Toward Rapid, Widely Available Autologous CAR-T Cell Therapy – Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital - Hort, Simon; Herbst, Laura; Bäckel, Niklas; Erkens, Frederik; Niessing, Bastian; Frye, Maik; König, Niels; Papantoniou, Ioannis; Hudecek, Michael; Jacobs, John J. L.; Schmitt, Robert H. (Frontiers in Medicine, 2022)
CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. It leads to a paradigm shift from centralized ATMP production towards point-of-care treatment. In this perspective, we will be discussing current challenges and opportunities of automated, AI-enabled CAR-T cell therapy manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.