机器学习加强了制药悬浮剂的配方设计
Nadina Zulbeari1, Fanjin Wang2, Sibel Selyatinova Mustafova1
1Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.
International journal of pharmaceutics
|November 20, 2024
概括
机器学习模型可以预测长效注射剂的纳米和微悬浮稳定性. 稳定剂度显著影响颗粒大小,改善配方开发.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 长效注射剂 (LAI) 对于治疗慢性疾病至关重要.
- 目前的LAI配方开发在很大程度上依赖于试错.
- 控制纳米和微悬浮中的颗粒大小是药物释放的关键.
研究的目的:
- 系统地调查影响纳米和微悬浮的配方参数.
- 应用统计和机器学习 (ML) 策略来优化配方.
- 开发一个可解释的ML模型来预测配方稳定性.
主要方法:
- 进行了全因数磨实验.
- 统计分析发现了重要的配方因素.
- 使用72个数据点构建和验证了一种机器学习分类模型.
- 沙普利增量解释 (SHAP) 用于模型解释.
主要成果:
- 稳定剂度是影响中位悬浮直径 (D50) 的高度显著因素 (p < 0.001).
- 一个ML模型实现了高预测精度 (0.91) 和F1得分 (0.91) 的配方稳定性.
- SHAP分析强调了稳定剂度和珠大小的关键作用.
结论:
- 可解释的ML建模可以显著提高对纳米和微悬浮设计的理解.
- 这种方法为传统的试错方法提供了数据驱动的替代方案.
- 开发的ML模型有助于优化LAI配方,以改善药物输送.
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