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使用近红外光谱法和化学计量学同时定量测定脂质体中辛伐他汀及其辅料的含量。

Simultaneous quantification of simvastatin and excipients in liposomes using near infrared spectroscopy and chemometry.

作者信息

Porfire Alina, Muntean Dana, Achim Marcela, Vlase Laurian, Tomuta Ioan

机构信息

Department of Pharmaceutical Technology and Biopharmaceutics, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania.

Department of Pharmaceutical Technology and Biopharmaceutics, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania.

出版信息

J Pharm Biomed Anal. 2015 Mar 25;107:40-9. doi: 10.1016/j.jpba.2014.12.013. Epub 2014 Dec 18.

Abstract

This work describes the development and validation of a near infrared (NIR) spectroscopy method coupled with an appropriate multivariate calibration algorithm for the simultaneous quantification of encapsulated drug, simvastatin (SIM) and excipients, L-α-phosphatidylcholine (LPC) and cholesterol (CHO) in liposomes. The development of calibration models for each compound was based on a D-optimal experimental design consisting of 63 standard mixtures containing LPC, CHO and SIM in chloroform. For each compound, different spectral pretreatment methods were applied in association with selected spectral regions. Partial least-square regression (PLS) was performed using OPUS 6.5 software. Calibration set and cross-validation was carried out in order to select the best model to be used further. Straight line subtraction (SLS) was the best pre-treatment method for each compound, although the selected spectral regions were different. The method developed for each compound was validated in terms of linearity, trueness, precision and accuracy. Finally, the method has been successfully used for simultaneous quantification of SIM and excipients in liposomes. The encapsulation efficiency of SIM determined by this method was similar with that obtained by the use of reference HPLC method.

摘要

本研究描述了一种近红外(NIR)光谱法的开发与验证,该方法结合了适当的多元校准算法,用于同时定量脂质体中包封的药物辛伐他汀(SIM)以及辅料L-α-磷脂酰胆碱(LPC)和胆固醇(CHO)。每种化合物校准模型的开发基于D-最优实验设计,该设计由63种含LPC、CHO和SIM的氯仿标准混合物组成。对于每种化合物,将不同的光谱预处理方法与选定的光谱区域相结合应用。使用OPUS 6.5软件进行偏最小二乘回归(PLS)。进行校准集和交叉验证,以选择进一步使用的最佳模型。直线扣除(SLS)是每种化合物的最佳预处理方法,尽管选定的光谱区域不同。针对每种化合物开发的方法在线性、真实性、精密度和准确度方面进行了验证。最后,该方法已成功用于同时定量脂质体中的SIM和辅料。通过该方法测定的SIM包封效率与使用参考HPLC方法获得的结果相似。

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