Abstract:
Breast cancer is responsible for most of the women deaths in the world, this situation leads to the importance of the design of new drug candidates. On the same way severe side effects and non selectivity of some drugs make treatment sensitive and non effective. Drug discovery has significant role of finding of novel promising prodrugs that may progress to clinical trials in rapid evaluation process depend on prediction approaches.
This study aim to discover new hits of inhibitors of recent targets of breast cancer by using of bioinformatic technology to identify structure, conserved domain ,active site and physical and chemical properties of each target. Virtual screening of flavonoid compounds and zinc database compounds to find out active inhibitors. Testing of druglike properties in terms of chemical structure properties.
Computer-Assisted drug design(CADD) approach particularly structure based drug design was adopted to discover novel drug candidates of breast cancer. In this study, molecular operating environment (MOE) was used to run a post-docking simulation of zinc database compounds. The compounds had been docked before by DOCK6 of zinc.org server, a free database of commercially-available compounds for virtual screening(VS), ZINC database contains over 13 million purchasable compounds in ready-to-dock. Representatives of famous targets such as cyclooxygenases2, Kinesin, Matrix Metalloproteinases 9,Epithelial Growth Factor Receptor and Janus Kinase were chosen in docking simulations.Flavonoids and flavonoid derivatives also were selected according
iii
to distinctive structures that have anticancer activity and were checked by docking simulation. Similar binding to the selected targets was observed.
The results predicted potential high and moderate anticancer activity as indicated by binding affinity comparable to drug standards. ZINC database compounds that had been selected exhibited moderate multitarget activity was less than that of the drug hence, less side effect is expected. Flavonoid derivatives compounds showed the same account of activity as well as preferred properties of lipiniski rule.
Quantitative Structure-Activity Relationship (QSAR) descriptors evaluated drug-likeness properties of compounds, namely logp, water solubility, Lipinski drug-like test, reactive molecules, and molar refractivity.
