Multivariate chemometric approach to fiber-optic dissolution testing

Area of Science:

• Analytical Chemistry
• Pharmaceutical Sciences

Background:

• In vitro dissolution testing is crucial for drug development.
• Interfering absorbance from excipients complicates data analysis in dissolution testing.
• Fiber optics enable full UV-Vis spectrum collection at each measurement point.

Purpose of the Study:

• To illustrate multivariate chemometric approaches for analyzing fiber-optic dissolution testing data.
• To address challenges posed by interfering absorbance from excipients.
• To enable accurate determination of active ingredients and excipient profiles.

Main Methods:

• Multivariate calibration using Partial Least Squares (PLS) regression.
• Curve resolution techniques: Multivariate Curve Resolution Alternating Least Squares (MCR-ALS), Generalized Rank Annihilation (GRAM), and Parallel Factor Analysis (PARAFAC).
• Application to glibenclamide tablets in hard gelatin capsules using fiber-optic UV-Vis spectroscopy.

Main Results:

• PLS successfully provided selective and accurate glibenclamide determination despite excipient interference.
• Curve resolution methods accurately estimated dissolution profiles and spectra for both glibenclamide and gelatin capsule excipients without precalibration.
• Both methods demonstrated effectiveness even with highly overlapping spectra and unresolved raw data.

Conclusions:

• Multivariate chemometric methods significantly enhance fiber-optic dissolution testing capabilities.
• These methods solve the problem of interfering absorbance, enabling detailed analysis of active ingredients and excipients.
• Future applications include analyzing multi-active pharmaceutical ingredients and gaining deeper insights into dissolution processes.