A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.

Autor: Karimi Alavijeh M; Department of Chemical Engineering The University of Melbourne Parkville Victoria Australia.; The Bio21 Molecular Science and Biotechnology Institute The University of Melbourne Parkville Victoria Australia., Lee YY; CSL Innovation Melbourne Victoria Australia., Gras SL; Department of Chemical Engineering The University of Melbourne Parkville Victoria Australia.; The Bio21 Molecular Science and Biotechnology Institute The University of Melbourne Parkville Victoria Australia.
Jazyk: angličtina
Zdroj: Engineering in life sciences [Eng Life Sci] 2024 Mar 20; Vol. 24 (7), pp. e2400023. Date of Electronic Publication: 2024 Mar 20 (Print Publication: 2024).
DOI: 10.1002/elsc.202400023
Abstrakt: Bioreactor scale-up and scale-down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail-safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale-up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale-up studies involving CHO cell-generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small- and large-scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale-sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large-scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.
Competing Interests: The authors declare that they have no conflicts of interest.
(© 2024 The Authors. Engineering in Life Sciences published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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