Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan.
Department of Mathematics and Statistics, University of Agriculture Faisalabad, Faisalabad, Pakistan.
Drug Dev Ind Pharm. 2024 Jul;50(7):619-627. doi: 10.1080/03639045.2024.2377331. Epub 2024 Jul 12.
To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol.
Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively.
As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model.
Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.
建立基于拉曼光谱的阿替洛尔原料药(API)固体制剂定量分析模型。
拉曼光谱是一种可靠、快速的药物检测方法,可用于药物的定量分析。本研究中,拉曼光谱用于定量分析不同浓度的阿替洛尔。
将 API 与赋形剂混合制备各种阿替洛尔固体制剂,形成不同的阿替洛尔固体制剂配方。采用主成分分析(PCA)和偏最小二乘回归(PLSR)等多元数据分析技术分别进行定性和定量分析。
随着制剂中药物浓度的增加,与 API(阿替洛尔)相关的独特拉曼光谱特征的峰强度逐渐增加。由于具有独特的光谱特征,拉曼光谱数据集采用 PCA 进行分类。此外,还使用 PLSR 分析构建了预测模型,以评估各种 API(阿替洛尔)浓度与光谱特征之间的定量关系。拟合值为 0.99,校准(RMSEC)和预测(RMSEP)的均方根误差分别为 1.0036 和 2.83mg。还使用 PLSR 模型确定了未知阿替洛尔制剂中 API 的含量。
基于这些结果,拉曼光谱可用于快速、准确地分析药物样品并进行定量测定。