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

Area of Science:

• Pharmaceutical Sciences
• Analytical Chemistry
• Process Analytical Technology (PAT)

Background:

• In-situ UV fiber optics dissolution systems (FODS) face signal saturation challenges with high-absorbance formulations, leading to inaccurate dissolution profiles.
• High absorbance impedes real-time assessment and can compromise the reliability of dissolution testing.

Purpose of the Study:

• To develop and validate multivariate modeling approaches to resolve inaccuracy in FODS caused by signal saturation.
• To assess the performance of linear and quadratic partial least squares (PLS) and principal component regression (PCR) models.

Main Methods:

• Development of linear and quadratic PLS and PCR models using full spectra and low absorbance regions.
• Comparison of model predictions against conventional dissolution testing (HPLC) for atenolol, ibuprofen, and metformin HCl immediate-release tablets.
• Evaluation of multivariate models' performance within the 'built-in' Opt-Diss system.

Main Results:

• Linear multivariate models (PLS and PCR) accurately predicted dissolution profiles, even with potential UV signal saturation.
• The 'built-in' Opt-Diss model and multivariate quadratic models failed to provide accurate dissolution predictions.
• Models demonstrated robust predictability across both low absorbance regions and full spectra.

Conclusions:

• Linear PLS and PCR models offer a statistically robust and flexible solution for accurate dissolution testing with FODS, even under signal saturation conditions.
• These linear multivariate models provide a viable alternative to conventional, labor-intensive UV or HPLC dissolution testing methods.