Spider chart, greenness and whiteness assessment of experimentally designed multivariate models for simultaneous determination of three drugs used as a combinatory antibiotic regimen in critical care units: Comparative study

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 313; p. 124115
• Main Authors: Taha, Asmaa M., Elmasry, Manal S., Hassan, Wafaa S., Sayed, Rania A.
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.05.2024
• Subjects: Levofloxacin; Linezolid; Meropenem; Multivariate coupled UV spectrophotometry; Plasma; Sustainability evaluation; Linezolid; Plasma; Levofloxacin; Meropenem; Sustainability evaluation; Multivariate coupled UV spectrophotometry
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2024.124115

[Display omitted] •Multivariate spectrophotometric methods were used to resolve antibiotic spectra overlap.•These methods were applied to pharmaceutical formulations and human plasma.•statistically compared to each other and with the reported methods.•Greenness and whitness were evaluated and compared to previously published methods.•A greenness index with spider charts was used for solvent sustainability assessment. In this study, five earth-friendly spectrophotometric methods using multivariate techniques were developed to analyze levofloxacin, linezolid, and meropenem, which are utilized in critical care units as combination therapies. These techniques were used to determine the mentioned medications in laboratory-prepared mixtures, pharmaceutical products and spiked human plasma that had not been separated before handling. These methods were named classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm partial least squares (GA-PLS), and artificial neural network (ANN). The methods used a five-level, three-factor experimental design to make different concentrations of the antibiotics mentioned (based on how much of them are found in the plasma of critical care patients and their linearity ranges). The approaches used for levofloxacin, linezolid, and meropenem were in the ranges of 3–15, 8–20, and 5–25 µg/mL, respectively. Several analytical tools were used to test the proposed methods' performance. These included the root mean square error of prediction, the root mean square error of cross-validation, percentage recoveries, standard deviations, and correlation coefficients. The outcome was highly satisfactory. The study found that the root mean square errors of prediction for levofloxacin were 0.090, 0.079, 0.065, 0.027, and 0.001 for the CLS, PCR, PLS, GA-PLS, and ANN models, respectively. The corresponding values for linezolid were 0.127, 0.122, 0.108, 0.05, and 0.114, respectively. For meropenem, the values were 0.230, 0.222, 0.179, 0.097, and 0.099 for the same models, respectively. These results indicate that the developed models were highly accurate and precise. This study compared the efficiency of artificial neural networks and classical chemometric models in enhancing spectral data selectivity for quickly identifying three antimicrobials. The results from these five models were subjected to statistical analysis and compared with each other and with the previously published ones. Finally, the whiteness of the methods was assessed by the recently published white analytical chemistry (WAC) RGB 12, and the greenness of the proposed methods was assessed using AGREE, GAPI, NEMI, Raynie and Driver, and eco-scale, which showed that the suggested approaches had the least negative environmental impact. Furthermore, to demonstrate solvent sustainability, a greenness index using a spider chart methodology was employed.