Almutairi Farooq M, Althobaiti Yusuf S, Abduljabbar Maram H, Alzhrani Rami M, Alnemari Reem M, Aldhafeeri Muneef M, Serag Ahmed, Almalki Atiah H
Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, University of Hafr AlBatin, Hafr AlBatin 39524, Saudi Arabia.
Department of Pharmacology and Toxicology, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Anal Methods. 2025 May 15;17(19):3933-3941. doi: 10.1039/d5ay00446b.
This study aimed to develop a green and sustainable analytical method for the quantitative determination of three statins-rosuvastatin, pravastatin, and atorvastatin-using their UV spectral fingerprints. Partial Least Squares (PLS) regression combined with the Firefly Algorithm (FFA) for variable selection was employed to optimize the analysis. A partial factorial design was used to construct a 25-sample synthetic calibration set, while a central composite design served for external validation. The FFA-PLS approach demonstrated superior performance over traditional PLS models, achieving relative root mean square errors of prediction of 1.68%, 1.04%, and 1.63% for rosuvastatin, pravastatin, and atorvastatin, respectively, compared to 2.85%, 2.77%, and 3.20% for conventional PLS. FFA-PLS also enabled model simplification, reducing latent variables from 4, 3, and 4 to 2, 2, and 3 for the respective statins while requiring fewer wavelengths. Validation in accordance with ICH guidelines further confirmed the method's accuracy, precision, and selectivity. Besides, application to real pharmaceutical samples yielded mean recoveries ranging from 99.23% to 99.90%, with RSD% below 2%. Furthermore, comparative analysis with reported chromatographic methods revealed no significant differences in terms of mean and variance as calculated by a two-tailed -test and -test, respectively. Finally, environmental impact assessment metrics demonstrated the method's superior sustainability (AGREE score: 0.78 0.64 for HPLC; RGB12 whiteness index: 91.4% 75.8% for HPLC-UV). In conclusion, the proposed UV-PLS-FFA method offers an effective, accurate, and environmentally friendly alternative for the determination of statins in pharmaceutical samples, aligning with the principles of green chemistry and sustainability and has potential for broader applicability beyond the scope of this study.
本研究旨在开发一种绿色可持续的分析方法，利用三种他汀类药物（瑞舒伐他汀、普伐他汀和阿托伐他汀）的紫外光谱指纹图谱对其进行定量测定。采用偏最小二乘法（PLS）回归结合萤火虫算法（FFA）进行变量选择以优化分析。使用部分因子设计构建一个包含25个样本的合成校准集，同时采用中心复合设计进行外部验证。与传统PLS模型相比，FFA-PLS方法表现出更优的性能，瑞舒伐他汀、普伐他汀和阿托伐他汀的预测相对均方根误差分别为1.68%、1.04%和1.63%，而传统PLS分别为2.85%、2.77%和3.20%。FFA-PLS还能简化模型，将各他汀类药物的潜在变量从4、3和4分别减少到2、2和3，同时所需波长更少。按照国际人用药品注册技术协调会（ICH）指南进行的验证进一步证实了该方法的准确性、精密度和选择性。此外，应用于实际药物样品时，平均回收率在99.23%至99.90%之间，相对标准偏差（RSD%）低于2%。此外，与已报道的色谱方法进行比较分析表明，通过双尾t检验和F检验计算的均值和方差方面无显著差异。最后，环境影响评估指标表明该方法具有卓越的可持续性（AGREE评分：高效液相色谱法（HPLC）为0.78比0.64；HPLC-紫外法的RGB12白度指数：91.4%比75.8%）。总之，所提出的紫外-PLS-FFA方法为药物样品中他汀类药物的测定提供了一种有效、准确且环境友好的替代方法，符合绿色化学和可持续性原则，并且在本研究范围之外具有更广泛的应用潜力。
