化学计量学辅助分光光度计新方法,用于同时测定不同剂量组合中的复方药物孟鲁司特、鲁帕他定和地氯雷他定

IF 4.3 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Marco M. Z. Sharkawi, Nehal F. Farid, Moataz H. Hassan, Said A. Hassan
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引用次数: 0

摘要

为同时测定孟鲁司特钠(MON)、富马酸鲁帕他定(RUP)和地氯雷他定(DES),开发了两种准确、精确和稳健的多元化学计量学方法。这些方法通过在药品质量控制中使用分光光度法,为色谱技术提供了一种具有成本效益的替代方法。利用遗传算法(GA)对所提出的偏最小二乘法-1(PLS-1)和人工神经网络(ANN)方法进行了优化,以选择最有影响力的波长,提高模型性能。采用五级三因子设计构建了一个包含 25 种混合物的校准集,MON、RUP 和 DES 的浓度范围分别为 3-19、5-25 和 4-20 µg.mL-1。采用独立的验证集来评估模型的性能。GA 明显改进了 RUP 和 DES 的 PLS-1 和 ANN 模型,但对 MON 的改进很小。这些方法成功地应用于药物制剂中化合物的同时定量,并证明可作为 RUP 的稳定性指示测定,因为 DES 是已知的降解产物。所开发的方法为药物分析中的杂质分析和质量控制提供了宝贵的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New chemometrics-assisted spectrophotometric methods for simultaneous determination of co-formulated drugs montelukast, rupatadine, and desloratadine in their different dosage combinations

Two accurate, precise and robust multivariate chemometric methods were developed for the simultaneous determination of montelukast sodium (MON), rupatadine fumarate (RUP) and desloratadine (DES). These methods provide a cost-effective alternative to chromatographic techniques by utilizing spectrophotometry in pharmaceutical quality control. The proposed approaches, partial least squares-1 (PLS-1) and artificial neural network (ANN), were optimized using genetic algorithm (GA) to select the most influential wavelengths, enhancing model performance. A five-level, three-factor design was employed to construct a calibration set with 25 mixtures, utilizing concentration ranges of 3–19, 5–25, and 4–20 µg.mL−1 for MON, RUP, and DES, respectively. An independent validation set was employed to assess the performance of the models. GA significantly improved the PLS-1 and ANN models for RUP and DES, though minimal enhancement was observed for MON. These methods were successfully applied to the simultaneous quantification of the compounds in pharmaceutical formulations and proved useful as stability-indicating assays for RUP, given that DES is a known degradation product. The developed methods offer a valuable tool for impurity profiling and quality control in pharmaceutical analysis.

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来源期刊
BMC Chemistry
BMC Chemistry Chemistry-General Chemistry
CiteScore
5.30
自引率
2.20%
发文量
92
审稿时长
27 weeks
期刊介绍: BMC Chemistry, formerly known as Chemistry Central Journal, is now part of the BMC series journals family. Chemistry Central Journal has served the chemistry community as a trusted open access resource for more than 10 years – and we are delighted to announce the next step on its journey. In January 2019 the journal has been renamed BMC Chemistry and now strengthens the BMC series footprint in the physical sciences by publishing quality articles and by pushing the boundaries of open chemistry.
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