Tubulin inhibitors： pharmacophore modeling,virtua screening and molecular docking

Aim： To construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities. Methods： Ligand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment （MOE） software and to biological evaluation in vitro. Results： Hypol was demonstrated to be the best pharmacophore model that exhibited the highest correlation coefficient （0.9582）, largest cost difference （70.905） and lowest RMSD value （0.6977）. Hypol consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypol was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypol was used in virtual screening of the Specs database, 952 drug-like compounds were revealed. After docking into the colchicine-binding site of tubulin, 5 drug-like compounds with the required interaction with the critical amino acid residues and the binding free energies 〈-4 kcal/mol were selected as representative leads. Compounds 1 and 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro. Conclusion： Hypol is a quantitative pharmacophore model for tubulin inhibitors, which not only provides a better understanding of their interaction with tubulin, but also assists in discovering new potential leads with antitumor activities.