The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
NSF Engineering Research Center (ERC) for Cell Manufacturing Technologies (CMaT), Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Biotechnol J. 2021 Mar;16(3):e2000277. doi: 10.1002/biot.202000277. Epub 2020 Oct 12.
Nascent advanced therapies, including regenerative medicine and cell and gene therapies, rely on the production of cells in bioreactors that are highly heterogeneous in both space and time. Unfortunately, advanced therapies have failed to reach a wide patient population due to unreliable manufacturing processes that result in batch variability and cost prohibitive production. This can be attributed largely to a void in existing process analytical technologies (PATs) capable of characterizing the secreted critical quality attribute (CQA) biomolecules that correlate with the final product quality. The Dynamic Sampling Platform (DSP) is a PAT for cell bioreactor monitoring that can be coupled to a suite of sensor techniques to provide real-time feedback on spatial and temporal CQA content in situ. In this study, DSP is coupled with electrospray ionization mass spectrometry and direct-from-culture sampling to obtain measures of CQA content in bulk media and the cell microenvironment throughout the entire cell culture process (≈3 weeks). Post hoc analysis of this real-time data reveals that sampling from the microenvironment enables cell state monitoring (e.g., confluence, differentiation). These results demonstrate that an effective PAT should incorporate both spatial and temporal resolution to serve as an effective input for feedback control in biomanufacturing.
新兴的先进治疗方法，包括再生医学、细胞和基因治疗，依赖于在生物反应器中生产细胞，这些细胞在空间和时间上都高度不均匀。不幸的是，由于制造工艺不可靠，导致批次间存在差异，且生产成本过高，先进的治疗方法未能惠及广泛的患者群体。这在很大程度上可以归因于缺乏现有的能够对与最终产品质量相关的分泌关键质量属性（CQA）生物分子进行特征描述的过程分析技术（PAT）。动态采样平台（DSP）是一种用于细胞生物反应器监测的 PAT，它可以与一系列传感器技术结合使用，为空间和时间 CQA 含量提供实时反馈原位。在这项研究中，DSP 与电喷雾电离质谱和直接从培养物中采样相结合，以获得整个细胞培养过程（约 3 周）中在批量介质和细胞微环境中的 CQA 含量的测量值。对这些实时数据的事后分析表明，从微环境中采样可以实现细胞状态监测（例如，汇合度、分化）。这些结果表明，有效的 PAT 应该同时具有空间和时间分辨率，作为生物制造中反馈控制的有效输入。
