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

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference32 articles.

1. (2023, December 04). AIDPATH: Artificial Intelligence-Driven Decentralized Production for Advanced Therapies in the Hospital. Available online: https://cordis.europa.eu/project/id/101016909/de.

2. Bäckel, N., Hort, S., Kis, T., Nettleton, D.F., Egan, J.R., Jacobs, J.J., Grunert, D., and Schmitt, R.H. (2023, December 04). Elaborating the Potential of Artificial Intelligence in Automated CAR-T Cell Manufacturing. Available online: https://www.frontiersin.org/articles/10.3389/fmmed.2023.1250508/full.

3. Toward rapid, widely available autologous CAR-T cell therapy–artificial intelligence and automation enabling the smart manufacturing hospital;Hort;Front. Med.,2022

4. Automated cell expansion: Trends & outlook of critical technologies;Wu;Cell Gene Ther. Insights,2018

5. Yamanaka, H., Murato, Y., and Cizdziel, P.E. (2021). Bioreactor Automation Driven by Real-Time Sensing: Enhancing Productivity through Accurate, Efficient Glucose Control, Yokogawa Corporation of America.

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