RESUMO
For the first time, clemastine was estimated in this work utilizing two validated resonance Rayleigh scattering (RRS) and fluorimetric methods. The methods relied on forming an association complex in an acidic medium between eosin Y reagent and clemastine. In the spectrofluorimetric approach, the investigated drug was quantified by quenching the fluorescence-emission intensity of eosin Y at 543.5 nm. The RRS method relied on enhancing the RRS spectrum at 331.8 nm, which is produced when eosin Y interacts with clemastine. Suitable conditions were established for the reaction to achieve maximum sensitivity. The linear values obtained from the spectrofluorimetric approach and the RRS method fall into the ranges of 0.2-1.5 µg mL- 1 and 0.25-2.0 µg mL- 1, respectively. It was established that the detection limits for these methods were 0.045 µg mL- 1 and 0.059 µg mL- 1, respectively. The developed methodologies yielded acceptable recoveries when used to estimate the quantity of clemastine in its pharmaceutical tablet dosage form. Regarding the use of greener solvents that were chosen, the suggested and reported methods were compared with the help of the Green Solvents Selecting (GSST) tool for assessing hazardous solvents to achieve sustainability. Furthermore, analytical Eco scale and comprehensive assessments of whiteness, blueness, and greenness were carried out utilizing Modified NEMI, ComplexGAPI, and AGREE evaluation tools. Additionally, recently developed tools such as BAGI and RGB 12 were applied to assess the blueness and the whiteness of the suggested methods.
RESUMO
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.
RESUMO
In this study, netilmicin (NTM) was selectively assessed in its dosage forms after a facile derivatization reaction. The proposed approach was based on the interaction between NTM and o-phthalaldehyde/2-mercaptoethanol (Roth's reagent). The reaction product was fluorometrically measured at λemission of 434 nm after λexcitation of 338 nm. All reaction conditions for achieving the optimum fluorescence switch-on activity were visualized and monitored. Moreover, the method was validated under ICH guidelines, and was linear over the range 30-210 ng/ml after plotting netilmicin concentrations against the corresponding fluorescence intensity values. In addition, the selectivity of the developed method was investigated against either the co-formulated drug (dexamethasone) or a common ophthalmic drop excipient (benzalkonium chloride) without interference from either of them. Furthermore, the developed method was applied to assay netilmicin in various samples of pharmaceutical eye drops with good recovery. Finally, multicriteria greenness and whiteness metrics were used to evaluate the sustainability, greenness, and whiteness of the approach. The applied tools were the AGREE algorithm, the RGB 12 algorithm, and HEXAGON.
