Fragment-based drug design and drug repositioning using multiple ligand simultaneous docking (MLSD): identifying celecoxib and template compounds as novel inhibitors of signal transducer and activator of transcription 3 (STAT3)

Development of new N-Arylbenzamides as STAT3 Dimerization Inhibitors

The O-tosylsalicylamide S3I-201 (10) was used as a starting point for design and synthesis of novel STAT-3 dimerization inhibitors with improved drug-like qualities. The phosphonic acid 12d and salicylic acids 13f, 13g with a shorter amide linker lacking the O-tosyl group had improved STAT-3 inhibitory activity. The equivalent potencies observed by the replacement of phosphonic acid moiety of 12d with 5-amino-2-hydroxybenzoic acid group as in 13f further validates 5-amino-2-hydroxybenzoic acid as a phosphotyrosine mimic. The salicylic acid 13f displayed improved whole cell activity. The focused library of salicylic acids 13 with benzamide linker indicated that hydrophobic heptyl and cyclohexyl are the best tolerated R groups and a biphenyl ether (as the Ar group) significantly contributes to STAT3 inhibitory activity. Our docking studies indicated that the acidic groups of 12d, 13f and 13g interact in the p-Tyr-705 binding site in a broadly similar manner, while the phenoxybenzoyl group and the cyclohexylbenzyl group occupying pY+1 and pY-X hydrophobic pockets respectively. The in vitro and cell based potency of 13f warrants further development of this scaffold as STAT3 inhibitors.

In silico deconstruction of ATP-competitive inhibitors of glycogen synthase kinase-3β

Fragment-based methods have emerged in the last two decades as alternatives to traditional high throughput screenings for the identification of chemical starting points in drug discovery. One arguable yet popular assumption about fragment-based design is that the fragment binding mode remains conserved upon chemical expansion. For instance, the question of the binding conservation upon fragmentation of a molecule is still unclear. A number of papers have challenged this hypothesis by means of experimental techniques, with controversial results, "underlining" the idea that a simple generalization, maybe, is not possible. From a computational standpoint, the issue has been rarely addressed and mostly to test novel protocols on limited data sets. To fill this gap, we here report on a computational retrospective study concerned with the in silico deconstruction of leadlike compounds, active on the pharmaceutically relevant enzyme glycogen synthase kinase-3β.

In silico drug repositioning: what we need to know

Drug repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among drugs, targets and diseases necessary for successful repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico drug repositioning.

Predicting new indications for approved drugs using a proteochemometric method

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching, or simple in silico ligand docking. We now describe a novel rapid computational proteochemometric method called "train, match, fit, streamline" (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology, and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3671 FDA approved drugs across 2335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84%, and 91% for agents ranked in the top 10, 20, 30, and 40, respectively, out of all 3671 drugs. Drugs ranked in the top 1-40 that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an antiparasitic with recently discovered and unexpected anticancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity and angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27 000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.

Celecoxib induces apoptosis and cell-cycle arrest in nasopharyngeal carcinoma cell lines via inhibition of STAT3 phosphorylation

AIM

To investigate the mechanisms underlying the anticancer effect of celecoxib on nasopharyngeal carcinoma (NPC).

METHODS

NPC cell lines, HNE1 and CNE1-LMP1, were treated with various concentrations of celecoxib for 48 h. The antiproliferative effect of celecoxib was assessed using MTT assay. Both cell cycle profiles and apoptosis were analyzed using flow cytometry. Western blot was used to measure the levels of signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3(Y705) (pSTAT3(Y705)), COX-2, Survivin, Mcl-1, Bcl-2 and Cyclin D1.

RESULTS

Celecoxib (10-75 μmol/L) inhibited the proliferation of the NPC cell lines in a dose-dependent manner. Celecoxib (25 and 50 μmol/L) induced apoptosis and cell-cycle arrest at the G(0)/G(1) checkpoint in the NPC cell lines, which was associated with significantly reduced STAT3 phosphorylation. The genes downstream of STAT3 (ie, Survivin, Mcl-1, Bcl-2 and Cyclin D1) were significantly down-regulated after exposure to celecoxib (25 and 50 μmol/L).

CONCLUSION

The anticancer effects of celecoxib on NPC cell lines results from inducing apoptosis and cell cycle arrest, which may be partly mediated through the STAT3 pathway.