机器学习增强的纳米纤维系统:控制药物释放的新前沿
Gabriella Onila Nascimento Soares1, Vitor Santi2, Andrey Coatrini Soares3
1Materials Engeneering Departament, University of Sao Paulo, Avenida joão Dagnone, 1100 - Santa Angelina, São Carlos, São Paulo, 13563120, BRAZIL.
Nanotechnology
|January 15, 2026
概括
机器学习 (ML) 加快了基于纳米纤维的药物输送系统 (N-DDS) 的发展. 机器学习模型预测和优化材料特性,制造和药物释放,推进精密医学.
科学领域:
- 生物材料科学 生物材料科学
- 纳米技术纳米技术
- 药理学 药理学是指药理学的学科.
背景情况:
- 基于纳米纤维的药物递送系统 (N-DDS) 由于其高表面积和可调节性质,为控制释放提供了显著的优势.
- 优化N-DDS涉及许多相互依存的参数,使传统的开发方法耗时且低效.
- 电是一种关键的制造技术,用于创建这些先进的药物输送平台.
研究的目的:
- 审查机器学习 (ML) 的应用,以加快N-DDS的设计和优化.
- 要突出ML如何用N-DDS开发的预测建模取代试错.
- 讨论ML集成N-DDS在未来治疗应用中的潜力.
主要方法:
- 对纳米纤维和药物输送系统 (DDS) 的文献进行了图书统计分析,重点是电.
- 在聚合物选择,电过程优化和封装策略中审查ML应用.
- 分析ML模型在预测纳米纤维形态,封装效率和药物释放动力学方面的性能.
主要成果:
- 机器学习模型在量身定制N-DDS特征方面表现出高的预测准确性.
- 案例研究表明,ML有效地优化了所需药物释放配置文件的参数.
- 该审查确定了临床翻译的关键挑战,包括数据质量和可扩展性.
结论:
- 机器学习的整合对于推动N-DDS开发超越传统方法至关重要.
- 基于ML的N-DDS有望为患者特定,可持续和可扩展的治疗解决方案提供希望.
- 机器学习和纳米纤维工程的协同作用为精密医学铺平了道路.
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