Variations in the solid state form of a pharmaceutical solid have profound impact on the product quality and clinical performance. Quantitative models that allow rapid and accurate determination of polymorphic changes in pharmaceutical products are essential in ensuring product quality throughout its lifecycle. This study reports the development and validation of chemometric models of Raman and near infrared spectroscopy (NIR) for quantifying the extent of pseudopolymorphic transitions of theophylline in extended release formulations. The chemometric models were developed using sample matrices consisting of the commonly used excipients and at the ratios in commercially available products. A combination of scatter removal (multiplicative signal correction and standard normal variate) and derivatization (Savitzky-Golay second derivative) algorithm were used for data pretreatment. Partial least squares and principal component regression models were developed and their performance assessed. Diagnostic statistics such as the root mean square error, correlation coefficient, bias and Q(2) were used as parameters to test the model fit and performance. The models developed had a good fit and performance as shown by the values of the diagnostic statistics. The model diagnostic statistics were similar for MSC-SG and SNV-SG treated spectra. Similarly, PLSR and PCR models had comparable performance. Raman chemometric models were slightly better than their corresponding NIR model. The Raman and NIR chemometric models developed had good accuracy and precision as demonstrated by closeness of the predicted values for the independent observations to the actual TMO content hence the developed models can serve as useful tools in quantifying and controlling solid state transitions in extended release theophylline products.
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