Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

The bio-pharmaceutical industry heavily relies on mammalian cells for the production of bio-therapeutic proteins. The complexity of implementing and high cost-of-goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion-based processes remain challenging due to resource and time constraints. Model-based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model-based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L-scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost-of-goods in bio-pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

Construction of a novel kinetic model for the production process of a CVA6 VLP vaccine in CHO cells

Bioprocess development benefits from kinetic models in many aspects, including scale-up, optimization, and process understanding. However, current models are unable to simulate the production process of a coxsackievirus A6 (CVA6) virus-like particle (VLP) vaccine using Chinese hamster ovary cell culture. In this study, a novel kinetic model was constructed, correlating (1) cell growth, death, and lysis kinetics, (2) metabolism of major metabolites, and (3) CVA6 VLP production. To construct the model, two batches of a laboratory-scale 2 L bioreactor cell culture were prepared and various pH shift strategies were applied to examine the effect of pH shift. The proposed model described the experimental data under various conditions with high accuracy and quantified the effect of pH shift. Next, cell culture performance with various pH shift timings was predicted by the calibrated model. A trade-off relationship was found between product yield and quality. Consequently, multiple objective optimization was performed by integrating desirability methodology with model simulation. Finally, the optimal operating conditions that balanced product yield and quality were predicted. In general, the proposed model improved the process understanding and enabled in silico process development of a CVA6 VLP vaccine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-023-00598-8.

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.

Identification of Mispairing Omic Signatures in Chinese Hamster Ovary (CHO) Cells Producing a Tri-Specific Antibody

Monoclonal antibody-based therapy has shown efficacy against cancer, autoimmune, infectious, and inflammatory diseases. Multispecific antibodies (MsAbs), including trispecifics (tsAbs), offer enhanced therapeutic potential by targeting different epitopes. However, when co-expressed from three or more different polypeptide chains, MsAb production can lead to incorrect chain assembly and co-production of mispaired species with impaired biological activity. Moreover, mispairing carries significant challenges for downstream purification, decreasing yields and increasing the cost of bioprocess development. In this study, quantitative transcriptomics and proteomics analyses were employed to investigate which signaling pathways correlated with low and high mispairing clone signatures. Gene and protein expression profiles of Chinese hamster ovary (CHO) clones producing an tsAb were analyzed in the exponential growth and stationary (tsAb production) phase of fed-batch culture. Functional analysis revealed activated endoplasmic reticulum stress in high mispairing clones in both culture phases, while low mispairing clones exhibited expression profiles indicative of activated protein translation, as well as higher endocytosis and target protein degradation, suggesting the clearance of unfolded proteins through ubiquitin-mediated mechanisms. In addition, through transcriptomic profiling, we identified a group of genes that have the potential to be used as a biomarker panel tool for identifying high mispairing levels in the early stages of bioprocess development.

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.

Improved process design for monoclonal antibody charge variants separation with multicolumn counter-current solvent gradient purification

The multicolumn counter-current solvent gradient purification (MCSGP) method has proven effective in addressing the issue of elution profile overlap for difficult-to-separate proteins, leading to improved purity and recovery. However, during the MCSGP process, the flow rate and proportion of loaded proteins undergo changes, causing a significant discrepancy between the elution profiles of batch process design and the actual MCSGP process. This mismatch negatively impacts the purity and recovery of the target protein. To address this challenge, an improved process design (reDesign) was proposed with the first-run MCSGP to mimic the actual continuous process and obtain elution profiles that closely resemble the real ones. The reDesign was demonstrated with both a model protein mixture and a sample of monoclonal antibody (mAb) with charge variants. For model protein mixture, the reDesign-based MCSGP process (reMCSGP) showed a remarkable improvement in recovery, increasing from 83.6% to 97.8% while maintaining a purity of more than 95%. For mAb sample, the recovery of reMCSGP improved significantly to 93.9%, surpassing the performance of normal MCSGP processes at a given purity level of more than 84%. In general, the new process design strategy developed in this work could generate a more representative elution profile that closely mirrors actual conditions in continuous processes, which enhances the separation performance of MCSGP.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

A reflection on the improvement of Chinese Hamster ovary cell-based bioprocesses through advances in proteomic techniques

Chinese hamster ovary (CHO) cells are the preferred mammalian host for the large-scale production of recombinant proteins in the biopharmaceutical industry. Research endeavors have been directed to the optimization of CHO-based bioprocesses to increase protein quantity and quality, often in an empirical manner. To provide a rationale for those achievements, a myriad of CHO proteomic studies has arisen in recent decades. This review gives an overview of significant advances in LC-MS-based proteomics and sheds light on CHO proteomic studies, with a particular focus on CHO cells with superior bioprocessing phenotypes (growth, viability, titer, productivity and cQA), that have exploited novel proteomic or sub-omic techniques. These proteomic findings expand the current knowledge and understanding about the underlying protein clusters, protein regulatory networks and biological pathways governing such phenotypic changes. The proteomic studies, highlighted herein, will help in the targeted modulation of these cell factories to the desired needs.

Increase recombinant antibody yields through optimizing vector design and production process in CHO cells

Chinese hamster ovary (CHO) cells are the most commonly used host cells for the production of recombinant monoclonal antibodies (mAbs) due to their several advantages. Although the yields of recombinant mAbs can be greatly increased by some strategies, such as medium formulation, culture conditions, and cell engineering, most studies focused on either upstream design or downstream processes. In the present study, we first expressed recombinant adalimumab through combination of the vector design and production process optimization in CHO cells. Bicistronic vector, monocistronic vector, and dual promoter vector were constructed, and the production process was optimized using low-temperature and fed-batch culture. The results showed that the dual promoter vector exhibited the highest yield under the transient and stable transfected cells among three different vector systems in CHO cells. In addition, low-temperature and fed-batch culture could further improve the yields of adalimumab. The purified antibody displayed tumor necrosis factor-α (TNF-α) binding activity. In conclusion, combination of expression vector design and production process optimization can achieve higher expression of recombinant mAbs in CHO cells. KEY POINTS: • The dual promoter vector is more effective for expressing recombinant antibodies. • The yields of antibodies are related to the LC chain expression level. • Low-temperature and feed addition can promote antibody production.

Insights into the Impact of Rosmarinic Acid on CHO Cell Culture Improvement through Transcriptomics Analysis

The use of antioxidants in Chinese hamster ovary (CHO) cell cultures to improve monoclonal antibody production has been a topic of great interest. Nevertheless, the antioxidants do not have consistent benefits of production improvement, which might be cell line specific and/or process specific. In this work, we investigated how treatment with the antioxidant rosmarinic acid (RA) improved cell growth and titer in CHO cell cultures using transcriptomics. In particular, transcriptomics analysis indicated that RA treatment modified gene expression and strongly affected the MAPK and PI3K/Akt signaling pathways, which regulate cell survival and cell death. Moreover, it was observed that these signaling pathways, which had been identified to be up-regulated on day 2 and day 6 by RA, were also up-regulated over time (from initial growth phase day 2 to slow growth or protein production phase day 6) in both conditions. In summary, this transcriptomics analysis provides insights into the role of the antioxidant RA in industrial cell culture processes. The current study also represents an example in the industry of how omics can be applied to gain an in-depth understanding of CHO cell biology and to identify critical pathways that can contribute to cell culture process improvement and cell line engineering.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.