Smart Sensor Control and Monitoring of an Automated Cell Expansion Process
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 pat...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/24/9676 |
| _version_ | 1797379404533334016 |
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| author | David F. Nettleton Núria Marí-Buyé Helena Marti-Soler Joseph R. Egan Simon Hort David Horna Miquel Costa Elia Vallejo Benítez-Cano Stephen Goldrick Qasim A. Rafiq Niels König Robert H. Schmitt Aldo R. Reyes |
| author_facet | David F. Nettleton Núria Marí-Buyé Helena Marti-Soler Joseph R. Egan Simon Hort David Horna Miquel Costa Elia Vallejo Benítez-Cano Stephen Goldrick Qasim A. Rafiq Niels König Robert H. Schmitt Aldo R. Reyes |
| author_sort | David F. Nettleton |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T20:22:45Z |
| format | Article |
| id | doaj.art-186c4a726fce45a28df4c125984ada20 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:22:45Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-186c4a726fce45a28df4c125984ada202023-12-22T14:40:11ZengMDPI AGSensors1424-82202023-12-012324967610.3390/s23249676Smart Sensor Control and Monitoring of an Automated Cell Expansion ProcessDavid F. Nettleton0Núria Marí-Buyé1Helena Marti-Soler2Joseph R. Egan3Simon Hort4David Horna5Miquel Costa6Elia Vallejo Benítez-Cano7Stephen Goldrick8Qasim A. Rafiq9Niels König10Robert H. Schmitt11Aldo R. Reyes12IRIS Technology Solutions, 08940 Barcelona, SpainAglaris Cell, 28760 Madrid, SpainIRIS Technology Solutions, 08940 Barcelona, SpainDepartment of Biochemical Engineering, University College London, London WC1E 6BT, UKFraunhofer Institute for Production Technology, 52074 Aachen, GermanyAglaris Cell, 28760 Madrid, SpainAglaris Cell, 28760 Madrid, SpainAglaris Ltd., Stevenage SG1 2FX, UKDepartment of Biochemical Engineering, University College London, London WC1E 6BT, UKDepartment of Biochemical Engineering, University College London, London WC1E 6BT, UKFraunhofer Institute for Production Technology, 52074 Aachen, GermanyFraunhofer Institute for Production Technology, 52074 Aachen, GermanyIRIS Technology Solutions, 08940 Barcelona, SpainImmune 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%.https://www.mdpi.com/1424-8220/23/24/9676smart sensorscell manufacturing platformcell expansion processconsensus |
| spellingShingle | David F. Nettleton Núria Marí-Buyé Helena Marti-Soler Joseph R. Egan Simon Hort David Horna Miquel Costa Elia Vallejo Benítez-Cano Stephen Goldrick Qasim A. Rafiq Niels König Robert H. Schmitt Aldo R. Reyes Smart Sensor Control and Monitoring of an Automated Cell Expansion Process Sensors smart sensors cell manufacturing platform cell expansion process consensus |
| title | Smart Sensor Control and Monitoring of an Automated Cell Expansion Process |
| title_full | Smart Sensor Control and Monitoring of an Automated Cell Expansion Process |
| title_fullStr | Smart Sensor Control and Monitoring of an Automated Cell Expansion Process |
| title_full_unstemmed | Smart Sensor Control and Monitoring of an Automated Cell Expansion Process |
| title_short | Smart Sensor Control and Monitoring of an Automated Cell Expansion Process |
| title_sort | smart sensor control and monitoring of an automated cell expansion process |
| topic | smart sensors cell manufacturing platform cell expansion process consensus |
| url | https://www.mdpi.com/1424-8220/23/24/9676 |
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