Karimi Alavijeh Masih, Lee Yih Yean, Gras Sally L
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.
Eng Life Sci. 2024 Mar 20;24(7):e2400023. doi: 10.1002/elsc.202400023. eCollection 2024 Jul.
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.
生物反应器的放大和缩小一直是生物制药行业的热门话题，尽管付出了巨大努力，但确定一种跨规模生物工艺开发的万无一失策略仍然是一项挑战。随着数字转型技术的普遍发展，基于计算机模型的新缩放方法可能会实现更有效的缩放。本研究旨在评估机器学习（ML）算法在生物反应器放大中的潜在应用，特别关注缩放参数的预测。确定了此类模型开发的关键因素，并从文献和公共来源整理了涉及CHO细胞产生的单克隆抗体产品的生物反应器放大研究数据，用于开发无监督和监督ML模型。跨规模生物反应器性能的比较确定了不同过程之间的相似性以及小型和大型生物反应器之间的主要差异。开展了一系列三个案例研究，以评估细胞生长与对规模敏感的生物反应器特征之间的关系。嵌入层提高了人工神经网络模型预测大规模细胞生长的能力，因为这种方法捕捉了过程之间的相似性。为预测缩放参数而构建的进一步模型展示了ML模型如何应用于辅助缩放过程。开发包含更多表征数据且在不同通气和搅拌条件下具有更大变异性的数据集，也将有助于未来用于生物反应器缩放的ML工具的开发。
