Tubulin inhibitors: pharmacophore modeling, virtual 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: Hypo1 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). Hypo1 consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypo1 was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypo1 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 land 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro. Conclusion: Hypo1 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.