Computational approach to the identification of novel Aurora-A inhibitors
- Computational approach to the identification of novel Aurora-A inhibitors
- 네아즈; 조용서; 서선희; 한기철; 양은경; 배애님
- Aurora-A; kinase; anticancer; Docking; Pharmacophore; Virtual screening; Tumor cell; Inhibitor
- Issue Date
- Bioorganic & medicinal chemistry
- VOL 19, NO 2, 907-916
- Aurora kinase A has been emerging as a key therapeutic target for the design of anticancer drugs. For the
purpose of finding biologically active and novel compounds and providing new ideas for drug-design, we
performed virtual screening using commercially available databases. A three-dimensional common feature
pharmacophore model was developed with the HipHop program provided in the Catalyst software
package, and this model was used as a query for screening the databases. A recursive partitioning (RP)
model was developed as a filtering system, which was able to classify active and inactive compounds.
Eventually, a step-wise virtual screening procedure was conducted by applying the common feature
pharmacophore and the RP model in succession to discover novel potent Aurora-A inhibitors. A total
of 68 compounds were selected for testing of their in vitro anticancer activities against various human
cancer cell lines. Based on the activity data, we have identified fifteen compounds that warrant further
investigation. Several compounds have a high inhibition rate (above 80% at 10 μM) and a GI50 lower than
5 μM for the cell lines DU145 and HT29. Enzyme assay for these compounds identified hits with micro
molar activity. Compound C11 has the highest activity (IC50 = 5.09 μM). The hits obtained from this
screening scheme could be potential drug candidates after further optimization.
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