Predictive Performance Comparison of Computed Linear and Quadratic Multivariate Models for In-Situ UV Fiber Optics Tablet Dissolution Testing

A present investigation aimed for multivariate modeling as a solution to resolve inaccuracy in dissolution testing experienced in the use of in-situ UV fiber optics dissolution systems (FODS) due to signal saturation problems. This problem is specifically encountered with high absorbance of moderate to high dose formulations. A high absorbance not only impede a real-time assessment but can also result in inaccurate dissolution profiles. Full spectra (F) and low absorbance regions (L) were employed to develop linear and quadratic (Q) partial least squares (PLS) and principal component regression (PCR) models. The conventional dissolution of atenolol, ibuprofen, and metformin HCl immediate-release (IR) tablets followed by HPLC analysis was used as a reference method to gauge multivariate models? performance in the ?built-in? Opt-Diss model. The linear multivariate modeling outputs resulted in accurate dissolution profiles, despite the potentially high UV signal saturation at later time points. Conversely, the ?built-in? Opt-Diss model and multivariate quadratic models failed to predict dissolution profiles accurately. The current studies show a good agreement in the predictions across both low absorbance region and full spectra, demonstrating the multivariate models? robust predictability. Overall, linear PLS and PCR models showed statistically similar results, which demonstrated their applicative flexibility for using FODS despite signal saturation and provides a unique alternative to traditional and labor-intensive UV or HPLC dissolution testing.

Automated summary

The study used multivariate modeling to address inaccuracy in dissolution testing caused by high absorbance in in-situ UV fiber optics dissolution systems. Linear PLS and PCR models showed accurate predictions, while quadratic models did not. Overall, the linear models demonstrated flexibility and accuracy in predicting dissolution profiles.