Water-Solvent Partition Coefficients and Δ Log P Values as Predictors for Blood-Brain Distribution; Application of the Akaike Information Criterion
Water-Solvent Partition Coefficients and Δ Log P Values as Predictors for Blood-Brain Distribution; Application of the Akaike Information Criterion
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DOI：
10.1002/jps.22010
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发表时间：
2010-05-01
影响因子：
3.8
通讯作者：
Cavanaugh, Joseph E.
中科院分区：
文献类型：
--
作者：
Abraham, Michael H.;Acree, William E., Jr.;Cavanaugh, Joseph E.
它示出，水-烷烃或水-环己烷分区的log P值，以及相应的Delta log P值，当用作描述符用于血脑分布时，作为log BB，产生具有非常差的相关系数但具有非常好的标准偏差的方程，S从0.25到0.33 log单位。使用相当大的数据集，我们已经验证了类似的S值适用于对数BB的预测。一个建议的模型，水十二烷和水十六烷分配系数的对数P的基础上，有109个数据点和拟合S = 0.254对数单位。在模型中必须包括挥发性化合物的指标变量和含有羧基的药物的指标变量。基于水-氯仿分配系数的类似方程具有83个数据点和拟合S = 0.287 log单位。在相关性或化学相似性方面，我们找不到这些log P值和log BB之间的因果关系，但得出结论，log P描述符将产生log BB的极好预测，前提是预测在用于建立模型的化合物的化学空间内。我们还表明，基于日志P（辛醇）和亚伯拉罕描述符的模型提供了一个简单易行的方法预测日志BB的误差不超过0.31个日志单位。我们已经使用赤池信息准则来研究最经济的模型为日志BB。(C)2009 Wiley-Liss，Inc.和American Pharmacologist Association J Pharm Sci 99：2492-2501，2010
It is shown that log P values for water-alkane or water-cyclohexane partitions, and the corresponding Delta log P values when used as descriptors for blood-brain distribution, as log BB, yield equations with very poor correlation coefficients but very good standard deviations, S from 0.25 to 0.33 log units. Using quite large data sets, we have verified that similar S-values apply to predictions of log BB. A suggested model, based on log P for water dodecane and water-hexadecane partition coefficients, has 109 data points and a fitted S = 0.254 log units. It is essential to include in the model an indicator variable for volatile compounds, and an indicator variable for drugs that contain the carboxylic group. A similar equation based on water-chloroform partition coefficients has 83 data points and a fitted S = 0.287 log units. We can find no causal connection between these log P values and log BB in terms of correlation or in terms of chemical similarity, but conclude that the log P descriptor will yield excellent predictions of log BB provided that predictions are within the chemical space of the compounds used to set up the model. We also show that model based on log P(octanol) and an Abraham descriptor provides a simple and easy method of predicting log BB with an error of no more than 0.31 log units. We have used the Akaike information criterion to investigate the most economic models for log BB. (C) 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:2492-2501, 2010