Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model

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.

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.

Trends in Monoclonal Antibody Production Using Various Bioreactor Syst

Monoclonal antibodies are widely used as diagnostic reagents and for therapeutic purposes, and their demand is increasing extensively. To produce these proteins in sufficient quantities for commercial use, it is necessary to raise the output by scaling up the production processes. This review describes recent trends in high-density cell culture systems established for monoclonal antibody production that are excellent methods to scale up from the lab-scale cell culture. Among the reactors, hollow fiber bioreactors contribute to a major part of high-density cell culture as they can provide a tremendous amount of surface area in a small volume for cell growth. As an alternative to hollow fiber reactors, a novel disposable bioreactor has been developed, which consists of a polymer-based supermacroporous material, cryogel, as a matrix for cell growth. Packed bed systems and disposable wave bioreactors have also been introduced for high cell density culture. These developments in high-density cell culture systems have led to the monoclonal antibody production in an economically favourable manner and made monoclonal antibodies one of the dominant therapeutic and diagnostic proteins in biopharmaceutical industry.

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.