Fibroblast growth factor receptor (FGFR) inhibitors as anticancer agents: 3D-QSAR, molecular docking and dynamics simulation studies of 1, 6-naphthyridines and pyridopyrimidines.

Fibroblast growth factor receptor (FGFR) plays a vital role in tissue regeneration, angiogenesis, and embryogenesis. 3D-QSAR and molecular modeling methods are widely used for designing novel compounds for the determination of inhibitory activity against the biological target. In the present study, 3D-QSAR (CoMFA and CoMSIA) analysis was performed on 1, 6-naphthyridines, and pyridopyrimidines as potential FGFR inhibitors as anticancer agents. The best CoMFA and CoMSIA models were generated from test and training set derivatives with leave-one-out correlation coefficients (q2) 0.591 and 0.667, cross-validated correlation coefficients (r2cv) 0.584 and 0.652, conventional coefficients (r2ncv) 0.978 and 0.975 respectively. The developed models were validated by a test set of 12 compounds providing acceptable predictive correlation coefficient (r2pred) 0.61 and 0.68 for both models. The generated CoMFA and CoMSIA contour maps could be used to design novel 1, 6-naphthyridine analogs. Molecular docking studies indicated that compound 75 occupied the active site of the FGFR kinase interacting with Glu520 in the catalytic region, Asp630 in the DFG motif, and Met524 in the hinge region which compared with standard drug Ponatinib. The molecular dynamics simulation analysis revealed that the inhibitor 75 displayed binding stability in the active site of the FGFR4 by making two hydrogen bonds and one π-cation interaction. Collectively the outcome of the study suggested that the applications of ligand-based and structure-based approaches could be applied for the design of new FGFR4 inhibitors as anticancer agents.Communicated by Ramaswamy H. Sarma.

Homology Modeling of Human Concentrative Nucleoside Transporters (hCNTs) and Validation by Virtual Screening and Experimental Testing to Identify Novel hCNT1 Inhibitors.

OBJECTIVE: The nucleoside transporter family is an emerging target for cancer, viral and cardiovascular diseases. Due to the difficulty in the expression, isolation and crystallization of membrane proteins, there is a lack of structural information on any of the mammalian and for that matter the human proteins. Thus the objective of this study was to build homology models for the three cloned concentrative nucleoside transporters hCNT1, hCNT2 and hCNT3 and validate them for screening towards the discovery of much needed inhibitors and probes. METHODS: The recently reported crystal structure of the Vibrio cholerae concentrative nucleoside transporter (vcCNT), has satisfactory similarity to the human CNT orthologues and was thus used as a template to build homology models of all three hCNTs. The Schrödinger modeling suite was used for the exercise. External validation of the homology models was carried out by docking a set of recently reported known hCNT1 nucleoside class inhibitors at the putative binding site using induced fit docking (IDF) methodology with the Glide docking program. Then, the hCNT1 homology model was subsequently used to conduct a virtual screening of a 360,000 compound library, and 172 compounds were obtained and biologically evaluated for hCNT 1, 2 and 3 inhibitory potency and selectivity. RESULTS: Good quality homology models were obtained for all three hCNTs as indicated by interrogation for various structural parameters and also external validated by docking of known inhibitors. The IDF docking results showed good correlations between IDF scores and inhibitory activities; particularly for hCNT1. From the top 0.1% of compounds ranked by virtual screening with the hCNT1 homology model, 172 compounds selected and tested for against hCNT1, hCNT2 and hCNT3, yielded 14 new inhibitors (hits) of (i.e., 8% success rate). The most active compound exhibited an IC50 of 9.05 µM, which shows a greater than 25-fold higher potency than phlorizin the standard CNT inhibitor (IC50 of 250 µM). CONCLUSION: We successfully undertook homology modeling and validation for all human concentrative nucleoside transporters (hCNT 1, 2 and 3). The proof-of-concept that these models are promising for virtual screening to identify potent and selective inhibitors was also obtained using the hCNT1 model. Thus we identified a novel potent hCNT1 inhibitor that is more potent and more selective than the standard inhibitor phlorizin. The other hCNT1 hits also mostly exhibited selectivity. These homology models should be useful for virtual screening to identify novel hCNT inhibitors, as well as for optimization of hCNT inhibitors.