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使用最少实验次数预测呋塞米在水-共溶剂混合物中的溶解度。

Solubility prediction for furosemide in water-cosolvent mixtures using the minimum number of experiments.

作者信息

Jouyban-Gharamaleki A, Dastmalchi S, Chan H K, Hanaee J, Javanmard A, Barzegar-Jalali M

机构信息

Faculty of Pharmacy, University of Sydney, NSW, Australia.

出版信息

Drug Dev Ind Pharm. 2001 Jul;27(6):577-83. doi: 10.1081/ddc-100105183.

Abstract

The mole fraction solubility of a poorly water soluble loop diuretic, furosemide, was determined in aqueous binary mixtures of ethanol, propylene glycol, and glycerol from 0% to 100% cosolvent concentrations at 25 degrees C. Solubility predictions based on the minimum number of experimental data points were performed using the commonly used accurate cosolvency models: the three-suffix excess free energy (3xEFE), the mixture response surface (MRS), the combined nearly ideal binary solvent/Redlich-Kister (CNIBS/R-K), and the general single model (GSM). This prediction method was tested using three sets of solubility data for furosemide generated in this study and 11 data sets collected from the literature. The average percentage deviations (APDs) were 8.4 +/- 3.8, 13.6 +/- 7.3, 7.4 +/- 2.8, and 7.6 +/- 2.9, respectively, for 3xEFE, MRS, CNIBS/R-K, and GSM models. Using 3xEFE, CNIBS/R-K, and GSM models, which are theoretically related, a mean predicted solubility (MPS) approach was also proposed. The APD for this method was 7.3 +/- 2.3. The mean differences between MRS and the others were statistically significant (p < .001). The results showed that one can employ solubility prediction based on a minimum of five experimental data points, and the expected APD is less than 10%.

摘要

在25℃下,测定了难溶于水的袢利尿剂呋塞米在乙醇、丙二醇和甘油的二元水混合溶剂中的摩尔分数溶解度,共溶剂浓度范围为0%至100%。基于最少实验数据点,使用常用的精确共溶剂模型进行溶解度预测:三后缀超额自由能(3xEFE)、混合响应面(MRS)、组合近理想二元溶剂/Redlich-Kister(CNIBS/R-K)和通用单模型(GSM)。使用本研究中生成的三组呋塞米溶解度数据以及从文献中收集的11组数据集对该预测方法进行了测试。对于3xEFE、MRS、CNIBS/R-K和GSM模型,平均百分比偏差(APD)分别为8.4±3.8、13.6±7.3、7.4±2.8和7.6±2.9。利用理论相关的3xEFE、CNIBS/R-K和GSM模型,还提出了一种平均预测溶解度(MPS)方法。该方法的APD为7.3±2.3。MRS与其他模型之间的平均差异具有统计学意义(p < 0.001)。结果表明,可以基于最少五个实验数据点进行溶解度预测,预期的APD小于10%。

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