title: Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.
abstract: The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters.
authors: Duquesne University Center for Pharmaceutical Technology, School of Pharmacy, Pittsburgh, Pennsylvania, 15282.
authors: Benoit
authors: B
authors: Igne
authors: Zhenqi
authors: Z
authors: Shi
authors: James K
authors: JK
authors: Drennen
authors: Carl A
authors: CA
authors: Anderson
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D002138:Calibration; D002482:Cellulose; D002626:Chemistry, Pharmaceutical; D004345:Drug Industry / Q000379:methods; D004777:Environment; D005079:Excipients; D008956:Models, Chemical; D015233:Models, Statistical; D010316:Particle Size; D004364:Pharmaceutical Preparations / Q000032:analysis*; D015203:Reproducibility of Results; D013056:Spectrophotometry, Ultraviolet; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013213:Starch; D013806:Theophylline / Q000032:analysis
chemical_list: D005079:Excipients; D004364:Pharmaceutical Preparations; D002482:Cellulose; D013213:Starch; D013806:Theophylline; C109691:microcrystalline cellulose
keywords: dynamic backward iterative PLS; excipients; iterative PLS; near-infrared spectroscopy; partial least squares; particle size; polymorph; raw material variability; variable selection