预测与酸盐成分和操作条件相关的增强药物溶解度:机器学习研究
Cong Wang1, Yuan Cheng1, Yuhong Ma1
1Department of Pharmaceutical Engineering, China Pharmaceutical University, Nanjing 211198, PR China.
International journal of pharmaceutics
|September 30, 2023
概括
机器学习准确地预测了使用酸盐复合的药物可溶性增强. 聚烯利 (PVP) 和基甲基纤维素 (HPMC) 分别显示了药物溶解百分比和溶解效率的卓越预测准确性.
科学领域:
- 制药科学 制药科学
- 物理化学 物理化学
- 计算化学计算化学
背景情况:
- 复杂化是改善难溶性药物的可溶性和生物利用性的关键策略.
- 基于酸盐成分和操作条件来预测提高药物溶解度仍然是一个重大挑战.
- 了解这些关系对于优化药物配方和输送至关重要.
研究的目的:
- 开发一种机器学习模型来预测通过酸盐的形成来提高药物溶解度.
- 为了同时预测药物溶解百分比和溶解效率.
- 分析各种类固醇化合物和操作条件对药物溶解性的影响.
主要方法:
- 利用机器学习来预测药物溶解百分比和溶解效率.
- 输入参数包括酸盐成分 (药物含量,分子量,摩尔比率) 和操作条件 (度,pH,压力,温度,溶解时间).
- 评估了作为主要溶解剂的不同辅助剂 (PEG,PVP,HPMC,环氧) 的性能.
主要成果:
- 机器学习模型成功预测了药物溶解百分比和溶解效率.
- 聚烯利 (PVP) 对药物溶解百分比的预测准确性更高.
- 基甲基纤维素 (HPMC) 对药物溶解效率的预测准确度更高.
结论:
- 本研究提出了一种新的机器学习方法,用于通过复杂化量化预测药物可溶性增强.
- 这些发现突出了PVP和HPMC等辅助剂对溶解性预测指标的差异影响.
- 为优化酸盐配方和预测药物性能提供了宝贵的见解.
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