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.
摘要：
塞来昔布和曲马多已被联合用于FDA批准的新型药物中，以解决在其他镇痛药无法耐受或无效时需要阿片类药物治疗的急性疼痛疾病。塞来昔布和曲马多的吸收光谱表现出明显的重叠，给他们的个人量化带来挑战。本研究介绍了使用主成分回归辅助模型对塞来昔布和曲马多进行分光光度定量的方法，以帮助解析重叠的光谱并定量两种药物的二元混合物。通过建立塞来昔布和曲马多混合物的校准和验证集来构建模型，采用五级，双因素实验设计，产生25个样本。测量并预处理来自这些混合物的光谱数据以消除200-210nm范围内的噪声和290-400nm范围内的零吸收值。因此,数据集简化为81个变量.将预测浓度与已知浓度的塞来昔布和曲马多进行比较，并证明了预测中的误差，计算了交叉验证的均方根误差和预测的均方根误差。验证结果证明了模型在预测结果方面的有效性；曲马多和塞来昔布的相对预测均方根误差（RRMSEP）值为0.052和0.164，证明了接近100％的回收率。分别。通过在潜在干扰药物存在下定量塞来昔布和曲马多进一步评价选择性。该模型在实验室制备的片剂中成功定量塞来昔布和曲马多，产生与以前建立的分光光度法报告的指标一致的指标。
