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. 2023 Sep 21:3:1250508.
doi: 10.3389/fmmed.2023.1250508. eCollection 2023.

Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing

Niklas Bäckel  1 , Simon Hort  1 , Tamás Kis  2 , David F Nettleton  3 , Joseph R Egan  4 , John J L Jacobs  5 , Dennis Grunert  1 , Robert H Schmitt  1   6
Affiliations

Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing

Niklas Bäckel et al. Front Mol Med. .

Abstract

This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improvement. CAR-T cell therapy was approved in 2018 as the first Advanced Therapy Medicinal Product (ATMP) for treating acute leukemia and lymphoma. ATMPs are cell- and gene-based therapies that show great promise for treating various cancers and hereditary diseases. While some new ATMPs have been approved, ongoing clinical trials are expected to lead to the approval of many more. However, the production of CAR-T cells presents a significant challenge due to the high costs associated with the manufacturing process, making the therapy very expensive (approx. $400,000). Furthermore, autologous CAR-T therapy is limited to a make-to-order approach, which makes scaling economical production difficult. First attempts are being made to automate this multi-step manufacturing process, which will not only directly reduce the high manufacturing costs but will also enable comprehensive data collection. AI technologies have the ability to analyze this data and convert it into knowledge and insights. In order to exploit these opportunities, this paper analyses the data potential in the automated CAR-T production process and creates a mapping to the capabilities of AI applications. The paper explores the possible use of AI in analyzing the data generated during the automated process and its capabilities to further improve the efficiency and cost-effectiveness of CAR-T cell production.

Keywords: ATMP; CAR-T manufacturing; advanced therapy; artificial intelligence; cell and gene therapy; data analytics; immunotherapy; machine learning.

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Conflict of interest statement

DN was employed by IRIS Technology Solutions. Author JJ was employed by ORTEC B.V. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
CAR-T cell therapy process and its challenges.
See this image and copyright information in PMC

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