Lubricant-Sensitivity Assessment of SPRESS®B820 by Near-Infrared Spectroscopy: A Comparison of Multivariate Methods.
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 24full 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.
Department of Pharmaceutical Sciences, St. John Fisher College, Wegmans School of Pharmacy, Rochester, New York 14618.
Vivek S
VS
Dave
Department of Pharmaceutical Sciences, St. John Fisher College, Wegmans School of Pharmacy, Rochester, New York 14618.
Michael
M
Popielarczyk
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, School of Pharmacy, Baltimore, Maryland 21201.
Heather
H
Boyce
Department of Pharmaceutical Sciences, College of Pharmacy & Health Sciences, Campbell University, Buies Creek, North Carolina 27506.
Antoine
A
Al-Achi
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, School of Pharmacy, Baltimore, Maryland 21201.
Enyioha
E
Ike-Amaechi
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, School of Pharmacy, Baltimore, Maryland 21201.
Stephen W
SW
Hoag
Department of Pharmaceutical Sciences, College of Pharmacy & Health Sciences, Campbell University, Buies Creek, North Carolina 27506. Electronic address: haware@campbell.edu.
Rahul V
RV
Haware
D002138:Calibration; D004339:Drug Compounding; D016018:Least-Squares Analysis; D054327:Lubricants / Q000737:chemistry*; D008153:Lubrication; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013607:Tablets
D054327:Lubricants; D013607:Tablets
breaking force; multivariate analysis; near-infrared spectroscopy; partial least square regression; principle component regression; tableting