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药物纳米颗粒超临界处理的数学建模和数值模拟优化绿色处理:人工智能分析

Khalid Aljohani¹

¹Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

|September 4, 2024

概括

这项研究使用人工智能增强了Oxaprozin在超临界二氧化碳 (SCCO2) 中的溶解性预测. NU-SVM模型实现了最高的准确性,优化了药物溶解度,以获得更好的治疗效果.

科学领域:

制药科学制药科学
化学工程是化学工程的重要组成部分.
计算化学的计算化学

背景情况:

药物溶解性是制药开发中的一个关键挑战.
超临界二氧化碳 (SCCO2) 是一种绿色且有效的溶剂,可提高药物溶解度.
准确的溶解性预测对于优化药物输送和疗效至关重要.

研究的目的:

使用人工智能 (AI) 改进用于估计Oxaprozin在SCCO2中的溶解度的预测模型.
为了确定最准确的AI模型来优化oxaprozin溶解度.
为最大限度地提高Oxaprozin在SCCO2.2中的溶解性提供最佳条件.

主要方法:

三种AI模型的开发和比较:NU-SVM,线性-SVM和决策树 (DT).
在一个数据集上训练模型,输入压力 (bar) 和温度 (K),输出溶解度.
模型性能使用R平方和根平均平方误差 (RMSE) 进行评估.

主要成果:

NU-SVM表现出优异的性能,其R平方值为0.994和RMSE为3.0982E-05.5.
线性SVM和DT的准确性较低,R平方值分别为0.854和0.950.
通过NU-SVM预测的最佳条件是T = 336.05 K和P = 400.0 bar,可溶性为0.00127.

结论:

NU-SVM是预测在SCCO2.中Oxaprozin可溶性的最精确的方法.
人工智能驱动的可溶性预测可以显著帮助制药配方开发.
在SCCO2中优化的溶解性增强了像Oxaprozin这样的溶解不良药物的潜在治疗效果.

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