Sustainable quantification of glycopyrronium, indacaterol, and mometasone along with two genotoxic impurities in a recently approved fixed-dose breezhaler formulations and biological fluids: A machine learning-augmented UV-spectroscopic approach

IF 4.9 2区 化学 Q1 CHEMISTRY, ANALYTICAL
Ahmed Emad F. Abbas, Mohammed Gamal, Ibrahim A. Naguib, Michael K. Halim, Basmat Amal M. Said, Mohammed M. Ghoneim, Mohmeed M.A. Mansour, Yomna A. Salem
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Abstract

This study presents an innovative, sustainable approach for the simultaneous quantification of glycopyrronium (2–14 μg/mL), indacaterol (6–18 μg/mL), and mometasone (4–20 μg/mL) in a recently approved fixed-dose breezhaler formulations and biological fluids, along with two genotoxic impurities: methyl -toluene sulfonate (2–10 μg/mL) and 4-dimethylamino pyridine (2–10 μg/mL). We developed robust UV spectrophotometric machine-learning chemometric models to address the limitations of existing chromatographic methods. The calibration set was carefully selected at five concentration levels using the multilevel-multifactor experimental design, resulting in 25 calibration mixtures. The Kennard-Stone Clustering Algorithm was employed to construct a representative 13-mixture validation set, overcoming biases associated with random data splitting. Five chemometric models (CLS, PCR, PLS, GA-PLS, and MCR-ALS) were rigorously evaluated, with MCR-ALS demonstrating superior performance. This model achieved 98–102 % recovery percentages for all analytes, with low root mean square error of calibration and prediction of (RMSEC: 0.0225 to 0.5246) and (RMSEP: 0.0039 to 0.4226). The method exhibited excellent relative root mean square error of prediction (RRMSEP: 0.1306 to 0.8517 %), a negligible bias-corrected mean square error of prediction (BCMSEP: −0.0073 to 0.0025), and good sensitivity (LOD: 0.022 to 0.893 μg/mL) across all analytes. Green solvents were selected using the Green Solvents Selection Tool and Greenness Index Spider Charts. The method’s sustainability was comprehensively evaluated using seven state-of-the-art assessment tools. This approach not only offers a green alternative to traditional chromatographic methods but also ensures high accuracy in quantifying both active ingredients and genotoxic impurities, thereby enhancing pharmaceutical quality control and patient safety.
对最近获批的固定剂量吸入剂配方和生物液体中的甘草酸铵、茚达特罗、莫美他松以及两种基因毒性杂质进行可持续定量:机器学习增强型紫外光谱法
本研究提出了一种创新的、可持续的方法,用于同时定量检测最近获批的固定剂量微喘器制剂和生物液体中的甘草酸铵(2-14 μg/mL)、茚达特罗(6-18 μg/mL)和莫美他松(4-20 μg/mL),以及两种基因毒性杂质:甲基甲苯磺酸盐(2-10 μg/mL)和 4-二甲氨基吡啶(2-10 μg/mL)。我们开发了稳健的紫外分光光度机器学习化学计量模型,以解决现有色谱方法的局限性。利用多层次多因素实验设计,在五个浓度水平上精心选择了校准集,从而得到了 25 种校准混合物。采用 Kennard-Stone 聚类算法构建了具有代表性的 13 种混合物验证集,克服了随机数据分割带来的偏差。对五种化学计量模型(CLS、PCR、PLS、GA-PLS 和 MCR-ALS)进行了严格的评估,其中 MCR-ALS 表现出卓越的性能。该模型对所有分析物的回收率都达到了 98-102%,校准和预测的均方根误差(RMSEC:0.0225-0.5246)和(RMSEP:0.0039-0.4226)都很低。该方法对所有分析物的预测相对均方根误差(RRMSEP:0.1306-0.8517 %)极小,偏差校正后的预测均方根误差(BCMSEP:-0.0073-0.0025)可忽略不计,灵敏度(LOD:0.022-0.893 μg/mL)良好。使用绿色溶剂选择工具和绿色指数蜘蛛图选择了绿色溶剂。使用七种最先进的评估工具对该方法的可持续性进行了全面评估。这种方法不仅是传统色谱法的绿色替代方法,还能确保活性成分和基因毒性杂质的高精度定量,从而加强药品质量控制和患者安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Microchemical Journal
Microchemical Journal 化学-分析化学
CiteScore
8.70
自引率
8.30%
发文量
1131
审稿时长
1.9 months
期刊介绍: The Microchemical Journal is a peer reviewed journal devoted to all aspects and phases of analytical chemistry and chemical analysis. The Microchemical Journal publishes articles which are at the forefront of modern analytical chemistry and cover innovations in the techniques to the finest possible limits. This includes fundamental aspects, instrumentation, new developments, innovative and novel methods and applications including environmental and clinical field. Traditional classical analytical methods such as spectrophotometry and titrimetry as well as established instrumentation methods such as flame and graphite furnace atomic absorption spectrometry, gas chromatography, and modified glassy or carbon electrode electrochemical methods will be considered, provided they show significant improvements and novelty compared to the established methods.
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