Porras Mauricio, Adrover María Esperanza, Pedernera Marisa, Bucalá Verónica, Gallo Loreana
CEDETS-Universidad Provincial del Sudoeste (UPSO), Ciudad de Cali 320, 8000 Bahía Blanca, Argentina.
Departamento de Ingeniería Química, Universidad Nacional del Sur (UNS), Av. Alem 1253, 8000 Bahía Blanca, Argentina; Planta Piloto de Ingeniería Química, PLAPIQUI (UNS-CONICET), Camino La Carrindanga Km 7, 8000 Bahía Blanca, Argentina.
J Pharm Biomed Anal. 2022 Jul 15;216:114830. doi: 10.1016/j.jpba.2022.114830. Epub 2022 May 11.
Albendazole is a crystalline drug that is poorly soluble in water, thus the dissolution rate in gastrointestinal fluids is limited. Mesoporous materials loaded with poorly water-soluble drugs become an interesting strategy to increase their solubility/dissolution rate as the drug state changes from crystalline to amorphous. In order to determine the drug loading content into mesoporous materials analytical methods such as elemental analysis, UV and HPLC are commonly used. However, elemental analysis and HPLC are destructive and relatively expensive. In addition, UV is time consuming. Moreover, UV and HPLC require the drug release from the mesoporous material before the quantification step. Therefore, the aim of this work was to develop quantifications techniques based on chemometric models combined with UV and FT-IR spectra without needing the drug release from the mesoporous material. Partial least squares regression (PLSR) was used as chemometric regression method. Albendazole content in the SBA-15 powders was first quantified by elemental analysis as reference measurement for multivariate calibration. The excellent drug loading predictions prove that robust calibration models can be obtained from both techniques (i.e., 0.999 and 0.998 adjusted correlation coefficient for UV and FT-IR, respectively). Additionally, the adjusted correlation coefficients determined from the validation models for UV and FT-IR are 0.963 and 0.930, respectively. It is important to highlight that the prediction adjustment of the FT-IR model (root-mean-square error of prediction=2.196%) presented lower error than the UV model (root-mean-square error of prediction=3.553%). Therefore, this development contributes to improve the overall time and cost of drug loading determination into mesoporous materials.
阿苯达唑是一种结晶药物,在水中溶解度较差,因此在胃肠液中的溶解速率有限。负载水溶性差的药物的介孔材料成为提高其溶解度/溶解速率的一种有趣策略,因为药物状态从结晶变为无定形。为了确定药物负载到介孔材料中的含量,通常使用元素分析、紫外和高效液相色谱等分析方法。然而,元素分析和高效液相色谱具有破坏性且相对昂贵。此外,紫外分析耗时。而且,紫外和高效液相色谱在定量步骤之前需要药物从介孔材料中释放出来。因此,这项工作的目的是开发基于化学计量学模型结合紫外和傅里叶变换红外光谱的定量技术,而无需药物从介孔材料中释放出来。偏最小二乘回归(PLSR)用作化学计量学回归方法。首先通过元素分析对SBA - 15粉末中的阿苯达唑含量进行定量,作为多变量校准的参考测量。出色的药物负载预测证明可以从这两种技术获得稳健的校准模型(即紫外和傅里叶变换红外的调整相关系数分别为0.999和0.998)。此外,紫外和傅里叶变换红外验证模型确定的调整相关系数分别为0.963和0.930。重要的是要强调,傅里叶变换红外模型的预测调整(预测均方根误差 = 2.196%)比紫外模型(预测均方根误差 = 3.553%)的误差更低。因此,这一进展有助于提高测定药物负载到介孔材料中的总体时间和成本。