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The recent outbreak of a novel coronavirus SARS-CoV-2 has posed a significant global public health threat and caused dramatic social and economic disruptions. A new research direction is attracting a significant amount of attention in the academic community of environmental sciences and engineering, in which rapid community-level monitoring could be achieved by applying the methodology of wastewater based epidemiology (WBE). Given the fact that the development of a mass balance on the total number of viral RNA copies in wastewater samples and the infected stool specimens is the heart of WBE, the result of the quantitative RNA detection in wastewater has to be highly sensitive, accurate, and reliable. Thus, applying effective concentration methods before the subsequent RNA extraction and RT-qPCR detection is a must-have procedure for the WBE. This review provides new insights into the primary concentration methods that have been adopted by the eighteen recently reported COVID-19 wastewater detection studies, along with a brief discussion of the mechanisms of the most commonly used virus concentration methods, including the PEG-based separation, electrostatically charged membrane filtration, and ultrafiltration. In the end, two easy and well-proven concentration strategies are recommended as below, aiming to maximize the practical significance and operational effectiveness of the SARS-CoV-2 virus concentration from wastewater samples. Strategy1: Prefiltration-Salt addition-Electronegative membrane filtration (for initial volume ≤ 50 mL). Strategy2: Prefiltration-PEG-based separation-Overnight standing (for initial volume from 50 to 1000 mL).

Kinetic modeling is the most suitable framework to describe the dynamic behavior of mammalian cell culture although its industrial application is still in its infancy. Herein, the authors reviewed mammalian bioprocess relevant kinetic models, and found that the simple unstructured-unsegregated approach utilizing empirical Monod-type kinetics based on limiting substrates and inhibitory metabolites is commonly used due to its traceability and simple formalism. Notably, the available kinetic models are typically small to moderate in size, and the development of large-scale models is severely hampered by the scarcity of kinetic data and limitations in current parameter estimation methods. The recent availability of abundant high-throughput multi-omics datasets from mammalian cell cultures have now paved the way to improve parameterization of kinetic models, and integrate regulatory, signaling, and product quality related intracellular events, as well as cellular metabolism within the modeling framework. Ultimately, the authors foresee that multi-scale modeling is the way forward in building predictive kinetic models of mammalian cell culture to advance biomanufacturing.

Large-scale bioprocessing is key to the successful manufacturing of a biopharmaceutical. However, cell viability and productivity are often lower in the scale-up from laboratory to production. In this study, we analyzed CHO cells, which showed lower percent viabilities and productivity in a 5-KL production scale bioreactor compared to a 20-L bench-top scale under seemingly identical process parameters. An increase in copper concentration in the media from 0.02 µM to 0.4 µM led to a doubling of percent viability in the production scale albeit still at a lower level than the bench-top scale. Combined metabolomics and proteomics revealed the increased copper reduced the presence of reactive oxygen species (ROS) in the 5-KL scale process. The reduction in oxidative stress was supported by the increased level of glutathione peroxidase in the lower copper level condition. The excess ROS was shown to be due to hypoxia (intermittent), as evidenced by the reduction in fibronectin with increased copper. The 20-L scale showed much less hypoxia and thus less excess ROS generation, resulting in little to no impact to productivity with the increased copper in the media. The study illustrates the power of 'Omics in aiding in the understanding of biological processes in biopharmaceutical production.

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