Matalqah Sina, Lafi Zainab, Mhaidat Qasim, Asha Nisreen, Yousef Asha Sara
Pharmacological and Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, Jordan.
King Hussein Cancer Center-Amman, Jordan.
Pharm Dev Technol. 2025 Jan;30(1):126-136. doi: 10.1080/10837450.2024.2448777. Epub 2025 Jan 11.
Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algorithms are accelerating formulation processes, predicting key parameters, and enabling personalized therapies. ML-driven approaches are restructuring formulation development by optimizing liposome size, stability, and encapsulation efficiency while refining drug release profiles. Additionally, the integration of ML enhances therapeutic outcomes by enabling precision-targeted delivery and minimizing side effects. This review presents current breakthroughs, challenges, and future opportunities in applying ML to liposomal systems, aiming to improve therapeutic efficacy and patient outcomes in various disease treatments.
机器学习（ML）已成为药物递送领域的一种变革性工具，尤其是在脂质体制剂的设计和优化方面。本综述聚焦于机器学习与脂质体技术的交叉领域，强调先进算法如何加速制剂工艺、预测关键参数并实现个性化治疗。机器学习驱动的方法正在通过优化脂质体大小、稳定性和包封效率，同时改善药物释放曲线，对制剂开发进行重塑。此外，机器学习的整合通过实现精准靶向递送和最小化副作用，提高了治疗效果。本综述介绍了将机器学习应用于脂质体系统的当前突破、挑战和未来机遇，旨在提高各种疾病治疗中的治疗效果和患者预后。
