Abstract

Celecoxib and tramadol have been combined in a novel FDA-approved medication to address acute pain disorders requiring opioid treatment when other analgesics proved either intolerable or ineffective. The absorbance spectra of celecoxib and tramadol exhibit significant overlap, posing challenges for their individual quantification. This study introduces a spectrophotometric quantification approach for celecoxib and tramadol using a principle component regression assistive model to assist resolving the overlapped spectra and quantifying both drugs in their binary mixture. The model was constructed by establishing calibration and validation sets for the celecoxib and tramadol mixture, employing a five-level, two-factor experimental design, resulting in 25 samples. Spectral data from these mixtures were measured and preprocessed to eliminate noise in the 200–210 nm range and zero absorbance values in the 290–400 nm range. Consequently, the dataset was streamlined to 81 variables. The predicted concentrations were compared with the known concentrations of celecoxib and tramadol, and the errors in the predictions were evidenced calculating root mean square error of cross-validation and root mean square error of prediction. Validation results demonstrate the efficacy of the models in predicting outcomes; recovery rates approaching 100 % are demonstrated with relative root mean square error of prediction (RRMSEP) values of 0.052 and 0.164 for tramadol and celecoxib, respectively. The selectivity was further evaluated by quantifying celecoxib and tramadol in the presence of potentially interfering drugs. The model demonstrated success in quantifying celecoxib and tramadol in laboratory-prepared tablets, producing metrics consistent with those reported in previously established spectrophotometric methods.

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