Application of machine learning assisted multi-variate UV spectrophotometric models augmented by kennard stone clustering algorithm for quantifying recently approved nasal spray combination of mometasone and olopatadine along with two genotoxic impurities: comprehensive sustainability assessment

• Published in: BMC chemistry Vol. 19; no. 1; pp. 98 - 19
• Main Authors: Abbas, Ahmed Emad F., Gamal, Mohammed, Naguib, Ibrahim A., Halim, Michael K., Said, Basmat Amal M., Ghoneim, Mohammed M., Mansour, Mohmeed M. A., Salem, Yomna A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 15.04.2025; Springer Nature B.V; BMC
• Subjects: Algorithms; Calibration; Chemistry; Chemistry and machine learning; Chemistry and Materials Science; Chemistry/Food Science; Chemometrics; Clustering; Cost effectiveness; Design of experiments; Footprint analysis; Genetic algorithms; Genotoxicity; Greenness whiteness and blueness evaluation; Impurities; Kennard stone clustering algorithm; Least squares; Machine learning; Machine learning models; Mixtures; Mometasone and olopatadine; Pharmaceuticals; Quality control; Quality standards; Robustness; Solvents; Solvents selection tool and spider charts; Spectrophotometry; Sustainability; Sustainable development; Toluene; Machine learning models; Kennard stone clustering algorithm; Mometasone and olopatadine; Greenness whiteness and blueness evaluation; Solvents selection tool and spider charts
• ISSN: 2661-801X; 2661-801X
• DOI: 10.1186/s13065-025-01391-8

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.