Department of Pharmaceutical Sciences, Jefferson School of Pharmacy, Thomas Jefferson University, 130 South 9th Street, Philadelphia, PA 19107, USA.
Eur J Med Chem. 2012 Jan;47(1):412-23. doi: 10.1016/j.ejmech.2011.11.010. Epub 2011 Nov 17.
The first three-dimensional (3D) pharmacophore model was developed for potent retinoidal retinoic acid metabolism blocking agents (RAMBAs) with IC(50) values ranging from 0.0009 to 5.84nM. The seven common chemical features in these RAMBAs as deduced by the Catalyst/HipHop program include five hydrophobic groups (hydrophobes), and two hydrogen bond acceptors. Using the pharmacophore model as a 3D search query against NCI and Maybridge conformational Catalyst formatted databases; we retrieved several compounds with different structures (scaffolds) as hits. Twenty-one retrieved hits were tested for RAMBA activity at 100nM concentration. The most potent of these compounds, NCI10308597 and HTS01914 showed inhibitory potencies less (54.7% and 53.2%, respectively, at 100nM) than those of our best previously reported RAMBAs VN/12-1 and VN/14-1 (90% and 86%, respectively, at 100nM). Docking studies using a CYP26A1 homology model revealed that our most potent RAMBAs showed similar binding to the one observed for a series of RAMBAs reported previously by others. Our data shows the potential of our pharmacophore model in identifying structurally diverse and potent RAMBAs. Further refinement of the model and searches of other robust databases is currently in progress with a view to identifying and optimizing new leads.
首个三维(3D)药效团模型是为具有 IC(50)值在 0.0009 到 5.84nM 之间的强效视黄酸代谢阻断剂(RAMBAs)而开发的。这些 RAMBAs 中由 Catalyst/HipHop 程序推断出的七个常见化学特征包括五个疏水区(疏水基团)和两个氢键受体。使用药效团模型作为 3D 搜索查询,针对 NCI 和 Maybridge 构象 Catalyst 格式化数据库进行搜索;我们检索到了一些具有不同结构(支架)的化合物作为命中。对这 21 个检索到的命中化合物在 100nM 浓度下进行了 RAMBA 活性测试。这些化合物中最有效的是 NCI10308597 和 HTS01914,它们在 100nM 时的抑制活性分别为 54.7%和 53.2%,低于我们之前报道的最好的 RAMBAs VN/12-1 和 VN/14-1(分别为 90%和 86%,在 100nM 时)。使用 CYP26A1 同源模型进行的对接研究表明,我们最有效的 RAMBAs 与其他人之前报道的一系列 RAMBAs 观察到的结合方式相似。我们的数据表明,我们的药效团模型在识别结构多样且强效的 RAMBAs 方面具有潜力。目前正在对模型进行进一步的细化,并对其他强大的数据库进行搜索,以期识别和优化新的先导化合物。