In Silico Drug-designing Studies on Sulforaphane Analogues: Pharmacophore Mapping, Molecular Docking and QSAR Modeling

(E-pub Ahead of Print)

Author(s): Neda Vaghefinezhad, Samaneh Fazeli Farsani, Sajjad Gharaghani*.

Journal Name: Current Drug Discovery Technologies

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Abstract:

Aims: In the presented work we successfully discovered several novel NQO1 inducers using the computational approaches.

Background: The phytochemical sulforaphane (SFN) is a potent inducer of carcinogen detoxication enzymes like NAD(P)H:quinone oxidoreductase 1 (NQO1) through the Kelch-like erythroid cell-derived protein with CNC homology[ECH]-associated protein 1 (Keap1)–[NF-E2]-related factor 2 (Nrf2) signaling pathway.

Objective: In this paper, we report the first QSAR and pharmacophore modeling study of sulforaphane analogues as NQO1 inducers. The pharmacophore model and understanding the relationships between the structures and activities of the known inducers will give useful information on the structural basis for NQO1 enzymatic activity and lead optimization for future rational design of new sulforaphane analogues as potent NQO1 inducers.

Method: In this study, a combination of QSAR modeling, pharmacophore generation, virtual screening and molecular docking was performed on a series of sulforaphane analogues as NQO1 inducers.

Result: In step of deriving QSAR model, the stepwise multiple linear regression gave a reliable model with the training set (N: 43, R: 0.971, RMSE: 0.216) and test set (N: 14, R: 0.870, RMSE: 0.324, Q2: 0.80) molecules. The best ligand-based pharmacophore model comprised two hydrophobic (HY), one ring aromatic (RA) and three hydrogen bond acceptor (HBA) sites. The model was validated by a testing set and decoys set, Güner–Henry (GH) scoring methods and etc. The enrichment of model was assessed by sensitivity (0.92) and specificity (0.95). Moreover, the values of enrichment factor (EF) and the area under the receiver operating characteristics curve (AUC) were 12 and 0.94, respectively. This well-validated model was applied to screen two Asinex libraries for novel NQO1 inducers. The hits were subsequently subjected to molecular docking after filtering by Lipinski’s, MDDR-like, and Veber rules and evaluating their interaction with three major drug-metabolizing P450 enzymes, CYP2C9, CYP2D6, CYP3A4. Ultimately, 12 hits filtered by molecular docking were subjected to validated QSAR model for calculating their inducer potencies and were introduced as potential NQO1 inducers for further investing action.

Conclusion: Conclusively, the validated QSAR model was applied on the hits to calculate their inducer potencies and these 12 hits were introduced as potential NQO1 inducers for further investing action.

Keywords: Sulforaphane, NQO1 inducer, QSAR, Pharmacophore, Molecular Docking, Drug Discovery

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1570163816666191112122047
Price: $95

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