The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr2 of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr2 of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q120 (percentage dissolved in 120 minutes), respectively.
sulfamethazine near infrared (NIR) corn starch paste granulation partial least squares (PLS) principal component analysis (PCA)