Smart Sensor Control and Monitoring of an Automated Cell Expansion Process
Author:
Nettleton David F.1ORCID, Marí-Buyé Núria2, Marti-Soler Helena1, Egan Joseph R.3ORCID, Hort Simon4, Horna David25, Costa Miquel25, Vallejo Benítez-Cano Elia5, Goldrick Stephen3ORCID, Rafiq Qasim A.3, König Niels4ORCID, Schmitt Robert H.46, R. Reyes Aldo1
Affiliation:
1. IRIS Technology Solutions, 08940 Barcelona, Spain 2. Aglaris Cell, 28760 Madrid, Spain 3. Department of Biochemical Engineering, University College London, London WC1E 6BT, UK 4. Fraunhofer Institute for Production Technology, 52074 Aachen, Germany 5. Aglaris Ltd., Stevenage SG1 2FX, UK 6. Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, 52074 Aachen, Germany
Abstract
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%.
Funder
EU project AIDPATH Spanish Ministerio de Economía y Competitividad Innovate UK
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference32 articles.
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