Sarhan Omnia M, Gebril Mostafa I, Elsegaie Doaa
Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Badr University in Cairo, Cairo 11829, Egypt.
Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Misr International University (MIU), Cairo, Egypt.
J Pharm Pharmacol. 2025 Dec 7. doi: 10.1093/jpp/rgaf113.
The integration of artificial intelligence (AI) and nanomedicine has initiated a revolutionary phase in pharmaceutical research, facilitating progress in targeted drug delivery, controlled release, and personalized therapeutics. This review explores how AI-driven methods are integrated with nanocarrier systems such as liposomes, polymeric nanoparticles, and dendrimers. By harnessing high-dimensional datasets and predictive modeling, advanced techniques like deep learning, reinforcement learning, and graph neural networks have greatly enhanced pharmacokinetic predictions. As a result, dose-response forecasts have become more accurate, development timelines have shortened, and the experimental workload has been reduced. These technologies confront challenges in data standardization, algorithmic transparency, and regulatory adherence. While agencies such as the Food and Drug Administration and European Medicines Agency continue to update their guidelines, there remains an urgent need for a unified, flexible framework that can keep pace with rapid technological progress. This article calls for stronger cross-disciplinary cooperation among computer scientists, pharmaceutical researchers, and regulatory experts to address these challenges and fully harness AI-Enabled Nanomedicine for transforming personalized drug development.
人工智能（AI）与纳米医学的整合已开启了药物研究的革命阶段，推动了靶向给药、控释和个性化治疗的进展。本综述探讨了人工智能驱动的方法如何与脂质体、聚合物纳米颗粒和树枝状大分子等纳米载体系统相结合。通过利用高维数据集和预测建模，深度学习、强化学习和图神经网络等先进技术极大地增强了药代动力学预测。结果，剂量反应预测变得更加准确，开发时间表缩短，实验工作量减少。这些技术在数据标准化、算法透明度和法规遵循方面面临挑战。虽然美国食品药品监督管理局和欧洲药品管理局等机构不断更新其指南，但仍迫切需要一个统一、灵活的框架，以跟上快速的技术进步。本文呼吁计算机科学家、药物研究人员和监管专家之间加强跨学科合作，以应对这些挑战，并充分利用人工智能支持的纳米医学来变革个性化药物开发。
