Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, Texas A&M University, College Station, Texas 77843.
Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, Texas A&M University, College Station, Texas 77843.
J Pharm Sci. 2019 Mar;108(3):1211-1219. doi: 10.1016/j.xphs.2018.10.023. Epub 2018 Oct 26.
Carbamazepine (CBZ) exists in anhydrous and dihydrate forms. These forms differ in their solubility, dissolution rate, and subsequently in their oral bioavailability. The objective of this study is to develop multivariate chemometric models for estimation of the low level of carbamazepine dihydrate (CBZ-DH) in the CBZ formulations containing excipients of the commercial formulation. The selected excipients were mixed in proportions to make sample matrices ranging from 0% to 50% CBZ-DH. Fourier transform infrared (FTIR), near infrared (NIR), and hyperspectral imaging data were mathematically pretreated before the development of partial least square and principal component analysis regression models. The developed partial least squares regression and principal component analysis models demonstrated predictability of CBZ and CBZ-DH by multiple scattering correction and standard normal variate processing methods. Among the spectroscopic techniques used the model performance parameters such as root-mean-square error, standard error, and bias were found to be low for NIR compared to FTIR. The treated data have shown better model fitting than without treatment, which was demonstrated by correlation coefficient of 0.9778, 0.9824, and 0.9852 for FTIR, NIR, and hyperspectral imaging, respectively. Furthermore, the predicted values were found to be very close to the selected low level of independent samples having 5% CBZ-DH in tablet formulation.
卡马西平(CBZ)有无水物和二水合物两种形式。这两种形式在溶解度、溶解速率方面存在差异,进而影响其口服生物利用度。本研究旨在建立多元化学计量学模型,以估算含有商业制剂辅料的 CBZ 制剂中低水平的卡马西平二水合物(CBZ-DH)。选择的辅料以不同比例混合,制成 CBZ-DH 含量从 0%到 50%的样品矩阵。傅里叶变换红外(FTIR)、近红外(NIR)和高光谱成像数据在建立偏最小二乘和主成分分析回归模型之前进行了数学预处理。所开发的偏最小二乘回归和主成分分析模型通过多重散射校正和标准正态变量处理方法证明了对 CBZ 和 CBZ-DH 的预测能力。在所使用的光谱技术中,与 FTIR 相比,NIR 的模型性能参数(如均方根误差、标准误差和偏差)较低。与未经处理的数据相比,处理后的数据显示出更好的模型拟合度,FTIR、NIR 和高光谱成像的相关系数分别为 0.9778、0.9824 和 0.9852。此外,预测值与含有 5%CBZ-DH 的片剂制剂中独立选择的低水平样本非常接近。