用机器学习分析和验证优化溶解度的混合溶剂里瓦罗克萨班溶解度的相关性
Muteb Alanazi1, Jowaher Alanazi2, Tareq Nafea Alharby3
1Department of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia. Ms.alanazi@uoh.edu.sa.
Scientific reports
|February 8, 2025
概括
这项研究利用混合溶剂和机器学习优化了里瓦罗克萨班的溶解性. AdaBoost高斯过程回归准确地预测了改善药物配方和生物可用性的最佳条件.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 里瓦罗克萨班具有较差的水溶性,限制了其口服生物可用性,并需要制定策略.
- 优化溶解度对于利瓦洛克萨班的有效输送和治疗结果至关重要.
- 混合溶剂系统有可能提高制药制造中的可溶性.
研究的目的:
- 为了研究里瓦罗克萨班在初级酒精-二甲混合物中的溶解性.
- 开发和评估用于预测利瓦洛克萨班溶解性的机器学习模型.
- 为了确定利瓦洛克萨班可溶性的最佳条件,用于制药应用.
主要方法:
- 在混合溶剂中对利瓦罗克萨班溶解性的实验研究.
- 开发了三个机器学习模型:ADAGPR,ADAMLP和ADALASSO.
- 使用R2,RMSE和MAPE指标对模型的评估.
主要成果:
- AdaBoost高斯过程回归 (ADAGPR) 显示出优异的预测性能 (R2 = [公式:参见文本]).
- ADAGPR实现了最低的预测错误 (RMSE = [公式:查看文本],MAPE = [公式:查看文本]).
- 在二甲-甲醇混合物中 (质量分数为0.8190) 预测的最佳溶解度为[公式:参见文本].
结论:
- 机器学习,特别是ADAGPR,可以有效地预测复杂溶剂系统中的里瓦洛克萨班溶解度.
- 优化的条件提高了溶解度,为制药开发提供了重要的价值.
- 预测建模与化学工程原理的整合促进了药物配方和过程优化.
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