Lead generation of Aurora-A kinase inhibitors: Using 3D-QSAR pharmacophore modeling, virtual screening, and molecular docking | ||||
| Archives of Pharmaceutical Sciences Ain Shams University | ||||
| Article 2, Volume 8, Issue 2, December 2024, Page 207-223 PDF (1.26 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/aps.2024.296909.1176 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sara A. Hesham1; Mai I. Shahin2; Samar Mowafy1; Rabah A. T. Serya2; Khaled Abouzid2; Nahla A. Farag   1
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| 1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Misr International University, Cairo, Egypt | ||||
| 2Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Ain Shams University, Cairo, 11566, Egypt | ||||
| Abstract | ||||
| Aurora- kinase is a key regulator of centrosome function during mitosis and meiosis and is involved in a number of mitotic events in the cell cycle. Aurora kinases, specifically Aurora-A are extensively expressed in many tumors. As a result, targeting Aurora-A kinase is established to be an important target in treatment of cancer. In our study, the 3DQSAR pharmacophore model generation using Hypogen algorithm protocol was performed to generate a valid predictive pharmacophore model using a data set of 35 reported pyrazolo and furano-pyrimidine analogues of aurora-A inhibitors. The pharmacophore model selected had a cost difference of 87.029 and a correlation coefficient of 0.97 and RMS of 0.83, thus it was proven to be statistically significant. The pharmacophore model showed one hydrogen bond donor, one hydrophobic, and two ring aromatic features. This model was selected to virtually screen different data bases (SPECS, and scPDB databases). Fit value was used to filter the screened ligands. Three top hits of this screening were furtherly subjected to docking studies in the binding site of Aurora-A kinase receptor (PDB ID: 4JBO). Docking results of the top three hits had a high binding affinity to Aurora-A kinase receptor and showed a similar pattern of binding interactions to the reference. Subsequently, the top hits are predicted to be potential Aurora-A kinase inhibitors. As a result, this research reveals potential promising Aurora-A kinase hits which can be furtherly optimized to act as novel Aurora-A kinase inhibitors with higher efficacy and safety profile. | ||||
| Keywords | ||||
| antitumor; Hypogen; Algorithms; drug; discovery | ||||
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