A novel high throughput 96‐well‐based fluorimetric method to measure amikacin in pharmaceutical formulations: development using response surface methodology

Abstract

An aminoglycoside antibiotic, amikacin, is used to treat severe and recurring bacterial infections. Due to the absence of a chromophore, however, amikacin must be extensively derivatized before being quantified, both in analytical and bioanalytical samples. In this study, for the first time, we developed a simple and sensitive method for measuring amikacin sulfate using spectrofluorimetry with a 96-well plate reader, based on the design of the experiment's approach. To develop a robust and reproducible spectrofluorimetric method, the influence of essential attributes, namely pH of the buffer, heating temperature, and concentration of reagents, were evaluated using univariate analysis followed by multivariate analysis (central composite design).International Conference of Harmonization guidelines were used to validate the optimized method. The developed technique is linear from 1.9 to 10 μg/ml with a regression coefficient of 0.9991. The detection and quantification limits were 0.649 μg/ml and 1.9 μg/ml, respectively. For the developed method, both intraday and interday precision (%RSD) were less than 5%. Using the method, amikacin concentrations were quantified in prepared amikacin liposomes and commercial formulations of Amicin ® . The developed method greatly reduces sample volume and is a rapid, high throughput microplate-based fluorescence approach for the convenient and costeffective measurement of amikacin in pharmaceutical formulations. In comparison with previously published approaches, the suggested method allowed for quick analysis of a high number of samples in a short amount of time (96 samples in 125 sec), resulting in an average duration of analysis of 1.3 sec per sample.

Cross-electrospun PVDF/PVDF-HFP nanofibrous membrane with central combination design and its waterproof and moisture permeable composite fabric

“…According to this model, the effect surface can be described, and the independent variable value corresponding to the effect value can be found from the effect surface, so as to obtain better experimental conditions. CCD has been rapidly developed and widely used in the chemical industry, biology, medicine, pharmaceuticals 39 and food science 40 and other fields, 41 because of its continuous prediction model and simple calculation for effectively solving practical problems and other advantages. It will make sense to apply it to the textile field to explore the optimal process parameters and response values.…”