Segmented linear modeling of CHO fed-batch culture and its application to large scale production

Dialysis rolled scaffold bioreactor allows extended production of monoclonal antibody with reduced media use

Rapidly expanding biopharmaceutical market demands more cost-effective platforms to produce protein therapeutics. To this end, novel approaches, such as perfusion culture or concentrated fed-batch, have been explored for higher yields and lower manufacturing costs. Although these new approaches produced promising results, but their wide-spread use in the industry is still limited. In this study, a dialysis rolled scaffold bioreactor was presented for long-term production of monoclonal antibodies with reduced media consumption. Media dialysis can selectively remove cellular bio-wastes without losing cells or produced recombinant proteins. The dialysis process was streamlined to significantly improve its efficiency. Then, extended culture of recombinant CHO cells for 41 days was successfully demonstrated with consistent production rate and minimal media consumption. The unique configuration of the developed bioreactor allows efficient dialysis for media management, as well as rapid media exchange to harvest produced recombinant proteins before they degrade. Taken together, it was envisioned that the developed bioreactor will enable cost-effective and long-term large-scale culture of various cells for biopharmaceutical production.

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.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

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.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed-batch conditions

Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.

Transient ammonia stress on Chinese hamster ovary (CHO) cells yield alterations to alanine metabolism and IgG glycosylation profiles

BACKGROUND

Ammonia concentrations typically increase during mammalian cell cultures, mainly due to glutamine and other amino acid consumption. An early ammonia stress indicator is a metabolic shift with respect to alanine. To determine the underlying mechanisms of this metabolic shift, a Chinese hamster ovary (CHO) cell line with two distinct ages (standard and young) was cultured in parallel fed-batch bioreactors with 0 mM or 10 mM ammonia added at 12 h. Reduced viable cell densities were observed for the stressed cells, while viability was not significantly affected. The stressed cultures had higher alanine, lactate, and glutamate accumulation. Interestingly, the ammonia concentrations were similar by Day 8.5 for all cultures. We hypothesized the ammonia was converted to alanine as a coping mechanism. Interestingly, no significant differences were observed for metabolite profiles due to cell age. Glycosylation analysis showed the ammonia stress reduced galactosylation, sialylation, and fucosylation. Transcriptome analysis of the standard-aged cultures indicated the ammonia stress had a limited impact on the transcriptome, where few of the significant changes were directly related metabolite or glycosylation reactions. These results indicate that mechanisms used to alleviate ammonia stress are most likely controlled post-transcriptionally, and this is where future research should focus.

Predictive macroscopic modeling of cell growth, metabolism and monoclonal antibody production: Case study of a CHO fed-batch production

We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.

Dynamic Optimization of a Fed-Batch Nosiheptide Reactor

Nosiheptide is a sulfur-containing peptide antibiotic, showing exceptional activity against critical pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) with livestock applications that can be synthesized via fed-batch fermentation. A simplified mechanistic fed-batch fermentation model for nosiheptide production considers temperature- and pH-dependence of biomass growth, substrate consumption, nosiheptide production and oxygen mass transfer into the broth. Herein, we perform dynamic simulation over a broad range of possible feeding policies to understand and visualize the region of attainable reactor performances. We then formulate a dynamic optimization problem for maximization of nosiheptide production for different constraints of batch duration and operability limits. A direct method for dynamic optimization (simultaneous strategy) is performed in each case to compute the optimal control trajectories. Orthogonal polynomials on finite elements are used to approximate the control and state trajectories allowing the continuous problem to be converted to a nonlinear program (NLP). The resultant large-scale NLP is solved using IPOPT. Optimal operation requires feedrate to be manipulated in such a way that the inhibitory mechanism of the substrate can be avoided, with significant nosiheptide yield improvement realized.

Reduced scale model qualification of 5-L and 250-ml bioreactors using multivariant visualization and Bayesian inferential methods

A novel method for the qualification of reduced scale models (RSMs) was illustrated using data from both a 250-ml advanced microscale bioreactor (ambr) and a 5-L bioreactor RSM for a 2,000-L manufacturing scale process using a CHO cell line to produce a recombinant monoclonal antibody. The example study showed how the method was used to identify process performance attributes and product quality attributes that capture important aspects of the RSM qualification process. The method uses two novel statistical approaches: multivariate dimension reduction and data visualization techniques, via partial least squares discriminant analysis (PLS-DA), and Bayesian multivariate linear modeling for inferential analysis. Bayesian multivariate linear modeling allows for individual probability distributions of the differences of the mean of each attribute for each scale, as well as joint probability statements on the differences of the means for multiple attributes. Depending on the results of this inferential procedure, PLS-DA is used to identify the process performance outputs at the different scales which have the greatest negative impact on the multivariate Bayesian joint probabilities. Experience with that particular process can then be leveraged to adjust operating conditions to minimize these differences, and then equivalence can be reassessed using the multivariate linear model.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

CHO-Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.