Integrating metabolome dynamics and process data to guide cell line selection in biopharmaceutical process development
Metabolic Engineering(2022)
Abstract
The successful development of mammalian cell culture for the production of therapeutic antibodies is a resource-intensive and multistage process which requires the selection of high performing and stable cell lines at different scale-up stages. Accordingly, science-based approaches exploiting biological information, such as metabolomics, can support and accelerate the selection of promising cell lines to progress. In fact, the integration of dynamic biological information with process data can provide valuable insights on the cell physiological changes as a consequence of the cultivation process.
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Key words
Bioprocess development,Scale up,Cell selection,Metabolomics,Machine learning,CHO
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