Dual aromatase-steroid sulfatase inhibitors (DASI's) for the treatment of breast cancer: a structure guided ligand based designing approach

Dual aromatase-steroid sulfatase inhibitors (DASIs) lead to significant deprivation of estrogen levels as compared to a single target inhibition and thereby exhibited an additive or synergistic effect in the treatment of hormone-dependent breast cancer (HDBC). Triazole-bearing DASI's having structural features of clinically available aromatase inhibitors are identified as lead structures for optimization as DASI's. To identify the spatial fingerprints of target-specific triazole as DASI's, we have performed molecular docking assisted Gaussian field-based comparative 3D-QSAR studies on a dataset with dual aromatase-STS inhibitory activities. Separate contours were generated for both aromatase and steroid sulphates showing respective pharmacophoric structural requirements for optimal activity. These developed 3D-QSAR models also showed good statistical measures with the excellent predictive ability with PLS-generated validation constraints. Comparative steric, electrostatic, hydrophobic, HBA, and HBD features were elucidated using respective contour maps for selective target-specific favourable activity. Furthermore, the molecular docking was used for elucidating the mode of binding as DASI's along with the MD simulation of 100 ns revealed that all the protease-ligand docked complexes are overall stable as compared to reference ligand (inhibitor ASD or Irosustat) complex. Further, the MM-GBSA study revealed that compound 24 binds to aromatase as well as STS active site with relatively lower binding energy than reference complex, respectively. A comparative study of these developed multitargeted QSAR models along with molecular docking and dynamics study can be employed for the optimization of drug candidates as DASI's.Communicated by Ramaswamy H. Sarma

Automated summary

The study used molecular docking and 3D-QSAR analysis to identify lead structures for dual aromatase-steroid sulfatase inhibitors (DASIs) and revealed their specific interactions with aromatase and steroid sulfatase. These findings can be used for drug candidate optimization in the treatment of hormone-dependent breast cancer.