药物释放动态的建模和验证,使用混合方法预测药物效率和新配方
Saad M Alshahrani1, Hadil Faris Alotaibi2, Mohammed Alqarni3
1Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
这项研究使用机器学习模型从聚合物矩阵中释放药物. 决策树回归实现了最高的准确性,为组织再生应用提供了精确的预测.
科学领域:
- 生物材料科学 生物材料科学
- 聚合物化学 聚合物化学
- 计算机建模 计算建模
背景情况:
- 了解聚合物矩阵中的药物释放动力学对于有效的组织再生疗法至关重要.
- 预测建模可以优化药物输送系统,提高治疗结果.
- 当前的模型往往难以捕捉影响药物释放的因素的复杂相互作用.
研究的目的:
- 开发和比较机器学习模型,用于从聚合物矩阵中预测药物释放.
- 评估决策树回归 (DTR),被动侵略回归 (PAR) 和二次多项式回归 (QPR) 模型的性能.
- 确定最准确的模型,以了解组织再生中的药物释放行为.
主要方法:
- 编制了药物释放的综合数据集,包括空间 (r,z) 和度 (C) 参数.
- 三种回归模型 (DTR, PAR, QPR) 应用于药物释放数据集.
- 使用基于序列模型的优化 (SMBO) 进行了超参数优化.
主要成果:
- 决策树回归 (DTR) 显示出卓越的预测准确性,R2为0.99887.
- 被动侵略回归 (PAR) 实现了0.94652的R2,显示了捕捉非线性关系的有效性.
- 二次多项式回归 (QPR) 给出了0.95382的R2,表明了处理二次复杂性的熟练程度.
结论:
- DTR对于从聚合物矩阵中预测药物释放动力学是非常有效的,其性能优于 PAR 和 QPR.
- 开发的模型为组织工程的控制药物释放机制提供了宝贵的见解.
- 机器学习提供了一种强大的方法来优化药物输送系统,以提高治疗效率.
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