Multi-spectroscopic assay methods for concurrent determination of recent anti-gout combination, a comparative study

Area of Science:

• Analytical Chemistry
• Pharmaceutical Analysis

Background:

• Spectrophotometric analysis is crucial for pharmaceutical quality control, especially for drug mixtures with overlapping spectra.
• Univariate methods have been standard, but chemometric approaches are gaining traction for complex analyses.

Purpose of the Study:

• To compare the efficacy of chemometric (multivariate) methods against traditional univariate techniques for analyzing drug mixtures.
• To evaluate if chemometric methods can effectively replace univariate methods in pharmaceutical analysis.

Main Methods:

• Three chemometric methods: Partial Least Squares (PLS), Artificial Neural Network (ANN), Genetic Algorithm-Partial Least Squares (GA-PLS).
• Seven univariate methods: derivative, ratio, and mean centering spectrophotometry.
• Analysis of mefenamic acid and febuxostat in raw materials, dosage forms, and spiked human plasma.

Main Results:

• All ten methods (univariate and chemometric) were found to be green, sensitive, rapid, and simple.
• Methods did not require pre-separation steps and showed good recoveries in various matrices.
• Statistical validation (t-test, F-test, ANOVA) confirmed the reliability of the developed methods.

Conclusions:

• Chemometric methods are suitable alternatives to univariate techniques for pharmaceutical quality control.
• The developed methods are validated according to ICH guidelines for routine analysis of drug mixtures.
• This study supports the adoption of advanced chemometric techniques in pharmaceutical quality control settings.