Abstract：
Hypertension and hyperlipidemia are two common conditions that require effective management to reduce the risk of cardiovascular diseases. Among the medications commonly used for the treatment of these conditions, valsartan and pitavastatin have shown significant efficacy in lowering blood pressure and cholesterol levels, respectively. In this study, synchronous spectrofluorimetry coupled to chemometric analysis tools, specifically concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS), was employed for the determination of valsartan and pitavastatin simultaneously. The developed models exhibited excellent predictive performance with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for valsartan and pitavastatin, respectively. Hence, these models were successfully applied to the analysis of synthetic samples and commercial formulations as well as plasma samples with high accuracy and precision. Besides, the greenness and blueness profiles of the determined samples were also evaluated to assess their environmental impact and analytical practicability. The results demonstrated excellent greenness and blueness scores with AGREE score of 0.7 and BAGI score of 75 posing the proposed method as reliable and sensitive approach for the determination of valsartan and pitavastatin with potential applications in pharmaceutical quality control, bioanalytical studies, and therapeutic drug monitoring.
摘要：
高血压和高脂血症是两种常见的疾病，需要有效的管理以降低心血管疾病的风险。在通常用于治疗这些疾病的药物中，缬沙坦和匹伐他汀在降低血压和胆固醇水平方面显示出显著的功效，分别。在这项研究中,同步荧光光谱法与化学计量分析工具相结合，特别是浓度残差增广经典最小二乘(CRACLS)和光谱残差增广经典最小二乘(SRACLS)，同时测定缬沙坦和匹伐他汀的含量。开发的模型表现出出色的预测性能，缬沙坦和匹伐他汀的预测相对均方根误差（RRMSEP）为2.253和2.1381，分别。因此，这些模型已成功应用于分析合成样品和商业配方以及血浆样品，具有很高的准确度和精密度。此外,还评估了所确定样品的绿色和蓝色轮廓，以评估其对环境的影响和分析实用性。结果显示了优异的绿色和蓝色评分，AGREE评分为0.7，BAGI评分为75，表明该方法是测定缬沙坦和匹伐他汀的可靠和灵敏的方法，在药物质量控制中具有潜在的应用。生物分析研究，和治疗药物监测。
