Eco-friendly synchronous spectrofluorimetry coupled with chemometrics for simultaneous determination of ezetimibe and propranolol in pharmaceutical formulations and spiked plasma samples.

Ali Alqahtani, Taha Alqahtani, Adel Al Fatease, Adil Alshehri, Ahmed A Almrasy
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Abstract

In this study, synchronous fluorescence spectroscopic methods coupled with chemometric techniques were developed and evaluated for the simultaneous quantification of ezetimibe and propranolol, two commonly prescribed cardiovascular drugs. Both drugs exhibit overlapping native fluorescence, posing a challenge for their selective determination. To address this, chemometric models including partial least squares (PLS) and genetic algorithm-based variable selection (GA) were constructed using a calibration dataset based on a 52 factorial design resulting in 25 synthetic mixtures. The developed method has been optimized to account for factors such as solvent composition, micellar systems, and excitation/emission wavelengths that affect the fluorescence signals. The PLS and GA-PLS models were validated using an independent test set of 13 samples based on central composite design revealing the GA-PLS model provided improved quantitative performance with relative root mean square error of prediction (RRMSEP) values of 1.3939 and 1.0005 % for ezetimibe and propranolol, respectively, compared to 2.2502 and 2.3526 % for the PLS models. Hence, the GA-PLS models were successfully applied for the determination of ezetimibe and propranolol in pharmaceutical formulations and spiked plasma samples. Furthermore, the greenness and blueness of the proposed methods were compared against reported HPLC procedures using the AGREE and BAGI tools, revealing a greener analytical footprint for the developed method and higher analytical practicability posing as an environmental-friendly alternative to the standard HPLC technique.

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环保型同步分光荧光测定法与化学计量学相结合,用于同时测定药物制剂和加标血浆样品中的依折麦布和普萘洛尔。
本研究开发并评估了同步荧光光谱方法与化学计量学技术,用于同时定量分析依折麦布和普萘洛尔这两种常用的心血管药物。这两种药物的原生荧光相互重叠,给选择性测定带来了挑战。为了解决这个问题,我们利用基于 52 个因子设计的校准数据集构建了化学计量学模型,包括偏最小二乘法 (PLS) 和基于遗传算法的变量选择 (GA),从而得到 25 种合成混合物。对所开发的方法进行了优化,以考虑到溶剂成分、胶束系统和激发/发射波长等影响荧光信号的因素。使用基于中心复合设计的 13 个样本的独立测试集对 PLS 和 GA-PLS 模型进行了验证,结果表明 GA-PLS 模型的定量性能有所提高,对依折麦布和普萘洛尔的预测相对均方根误差(RRMSEP)值分别为 1.3939 % 和 1.0005 %,而 PLS 模型的预测相对均方根误差(RRMSEP)值分别为 2.2502 % 和 2.3526 %。因此,GA-PLS 模型成功地应用于药物制剂和血浆样品中依折麦布和普萘洛尔的测定。此外,利用 AGREE 和 BAGI 工具将所提出方法的绿色度和蓝色度与已报道的 HPLC 程序进行了比较,结果表明所开发方法的分析足迹更环保,分析实用性更高,是标准 HPLC 技术的环保型替代方法。
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