therefore, the mechanisms underlying RTP degradation and strategies for avoiding degradation have regained an interest from academia and industry. In this review, we outline recent progress in this field, with a focus on factors that cause degradation during RTP production and the development of strategies for overcoming RTP degradation. KEY POINTS: • The recombinant therapeutic protein degradation in CHO cell systems is reviewed. • Enzymatic factors and non-enzymatic methods influence recombinant therapeutic protein degradation. • Reducing the degradation can improve the quality of recombinant therapeutic proteins.

Development of an in-line Raman analytical method for commercial-scale CHO cell culture process monitoring: Influence of measurement channels and batch number on model performance

The mammalian cell culture process is a key step in commercial therapeutic protein production and needs to be monitored and controlled due to its complexity. Raman spectroscopy has been reported for cell culture process monitoring by analysis of many important parameters. However, studies on in-line Raman monitoring of the cell culture process were mainly conducted on small or pilot scale. Developing in-line Raman analytical methods for commercial-scale cell culture process monitoring is more challenging. In this study, an in-line Raman analytical method was developed for monitoring glucose, lactate, and viable cell density (VCD) in the Chinese hamster ovary (CHO) cell culture process during commercial production of biosimilar adalimumab (1500 L). The influence of different Raman measurement channels was considered to determine whether to merge data from different channels for model development. Raman calibration models were developed and optimized, with minimum root mean square error of prediction of 0.22 g L-1 for glucose in the range of 1.66-3.53 g L-1 , 0.08 g L-1 for lactate in the range of 0.15-1.19 g L-1 , 0.31 E6 cells mL-1 for VCD in the range of 0.96-5.68 E6 cells mL-1 on test sets. The developed analytical method can be used for cell culture process monitoring during manufacturing and meets the analytical purpose of this study. Further, the influence of the number of batches used for model calibration on model performance was also studied to determine how many batches are needed basically for method development. The proposed Raman analytical method development strategy and considerations will be useful for monitoring of similar bioprocesses.

Amino acid degradation pathway inhibitory by-products trigger apoptosis in CHO cells

Chinese hamster ovary (CHO) cells are widely used to produce complex biopharmaceuticals. Improving their productivity is necessary to fulfill the growing demand for such products. One way to enhance productivity is by cultivating cells at high densities, but inhibitory by-products, such as metabolite derivatives from amino acid degradation, can hinder achieving high cell densities. This research examines the impact of these inhibitory by-products on high-density cultures. We cultured X1 and X2 CHO cell lines in a small-scale semi-perfusion system and introduced a mix of inhibitory by-products on day 10. The X1 and X2 cell lines were chosen for their varied responses to the by-products; X2 was susceptible, while X1 survived. Proteomics revealed that the X2 cell line presented changes in the proteins linked to apoptosis regulation, cell building block synthesis, cell growth, DNA repair, and energy metabolism. We later used the AB cell line, an apoptosis-resistant cell line, to validate the results. AB behaved similar to X1 under stress. We confirmed the activation of apoptosis in X2 using a caspase assay. This research provides insights into the mechanisms of cell death triggered by inhibitory by-products and can guide the optimization of CHO cell culture for biopharmaceutical manufacturing.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Automated Raman feed-back control of multiple supplemental feeds to enable an intensified high inoculation density fed-batch platform process

Biologics manufacturing is increasingly moving toward intensified processes that require novel control strategies in order to achieve higher titers in shorter periods of time compared to traditional fed-batch cultures. In order to implement these strategies for intensified processes, continuous process monitoring is often required. To this end, inline Raman spectroscopy was used to develop partial least squares models to monitor changes in residual concentrations of glucose, phenylalanine and methionine during the culture of five different glutamine synthetase piggyBac® Chinese hamster ovary clones cultured using an intensified high inoculation density fed-batch platform process. Continuous monitoring of residual metabolite concentrations facilitated automated feed-rate adjustment of three supplemental feeds to maintain glucose, phenylalanine, and methionine at desired setpoints, while maintaining other nutrient concentrations at acceptable levels across all clones cultured on the high inoculation density platform process. Furthermore, all clones cultured on this process achieved high viable cell concentrations over the course of culture, indicating no detrimental impacts from the proposed feeding strategy. Finally, the automated control strategy sustained cultures inoculated at high cell densities to achieve product concentrations between 5 and 8.3 g/L over the course of 12 days of culture.

Novel Approaches to the Establishment of Local Microenvironment from Resorbable Biomaterials in the Brain In Vitro Models

The development of brain in vitro models requires the application of novel biocompatible materials and biopolymers as scaffolds for controllable and effective cell growth and functioning. The "ideal" brain in vitro model should demonstrate the principal features of brain plasticity like synaptic transmission and remodeling, neurogenesis and angiogenesis, and changes in the metabolism associated with the establishment of new intercellular connections. Therefore, the extracellular scaffolds that are helpful in the establishment and maintenance of local microenvironments supporting brain plasticity mechanisms are of critical importance. In this review, we will focus on some carbohydrate metabolites-lactate, pyruvate, oxaloacetate, malate-that greatly contribute to the regulation of cell-to-cell communications and metabolic plasticity of brain cells and on some resorbable biopolymers that may reproduce the local microenvironment enriched in particular cell metabolites.

Addressing amino acid-derived inhibitory metabolites and enhancing CHO cell culture performance through DOE-guided media modifications

Previously, we identified six inhibitory metabolites (IMs) accumulating in Chinese hamster ovary (CHO) cultures using AMBIC 1.0 community reference medium that negatively impacted culture performance. The goal of the current study was to modify the medium to control IM accumulation through design of experiments (DOE). Initial over-supplementation of precursor amino acids (AAs) by 100% to 200% in the culture medium revealed positive correlations between initial AA concentrations and IM levels. A screening design identified 5 AA targets, Lys, Ile, Trp, Leu, Arg, as key contributors to IMs. Response surface design analysis was used to reduce initial AA levels between 13% and 33%, and these were then evaluated in batch and fed-batch cultures. Lowering AAs in basal and feed medium and reducing feed rate from 10% to 5% reduced inhibitory metabolites HICA and NAP by up to 50%, MSA by 30%, and CMP by 15%. These reductions were accompanied by a 13% to 40% improvement in peak viable cell densities and 7% to 50% enhancement in IgG production in batch and fed-batch processes, respectively. This study demonstrates the value of tuning specific AA levels in reference basal and feed media using statistical design methodologies to lower problematic IMs.

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

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

Critical challenges and advances in recombinant adeno-associated virus (rAAV) biomanufacturing

Gene therapy is a promising therapeutic approach for genetic and acquired diseases nowadays. Among DNA delivery vectors, recombinant adeno-associated virus (rAAV) is one of the most effective and safest vectors used in commercial drugs and clinical trials. However, the current yield of rAAV biomanufacturing lags behind the necessary dosages for clinical and commercial use, which embodies a concentrated reflection of low productivity of rAAV from host cells, difficult scalability of the rAAV-producing bioprocess, and high levels of impurities materialized during production. Those issues directly impact the price of gene therapy medicine in the market, limiting most patients' access to gene therapy. In this context, the current practices and several critical challenges associated with rAAV gene therapy bioprocesses are reviewed, followed by a discussion of recent advances in rAAV-mediated gene therapy and other therapeutic biological fields that could improve biomanufacturing if these advances are integrated effectively into the current systems. This review aims to provide the current state-of-the-art technology and perspectives to enhance the productivity of rAAV while reducing impurities during production of rAAV.

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.

Optimization of medium with perfusion microbioreactors for high density CHO cell cultures at very low renewal rate aided by design of experiments

A novel approach of design of experiment (DoE) is developed for the optimization of key substrates of the culture medium, amino acids, and sugars, by utilizing perfusion microbioreactors with 2 mL working volume, operated in high cell density continuous mode, to explore the design space. A mixture DoE based on a simplex-centroid is proposed to test multiple medium blends in parallel perfusion runs, where the amino acids concentrations are selected based on the culture behavior in presence of different amino acid mixtures, and using targeted specific consumption rates. An optimized medium is identified with models predicting the culture parameters and product quality attributes (G0 and G1 level N-glycans) as a function of the medium composition. It is then validated in runs performed in perfusion microbioreactor in comparison with stirred-tank bioreactors equipped with alternating tangential flow filtration (ATF) or with tangential flow filtration (TFF) for cell separation, showing overall a similar process performance and N-glycosylation profile of the produced antibody. These results demonstrate that the present development strategy generates a perfusion medium with optimized performance for stable Chinese hamster ovary (CHO) cell cultures operated with very high cell densities of 60 × 106 and 120 × 106  cells/mL and a low cell-specific perfusion rate of 17 pL/cell/day, which is among the lowest reported and is in line with the framework recently published by the industry.

Dynamics of Amino Acid Metabolism, Gene Expression, and Circulomics in a Recombinant Chinese Hamster Ovary Cell Line Adapted to Moderate and High Levels of Extracellular Lactate

The accumulation of metabolic wastes in cell cultures can diminish product quality, reduce productivity, and trigger apoptosis. The limitation or removal of unintended waste products from Chinese hamster ovary (CHO) cell cultures has been attempted through multiple process and genetic engineering avenues with varied levels of success. One study demonstrated a simple method to reduce lactate and ammonia production in CHO cells with adaptation to extracellular lactate; however, the mechanism behind adaptation was not certain. To address this profound gap, this study characterizes the phenotype of a recombinant CHO K-1 cell line that was gradually adapted to moderate and high levels of extracellular lactate and examines the genomic content and role of extrachromosomal circular DNA (eccDNA) and gene expression on the adaptation process. More than 500 genes were observed on eccDNAs. Notably, more than 1000 genes were observed to be differentially expressed at different levels of lactate adaptation, while only 137 genes were found to be differentially expressed between unadapted cells and cells adapted to grow in high levels of lactate; this suggests stochastic switching as a potential stress adaptation mechanism in CHO cells. Further, these data suggest alanine biosynthesis as a potential stress-mitigation mechanism for excess lactate in CHO cells.

A novel hybrid bioprocess strategy addressing key challenges of advanced biomanufacturing

Monoclonal antibodies (mAb) are commonly manufactured by either discontinuous operations like fed-batch (FB) or continuous processes such as steady-state perfusion. Both process types comprise opposing advantages and disadvantages in areas such as plant utilization, feasible cell densities, media consumption and process monitoring effort. In this study, we show feasibility of a promising novel hybrid process strategy that combines beneficial attributes of both process formats. In detail, our strategy comprises a short duration FB, followed by a fast media exchange and cell density readjustment, marking the start of the next FB cycle. Utilizing a small-scale screening tool, we were able to identify beneficial process parameters, including FB interval duration and reinoculation cell density, that allow for multiple cycles of the outlined process in a reproducible manner. In addition, we could demonstrate scalability of the process to a 5L benchtop system, using a fluidized-bed centrifuge as scalable media exchange system. The novel process showed increased productivity (+217%) as well as longer cultivation duration, in comparison to a standard FB with a significantly lower media consumption per produced product (-50%) and a decreased need for process monitoring, in comparison to a perfusion cultivation. Further, the process revealed constant glycosylation pattern in comparison to the perfusion cultivation and has strong potential for further scale-up, due to the use of fully scalable cultivation and media exchange platforms. In summary, we have developed a novel hybrid process strategy that tackles the key challenges of current biomanufacturing of either low productivity or high media consumption, representing a new and innovative approach for future process intensification efforts.

Employing active learning in the optimization of culture medium for mammalian cells

Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectively. Both modes successfully fine-tuned 29 components to generate a medium for improved cell culture. Intriguingly, the two modes provided different predictions for the concentrations of vitamins and amino acids, and a significant decrease was commonly predicted for fetal bovine serum (FBS) compared to the commercial medium. In addition, active learning-assisted medium optimization significantly increased the cellular concentration of NAD(P)H, an active chemical with a constant abundance in living cells. Our study demonstrated the efficiency and practicality of active learning for medium optimization and provided valuable information for employing machine learning technology in cell biology experiments.

CRISPR Technologies in Chinese Hamster Ovary Cell Line Engineering

The Chinese hamster ovary (CHO) cell line is a well-established platform for the production of biopharmaceuticals due to its ability to express complex therapeutic proteins with human-like glycopatterns in high amounts. The advent of CRISPR technology has opened up new avenues for the engineering of CHO cell lines for improved protein production and enhanced product quality. This review summarizes recent advances in the application of CRISPR technology for CHO cell line engineering with a particular focus on glycosylation modulation, productivity enhancement, tackling adventitious agents, elimination of problematic host cell proteins, development of antibiotic-free selection systems, site-specific transgene integration, and CRISPR-mediated gene activation and repression. The review highlights the potential of CRISPR technology in CHO cell line genome editing and epigenetic engineering for the more efficient and cost-effective development of biopharmaceuticals while ensuring the safety and quality of the final product.

Fast-Training Deep Learning Algorithm for Multiplex Quantification of Mammalian Bioproduction Metabolites via Contactless Short-Wave Infrared Hyperspectral Sensing

Within the biopharmaceutical sector, there exists the need for a contactless multiplex sensor, which can accurately detect metabolite levels in real time for precise feedback control of a bioreactor environment. Reported spectral sensors in the literature only work when fully submerged in the bioreactor and are subject to probe fouling due to a cell debris buildup. The use of a short-wave infrared (SWIR) hyperspectral (HS) cam era allows for efficient, fully contactless collection of large spectral datasets for metabolite quantification. Here, we report the development of an interpretable deep learning system, a convolution metabolite regression (CMR) approach that detects glucose and lactate concentrations using label-free contactless HS images of cell-free spent media samples from Chinese hamster ovary (CHO) cell growth flasks. Using a dataset of <500 HS images, these CMR algorithms achieved a competitive test root-mean-square error (RMSE) performance of glucose quantification within 27 mg/dL and lactate quantification within 20 mg/dL. Conventional Raman spectroscopy probes report a validation performance of 26 and 18 mg/dL for glucose and lactate, respectively. The CMR system trains within 10 epochs and uses a convolution encoder with a sparse bottleneck regression layer to pick the best-performing filters learned by CMR. Each of these filters is combined with existing interpretable models to produce a metabolite sensing system that automatically removes spurious predictions. Collectively, this work will advance the safe and efficient adoption of contactless deep learning sensing systems for fine control of a variety of bioreactor environments.

Boosting Productivity for Advanced Biomanufacturing by Re-Using Viable Cells

Monoclonal antibodies (mAb) have gained enormous therapeutic application during the last decade as highly efficient and flexible tools for the treatment of various diseases. Despite this success, there remain opportunities to drive down the manufacturing costs of antibody-based therapies through cost efficiency measures. To reduce production costs, novel process intensification methods based on state-of-the-art fed-batch and perfusion have been implemented during the last few years. Building on process intensification, we demonstrate the feasibility and benefits of a novel, innovative hybrid process that combines the robustness of a fed-batch operation with the benefits of a complete media exchange enabled through a fluidized bed centrifuge (FBC). In an initial small-scale FBC-mimic screening, we investigated multiple process parameters, resulting in increased cell proliferation and an elongated viability profile. Consecutively, the most productive process scenario was transferred to the 5-L scale, further optimized and compared to a standard fed-batch process. Our data show that the novel hybrid process enables significantly higher peak cell densities (163%) and an impressive increase in mAb amount of approximately 254% while utilizing the same reactor size and process duration of the standard fed-batch operation. Furthermore, our data show comparable critical quality attributes (CQAs) between the processes and reveal scale-up possibilities and no need for extensive additional process monitoring. Therefore, this novel process intensification strategy yields strong potential for transfer into future industrial manufacturing processes.

Microevolutionary dynamics of eccDNA in Chinese hamster ovary cells grown in fed-batch cultures under control and lactate-stressed conditions

Chinese hamster ovary (CHO) cell lines are widely used to manufacture biopharmaceuticals. However, CHO cells are not an optimal expression host due to the intrinsic plasticity of the CHO genome. Genome plasticity can lead to chromosomal rearrangements, transgene exclusion, and phenotypic drift. A poorly understood genomic element of CHO cell line instability is extrachromosomal circular DNA (eccDNA) in gene expression and regulation. EccDNA can facilitate ultra-high gene expression and are found within many eukaryotes including humans, yeast, and plants. EccDNA confers genetic heterogeneity, providing selective advantages to individual cells in response to dynamic environments. In CHO cell cultures, maintaining genetic homogeneity is critical to ensuring consistent productivity and product quality. Understanding eccDNA structure, function, and microevolutionary dynamics under various culture conditions could reveal potential engineering targets for cell line optimization. In this study, eccDNA sequences were investigated at the beginning and end of two-week fed-batch cultures in an ambr®250 bioreactor under control and lactate-stressed conditions. This work characterized structure and function of eccDNA in a CHO-K1 clone. Gene annotation identified 1551 unique eccDNA genes including cancer driver genes and genes involved in protein production. Furthermore, RNA-seq data is integrated to identify transcriptionally active eccDNA genes.

Comparative study of commercial media to improve GMP manufacturing of recombinant human interferon β-1a by CHO cells in perfusion bioreactor

Chinese hamster ovary cells are the main cellular factories for production of a wide range of recombinant proteins in biopharmaceutical industry. Recombinant human Interferon beta-1a (rh-IFN β-1a), as a cytokine is broadly used to treat multiple sclerosis. In this work, the cell line producing rh-IFN β-1a was studied to improve cell density along with the specific expression. For this reason different cell culture experiments were done using different commercial serum-free media to find the appropriate media providing higher cell density. It was shown DMEMF12, DMEM:ProCHO5, and CHO-S-SFM II led to higher cell density and shorter doubling time. Next, using these media, fed-batch, and perfusion culture with temperature shift were implemented to investigate the best condition for industrial-scale manufacturing of rh-IFN β-1a in terms of higher cell density and product expression yield. The results demonstrated that CHO-S-SFM II media and a thermally biphasic condition provide enhanced expression of rh-IFN β-1a in perfusion bioreactor.

Flavin Mononucleotide-Dependent l-Lactate Dehydrogenases: Expanding the Toolbox of Enzymes for l-Lactate Biosensors

The development of L-lactate biosensors has been hampered in recent years by the lack of availability and knowledge about a wider range and diversity of L-lactate-oxidizing enzymes that can be used as bioelements in these sensors. For decades, L-lactate oxidase of Aerococcus viridans (AvLOx) has been used almost exclusively in the field of L-lactate biosensor development and has achieved somewhat like a monopoly status as a biocatalyst for these applications. Studies on other L-lactate-oxidizing enzymes are sparse and are often missing biochemical data. In this work, we made use of the vast amount of sequence information that is currently available on protein databases to investigate the naturally occurring diversity of L-lactate-utilizing enzymes of the flavin mononucleotide (FMN)-dependent α-hydroxy acid oxidoreductase (HAOx) family. We identified the HAOx sequence space specific for L-lactate oxidation and additionally discovered a not-yet described class of soluble and FMN-dependent L-lactate dehydrogenases, which are promising for the construction of second-generation biosensors or other biotechnological applications. Our work paves the way for new studies on α-hydroxy acid biosensors and proves that there is more to the HAOx family than AvLOx.