Chan Zhi Xian, Chelvam Shruthi Pandi, Sin Wei-Xiang, Teo Denise Bei Lin, Abdul Rahim Ahmad Amirul Bin, Wu Ying Ying, Liu Dan, Birnbaum Michael E, Yong Derrick, Ram Rajeev J
Critical Analytics for Manufacturing Personalized Medicine, Singapore MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Biomanufacturing Technology, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Front Bioeng Biotechnol. 2025 Jul 31;13:1612648. doi: 10.3389/fbioe.2025.1612648. eCollection 2025.
Current workflows in autologous cell therapy manufacturing are reliant on manual processes that are difficult to scale out to meet patient demands. High throughput bioreactor systems that enable multiple cultures to occur in parallel can address this need, but require good bioprocess monitoring workflows to produce good quality cell therapy products. Commercial sampling systems have thus been developed for better feedback control and monitoring capabilities. However, they are targeted towards large scale processes and often bioreactor specific, making them less robust for integration across different bioreactor scales and types, such as perfusion-capable microbioreactors which allows for greater process intensification. Here, an automated cell culture sampling system (Auto-CeSS) was developed to eliminate laborious manual sampling while minimizing sterility risks for cell therapy manufacturing processes. The system is aseptically integrated with a variety of bioreactors of different working volumes. This system can accurately and aseptically sample a minimum volume of 30 μL and can consistently perform periodic sampling of supernatant over a minimum interval of 15 min. We integrated Auto-CeSS with a 2 mL perfusion microbioreactor and a 8 mL gas-permeable well-plate for T cell culture, collecting 200 μL of supernatant samples daily for metabolite analysis. Comparison of the metabolic profiles of the samples collected via Auto-CeSS versus manual sampling revealed insignificant differences in metabolite levels, including glucose, lactate, glutamine, and glutamate. This report demonstrates the potential of Auto-CeSS as an at-line sampling platform in a real-time T cell production run to facilitate in-process culture monitoring.
当前自体细胞治疗产品制造中的工作流程依赖于手工操作，这些操作难以扩大规模以满足患者需求。能够使多种培养物并行进行的高通量生物反应器系统可以满足这一需求，但需要良好的生物过程监测工作流程来生产高质量的细胞治疗产品。因此，已经开发出商业采样系统以实现更好的反馈控制和监测能力。然而，它们针对的是大规模过程，并且通常是特定于生物反应器的，这使得它们在跨不同生物反应器规模和类型（例如具有灌注能力的微生物反应器，其可实现更大程度的过程强化）进行集成时不够稳健。在此，开发了一种自动化细胞培养采样系统（Auto-CeSS），以消除繁琐的手动采样，同时将细胞治疗制造过程中的无菌风险降至最低。该系统与各种不同工作体积的生物反应器无菌集成。该系统能够准确、无菌地采集最小体积为30 μL的样品，并且能够在至少15分钟的最小间隔内持续对上清液进行定期采样。我们将Auto-CeSS与一个2 mL的灌注微生物反应器和一个用于T细胞培养的8 mL透气孔板集成在一起，每天收集200 μL的上清液样品用于代谢物分析。通过Auto-CeSS采集的样品与手动采样的样品的代谢谱比较显示，代谢物水平（包括葡萄糖、乳酸、谷氨酰胺和谷氨酸）的差异不显著。本报告证明了Auto-CeSS作为实时T细胞生产过程中的在线采样平台，在促进过程中培养监测方面的潜力。
