VolSurf: a new tool for the pharmacokinetic optimization of lead compounds
VolSurf: a new tool for the pharmacokinetic optimization of lead compounds
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DOI：
10.1016/s0928-0987(00)00162-7
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发表时间：
2000-10-01
影响因子：
4.6
通讯作者：
Guba, W
中科院分区：
文献类型：
--
作者：
Cruciani, G;Pastor, M;Guba, W
在不同的实验数据集上，研究了基于计算分子相互作用场和多元统计的药代动力学性质的建模和预测方法。程序VolSurf用于将3D分子结构与物理化学和药代动力学性质相关联。在膜分配中，VolSurf产生了一个双组分模型，解释了94%的总变异，预测q（2）为0.90。这一结果是在没有构象取样和没有任何量子化学计算的情况下实现的。对于血脑屏障渗透的预测，VolSurf模型能够预测外部预测集中大多数药物的BBB曲线。在Caco-2和MDCK渗透实验中，VolSurf成功地用于建立统计模型和预测新化合物的行为。因此，该方法出现作为一个有价值的新的属性过滤器在虚拟筛选和作为一种新的工具，在优化药物相关化合物的药代动力学曲线。(C)2000 Elsevier Science B. V.保留所有权利。
A method for the modeling and prediction of pharmacokinetic properties based on computed molecular interaction fields and multivariate statistics has been investigated in different experimental datasets. The program VolSurf was used to correlate 3D molecular structures with physico-chemical and pharmacokinetic properties. In membrane partitioning, VolSurf produced a two-component model explaining 94% of the total variation with a predictive q(2) of 0.90. This result was achieved without conformational sampling and without any quantum-chemical calculation. For the prediction of blood-brain barrier penetration the VolSurf model was able to predict the BBB profile for most of the drugs in the external prediction set. In Caco-2 and MDCK permeation experiments, VolSurf was used with success to establish statistical models and to predict the behaviour of new compounds. The method thus appears as a valuable new property filter in virtual screening and as a novel tool in optimizing the pharmacokinetic profile of pharmaceutically relevant compounds. (C) 2000 Elsevier Science B.V. All rights reserved.