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.

The development of sustainable analytical methodologies that minimize hazards, waste generation, and energy consumption has become crucial. This study introduces pioneering green‒blue-white approaches for the simultaneous quantification of montelukast sodium (MLK) and fexofenadine hydrochloride (FEX) in combination formulations. The first approach employs an ultra-performance liquid chromatographic method (UPLC) with a green micellar mobile phase of 0.02 M sodium dodecyl sulfate and 10% 1-pentanol (65:35%). The method demonstrated excellent resolution, peak symmetry, and a short analysis time, with retention times of 3.53 min for MLK and 1.67 min for FEX. The MLK and FEX linearities were 1–260 and 1.2–312 μg/mL, respectively. The second approach involves complementary built-in spectroscopic techniques (second derivative, third derivative, and ratio difference methods) using water as a solvent, providing a green, simple, low-cost alternative in laboratories where expensive chromatographic devices may not be readily available. The MLK and FEX linearities were 3–50 and 3–60 μg/mL, respectively. All methods were comprehensively validated and showed satisfactory results. The proposed methods demonstrated excellent linearity (r² ≥ 0.9990), accuracy (recovery 98.5–101.5%), and precision (RSD ≤ 2%) across wide concentration ranges. A multifaceted evaluation was conducted to assess the environmental sustainability, real-world applicability, and economic viability of the proposed methods in comparison with previously reported techniques. This comprehensive assessment leveraged several state-of-the-art tools, including NEMI, ComplexGAPI, AGREE, ESA, BAGI, and RGB12. The suggested approaches exhibited favorable quadrant profiles in the NEMI and ComplexGAPI assessments, coupled with higher AGREE scores (0.90, 0.86) than reported (0.62, 0.74, 0.75, 0.69, 0.74, 0.74, and 0.75), in addition to higher ESA score (88, 92) than reported (75, 84, 85, 79, 82, 82, and 83), collectively affirming their environmentally friendly credentials. Moreover, we embraced the innovative notions of 'blueness' and 'whiteness' assessment by harnessing the recently formulated BAGI and RGB12 algorithms. The higher BAGI score (90, 82.5) than reported (72.5, 70, 70, 67.5, 67.5, 67.5, and 72.5), confirmed the excellent real-world applicability of the proposed methods, while the notable RGB12 indices (89.8, 88.1) than reported (67.8, 72.8, 71.5, 67.1, 73.7, 70.3, and 73.2), validated their cost-effectiveness and overall sustainability, contributing to an eco-friendly future for quality control processes.

A simple, cheap, sensitive, and time-saving square wave voltammetric (SWV) procedure using a carbon paste electrode modified with aluminum oxide nanoparticle decorated multi-walled carbon nanoparticles (Al₂O₃-NPs/MWCNTs/CPE) is presented for the ultra-sensitive determination of tamsulosin (TAM) and solifenacin (SOL), one of the most prescribed pharmaceutical combinations in urology. Characterization of the developed electrode was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD) patterns, energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM) and FT-IR spectrophotometry. The voltammetric behavior of TAM/SOL was evaluated using Al₂O₃-NPs in different content and electrode compositions. The use of Al₂O₃ functionalized MWCNTs as a CPE modifier increased the process of electron transfer as well as improved the electrode active surface area therefore, ultra-sensitive results were acquired with a linear range of 10–100 and 12–125 ng ml⁻¹ for TAM and SOL respectively, and a limit of the detection value of 2.69 and 3.25 ng ml⁻¹ for TAM and SOL, respectively. Interestingly, the proposed method succeeded in quantifying TAM and SOL with acceptable percentage recoveries in dosage forms having diverged concentration ranges and in the biological fluids with very low peak plasma concentration (C_max). Furthermore, the proposed method was validated, according to the ICH criteria, and shown to be accurate and reproducible.

The recent approval of the nasal spray combination of mometasone (MOM) and olopatadine (OLO) presents a significant analytical challenge, as only a single reported method exists for its determination, deviating from eco-friendly practices. This study addresses this critical gap by pioneering the application of machine learning techniques to develop robust UV spectrophotometric approach for the simultaneous quantification of MOM and OLO, along with two genotoxic impurities: 4-dimethylamino pyridine (DAP) and methyl para-toluene sulfonate (MTS). By simultaneously determining these highly concerning genotoxic impurities and active pharmaceutical ingredients, this method underscores its paramount significance in upholding rigorous pharmaceutical quality standards and safeguarding patient safety. Applying the multilevel-multifactor experimental design, the calibration set was meticulously chosen at five different concentrations, yielding 25 calibration mixtures with central levels of 4, 46.5, 2.5, and 3 µg/mL for MOM, OLA, MTS, and DAP, respectively. The key innovation lies in the strategic implementation of the Kennard-Stone Clustering Algorithm to create a robust validation set of thirteen mixtures, resolving the limitations of reported chemometric methods' random data splitting. This approach ensures unbiased evaluation across the full concentration space, improving the method's reliability and sustainability. The robustness of this approach was rigorously tested using five distinct chemometric models: principal component regression, classical least squares, partial least squares, genetic algorithm-partial least squares, and multivariate curve resolution-alternating least squares, demonstrating its broad applicability across diverse modeling techniques. All models successfully determined all components with excellent recovery, low bias-corrected prediction, and adequate limits of detection. The Greenness Index Spider Charts and the Green Solvents Selection Tool were used to choose environmentally conscious solvents. A comprehensive sustainability assessment employed six state-of-the-art tools, including the national environmental method index, complementary green analytical procedure index, analytical greenness metric, blue applicability grade index, carbon footprint analysis, and the red-green-blue 12 metrics. Favorable results across all metrics affirmed the method's eco-friendliness, real-world applicability, and cost-effectiveness, supporting sustainable development goals in pharmaceutical quality control processes.