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 1 and 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.

Pharmacophore modeling and virtual screening for the discovery of new type 4 cAMP phosphodiesterase (PDE4) inhibitors.

Type 4 cAMP phosphodiesterase (PDE4) inhibitors show a broad spectrum of anti-inflammatory effects in almost all kinds of inflamed cells, by an increase in cAMP levels which is a pivotal second messenger responsible for various biological processes. These inhibitors are now considered as the potential drugs for treatment of chronic inflammatory diseases. However, some recently marketed inhibitors e.g., roflumilast, have shown adverse effects such as nausea and emesis, thus restricting its use. In order to identify novel PDE4 inhibitors with improved therapeutic indexes, a highly correlating (r = 0.963930) pharmacophore model (Hypo1) was established on the basis of known PDE4 inhibitors. Validated Hypo1 was used in database screening to identify chemical with required pharmacophoric features. These compounds are further screened by using the rule of five, ADMET and molecular docking. Finally, twelve hits which showed good results with respect to following properties such as estimated activity, calculated drug-like properties and scores were proposed as potential leads to inhibit the PDE4 activity. Therefore, this study will not only assist in the development of new potent hits for PDE4 inhibitors, but also give a better understanding of their interaction with PDE4. On a wider scope, this will be helpful for the rational design of novel potent enzyme inhibitors.