机器学习授权制剂设计,优化和纳米颗粒药物输送系统的特征:当前的应用,挑战和未来的前景
Chunyan Shen1,2, Mengyan Zhang3, Meiting Lu1,2
1Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510515, China.
Acta pharmaceutica Sinica. B
|February 13, 2026
概括
人工智能,特别是机器学习 (ML),正在加速纳米颗粒药物递送系统 (NDDS) 的发展. 机器学习有助于预测关键质量属性和优化纳米药物的配方.
科学领域:
- 制药科学 制药科学
- 生物技术是生物技术.
- 材料科学 材料科学 材料科学
背景情况:
- 纳米颗粒药物递送系统 (NDDS) 已经显著推进了医学,但它们的开发是复杂的.
- 超过90种纳米药物已获得批准,但制剂方面的挑战仍然存在.
研究的目的:
- 审查机器学习 (ML) 在NDDS的配方开发中的应用.
- 探索ML在预测关键质量属性和优化纳米医学配方方面的潜力.
主要方法:
- 对各种NDDS配方的ML应用中的最新进展进行系统审查.
- 用于预测NDDS关键质量属性 (CQAs) 的ML算法的摘要.
主要成果:
- ML在各种NDDS中应用,包括聚合物纳米粒子,脂质体和脂质纳米粒子.
- 机器学习算法可以预测关键的CQA,如粒子大小,药物负载和稳定性.
- ML有助于优化配方设计和表征.
结论:
- 机器学习提供了强大的预测和分析能力,以克服NDDS开发挑战.
- ML具有改造潜力,可以增强纳米药物配方并加速临床转化.
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