Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.
Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.
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DOI：
10.1016/j.jmgm.2010.03.010
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发表时间：
2010-06
影响因子：
2.9
通讯作者：
Costanzi, Stefano
中科院分区：
文献类型：
--
作者：
Vilar, Santiago;Chakrabarti, Mayukh;Costanzi, Stefano
关键词：
化合物在血液和脑中的分布是新的候选药物分子非常重要的考虑因素。在这篇文章中，我们描述了两个线性判别分析(LDA)模型的推导，用于预测被动血脑分割，以对数Bb值表示。这些模型是基于计算得出的物理化学描述符，即辛醇/水分配系数(LogP)、拓扑极性表面积(TPSA)和酸性和碱性原子总数，并使用307个化合物的均匀训练集获得，所有这些化合物的已发表的实验LOG BB数据都已在体内确定。特别是，由于LOG BB>；0.3的分子很容易穿过血脑屏障，而LOG BB<；−1的分子在大脑中的分布很差，在这些阈值的基础上，我们得出了两个不同的模型，两个模型都显示了大约80%的良好分类百分比。值得注意的是，我们的模型的预测能力通过对大量外部化合物数据集的分析得到了证实，这些化合物的报告对中枢神经系统(CNS)有活性或没有活性。通过我们的模型预测新化合物的对数BB，只需计算简单的物理化学描述符，就可以方便地与药物设计和虚拟筛选相结合。
The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings.
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影响因子：
2.6
作者：
Banks WA
通讯作者：
Banks WA
DOI：
10.1016/j.jchromb.2004.04.004
发表时间：
2004-08-05
影响因子：
3
作者：
Escuder-Gilabert, L;Molero-Monfort, A;Medina-Hernández, MJ
通讯作者：
Medina-Hernández, MJ
DOI：
10.1021/ci034205d
发表时间：
2004-01-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
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作者：
Adenot, M;Lahana, R
通讯作者：
Lahana, R
影响因子：
6.7
作者：
Abraham, Michael H.;Ibrahim, Adam;Acree, William E., Jr.
通讯作者：
Acree, William E., Jr.
影响因子：
7.3
作者：
Lombardo, F;Blake, JF;Curatolo, WJ
通讯作者：
Curatolo, WJ