Sustainable Chemometric Approach Resolving Ternary Anti-COVID-19 Mixture; Statistically Guided Impurity Profiling Using Comparative Assessment Tools and Molecular Docking
Abstract
This study used chemometric models to measure caffeine (CAF), aspirin (ASP), and paracetamol (PAR) in the presence of three hazardous impurities: salicylic acid (SAL), P-nitrophenol (PNP), and P-chloroacetanilide (PCA). A molecular docking study was used to examine how impurities might inhibit cyclooxygenase-2, highlighting the importance of controlling their levels if present in the dosage form. Four sustainable chemometric models: principal component regression, multivariate curve resolution-alternating least squares, artificial neural networks, and partial least squares, were developed. The quantitative analytical performance of all proposed models was evaluated using the per cent recovery, standard error of prediction, and root mean square error of prediction. The developed models covered the concentration ranges (in µg/mL) of PNP (2.00-6.00), PCA (0.50-0.90), SAL (6.00-14.00), ASP (6.00-14.00), CAF (3.00-19.00), and PAR (2.00-10.00). These models effectively addressed collinearity and spectral overlaps. Seven innovative tools, including the Carbon Footprint Reduction Index, Click Analytical Chemistry Index, Multicolour assessment tool, Analytical Green Star Area, spider chart, green solvent selection tool, and Modified Green Analytical Procedure, were used to calculate the sustainability and whiteness of the developed models.
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Evidence Hub reads this as a preprint, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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