Background:
In spite of the availability of various treatment approaches including
surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a
significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer
with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects
associated with currently available anti-breast cancer agents, there is emergent requirement to
develop target-specific AIs with safer anti-breast cancer profile.
Methods:
It is challenging task to design target-specific and less toxic SAIs, though the molecular
modeling tools viz. molecular docking simulations and QSAR have been continuing for more than
two decades for the fast and efficient designing of novel, selective, potent and safe molecules
against various biological targets to fight the number of dreaded diseases/disorders. In order to
design novel and selective SAIs, structure guided molecular docking assisted alignment dependent
3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal
scaffold with wide range of aromatase inhibitory activity.
Results:
3D-QSAR model developed using molecular weighted (MW) extent alignment approach
showed good statistical quality and predictive ability when compared to model developed using
moments of inertia (MI) alignment approach.
Conclusion:
The explored binding interactions and generated pharmacophoric features (steric and
electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and
development of new potential safer SAIs, that can be effective to reduce the mortality and
morbidity associated with breast cancer.
Correction: Xie, H.; et al. 3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors. Int. J. Mol. Sci. 2014, 15, 20927–20947
