Mainali Dipak, Li Jane, Yehl Peter, Chetwyn Nicholas
Department of Chemistry, Iowa State University, 1605 Gilman Hall, Ames, IA 50011, United States of America.
Small Molecules Analytical Chemistry and Quality Control, Genentech, 1 DNA Way, South San Francisco, CA 94080, United States of America.
J Pharm Biomed Anal. 2014 Jul;95:169-75. doi: 10.1016/j.jpba.2014.03.001. Epub 2014 Mar 12.
Near infrared (NIR) spectroscopy has been widely used for the determination of water content in a wide variety of samples. With few exceptions, all methods employ a calibration model developed and applicable for a single product. The current study describes a NIR method using a single, comprehensive calibration model to predict the water content in tablets containing different active pharmaceutical ingredients (API). The calibration model was developed for water content range of 2-13% (w/w) using tablets containing three different APIs and different formulation compositions. To develop a robust comprehensive model, individual calibration models were sequentially developed starting from a simple model for one product to including tablets from all three projects in the final model using partial least square analysis method. Data pretreatments and spectral region selections were performed during the method development to optimize the number of factors and the correlation coefficients for cross-validation and prediction by the comprehensive model. The model reliably predicted the water content in tablet samples of these three products, and can be updated for water measurements of new drug products by adding to the model two samples of the new product for calibration purpose.
近红外（NIR）光谱法已被广泛用于测定各种样品中的水分含量。几乎无一例外，所有方法都采用针对单一产品开发且适用于该产品的校准模型。本研究描述了一种近红外方法，该方法使用单一的综合校准模型来预测含有不同活性药物成分（API）的片剂中的水分含量。该校准模型是针对水分含量范围为2 - 13%（w/w）开发的，使用了含有三种不同API和不同制剂组成的片剂。为了开发一个稳健的综合模型，从针对一种产品的简单模型开始，依次开发各个校准模型，最终使用偏最小二乘法分析将来自所有三个项目的片剂纳入最终模型。在方法开发过程中进行了数据预处理和光谱区域选择，以优化综合模型的因子数量以及交叉验证和预测的相关系数。该模型可靠地预测了这三种产品片剂样品中的水分含量，并且通过为校准目的向模型中添加两种新产品样品，可以针对新药产品的水分测量对模型进行更新。
