Cheminformatic models to predict binding affinities to human serum albumin
Cheminformatic models to predict binding affinities to human serum albumin
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DOI：
10.1021/jm010960b
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发表时间：
2001-12-06
影响因子：
7.3
通讯作者：
Lavandera, JL
中科院分区：
文献类型：
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作者：
Colmenarejo, G;Alvarez-Pedraglio, A;Lavandera, JL
预测与人血清白蛋白（HSA）结合亲和力的模型在制药工业中应该是非常有用的，以加速新化合物的设计，特别是就药物动力学而言。我们通过高效亲和色谱实验测定了95种不同药物和类药物化合物与HSA的结合亲和力。这些数据使我们能够推导出定量结构-活性关系模型，以根据其结构预测新化合物与HSA的结合亲和力。对于特定的化合物家族（r（2）> 0.80; q（2）> 0.62），已经推导出简单的线性单变量模型：β-肾上腺素能拮抗剂、类固醇、考克斯抑制剂和三环类抗抑郁药。此外，通过使用上述HSA结合常数的完整数据库，已经推导出适用于整个药物化学空间的全局模型。为了这个目的，遗传算法已被用来彻底搜索和选择多变量和非线性方程，从一个大的池的分子描述符。所得模型显示出与实验数据的良好拟合（r（2）大于或等于0.78; LOF小于或等于0.12）。此外，内部（交叉验证和随机化）和外部验证测试都证明这些模型具有良好的预测能力（q（2）大于或等于0.73; PRESS/SSY小于或等于0.23;外部集的r（2）大于或等于0.82）。方程群体的统计分析表明，疏水性（如通过ClogP测量的）是决定与HSA结合程度的最重要变量。此外，结构因素（特别是拓扑6卡（环）指数和一些朱尔斯描述符）也经常出现在最佳方程的描述符。因此，与HSA的结合是由疏水力与一些调节形状因子的组合决定的。这与HSA单独或与配体结合的X射线结构一致，其中位点I和II的结合口袋主要由疏水残基组成。
Models to predict binding affinities to human serum albumin (HSA) should be very useful in the pharmaceutical industry to speed up the design of new compounds, especially as far as pharmacokinetics is concerned-We have experimentally determined through high-performace affinity chromatography the binding affinities to HSA of 95 diverse drugs and druglike compounds. These data have allowed us the derivation of quantitative structure-activity relationship models to predict binding affinities to HSA of new compounds on the basis of their structure. Simple linear, one-variable models have been derived for specific families of compounds (r(2) > 0.80; q(2) > 0.62): beta -adrenergic antagonists, steroids, COX inhibitors, and tricyclic antidepressants. Also, global models have been derived to be applicable to the' whole medicinal chemical space by using the full database of HSA binding constants described above. For this aim, a genetic algorithm has been used to exhaustively search and select for multivariate and nonlinear equations, starting from a large pool of molecular descriptors. The resulting models display good fits to the experimental data (r(2) greater than or equal to 0.78; LOF less than or equal to 0.12). In addition, both internal (cross validation and randomization) and external validation tests have demonstrated that these models have good predictive power (q(2) greater than or equal to 0.73; PRESS/SSY less than or equal to 0.23; r(2) greater than or equal to 0.82 for the external set). Statistical analysis of the equation populations indicates that hydrophobicity (as measured by the ClogP) is the most important variable determining the binding extent to HSA. In addition, structural factors (especially the topological 6 chi (ring) index and some Jurs descriptors) also frequently appear as descriptors in the best equations. Therefore, binding to HSA turns out to be determined by a combination of hydrophobic forces together with some modulating shape factors. This agrees with X-ray structures of HSA alone or bound to ligands, where the binding pockets of both sites I and II are composed mainly of hydrophobic residues.