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

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

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

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Perfusion of MC3T3E1 Preosteoblast Spheroids within Polysaccharide-Based Hydrogel Scaffolds: An Experimental and Numerical Study at the Bioreactor Scale

The traditional 3D culture systems in vitro lack the biological and mechanical spatiotemporal stimuli characteristic to native tissue development. In our study, we combined porous polysaccharide-based hydrogel scaffolds with a bioreactor-type perfusion device that generates favorable mechanical stresses while enhancing nutrient transfers. MC3T3E1 mouse osteoblasts were seeded in the scaffolds and cultivated for 3 weeks under dynamic conditions at a perfusion rate of 10 mL min^-1. The spatial distribution of the cells labeled with superparamagnetic iron oxide nanoparticles was visualized by MRI. Confocal microscopy was used to assess cell numbers, their distribution inside the scaffolds, cell viability, and proliferation. The oxygen diffusion coefficient in the hydrogel was measured experimentally. Numerical simulations of the flow and oxygen transport within the bioreactor were performed using a lattice Boltzmann method with a two-relaxation time scheme. Last, the influence of cell density and spheroid size on cell oxygenation was investigated. The cells spontaneously organized into spheroids with a diameter of 30-100 μm. Cell viability remained unchanged under dynamic conditions but decreased under static culture. The cell proliferation (Ki67 expression) in spheroids was not observed. The flow simulation showed that the local fluid velocity reached 27 mm s^-1 at the height where the cross-sectional area of the flow was the smallest. The shear stress exerted by the fluid on the scaffolds may locally rise to 100 mPa, compared with the average value of 25 mPa. The oxygen diffusion coefficient in the hydrogel was 1.6×10-9 m² s^-1. The simulation of oxygen transport and consumption confirmed that the cells in spheroids did not suffer from hypoxia when the bioreactor was perfused at 10 mL min^-1, and suggested the existence of optimal spheroid size and spacing for appropriate oxygenation. Collectively, these findings enabled us to define the optimal conditions inside the bioreactor for an efficient in vitro cell organization and survival in spheroids, which are paramount to future applications with organoids.

Biopharmaceutical-Type Chinese Hamster Ovary Cell Cultivation Under Static Magnetic Field Exposure: A Study of Genotoxic Effect

The lack of a sufficient research base is the reason for the ongoing discussion regarding the genotoxic effect of magnetic field (MF) exposure on mammalian cell cultures. Chinese hamster ovary (CHO) suspension-type cells, which are widely used for biopharmaceutical production, are potentially subjected to an increased MF when cultivated in bioreactors equipped with bottom-placed magnetically coupled stirring mechanisms. The main challenge for conducting research in this field remains the availability of a suitable experimental setup that generates an appropriate MF for the raised research question. In the present study, a simple and cost-effective experimental setup was developed that generated a static MF, similar to what has been modeled in large-scale bioreactors and, at the same time, was suitable for experimental cell cultivation in laboratory conditions. The measured maximum magnetic flux density to which the cells were exposed was 0.66 T. To assess the possible genotoxic effect, cells were continuously subcultivated in laboratory petri dishes for a period of 14 days, corresponding to a typical duration of a biopharmaceutical production process in a conventional fed-batch regime. The genotoxic effect was assessed using the cytokinesis-block micronucleus assay with fluorescent staining. Results showed that a 0.66-T static MF exposure had no significant long-term effect on cell viability and chromosomal damage but demonstrated a short-term effect on cell apoptosis. Significant increase in nuclear bud formation was observed. These findings may encourage other researchers in future studies investigating cellular responses to MF exposure and contribute relevant data for comparison.

Ultrasensitivity and bistability in covalent-modification cycles with positive autoregulation

Switch-like behaviours in biochemical networks are of fundamental significance in biological signal processing, and exist as two distinct types: ultra-sensitivity and bistability. Here we propose two new models of a reversible covalent-modification cycle with positive autoregulation (PAR), a motif structure that is thought to be capable of both ultrasensitivity and bistability in different parameter regimes. These new models appeal to a modelling framework that we call complex-complete, which accounts fully for the molecular complexities of the underlying signalling mechanisms. Each of the two new models encodes a specific molecular mechanism for PAR. We demonstrate that the modelling simplifications for PAR models that have been used in previous work, which rely on Michaelian approximations, are unable to accurately recapitulate the qualitative signalling responses supported by our detailed models. Strikingly, we show that complex-complete PAR models are capable of new qualitative responses such as one-way switches and a 'prozone' effect, depending on the specific PAR-encoding mechanism, which are not supported by Michaelian simplifications. Our results highlight the critical importance of accurately representing the molecular details of biochemical signalling mechanisms, and strongly suggest that the Michaelian approximation is inadequate for predictive models of enzyme-mediated chemical reactions with added regulations such as PAR.

Incorporating RNA-Seq transcriptomics into glycosylation-integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed-batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA-Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed-batch process, which is a significant improvement of the modelling technique used.