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首页> 外文期刊>Pharmaceutical research >A quantitative structure-property relationship for predicting drug solubility in PEG 400/water cosolvent systems.
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A quantitative structure-property relationship for predicting drug solubility in PEG 400/water cosolvent systems.

机译：预测PEG 400 /水助溶剂系统中药物溶解度的定量结构-性质关系。

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PURPOSE: A quantitative structure-property relationship (QSPR) was developed to predict drug solubility in binary mixtures of polyethylene glycol (PEG) 400 and water. The ability of the QSPR model to predict solubility was assessed and compared to the classic log-linear cosolvency model. METHODS: The solubility of 122 drugs, ranging in log P from -2.4 to 7.5, was determined in 0%, 25%, 50%, and 75% PEG (v/v in water) by the shake-flask method. Solubility data from 84 drugs were fit by linear regression using the following molecular descriptors: molecular weight, volume, radius of gyration, density, number of rotatable bonds, hydrogen-bond donors, and hydrogen-bond acceptors. The multiple linear regression model was optimized by a genetic algorithm guided selection method. The remaining 38 compounds were used to test the predictability of the model. RESULTS: QSPR-based models developed at each volume fraction with the training set compounds showed a reasonable correlation coefficient (r) of approximately 0.9 and a root mean square (rms) error of <0.5 log unit. The model predicted solubility values of approximately 78% of the testing set compounds within 1 log unit. The log-linear model was as effective as the QSPR-based model in predicting the testing set solubilities; however, many drugs, as expected, showed significant deviation from log-linearity. CONCLUSIONS: The QSPR model requires only the chemical structure of the drug and has utility for guiding vehicle identification for early preclinical in vivo studies, especially when compound availability is limited and experimental data such as aqueous solubility and melting point are unknown. When experimental data are available, the log-linear model was verified to be a useful predictive tool.
机译：目的：建立定量结构-性质关系（QSPR）以预测药物在聚乙二醇（PEG）400和水的二元混合物中的溶解度。评估了QSPR模型预测溶解度的能力，并将其与经典的对数线性共溶解度模型进行了比较。方法：采用摇瓶法测定了0％，25％，50％和75％PEG（水中v / v）中122种药物的溶解度，log P从-2.4至7.5。使用以下分子描述符通过线性回归拟合来自84种药物的溶解度数据：分子量，体积，回转半径，密度，可旋转键数，氢键供体和氢键受体。通过遗传算法指导的选择方法优化了多元线性回归模型。其余38种化合物用于测试模型的可预测性。结果：在每个体积分数下使用训练集化合物开发的基于QSPR的模型显示合理的相关系数（r）约为0.9，均方根（rms）误差小于0.5 log单位。该模型预测在1 log单位内约有78％的测试化合物的溶解度值。对数线性模型在预测测试集溶解度方面与基于QSPR的模型一样有效。然而，正如预期的那样，许多药物显示出与对数线性显着偏离。结论：QSPR模型仅需要药物的化学结构，可用于指导载体鉴定，以进行早期临床前体内研究，尤其是当化合物的可获得性有限且水溶性和熔点等实验数据未知时。当可获得实验数据时，对数线性模型被证明是有用的预测工具。

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