article · BMC Chemistry
Researchers developed a sustainable analytical technique to quantify montelukast sodium and levocetirizine dihydrochloride, two widely prescribed medications with therapeutic potential against COVID-19. Addressing the environmental drawbacks and expenses of conventional testing, the approach combines ultraviolet spectroscopy with multivariate calibration models. Using a multilevel multifactor design and Latin hypercube sampling for validation, four chemometric models were tested across the 210 to 400 nanometre spectral range. The genetic algorithm partial least squares model achieved high precision, delivering analyte recoveries between 98 and 102 percent along with low prediction errors and detection limits. Practical testing on pharmaceutical samples was verified using standard additions. Comprehensive sustainability profiling confirmed that the procedure generates a minimal carbon footprint, avoids toxic solvents, and provides a cost-effective alternative for routine medicine testing.
Standard pharmaceutical quality control frequently relies on toxic chemical solvents and expensive, energy-intensive machinery. Replacing these processes with ultraviolet spectroscopy and mathematical modelling lowers operating costs, prevents hazardous chemical waste, and cuts laboratory carbon emissions. This transition supports cleaner manufacturing practices while helping pharmaceutical facilities maintain rigorous quality standards for essential medications more affordably.
This technique is designed for pharmaceutical manufacturers, contract testing organisations, and regulatory quality control laboratories. By relying on standard ultraviolet spectrophotometers rather than complex chromatography platforms, it presents an accessible route to lowering equipment and solvent expenses. Having been applied and tested successfully on practical pharmaceutical samples using standard additions, the workflow appears ready for trial adoption in commercial quality control and formulation analysis settings.
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Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210-400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.
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DOI: 10.1186/s13065-024-01158-7
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