Driving towards digital biomanufacturing by CHO genome-scale models.

Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.

Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis.

There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.

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.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues.