Lead optimization of 4-(thio)-chromenone 6-O-sulfamate analogs using QSAR, molecular docking and DFT – a combined approach as steroidal sulfatase inhibitors

Abstract

Aromatase and steroidal sulfatase (STS) are steroidogenic enzyme that increases the concentration of estrogens in circulation, a primary factor leading to breast cancer. At molecular level, 87% of STS is expressed and an inhibitor targeting STS could decrease the level of estrogens. In an attempt to identify the chemical structural requirement targeting placental STS inhibition, 26 compounds with pIC₅₀ ranging from 4.61 to 9.46 were subjected to computational studies including Quantitative Structural–Activity Relationship (QSAR), MolecularDocking followed by Density Functional Theory (DFT) studies. A robust and predictable model were developed with good R² (0.834) and cross-validated correlation coefficient value Q²_LOO (0.786) explaining the relationship quantitatively. The regression graphs suggests that the STS inhibition was greatly dependent on the electro topological state of an atom, sum of the atom type E-state (SdssC), maximum E-states for strong hydrogen bond acceptors (maxHBa) and basic group count descriptor (BCUTp-1h). Furthermore, docking results showed favorable interactions of sulfamate analogs with catalytically important amino acid residues such as LEU74, VAL101, and VAL486. The interactions of the best active compound 3j when compared with standard Irosustat show similar binding energies. DFT studies further confirm the presence of HOMO orbital centered on chromenone ring further highlighting its importance for receptor ligand hydrophobic interaction. The study reveals that substitution of thio in chromenone nucleus and introduction of adamantyl substitution at second position are favorable in inhibiting the enzyme STS.

Graphical Abstract

Keywords:

• Steroidal sulfatase
• 4(thio)chromenone 6-O-sulfamate analogs
• QSAR
• molecular docking
• DFT
• SAR

Acknowledgements

The authors thank the Research Council of SRMIST and The Dean, SRM College of Pharmacy for their valuable support. The authors also express their gratitude to Prof. Paolo Gramatica, University of Insurbia, Varese, Italy for providing access to QSARINS software.

Disclosure statement

The authors declare no conflict of interest.