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

• Published in: Engineering in life sciences Vol. 24; no. 7; pp. e2400023 - n/a
• Main Authors: Karimi Alavijeh, Masih, Lee, Yih Yean, Gras, Sally L.
• Format: Journal Article
• Language: English
• Published: Germany John Wiley and Sons Inc 01.07.2024; Wiley-VCH
• Subjects: bioprocessing; bioreactor; data‐driven modeling; machine learning; mammalian cell; bioprocessing; mammalian cell; bioreactor; machine learning; data‐driven modeling
• ISSN: 1618-0240; 1618-2863
• DOI: 10.1002/elsc.202400023

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. Lay summary: This study examined the potential of machine learning to assist in bioreactor scale‐up. The findings demonstrated the capability of these algorithms to uncover complex non‐linear relationships among scale‐sensitive features, transfer knowledge, and predict process performance across scales. A method for predicting scaling factors for equivalent performance across scales was also developed and the characteristics of ideal datasets for future application of machine learning to scaling described.