Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.
Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.
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DOI：
10.3390/s23249676
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发表时间：
2023-12-07
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通讯作者：
R Reyes A
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作者：
Nettleton DF;Marí-Buyé N;Marti-Soler H;Egan JR;Hort S;Horna D;Costa M;Vallejo Benítez-Cano E;Goldrick S;Rafiq QA;König N;Schmitt RH;R Reyes A
癌症患者的免疫治疗是一个新的有希望的领域，未来可能会补充传统的化疗。细胞扩增阶段是从患者的初始样本中生产大量高质量的转基因免疫细胞的过程链中的关键部分。智能传感器增强了过程控制和监控系统对关键控制参数变化做出实时反应的能力，适应不同的患者特征，并优化过程。目前工作的目的是开发和校准智能传感器，以便将其部署在真实的生物反应器平台上，并对不同的患者/供体细胞图谱进行自适应控制和监测。已经实施了一组对比鲜明的智能传感器，并在自动单元扩展批处理运行中进行了测试，这些传感器结合了先进的数据驱动的机器学习和统计技术，以检测关键系统功能的变化和干扰。此外，在六个智能传感器警报中应用了“共识”方法作为置信度因素，帮助操作员识别需要注意的重大事件。初步结果表明，智能传感器可以有效地对Aglaris Facer生物反应器产生的数据进行建模和跟踪，在30分钟的时间窗口内预测事件，并缓解扰动，从而优化细胞数量和质量的关键性能指标。在事件检测的定量方面，所有批次运行的传感器的共识显示出良好的稳定性：基于人工智能的智能传感器(模糊和加权聚合)的一致性分别为88%和86%，而基于统计学的(稳定性检测器和布林格)的一致性分别为25%和42%，所有六个传感器的平均一致性为65%。不同的结果反映了不同的理论方法。最后，跨传感器批量运行的共识给出了更高的稳定性，范围从57%到98%，平均共识为80%。
Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a ‘consensus’ approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%.