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在安替比林法定杂质和盐酸苯佐卡因降解产物存在下测定安替比林和盐酸苯佐卡因的生态多元辅助分光光度法:迈向绿色与白色

Ecological multivariate assisted spectrophotometric methods for determination of antipyrine and benzocaine HCl in presence of antipyrine official impurity and benzocaine HCl degradant: toward greenness and whiteness.

作者信息

Kelani Khadiga M, Hegazy Maha A, Hassan Amal M, Nadim Ahmed H

机构信息

Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information, El-Hadaba El-Wosta, Mokatam, 5th district, Cairo, Egypt.

Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt.

出版信息

BMC Chem. 2024 Dec 24;18(1):250. doi: 10.1186/s13065-024-01352-7.

DOI:10.1186/s13065-024-01352-7
PMID:39719601
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11669231/
Abstract

A simple and green chemometrics-assisted spectrophotometric technique has beendeveloped and validated for the determination of antipyrine (ANT) and benzocaine HCl (BEN) along with the official impurity of ANT, antipyrine impurity A (ANT imp-A), and the degradation product of BEN, p-amino benzoic acid (PABA), in their quaternary mixture. Three models were developed and compared: partial least squares (PLS), artificial neural networks (ANN), and multivariate curve resolution-alternating least squares (MCR-ALS) where the four studied drugs were successfully quantified. The quantitative determination of the studied drugs was assessed using percentage recoveries, standard errors of prediction, and root mean square errors of prediction. The ANN model demonstrated the lowest error and the best correlation making it the most accurate method for analysis. The models were constructed in the ranges of 5.0-9.0 µg mL for ANT, 1.0-5.0 µg mL for BEN, 0.5-2.5 µg mL for ANT imp-A, and 0.25-1.25 µg mL for PABA. The established models successfully determined ANT, BEN, ANT imp-A, and PABA with detection limits of 0.312, 0.178, 0.093, and 0.042 µg mL for PLS, 0.185, 0.085, 0.001, and 0.034 µg mL for ANN; and 0.473, 0.240, 0.073, and 0.069 µg mL for MCR-ALS, respectively. The greenness and the whiteness of the proposed method were assessed using two green evaluating approaches: analytical Eco-scale, and AGREE, along with one white analytical chemistry evaluating tool, RGB. The three proposed models were successfully applied for determination of ANT and BEN in their pharmaceutically co-formulated dosage forms. They are also recommended for stability assays and purity testing of these drugs in quality control laboratories.

摘要

已开发并验证了一种简单且绿色的化学计量学辅助分光光度技术,用于测定安替比林(ANT)和盐酸苯佐卡因(BEN)以及安替比林的法定杂质安替比林杂质A(ANT imp-A)和苯佐卡因的降解产物对氨基苯甲酸(PABA)在其四元混合物中的含量。开发并比较了三种模型:偏最小二乘法(PLS)、人工神经网络(ANN)和多元曲线分辨交替最小二乘法(MCR-ALS),成功对四种研究药物进行了定量。使用回收率百分比、预测标准误差和预测均方根误差对研究药物的定量测定进行评估。ANN模型显示出最低的误差和最佳的相关性,使其成为最准确的分析方法。模型构建的范围为:ANT为5.0 - 9.0 μg/mL,BEN为1.0 - 5.0 μg/mL,ANT imp-A为0.5 - 2.5 μg/mL,PABA为0.25 - 1.25 μg/mL。所建立的模型成功测定了ANT、BEN、ANT imp-A和PABA,PLS的检测限分别为0.312、0.178、0.093和0.042 μg/mL,ANN的检测限分别为0.185、0.085、0.001和0.034 μg/mL;MCR-ALS的检测限分别为0.473、0.240、0.073和0.069 μg/mL。使用两种绿色评估方法:分析生态尺度和AGREE,以及一种白色分析化学评估工具RGB,对所提出方法的绿色度和白度进行了评估。所提出的三种模型成功应用于其药物共混剂型中ANT和BEN的测定。它们也推荐用于质量控制实验室中这些药物的稳定性测定和纯度检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/98063f8fe0d3/13065_2024_1352_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/96a6629512b7/13065_2024_1352_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/3d1f5f45da5f/13065_2024_1352_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a4eb34b96192/13065_2024_1352_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a31a843c3330/13065_2024_1352_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a694e53a48c3/13065_2024_1352_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/98063f8fe0d3/13065_2024_1352_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/96a6629512b7/13065_2024_1352_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/3d1f5f45da5f/13065_2024_1352_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a4eb34b96192/13065_2024_1352_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a31a843c3330/13065_2024_1352_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/a694e53a48c3/13065_2024_1352_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d442/11669231/98063f8fe0d3/13065_2024_1352_Fig6_HTML.jpg

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