Han Youngji, Kim Dong Hyun, Pack Seung Pil
Bio-Medical Research Institute, Kyungpook National University Hospital, Daegu 41940, Republic of Korea.
Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.
Int J Mol Sci. 2025 Nov 17;26(22):11121. doi: 10.3390/ijms262211121.
Nanomaterials have revolutionized drug delivery by enabling precise control over solubility, stability, circulation time, and targeted release, yet translation from bench to bedside remains challenging due to complex synthesis, unpredictable biological interactions, and regulatory hurdles. Recent advances in artificial intelligence (AI) and big data analytics offer powerful solutions to these bottlenecks by integrating multidimensional datasets-encompassing physicochemical characterization, pharmacokinetics, omics profiles, and preclinical outcomes-to generate predictive models for rational nanocarrier design. Machine learning and deep learning approaches enable the prediction of key parameters such as particle size, drug loading efficiency, and biodistribution, while generative algorithms explore novel chemistries and architectures optimized for specific clinical applications. Nanoinformatics platforms and large-scale data repositories further enhance reproducibility and cross-study comparisons, supporting regulatory science and accelerating clinical translation. This review provides a comprehensive overview of nanomaterial-based drug delivery systems, highlights AI-driven strategies for predictive modeling and optimization, and discusses translational and regulatory perspectives. By bridging nanotechnology, computational modeling, and precision medicine, AI-assisted nanomaterial design has the potential to transform drug delivery into a more efficient, reproducible, and patient-centered discipline.
纳米材料通过实现对溶解度、稳定性、循环时间和靶向释放的精确控制，彻底改变了药物递送方式。然而，由于合成过程复杂、生物相互作用不可预测以及监管障碍，从实验室到临床的转化仍然具有挑战性。人工智能（AI）和大数据分析的最新进展通过整合多维数据集（包括物理化学表征、药代动力学、组学图谱和临床前结果），为这些瓶颈提供了强大的解决方案，以生成用于合理纳米载体设计的预测模型。机器学习和深度学习方法能够预测诸如粒径、载药效率和生物分布等关键参数，而生成算法则探索针对特定临床应用进行优化的新型化学物质和结构。纳米信息学平台和大规模数据存储库进一步提高了可重复性和跨研究比较，支持监管科学并加速临床转化。本综述全面概述了基于纳米材料的药物递送系统，强调了用于预测建模和优化的人工智能驱动策略，并讨论了转化和监管方面的观点。通过将纳米技术、计算建模和精准医学联系起来，人工智能辅助的纳米材料设计有可能将药物递送转变为一个更高效、可重复且以患者为中心的学科。
