机器学习引导口腔微乳液系统的优化:贝叶斯优化方法
bioRxiv : the preprint server for biology
|February 12, 2026
概括
贝叶斯优化简化了微乳液 (ME) 配方设计,减少了开发时间. 这种方法确定了稳定的,高性能的ME,用于溶解性较差的药物,其药物输送能力得到改善.
科学领域:
- 制药科学 制药科学
- 药物输送系统 药物输送系统
- 制定 发展 制定 发展
背景情况:
- 口服微乳液 (ME) 增强了水溶性较差的药物的口服生物可用性.
- 传统的ME配方是耗时和资源密集的,经常产生不理想的结果.
- 需要有效的方法来简化ME配方设计.
研究的目的:
- 采用一个批量贝叶斯优化策略来设计优化的微乳液配方.
- 为了加快稳定和高性能微乳液的识别.
- 提高微乳液的物理化学特性和治疗性能.
主要方法:
- 一个批量贝叶斯优化策略在5次代 (每批5次实验) 实施.
- 一种两相建模方法在复杂的实验空间中导航了多种油,表面活性剂和表面活性剂.
- 一个由22个实验组成的训练数据集被用来构建优化模型.
主要成果:
- 成功设计了五种高性能空白微乳液.
- 其中4种微乳液的物理稳定性高达30天.
- 当装满模型药物时,三种微乳液显示了高药物负载,良好的稳定性和改善的体内透性.
结论:
- 贝叶斯优化显著简化了微乳液配方的设计.
- 开发的方法有效地识别出用于药物输送的稳定和有效的微乳液.
- 优化的微乳液显示出对难溶性药物的口服输送有前途.
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