Laboratório de Planejamento e Síntese de Quimioterápicos Potenciais Contra Endemias Tropicais, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, Av. Prof. Lineu Prestes 580, 05508-900 São Paulo, SP, Brazil.
J Mol Model. 2011 May;17(5):921-8. doi: 10.1007/s00894-010-0779-4. Epub 2010 Jul 6.
Histamine is an important biogenic amine, which acts with a group of four G-protein coupled receptors (GPCRs), namely H(1) to H(4) (H(1)R - H(4)R) receptors. The actions of histamine at H(4)R are related to immunological and inflammatory processes, particularly in pathophysiology of asthma, and H(4)R ligands having antagonistic properties could be helpful as antiinflammatory agents. In this work, molecular modeling and QSAR studies of a set of 30 compounds, indole and benzimidazole derivatives, as H(4)R antagonists were performed. The QSAR models were built and optimized using a genetic algorithm function and partial least squares regression (WOLF 5.5 program). The best QSAR model constructed with training set (N = 25) presented the following statistical measures: r (2) = 0.76, q (2) = 0.62, LOF = 0.15, and LSE = 0.07, and was validated using the LNO and y-randomization techniques. Four of five compounds of test set were well predicted by the selected QSAR model, which presented an external prediction power of 80%. These findings can be quite useful to aid the designing of new anti-H(4) compounds with improved biological response.
组胺是一种重要的生物胺,它与一组四个 G 蛋白偶联受体(GPCR),即 H(1)至 H(4)(H(1)R-H(4)R)受体相互作用。组胺在 H(4)R 上的作用与免疫和炎症过程有关,特别是在哮喘的病理生理学中,具有拮抗作用的 H(4)R 配体可能有助于作为抗炎剂。在这项工作中,对一组 30 种化合物(吲哚和苯并咪唑衍生物)作为 H(4)R 拮抗剂进行了分子建模和 QSAR 研究。使用遗传算法函数和偏最小二乘回归(WOLF 5.5 程序)构建和优化了 QSAR 模型。用训练集(N=25)构建的最佳 QSAR 模型呈现出以下统计度量:r(2)=0.76,q(2)=0.62,LOF=0.15,LSE=0.07,并使用 LNO 和 y-随机化技术进行了验证。测试集中的四种化合物中的五种化合物被选定的 QSAR 模型很好地预测,其外部预测能力为 80%。这些发现对于辅助设计具有改善的生物学反应的新型抗-H(4)化合物非常有用。