基于自然聚合物的药物输送系统的AI-enabled,QbD-aligned预测和可持续设计基于天然聚合物的药物输送系统
Jirapornchai Suksaeree1, Pattwat Maneewattanapinyo2, Chaowalit Monton3,4
1Department of Pharmaceutical Chemistry, College of Pharmacy, Rangsit University, Muang, Pathum Thani, 12000, Thailand. jirapornchai.s@rsu.ac.th.
AAPS PharmSciTech
|January 29, 2026
概括
人工智能 (AI) 通过预测配方性能来增强天然聚合物药物输送. 这种数据驱动的方法超出了试错的范围,以实现可持续和可扩展的制药创新.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 生物技术是生物技术.
背景情况:
- 自然聚合物为药物输送提供生物相容性和可持续性.
- 不一致的性能和可扩展性阻碍了天然聚合物配方.
- 人工智能 (AI) 和机器学习 (ML) 是新兴的解决方案.
研究的目的:
- 审查自然聚合物药物输送系统的AI辅助建模和优化.
- 突出AI在克服制定挑战中的作用.
- 讨论通往数据驱动,可持续制药的途径.
主要方法:
- 综合目前AI/ML在天然聚合物配方方面的进展.
- 预测算法的分析 (神经网络,贝叶斯优化).
- 讨论混合机械-人工智能和物理信息的神经网络.
- 审查数据生成工作流程,FAIR标准和聚合物信息学数据库.
主要成果:
- 人工智能模型准确地预测封装效率和溶解概况.
- 混合人工智能方法通过整合物理定律来提高模型的解释性.
- 数据基础设施 (工作流程,标准,数据库) 可以实现可重复的建模.
结论:
- 人工智能集成为数据驱动的,可持续的制药提供了一条途径.
- 预测性和生态设计的配方可以取代经验方法.
- 解决标准化,透明度和验证方面的挑战对于翻译至关重要.
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