增强经典最小平方模型的比较研究,用于对联合配制的抗发药物进行紫外线检测,并与相关的杂质一起进行
Muneera S M Al-Saleem1, Hany W Darwish2, Ibrahim A Naguib3
1Department of Chemistry, Science College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
经典最小方形 (CLS) 模型和增强版本被用于分析素HCl,环素HCl和麦克素HCl. 直接正交信号校正CLS (DOSC-CLS) 模型显示出在剂量形式中量化这些分析物的卓越性能.
科学领域:
- 分析化学 分析化学
- 药品分析 药品分析
- 化学测量 化学测量 化学测量
背景情况:
- 精确量化活性药物成分 (API) 和杂质对于药物的安全性和有效性至关重要.
- 经典最小平方 (CLS) 和先进的化学测量方法为使用光谱数据分析复杂混合物提供了强大的工具.
- 识别和量化具有已知的不良影响的杂质,如hydrol (BEH) 和4-Chlorobenzophenone (BEP),是制药质量控制的一个关键方面.
研究的目的:
- 开发和验证增强的经典最小方形 (CLS) 模型,以在存在相关杂质时同时量化素HCl (PYR),环素HCl (CYC) 和环素HCl (MEC).
- 将标准CLS与三个增强型CLS模型的性能进行比较:直角信号校正CLS (OSC-CLS),直角信号校正CLS (DOSC-CLS) 和净分析物处理CLS (NAP-CLS).
- 评估这些模型在使用紫外线光谱分析原材料和制药剂型的预测能力.
主要方法:
- 开发和验证四个基于CLS的模型:CLS,OSC-CLS,DOSC-CLS和NAP-CLS.
- 利用紫外线光谱数据进行定量分析.
- 设计一个全面的实验策略,包括一个训练集 (25种混合物) 和一个验证集 (16个样本),基于五级,五因素校准设计.
- 使用根平均平方预测误差 (RMSEP) 的模型性能统计比较,并与已建立的高性能液态染色学 (HPLC) 方法进行比较.
主要成果:
- 所有拟议的CLS增强模型都显示了可接受的性能,用于对三个分析物 (PYR,CYC,MEC) 的测定.
- 直接直角信号校正CLS (DOSC-CLS) 模型表现出最好的预测准确性和性能,用于量化其剂型中的分析物.
- 统计比较证实了开发的化学测量模型的有效性和可靠性.
结论:
- 增强的CLS模型,特别是DOSC-CLS,为药物配方中PYR,CYC和MEC的同时定量提供了强大而准确的方法.
- 开发的化学测量方法为传统的HPLC方法提供了可行的替代方案,有可能实现更快,更有效的质量控制.
- 该研究强调了化学测量技术在制药分析中的重要性,以管理复杂的混合物并确保药品质量.
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