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Sustainable HPLC technique for measurement of antidiabetic drugs: Appraisal of green and white metrics, content uniformity, and in vitro dissolution

A. Al-Wasidi, Hoda A. Ahmed, Samar M. Mahgoub, M. A. Mohamed, Hossam F. Nassar

Analitical chemistry (Tel Aviv, 1993), 2024

8 citations

Abstract

Abstract Green chemistry and white chemistry are two approaches to chemistry that prioritize sustainability and environmental protection. The pursuit of green chemistry is to develop chemical processes and products that decrease or stop the use and generation of dangerous materials. In contrast, white chemistry focuses on developing energy-efficient, sustainable chemical processes that produce minimal waste. Our study evaluated the environmental friendliness of the suggested approach, using eight greenness appraisal techniques, including analytical eco-scale, analytical method volume intensity, HPLC-environmental assessment tool, analytical method greenness score, analytical greenness, analytical greenness metric for sample preparation, green analytical procedure index, and complexgreen analytical procedure index, in addition to the unique metric blue applicability grade index of white chemistry. We have developed and validated a highly effective and reliable method for concurrently analyzing designated pharmaceutical medications characterized in metformin (MET) and empagliflozin (EMP) formulations, including their degraded products. This method is cost-effective, specific, and environmentally friendly, utilizing reversed-phase high-performance liquid chromatography with an XBridge BEH C8 column (150 mm × 4.6 mm, 5 μm) at a flow rate of 1.0 mL·min−1, an injection volume of 5.0 μL, a column oven temperature of 50°C, a wavelength of 224 nm, and a mobile phase comprised of…

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Evidence Hub reads this as a experimental / empirical study, done in cell cultures (in vitro), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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