Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors

Abstract

Cyclooxygenase-2 (COX-2) enzyme inhibitors have not eliminated the necessity for developed drugs not only in the nonsteroidal anti-inflammatory drug (NSAIDs) area, but also in other therapeutic applications including prevention of cancer and Alzheimer's disease. A series of novel substituted cyclic imides have been reported as selective COX-2 inhibitors. To understand the structural features responsible for their activity, a 3D validated pharmacophore and quantitative structure-activity relationship (QSAR) model have been developed. The values of enrichment factor (EF), goodness of hit score (GH), area under the ROC curve (AUC), sensitivity, and specificity refer to the good ability of the pharmacophore model to identify active compounds. Multiple linear regression (MLR) produced statistically significant QSAR model with (R² _training = 0.763, R² _test = 0.96) and predictability (Q² _training = 0.66, Q² _test = 0.84). Then, using the pharmacophore and QSAR models, eight authenticated botanicals in two herbal medicines and the ZINC compounds database, were virtually screened for ligands to COX-2. The retrieved hits which also obey lipinski's rule of five (RO5) were docked in the COX-2 3D structure to investigate their binding mode and affinity. Finally, based on the docking results, nine molecules were prioritized as promising hits that could be used as leads to discover novel COX-2 inhibitors. COX-2 inhibition of most of these hits has not been reported previously. Ten-nanosecond molecular dynamics simulation (10-ns MD) was performed on the initial structure COX-2 complex with ZINC000113253375 and ZINC000043170560 resulted from the docking. Our utilization of the 3D pharmacophore model, QSAR, molecular docking, and molecular dynamics simulation trials can be a potent strategy to successfully predict activity, efficiently design drugs, and screen large numbers of new compounds as active drug candidates.

Keywords: Cyclooxygenase-2; Docking; Molecular dynamics simulation; Pharmacophore; QSAR; Virtual screening.

Figure 1

Mapping the most active training compound on the model in 2D (a) and 3D (b).

Figure 2

ROC plot of the pharmacophore model.

Figure 3

Plot of predicted pIC₅₀ versus their experimental values.

Figure 4

William plot for standardized residual versus leverage.

Figure 5

Schematic representation of the interactions between compound ZINC000113253375 and COX-2 active site.

Figure 6

Schematic representation of the interactions between Astragalin and COX-2active site.

Figure 7

RMSD values of protein backbone for COX-2 and COX-2 complex with compound 375 and 560 during 10 ns MD simulation.

Figure 8

Time evolution of the radius of gyration (Rg) for COX-2 and COX-2 complex with compound 375 and 560 during 10-ns of MD simulation.