Search

Quantification analysis with near-infrared (NIR) spectroscopy typically requires utilizing chemometric techniques, such as partial least squares (PLS) method, to achieve the desired selectivity. This article points out a major limitation of these statistical-based calibration methods. The limitation is that the techniques suffer from the potential for chance correlation. In this article, the impact of chance correlation on the robustness of PLS model was illustrated via a pharmaceutical application with NIR to the content uniformity determination of tablets. The procedure involves evaluating the PLS models generated with two sets of calibration tablets incorporated with distinct degree of concentration correlation between the active pharmaceutical ingredient (API) and excipients. The selectivity and robustness of the two models were examined by using a series of data sets associated with placebo tablets and tablets incorporated with variations from excipient content, hardness and particle size. The result clearly revealed that the strong correlation observed in the PLS model created by the correlated design was not solely based on the API information, and there was an intrinsic difference in the variances described by the two calibration models. Diagnostic tools that enable the characterization of the chemical selectivity of the calibration model were also proposed for pharmaceutical quantitative analysis. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:1155–1166, 2009 [ABSTRACT FROM AUTHOR]

The objective of this study was to examine the effects of testing parameters and formulation variables on the segregation tendency of pharmaceutical powders measured by the ASTM D 6940-04 segregation tester using design of experiments (DOE) approaches. The test blends consisted of 4% aspirin (ASP) and 96% microcrystalline cellulose (MCC) with and without magnesium stearate (MgS). The segregation tendency of a blend was determined by measuring the last/first (L/F) ratio, the ratio of aspirin concentrations between the first and last samples discharged from the tester. A 22 factorial design was used to determine the effects of measurement parameters [amount of material loaded (W), number of segregation cycles] with number of replicates 6. ANOVA showed that W was a critical parameter for segregation testing. The L/F value deviated further from 1 (greater segregation tendency) with increasing W. A 23 full factorial design was used to assess the effects of formulation variables: grade of ASP (unmilled, milled), grade of MCC, and amount of lubricant, MgS. MLR and ANOVA showed that the grade of ASP was the main effect contributing to segregation tendency. Principal Component Regression Analysis established a correlation between L/F and the physical properties of the blend related to ASP and MCC, the ASP/MCC particle size ratio (PSR) and powder cohesion. The physical properties of the blend related to density and flow were not influenced by the grade of ASP and were not related to the segregation tendency of the blend. The direct relationship between L/F and PSR was determined by univariate analysis. Segregation tendency increased as the ASP to MCC particle size increased. This study highlighted critical test parameters for segregation testing and identified critical physical properties of the blends that influence segregation tendency. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:4485–4497, 2008 [ABSTRACT FROM AUTHOR]