REVIEW article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1635159
This article is part of the Research TopicNanomaterials Mediated Immunomodulation in Cancer: Current Perspective from Bench to ClinicView all 3 articles
Smart CAR-T Nanosymbionts: Archetypes and Proto-models
Provisionally accepted- 1Facultad de Ciencias de la Salud, Universidad Icesi, Cali, Colombia
- 2LiliCAR-T Group, Fundación Valle del Lili, ICESI University,, Cali 760032, Colombia, Colombia
- 3LiliCAR-T Group, Fundación Valle del Lili, ICESI University, Cali 760032, Colombia, Colombia
- 4LiliCAR-T Group, Fundación Valle del Lili, ICESI University, Cali, Colombia
- 5Rio Grande Urology., El Paso, Texas,, United States
- 6Instituto de Cancer Hemato-oncologos, Cali, Colombia
- 7Universidad Icesi, Cali, Colombia
- 8Artificial Intelligence Unit, Fundación Valle del Lili., Cali, Colombia
- 9Hospital Clinico Universitario, Valencia, Spain
- 10Imperial College London, London, United Kingdom
- 11Department of Chemistry and Materials Science, National Institute of Technology., Gunma College, Maebashi 371-8530, Japan, Japan
- 12Laboratory of Engineering Nanobiotechnology, University of Mining and Geology, St. Ivan Rilski”,1700 Sofia, Bulgaria, Bulgaria
- 13Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA., United States
- 14Division of Cancer, Department of Surgery and Cancer., Faculty of Medicine, Imperial College London, London, UK., United Kingdom
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Personalized medicine has redefined cancer treatment by aligning therapies with each patient's unique biological profile. A key example is chimeric antigen receptor T-cell (CAR-T) therapy, in which a patient’s own T cells are genetically modified to recognize and destroy cancer cells. This approach has delivered remarkable results in hematologic malignancies and is beginning to show promise in solid tumors and autoimmune diseases. However, its broader adoption is limited by major challenges, including complex manufacturing, high costs, limited efficacy in solid tumors, and potentially severe toxicities. Nanotechnology offers exciting possibilities to overcome many of these barriers. Engineered nanoparticles can improve gene delivery, target tumors more precisely, enhance immune cell function, and enable in vivo CAR-T production, reducing the need for labor-intensive ex vivo processes. However, despite this promise, translation into clinical settings remains difficult due to regulatory hurdles, scalability issues, and inconsistent reproducibility in human models. At the same time, artificial intelligence (AI), with its powerful algorithms for data analysis and predictive modeling, is transforming how we design, evaluate, and monitor advanced therapies, including the optimization of manufacturing processes. In the context of CAR-T, AI holds strong potential for better patient stratification, improved prediction of treatment response and toxicity, and faster, more precise design of CAR constructs and delivery systems. Leveraging these three technological pillars, this review introduces the concept of Smart CART Nanosymbionts, an integrated framework in which AI guides the design and deployment of nanotechnology-enhanced CAR-T therapies. We explore how this convergence enables optimization of lipid nanoparticle formulations for mRNA transfection, specific targeting and modification of the tumor microenvironment, real-time monitoring of CAR-T cell behavior and toxicity, and improved in vivo CAR-T generation and overcoming barriers in solid tumors. Finally, it's important we also address the ethical and regulatory considerations surrounding this emerging interface of living therapies and computational driven systems. The Smart CART Nanosymbionts framework(Image 1) represents a transformative step forward, promising to advance personalized cancer treatment toward greater precision, accessibility, and overall effectiveness.
Keywords: CAR-T therapy, Nanotechnology, artificial intelligence, machine learning, deep learning, Immunotherapy, manufacturing, personalized medicine
Received: 26 May 2025; Accepted: 01 Jul 2025.
Copyright: © 2025 BAENA, VICTORIA, Toro-Pedroza, Aragón, Ortiz-Guzman, Garcia-Robledo, Torres, Rios, Albornoz, Rosales, Cañas, Adolfo Cruz-Suarez, Osorio, Fleitas, Laponogov, Loukanov and Veselkov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: JUAN camilo BAENA, Facultad de Ciencias de la Salud, Universidad Icesi, Cali, Colombia
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