共同包装的帕克斯洛维德:探索先进的采样技术和多变量处理工具
Shymaa S Soliman1, Nisreen F Abo- Talib2, Mohamed R Elghobashy3,4
1Analytical Chemistry Department, Faculty of Pharmacy, October 6 University, October 6 City, Giza, 12858, Egypt. shimaasayed@o6u.edu.eg.
BMC chemistry
|July 10, 2025
概括
拉丁式超立方体采样 (LHS) 显著提高了分析Paxlovid等药物的化学测量模型的准确性和稳定性,优于随机采样方法. 这种开创性的技术提高了预测能力,同时与环保分析实践保持一致.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 计算统计学 计算统计学
背景情况:
- 随机抽样方法在复杂数据集的准确性和预测有效性方面存在局限性.
- 开发强大高效的分析方法对于制药分析和质量控制至关重要.
- 现有的化学测量模型需要优化,以提高数据覆盖率和预测性能.
研究的目的:
- 将拉丁式超立方体采样 (LHS) 与各种多变量化学测量模型 (PLS,GA-PLS,ANN,MCR-ALS) 集成.
- 为了提高这些模型的预测准确性和稳定性,使用LHS改进数据采样.
- 证明LHS在分析抗COVID-19药物Paxlovid (与里托纳维尔增强的尼马特里尔维尔) 的应用和益处.
主要方法:
- 拉丁式超立方采样 (LHS) 与部分最小平方 (PLS),遗传算法-部分最小平方 (GA-PLS),人工神经网络 (ANN) 和多变量曲线分辨率-交替最小平方 (MCR-ALS) 的集成.
- 在Paxlovid中应用LHS进行样本选择,以分析里托纳维尔 (RNV) 和涅马特里尔维尔 (NMV).
- 开发混合变量选择策略 (GA-ICOMP-PLS) 和评估环保分析指标 (可持续性样本准备指标,分析绿色指标,RGB12,蓝色适用度指数).
主要成果:
- LHS提供了经过良好的解释的样本,在不增加样本大小的情况下捕获了基本的变异性,优于随机抽样.
- 在ANN和MCR-ALS模型中,RNV和NMV的预测误差 (RMSEP) 显著减少 (14.1-53.1%) .
- 该GA-ICOMP-PLS模型实现了较低的预测误差 (RNV为0.15,NMV为0.14),展示了强大的预测能力和概括性.
- 环保评估显示,可持续性得分高,适用性广泛,符合绿色分析化学原则.
结论:
- 拉丁式超立方采样 (LHS) 是一种强大的技术,可以提高化学模型的稳定性和预测准确性.
- 开发的模型和采样策略为Paxlovid等药物化合物提供了高效和可靠的分析.
- 该研究强调了在制药分析中成功实施可持续和环保的分析实践.
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