Integrating metabolome dynamics and process data to guide cell line selection in biopharmaceutical process development.

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. This work studies the industrial development of monoclonal antibodies at micro-bioreactor scale (Ambr®15) and aims at accelerating the selection of the better performing cell lines. To that end, we apply a machine learning approach to integrate time-varying process and biological information (i.e., metabolomics), explicitly exploiting their dynamics. Strikingly, cell line performance during the cultivation can be predicted from early process timepoints by exploiting the gradual temporal evolution of metabolic phenotypes. Furthermore, product titer is estimated with good accuracy at late process timepoints, providing insights into its relationship with underlying metabolic mechanisms and enabling the identification of biomarkers to be further investigated. The biological insights obtained through the proposed machine learning approach provide data-driven metabolic understanding allowing early identification of high performing cell lines. Additionally, this analysis offers the opportunity to identify key metabolites which could be used as biomarkers for industrially relevant phenotypes and onward fit into our commercial manufacturing platforms. • Machine learning to integrate process and biological dynamic information. • Accelerating the selection of better performing cell lines through data analytics. • Early prediction of cell lines performance from temporal evolution of metabolomics. [ABSTRACT FROM AUTHOR]