Integrated molecular medicine approaches in inflammation: pharmacoinformatic modelling, virtual screening, 3D-QSAR and free energy landscape analysis of dronedarone derivatives for targeting NLRP3 inflammasome

Chronic inflammatory diseases are the most significant cause of death in the world. NLRP3 is an attractive target for the design of anti-inflammatory agents. In the present work, pharmacophore hypothesis, ligand-based virtual screening and 3D-QSAR studies were carried out for the reported anti-inflammatory dronedarone derivatives using Schrodinger 2022–4. A five-point pharmacophore hypothesis model was generated. The top pharmacophore hypothesis AAHHH_1 model consists of two-hydrogen bond acceptors and three hydrophobic groups. The hypothesis model AAHHH_1 underwent virtual screening using the molecules from the ChEMBL database. The screened molecules were further filtered using ADMET property and docking. Further, an atom-based 3D-QSAR analysis was carried out to determine the contribution of individual atoms to model development. The top QSAR model was chosen based on values of R2 (0.8852) and Q2 (0.7955). Based on contour plot analysis NCEs were designed and ADMET studies were carried out. The molecular docking studies were also conducted on top-screened molecules and top-designed molecules on target protein (PDB ID: 7ALV). The virtual screen compounds and NCEs (by 3D-QSAR) have shown similar docking interaction with amino acid residues as shown by standard dronedarone drug. Based on Molecular docking studies the MD simulation runs for 100 ns on docked compounds using Desmond revealed fairly stable interactions. Post MD simulation, MM-GBSA studies and PCA of molecular docking trajectories were also performed for these top four molecules, also showed good binding affinities and FEL respectively. Therefore, the findings in the present study can be helpful in the development of more potent NLRP3 inhibitors.