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A Validated HPLC Multichannel DAD Method for the Simultaneous Determination of Amoxicillin and Doxycycline in Pharmaceutical Formulations and Wastewater Samples
- Source :
- Applied Sciences, Vol 12, Iss 19, p 9789 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- The quality of marketed pharmaceutical formulations must be guaranteed to attain better remedial effects and lower toxicity. The wide exploitation of antibiotics may lead to their presence as residues in body fluids and wastewaters, potentially toxic to human health. Consequently, determining antibiotics in pharmaceutical formulations and water samples is of significant importance. This paper aims to explore the possibilities of a high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) method to obtain a simple, fast, and efficient analytical tool for the simultaneous determination of antibiotics in pharmaceutical formulations and environmental samples. The method was completely validated with regard to specificity, linearity, detection and quantification limits, precision, accuracy, and robustness according to the requirements of existing guidelines, and was proven to be reliable and suitable for the envisioned application. The linearity study was conducted for the calibration curves in the range of 10–100 μg/mL. The limits of detection and quantification were found to be 0.2 and 0.7 μg/mL for amoxicillin and 0.3 and 1.0 μg/mL for doxycycline, respectively. The high recovery of drugs from their commercial pharmaceutical formulations (93%) and from wastewater samples (98%) indicated good accuracy and precision. The method is robust for small or deliberate changes to the chromatographic parameters, and it was successfully applied for the quantitative determination of amoxicillin and doxycycline in wastewater and commercial tablets. The obtained results proved that the validated method is appropriate for its intended use in the routine quality control and assay of both antibiotics studied.
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 12
- Issue :
- 19
- Database :
- Directory of Open Access Journals
- Journal :
- Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.9d3fe91287c424d8e23cfb0eb7ccb72
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/app12199789