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利用 CONTOUR 中基于生长的新型筛选和优化方案发现高效 11β-羟甾类脱氢酶 1 抑制剂

Discovery of Potent Inhibitors of 11β-Hydroxysteroid Dehydrogenase Type 1 Using a Novel Growth-Based Protocol of Screening and Optimization in CONTOUR.

机构信息

Allergan Plc , 2525 Dupont Drive , Irvine , California 92612 , United States.

Vitae Pharmaceuticals, Inc. , 502 West Office Center Drive , Fort Washington , Pennsylvania 19034 , United States.

出版信息

J Chem Inf Model. 2019 Aug 26;59(8):3422-3436. doi: 10.1021/acs.jcim.9b00198. Epub 2019 Aug 8.

Abstract

With the continuous progress in ultralarge virtual libraries which are readily accessible, it is of great interest to explore this large chemical space for hit identification and lead optimization using reliable structure-based approaches. In this work, a novel growth-based screening protocol has been designed and implemented in the structure-based design platform CONTOUR. The protocol was used to screen the ZINC database and optimize hits to discover 11β-HSD1 inhibitors. In contrast to molecular docking, the virtual screening process makes significant improvements in computational efficiency without losing chemical equities through partitioning 1.8 million ZINC compounds into fragments, docking fragments to form key hydrogen bonds with anchor residues, reorganizing molecules into molecular fragment trees using matched fragments and common substructures, and then regrowing molecules with the help of developed intelligent growth features inside the protein binding site to find hits. The growth-base screening approach is validated by the high hit rate. A total of 50 compounds have been selected for testing; of these, 15 hits having diverse scaffolds are found to inhibit 11β-HSD1 with IC values of less than 1 μM in a biochemical enzyme assay. The best hit which exhibits an enzyme IC of 33 nM is further developed to a novel series of bicyclic 11β-HSD1 inhibitors with the best inhibition of enzyme IC of 3.1 nM. The final lead candidate exhibits IC values of 7.2 and 21 nM in enzyme and adipocyte assays, respectively, displayed greater than 1000-fold of selectivity over 11β-HSD2 and two other related hydroxysteroid dehydrogenases, and can serve as good starting points for further optimization to develop clinical candidates.

摘要

随着易于访问的超大型虚拟库的不断发展,使用可靠的基于结构的方法探索这个大型化学空间以进行命中鉴定和先导化合物优化具有重要意义。在这项工作中,我们设计并在基于结构的设计平台 CONTOUR 中实现了一种新的基于生长的筛选方案。该方案用于筛选 ZINC 数据库并优化命中化合物以发现 11β-HSD1 抑制剂。与分子对接相比,虚拟筛选过程通过将 180 万 ZINC 化合物分割成片段,将片段与锚定残基形成关键氢键,使用匹配片段和公共亚结构将分子重新组织成分子片段树,然后在蛋白质结合位点内部利用开发的智能生长特征重新生长分子,从而在不丧失化学等价性的情况下显著提高计算效率。基于生长的筛选方法得到了高命中率的验证。共选择了 50 种化合物进行测试;其中,发现 15 个具有不同骨架的化合物具有抑制 11β-HSD1 的活性,在生化酶测定中其 IC 值小于 1 μM。表现出最佳酶 IC 值为 33 nM 的最佳化合物被进一步开发为新型双环 11β-HSD1 抑制剂系列,其对酶的最佳抑制 IC 值为 3.1 nM。最终的先导化合物在酶和脂肪细胞测定中的 IC 值分别为 7.2 和 21 nM,对 11β-HSD2 和其他两种相关羟甾脱氢酶的选择性大于 1000 倍,可作为进一步优化以开发临床候选药物的良好起点。

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