Recent Developments in Bioprocess Monitoring Systems

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https://link.springer.com/chapter/10.1007/978-3-031-45669-5_2

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https://doi.org/10.1007/978-3-031-45669-5_2
 http://hdl.handle.net/11603/31589

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UMBC Chemical, Biochemical & Environmental Engineering Department
UMBC Center for Advanced Sensor Technology (CAST)
UMBC Faculty Collection
UMBC Student Collection

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https://orcid.org/0000-0001-6140-7582

Date

2024-01-11

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journal articles
postprints
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Citation of Original Publication

Rahmatnejad, Vida, Yunqian Wei, and Govind Rao. “Recent Developments in Bioprocess Monitoring Systems.” In Biopharmaceutical Manufacturing: Progress, Trends and Challenges, edited by Ralf Pörtner, 39–66. Cell Engineering. Cham: Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-45669-5_2.

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This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-45669-5
Access to this item will begin on 01-11-2025.

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Abstract

Bioprocess monitoring systems are essential tools for achieving an optimal manufacturing process for pharmaceuticals. In recent years, there have been advancements in the development of these systems. One of the most notable developments in bioprocess monitoring systems is the use of biosensors, which can detect and measure biological molecules such as enzymes and proteins. The use of imaging technologies, such as microscopy and flow cytometry, has allowed for the non-invasive monitoring of cell culture, providing valuable information for process optimization. Other non-invasive monitoring systems are being developed with the aim of reducing the risk of contamination. Another important advancement is the use of machine learning and artificial intelligence in bioprocess monitoring systems. These technologies have the ability to learn from historical data and predict future outcomes, which can be beneficial for optimizing bioprocesses in real-time. Overall, recent advancements in sensor technology can help in improving the efficiency and effectiveness of bioprocessing. The integration of advanced sensing technologies, and machine learning can provide valuable insights and improve the ability to monitor and control bioprocesses online. In this chapter, we will discuss recent advancements in bioprocess monitoring systems, specifically for monoclonal antibody and cell and gene therapy manufacturing processes.