Baena Juan C, Victoria Juan Sebastián, Toro-Pedroza Alejandro, Aragón Cristian C, Ortiz-Guzman Joshua, Garcia-Robledo Juan Esteban, Torres David, Rios-Serna Lady J, Albornoz Ludwig, Rosales Joaquin D, Cañas Carlos A, Adolfo Cruz-Suarez Gustavo, Osorio Felipe Ocampo, Fleitas Tania, Laponogov Ivan, Loukanov Alexandre, Veselkov Kirill
Division of Oncology, Department of Medicine, Fundación Valle del Lili, ICESI University, Cali, Colombia.
LiliCAR-T Group, Fundación Valle del Lili, ICESI, Cali, Colombia.
Front Immunol. 2025 Aug 12;16:1635159. doi: 10.3389/fimmu.2025.1635159. eCollection 2025.
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 CAR-T production, reducing the need for labor-intensive 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 , 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 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 framework (:) represents a transformative step forward, promising to advance personalized cancer treatment toward greater precision, accessibility, and overall effectiveness.
个性化医疗通过使治疗与每个患者独特的生物学特征相匹配，重新定义了癌症治疗。一个关键的例子是嵌合抗原受体T细胞（CAR-T）疗法，在这种疗法中，患者自身的T细胞经过基因改造，以识别和摧毁癌细胞。这种方法在血液系统恶性肿瘤中取得了显著成果，并开始在实体瘤和自身免疫性疾病中显示出前景。然而，其更广泛的应用受到重大挑战的限制，包括复杂的生产过程、高成本、在实体瘤中的疗效有限以及潜在的严重毒性。纳米技术为克服其中许多障碍提供了令人兴奋的可能性。工程纳米颗粒可以改善基因传递、更精确地靶向肿瘤、增强免疫细胞功能，并实现CAR-T的生产，减少对劳动密集型过程的需求。然而，尽管有这些前景，但由于监管障碍、可扩展性问题以及人体模型中不一致的可重复性，将其转化为临床应用仍然困难。与此同时，人工智能（AI）凭借其强大的数据分析和预测建模算法，正在改变我们设计、评估和监测先进疗法的方式，包括制造过程的优化。在CAR-T的背景下，人工智能在更好的患者分层、改善治疗反应和毒性预测以及更快、更精确地设计CAR构建体和递送系统方面具有巨大潜力。利用这三个技术支柱，本综述介绍了 的概念，这是一个综合框架，其中人工智能指导纳米技术增强的CAR-T疗法的设计和部署。我们探讨了这种融合如何实现用于mRNA转染的脂质纳米颗粒制剂的优化、肿瘤微环境的特异性靶向和修饰、CAR-T细胞行为和毒性的实时监测，以及改善CAR-T的生成并克服实体瘤中的障碍。最后，我们还必须解决围绕这种新兴的生物疗法与计算驱动系统界面的伦理和监管问题。 框架（:）代表了向前迈出的变革性一步，有望推动个性化癌症治疗朝着更高的精准度、可及性和总体有效性发展。
