Lubricant-Sensitivity Assessment of SPRESS^® B820 by Near-Infrared Spectroscopy: A Comparison of Multivariate Methods

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• 作者：Vivek S. Dave¹ ; Michael Popielarczyk¹ ; Heather Boyce² ; Antoine Al-Achi³ ; Enyioha Ike-Amaechi² ; Stephen W. Hoag² ; Rahul V. Haware³ ; ^{haware@campbell.edu}
• 关键词：near-infrared spectroscopy ; tableting ; breaking force ; multivariate analysis ; partial least square regression ; principle component regression
• 刊名：Journal of Pharmaceutical Sciences
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：106
• 期：2
• 页码：537-545
• 全文大小：2208 K
• 卷排序：106

文摘

The predictability of multivariate calibration models, calculated with offline near-infrared spectroscopy (NIRS), assessing impact of magnesium stearate (MgSt) fraction, blending time, and compression force on the tablet breaking force (TBF) of SPRESS® B820 was statistically compared. Tablets of lubricated SPRESS® B820 were prepared by varying lubrication and compression conditions using 24 full factorial design. Tablets were scanned in reflection mode on a benchtop NIRS. A qualitative principal component analysis and quantitative principal component regression (PCR) and partial least square (PLS) regression relationship between lubricant concentration, blending time, compression force, preprocessed NIR spectra, and measured TBF was calculated with calibration data set. The predictability of calibration models was validated with independent data set. Expected qualitative correlations between MgSt blending time and TBF and a nonlinear relationship between MgSt fraction and TBF were observed. Predictability of PLS comprehensive (0.25%-1% w/w MgSt) model was significantly different from individual 0.25%, 0.5%, and 1.0% w/w MgSt PLS models. In addition, PLS calibration models' predictability was different from PCR calibration models. Thus, added lubrication fraction and adopted multivariate methodology should be selected carefully during the calibration and validation stages as it may have a significant impact on the predictability of the developed models.