Hattori Yusuke, Seko Yurie, Peerapattana Jomjai, Otsuka Kuniko, Sakamoto Tomoaki, Otsuka Makoto
Research Institute of Pharmaceutical Sciences, Musashino University, 1-1-20 Shinmachi, Nishi-Tokyo, 202-8585, Japan.
Center for Research and Development of Herbal Health Products, Faculty of Pharmaceutical Sciences, No.123, Naimaung, Muang Khon Kaen University, Khon Kaen, 40002, Thailand.
Biomed Mater Eng. 2018;29(1):1-14. doi: 10.3233/BME-171708.
Since it can take an enormous amount of time and cost to discriminate counterfeit medicines by using conventional methods, counterfeit medicines has been spread in the world markets.
The purpose of this study was to develop a rapid and simple analytical method to discriminate counterfeit drugs using near infrared (NIR) spectroscopy.
Seven types of brand name tablet and generic tablets containing atorvastatin calcium sesquihydrate (AT) preparations were used as simulated counterfeit medicines. NIR spectra of 35 AT tablet products were measured using a diffuse reflection method.
The NIR spectral data were analyzed by principal component analysis (PCA). The PCA results suggested that the model had sufficient accuracy to discriminate the 7 types for AT tablets. The NIR spectral data were also analyzed using a soft independent modeling of class analogy (SIMCA) method. Predicting the classification of the AT tablet samples was performed based on all the validated AT tablet data using the SIMCA model, and the probability of classification of 7 types was 100%. The discrimination power spectrum of the SIMCA model indicated significant patterns based on diluents.
The PCA and SIMCA classification of the AT tablets were depended on the major excipient combinations.
由于使用传统方法鉴别假药可能需要耗费大量时间和成本，假药已在世界市场上蔓延。
本研究的目的是开发一种快速简便的分析方法，利用近红外（NIR）光谱鉴别假药。
使用七种含有阿伐他汀钙倍半水合物（AT）制剂的品牌片剂和仿制药片作为模拟假药。采用漫反射法测量35种AT片剂产品的近红外光谱。
通过主成分分析（PCA）对近红外光谱数据进行分析。PCA结果表明，该模型具有足够的准确性来鉴别7种类型的AT片剂。还使用类类比软独立建模（SIMCA）方法对近红外光谱数据进行了分析。基于所有经过验证的AT片剂数据，使用SIMCA模型对AT片剂样品的分类进行预测，7种类型的分类概率为100%。SIMCA模型的鉴别功率谱显示了基于稀释剂的显著模式。
AT片剂的PCA和SIMCA分类取决于主要辅料组合。
