Elevated pCO2 affects the lactate metabolic shift in CHO cell culture processes

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.

Multi-cell-line learning for the data-driven construction of mechanistic metabolic models

Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth-signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed-batch culture data and time-series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi-cell-line (MCL) learning, which combines data sets from different cell lines and trains the individual cell-line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell-line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell-line development for the biomanufacturing of new protein therapeutics.

Modeling of cell cultivation for monoclonal antibody production processes considering lactate metabolic shifts

Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet been fully incorporated in such models. This paper offers a new mechanistic model considering variations in dissolved oxygen and glutamine depletion on cell metabolism applied to a novel Chinese hamster ovary (CHO) cell line. Expressions for the specific rates of lactate production, glutamine consumption, and mAb production were formulated for stirred and shaken-tank reactors. A deeper understanding of lactate metabolic shifts was obtained under different combinations of experimental conditions. Lactate consumption was more pronounced in conditions with higher DO and low glutamine concentrations. The model offers mechanistic insights that are useful for designing advanced operation strategies. It can be used in design space generation and process optimization for better productivity and product quality.

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.

Development of bioreactor scale-down model using orthogonal projections to latent structures method and CO2 supplementation

Scale-down model qualification is an important step for developing a large-scale cell culture process to enhance process understanding and support process characterization studies. Traditionally, only harvest data are used to show consistency between small-scale and large-scale bioreactor performance, allowing attributes that are dynamic over the cell culture period to be overlooked. A novel statistical method, orthogonal projections to latent structures (OPLS) analysis, can be utilized to compare time-course cell culture data across scales. Here we describe an example where OPLS is used to identify gaps between small-scale and large-scale bioreactor performances. In this case, differences in the partial pressure of carbon dioxide (pCO2) and lactate profiles were observed between small- and large-scale bioreactors, which were linked to differences in the product-quality attributes fragments and galactosylation. An improved small-scale model was developed, leading to improved consistency in the process performance and product qualities across scales and qualification of the scale-down model for regulatory submissions. This new statistical approach can provide valuable insights into process understanding and process scale-up.

Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

Evaluation of single-use optical and electrochemical pH sensors in upstream bioprocessing

Culture pH is a critical process parameter during CHO cell bioreactor operations that is key for proper cell growth, protein production, and maintaining the critical quality attributes of a monoclonal antibody drug substance. The traditional means of measuring pH in bioreactors is with an electrochemical probe that can withstand and maintain accuracy through repeated sterilization cycles. An alternative technique for measuring pH is an optical sensor composed of a fluorescent dye that is sensitive to the hydrogen ion concentration. In this work we explore single-use electrochemical and single-use optical pH sensors in stirred-tank and rocking bioreactors, respectively, to understand how their overall performance compares to traditional electrochemical probes in benchtop glass stirred tank bioreactors. We found that the single-use optical pH sensors were generally less accurate than the electrochemical probes, especially in detecting large pH drifts from the setpoint. The single-use electrochemical probes were increasingly accurate as pH was increased from <7.0 to 7.5 but tended to decrease in accuracy as the batch age increased. In conclusion, single-use pH sensors offer a convenient means to measure pH during an upstream bioprocess, but the limitations of these sensors should be built into process control such that deviations in process pH, and consequently potential fluctuations in product quality, can be avoided.

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.

An Investigation into the Metabolic Differences between Conventional and High Seeding Density Fed-Batch Cell Cultures by Applying a Segmented Modeling Approach

The conventional fed-batch process characterized by a low titer currently challenges pharmaceutical development. Process optimization by applying a perfusion process in the pre-stage and subsequent production phase at a high seeding density (HSD) can meet this challenge. In this study, we employed a simplified approach based on measured experiments, namely segmented modeling, to systematically analyze an HSD fed-batch process compared to a standard process. A comparison indicated that the metabolic phases of HSD processes are not only shifted in time, but metabolite trends show an altered metabolism. In an extended study, we integrated the intracellular fluxes determined by a metabolic flux analysis into the segmented modeling approach. Compared to using only extracellular rates, similar phases are identified, and this highlights the reliability of phase identification modeling using extracellular rates only. Furthermore, the segmented linear regression approach is used to create a model that describes cellular behavior and that can be used to predict potential improvements in the feeding strategy and in harvest viability. Here, overfeeding was eliminated and a significantly higher titer was achieved. This work provides insights into the overall metabolic changes in the HSD process and paves the way towards the optimization of the feeding regime.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

Acetate accumulation and regulation by process parameters control in Chinese hamster ovary cell culture

Chinese hamster ovary (CHO) cells represent a group of predominantly used mammalian hosts for producing recombinant therapeutic proteins. Known for their rapid proliferation rates, CHO cells undergo aerobic glycolysis that is characterized by fast glucose consumption, that ultimately gives rise to a group of small-molecule organic acids. However, only the function of lactate has been extensively studied in CHO cell culture. In this study, we observed the accumulation of acetate from the late exponential phase to harvest day, potentially contributing to the pH decline in late culture stage regardless of lactate consumption. In addition, we evaluated the acidification of the fresh media and the cell culture suspension, and the data revealed that acetate presented a lower acidification capacity compared to lactate and exhibited limited inhibitory effect on cells with less than 20 mM supplemented in the media. This study also explored the ways to control acetate accumulation in CHO cell culture by manipulating the process parameters such as temperature, glucose, and pH control. The positive correlation between the specific glucose consumption rate and acetate generation rate provides evidence of the endogenous acetate generation from overflow metabolism. Reducing these parameters (temperature, glucose consumption) and HCl-controlled low pH ultimately suppress acetate build-up. In addition, the specific acetate generation rate and relevant glucose consumption rate are found to be a metabolic trait associated with specific cell lines. Taken together, the results presented in these experiments provide a means to advance industrial CHO cell culture process control and development.

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.

Improved Time Resolved KPI and Strain Characterization of Multiple Hosts in Shake Flasks Using Advanced Online Analytics and Data Science

Shake flasks remain one of the most widely used cultivation systems in biotechnology, especially for process development (cell line and parameter screening). This can be justified by their ease of use as well as their low investment and running costs. A disadvantage, however, is that cultivations in shake flasks are black box processes with reduced possibilities for recording online data, resulting in a lack of control and time-consuming, manual data analysis. Although different measurement methods have been developed for shake flasks, they lack comparability, especially when changing production organisms. In this study, the use of online backscattered light, dissolved oxygen, and pH data for characterization of animal, plant, and microbial cell culture processes in shake flasks are evaluated and compared. The application of these different online measurement techniques allows key performance indicators (KPIs) to be determined based on online data. This paper evaluates a novel data science workflow to automatically determine KPIs using online data from early development stages without human bias. This enables standardized and cost-effective process-oriented cell line characterization of shake flask cultivations to be performed in accordance with the process analytical technology (PAT) initiative. The comparison showed very good agreement between KPIs determined using offline data, manual techniques, and automatic calculations based on multiple signals of varying strengths with respect to the selected measurement signal.

Real-time amino acid and glucose monitoring system for the automatic control of nutrient feeding in CHO cell culture using raman spectroscopy

An innovative, Raman spectroscopy-based monitoring and control system is introduced in this paper for designing dynamic feeding strategies that allow the maintenance of key cellular nutrients at an ideal level in Chinese hamster ovary cell culture. The Partial Least Squares calibration models built for glucose, lactate and 16 (out of 20) individual amino acids had very good predictive power with low root mean square errors values and high square correlation coefficients. The developed models used for real-time measurement of nutrient and by-product concentrations allowed us to gain better insight into the metabolic behavior and nutritional consumption of cells. To establish a more beneficial nutritional environment for the cells, two types of dynamic feeding strategies were used to control the delivery of two-part multi-component feed media according to the prediction of Raman models (glucose or arginine). As a result, instead of high fluctuations, the nutrients (glucose together with amino acids) were maintained at the desired level providing a more balanced environment for the cells. Moreover, the use of amino acid-based feeding control enabled to prevent the excessive nutrient replenishment and was economically beneficial by significantly reducing the amount of supplied feed medium compared to the glucose-based dynamic fed culture. This article is protected by copyright. All rights reserved.

Influence of the Hypercapnic Tumor Microenvironment on the Viability of Hela Cells Screened by a CO2-Gradient-Generating Device

Carbon dioxide (CO₂) levels outside of the physiological range are frequently encountered in the tumor microenvironment and laparoscopic pneumoperitoneum during clinical cancer therapy. Controversies exist regarding the biological effects of hypercapnia on tumor proliferation and metastasis concerning time frame, CO₂ concentration, and cell type. Traditional control of gaseous microenvironments for cell growth is conducted using culture chambers that allow for a single gas concentration at a time. In the present paper, Hela cells were studied for their response to varying levels of CO₂ in an aerogel-based gas gradient-generating apparatus capable of delivering a stable and quantitative linear CO₂ profile in spatial and temporal domains. Cells cultured in the standard 96-well plate sandwiched in between the device were interfaced with the gas gradient generator, and the cells in each row were exposed to a known level of CO₂ accordingly. Both the ratiometric pH indicator and theoretical modeling have confirmed the efficient mass transport of CO₂ through the air-permeable aerogel monolith in a short period of time. Tumor cell behaviors in various hypercapnic microenvironments with gradient CO₂ concentrations ranging from 12 to 89% were determined in terms of viability, morphology, and mitochondrial metabolism under acute exposure for 3 h and over a longer cultivation period for up to 72 h. A significant reduction in cell viability was noticed with increasing CO₂ concentration and incubation time, which was closely associated with intracellular acidification and elevated cellular level of reactive oxygen species. Our modular device demonstrated full adaptability to the standard culture systems and high-throughput instruments, which provide the potential for simultaneously screening the responses of cells under tunable gaseous microenvironments.

Continuous Feeding Reduces the Generation of Metabolic Byproducts and Increases Antibodies Expression in Chinese Hamster Ovary-K1 Cells

Chinese hamster ovary (CHO) cells are the most important host system used for monoclonal antibody (mAb) expression. Moreover, the fed-batch culture mode is the most widely used method to increase mAb expression in CHO cells by increasing the amount of feed. However, a high amount of culture feed results in the production of metabolic byproducts. In this work, we used a continuous feeding strategy to reduce metabolic byproducts and improve mouse–human chimeric anti-epidermal growth factor receptor vIII (EGFRvIII) antibody C12 expression in Chinese hamster ovary-K1 cells. Moreover, the effects of the feeding strategy on the cell culture and monoclonal antibody production were evaluated in chemically defined suspension cultures of recombinant CHO-K1 cells. Compared with bolus feeding methods, the continuous feeding method did not have any advantages when the feeding amount was low, but with a high feeding amount, the continuous feeding method significantly reduced the concentrations of lactate and NH₄⁺ in the later culture stage. At the end of the culture stage, compared with bolus feeding methods, the lactate and NH₄⁺ concentrations under the continuous feeding mode were reduced by approximately 45% and 80%, respectively. In addition, the antibody C12 expression level was also increased by almost 10%. Compared to the bolus feeding method, the antibody C12 produced by the continuous feeding method had a lower content of high-mannose glycoforms. Further analysis found that the osmolality of the continuous feeding method was lower than that of the typical fed-batch bolus feeding method. Conclusively, these results indicate that the continuous feeding method is very useful for reducing metabolic byproducts and achieving higher levels of mAb production.

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.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Understanding gradients in industrial bioreactors

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

Dynamic multiscale metabolic network modeling of Chinese hamster ovary cell metabolism integrating N-linked glycosylation in industrial biopharmaceutical manufacturing

Experimental and modeling work, described in this article, is focused on the metabolic pathway of Chinese hamster ovary (CHO) cells, which are the preferred expression system for monoclonal antibody protein production. CHO cells are one of the primary hosts for monoclonal antibodies production, which have extensive applications in multiple fields like biochemistry, biology and medicine. Here, an approach to explain cellular metabolism with in silico modeling of a microkinetic reaction network is presented and validated with unique experimental results. Experimental data of 25 different fed-batch bioprocesses included the variation of multiple process parameters, such as pH, agitation speed, oxygen and CO₂ content, and dissolved oxygen. A total of 151 metabolites were involved in our proposed metabolic network, which consisted of 132 chemical reactions that describe the reaction pathways, and include 25 reactions describing N-glycosylation and additional reactions for the accumulation of the produced glycoforms. Additional eight reactions are considered for accumulation of the N-glycosylation products in the extracellular environment and one reaction to correlate cell degradation. The following pathways were considered: glycolysis, pentose phosphate pathway, nucleotide synthesis, tricarboxylic acid cycle, lipid synthesis, protein synthesis, biomass production, anaplerotic reactions, and membrane transport. With the applied modeling procedure, different operational scenarios and fed-batch techniques can be tested.

Investigation of cell line specific responses to pH inhomogeneity and consequences for process design

With increasing bioreactor volumes, the mixing time of the reactor increases as well, which creates an inhomogeneous environment for the cells. This can result in impaired process performance in large-scale production reactors. Particularly the addition of base through the reactor headspace can be problematic, since it creates an area, where cells are repeatedly exposed to an increased pH. The aim of this study is to simulate this large-scale phenomenon at lab-scale and investigate its impact. Two different cell lines were exposed to pH amplitudes of a maximal magnitude of 0.05 units (pH of 6.95). Both cell lines showed similar responses, like decreased viable cell counts, but unaffected lactate levels. However, cell line B showed an initially increased specific productivity in response to the introduced amplitudes, whereas cell line A showed a consistently lower specific productivity. Furthermore, the time point at which base addition is started influences the impact, which pH amplitudes have on process performance. When pH control was started earlier in the process, maximal viable cell counts decreased and the lactate metabolic shift was less pronounced. These results show that the potential negative impact of pH amplitudes can be minimized by strategic process design.

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

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

Spatially Resolved Effects of Protein Freeze-Thawing in a Small-Scale Model Using Monoclonal Antibodies

Protein freeze-thawing is frequently used to stabilize and store recombinantly produced proteins after different unit operations in upstream and downstream processing. However, freeze-thawing is often accompanied by product damage and, hence, loss of product. Different effects are responsible, including cold denaturation, aggregation effects, which are caused by inhomogeneities in protein concentration, as well as pH and buffer ingredients, especially during the freeze cycle. In this study, we tested a commercially available small-scale protein freezing unit using immunoglobin G (IgG) as monoclonal antibody in a typical formulation buffer containing sodium phosphate, sodium chloride, and Tween 80. Different freezing rates were used respectively, and the product quality was tested in the frozen sample. Spatially resolved tests for protein concentration, pH, conductivity, and aggregation revealed high spatial differences in the frozen sample. Usage of slow freezing rates revealed high inhomogeneities in terms of buffer salt and protein distribution, while fast rates led to far lower spatial differences. These protein and buffer salt inhomogeneities can be reliably monitored using straight forward analytics, like conductivity and photometric total protein concentration measurements, reducing the need for HPLC analytics in screening experiments. Summarizing, fast freezing using steep rates shows promising results concerning homogeneity of the final frozen product and inhibits increased product aggregation.

Feeding tricarboxylic acid cycle intermediates improves lactate consumption and antibody production in Chinese hamster ovary cell cultures

Media components play an important role in modulating cell metabolism and improving product titer in mammalian cell cultures. To sustain cell productivity, highly active oxidative metabolism is desired. Here we explored the effect of tricarboxylic acid (TCA) cycle intermediates supplementation on lactate metabolism and productivity in Chinese hamster ovary fed-batch cultures. Direct addition of 5 mM alpha-ketoglutarate (α-KG), malic acid, or succinic acid in the basal medium did not have any significant impact on culture performance. On the other hand, feeding α-KG, malic acid, and succinic acid in the stationary phase, either as a single solution or as a mixture, significantly improved lactate consumption, reduced ammonium accumulation, and led to higher cell specific productivity and antibody titer (~35% increase for the best condition). Delivering those intermediates as an acidic solution for pH control eliminated CO₂ sparging and accumulation. Feeding TCA cycle intermediates was also demonstrated to be superior to feeding lactic acid or pyruvic acid in titer improvement. Taken together, feeding TCA cycle intermediates was effective in improving lactate consumption and increasing product titer, which is likely due to enhanced oxidative metabolism in an extended duration.

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

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

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

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

Metabolic flux balance analysis during lactate and glucose concomitant consumption in HEK293 cell cultures

At early stages of the exponential growth phase in HEK293 cell cultures, the tricarboxylic acid cycle is unable to process all the amount of NADH generated in the glycolysis pathway, being lactate the main by-product. However, HEK293 cells are also able to metabolize lactate depending on the environmental conditions. It has been recently observed that one of the most important modes of lactate metabolization is the cometabolism of lactate and glucose, observed even during the exponential growth phase. Extracellular lactate concentration and pH appear to be the key factors triggering the metabolic shift from glucose consumption and lactate production to lactate and glucose concomitant consumption. The hypothesis proposed for triggering this metabolic shift to lactate and glucose concomitant consumption is that HEK293 cells metabolize extracellular lactate as a response to both extracellular protons and lactate accumulation, by means of cotransporting them (extracellular protons and lactate) into the cytosol. At this point, there exists a considerable controversy about how lactate reaches the mitochondrial matrix: the first hypothesis proposes that lactate is converted into pyruvate in the cytosol, and afterward, pyruvate enters into the mitochondria; the second alternative considers that lactate enters first into the mitochondria, and then, is converted into pyruvate. In this study, lactate transport and metabolization into mitochondria is shown to be feasible, as evidenced by means of respirometry tests with isolated active mitochondria, including the depletion of lactate concentration of the respirometry assay. Although the capability of lactate metabolization by isolated mitochondria is demonstrated, the possibility of lactate being converted into pyruvate in the cytosol cannot be excluded from the discussion. For this reason, the calculation of the metabolic fluxes for an HEK293 cell line was performed for the different metabolic phases observed in batch cultures under pH controlled and noncontrolled conditions, considering both hypotheses. The main objective of this study is to evaluate the redistribution of cellular metabolism and compare the differences or similarities between the phases before and after the metabolic shift of HEK293 cells (shift observed when pH is not controlled). That is from a glucose consumption/lactate production phase to a glucose-lactate coconsumption phase. Interestingly, switching to a glucose and lactate cometabolization results in a better-balanced cell metabolism, with decreased glucose and amino acids uptake rates, affecting minimally cell growth. This behavior could be applied to further develop new approaches in terms of cell engineering and to develop improved cell culture strategies in the field of animal cell technology.