A disposable impedance-based sensor for in-line cell growth monitoring in CAR-T cell manufacturing

Liu, Zhaonan; Jiang, Xuzhou; Li, Shuai; Jiang, Chen; Wang, Kan; Zhang, Chuck; Wang, Ben

doi:10.1016/j.bioelechem.2023.108416

Bioelectrochemistry

2023

DOI: 10.1016/j.bioelechem.2023.108416

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A disposable impedance-based sensor for in-line cell growth monitoring in CAR-T cell manufacturing

Zhaonan Liu

Xuzhou Jiang

Shuai Li

et al.

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Cited by 6 publications

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Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Ferdous,

Shihab,

Chowdhury

et al. 2024

Biotech & Bioengineering

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Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T‐cell activation by antigen‐presenting beads and their subsequent expansion is formulated in silico. The simulation is used as an environment to train RL‐agents to dynamically control the number of beads in culture to maximize the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym, enabling testing of different environments, cell types, RL‐agent algorithms, and state inputs to the RL‐agent. RL‐agent training is demonstrated with three different algorithms (PPO, A2C, and DQN), each sampling three different state input types (tabular, image, mixed); PPO‐tabular performs best for this simulation environment. Using this approach, training of the RL‐agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input‐noise (sensor performance), number of control step interventions, and advantages of pre‐trained RL‐agents are also evaluated. Therefore, we present an RL framework to maximize the population of robust effector cells in CAR T‐cell therapy production.

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Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Ferdous,

Shihab,

Chowdhury

et al. 2024

Biotech & Bioengineering

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Advances in sensor developments for cell culture monitoring

Kim,

Yeo

2023

BMEMat

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Cell culture encompasses procedures for extracting cells from their natural tissue and cultivating them under controlled artificial conditions. During this process, various factors, including cell physiological/morphological properties, culture environments, metabolites, and contaminants, have to be precisely controlled and monitored for the survival of cells and the pursuit of the desired properties of the cells. This review summarizes recent advances in sensor technologies and manufacturing strategies for various cell culture platforms using traditional plastics, microfluidic chips, and scalable bioreactors. We share the details of newly developed biological sensors, chemical sensors, optical sensors, electronic chip technologies, and material integration methods. The precise control of parameters based on the feedback by these sensors and electronics enhances cell culture quality and throughput.

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Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Dong,

He,

Yan

et al. 2024

Chinese Journal of Chemical Engineering

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A disposable impedance-based sensor for in-line cell growth monitoring in CAR-T cell manufacturing

Cited by 6 publications

References 36 publications

Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Advances in sensor developments for cell culture monitoring

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

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