实施部分最小方程和机器学习回归模型,用于预测药物释放在向药物递送应用程序中的药物释放
Anupam Yadav1, B Jayaprakash2, Laith Hussein Jasim3,4
1Department of Computer Engineering and Application, GLA University, Mathura, 281406, India. anupam.yadav@gla.ac.in.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的化学测量和机器学习方法,用于预测聚糖涂层结肠药物输送. 先进的AdaBoost-MLP模型使用拉曼光谱和配方因子准确估计药物释放.
科学领域:
- 制药科学 制药科学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 准确预测来自结肠输送系统的药物释放对于治疗疗效至关重要.
- 多糖涂层广泛用于针对性结肠药物输送,但它们的释放概况可能很复杂.
- 化学测量和机器学习为分析复杂的光谱数据和构建预测模型提供了强大的工具.
研究的目的:
- 开发和验证一种预测模型,用于估计多糖涂层结肠配方中的药物释放量.
- 将拉曼光谱与机器学习相结合,用于药物输送的定量分析.
- 探索涂层类型,介质和释放时间对药物释放动态的影响.
主要方法:
- 使用化学测量和机器学习回归模型的综合方法被采用.
- 拉曼光谱法用于测量药物释放,生成光谱数据.
- 部分最小正方形 (PLS) 用于缩小维度,并使用线性回归的AdaBoost,多层感知子 (MLP) 和Theil-Sen回归.
- 虫群优化 (GSO) 已集成用于超参数调整.
主要成果:
- AdaBoost-MLP模型展示了最高的预测性能,达到0.994的R2和0.000368.8的平均平方误差 (MSE).
- 预测模型有效地利用了光谱数据以及涂层类型,介质和释放时间.
- 虫群的优化显著提高了模型的准确性和效率.
结论:
- 该研究使用光谱数据和机器学习成功建立了结肠药物释放的强大预测模型.
- 这种综合方法为评估向结肠输送配方提供了全面的基础.
- 这些发现突出了将光谱技术与先进的计算方法结合起来,为制药开发带来的潜力.
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