A series of novel 1,5-diaryl pyrazole derivatives targeting the COX enzyme were designed by combined ligand and structure-based approach. The designed molecules were then further subjected to ADMET and molecular docking studies. Out of 34 designed compounds, the top-10 molecules from the computation studies were synthesized, characterized, and evaluated for COX-2 inhibition and anti-cancer activity. Initially, the target compounds were screened for the protein denaturation assay. The results of the top-five molecules T2, T3, T5, T6, and T9 were further subjected to in vitro COX-2 enzymatic assay and anti-cancer activity. As far as COX-2 inhibitory activity is considered, two compounds, T3 and T5, exhibited the half maximum inhibitory concentration (IC₅₀) at 0.781 µM and 0.781 µM respectively. Further, the two compounds T3 and T5, when evaluated for COX-1 inhibition, exhibited excellent inhibitory activity with T3 IC₅₀ of 4.655μM and T5 with IC₅₀ of 5.596 μM. The compound T5 showed more significant human COX-2 inhibition, with a selectivity index of 7.16, when compared with T3, which had a selectivity index of 5.96. Further, in vitro anti-cancer activity was screened against two cancer cell lines in which compounds T2 and T3 were active against A549 cell lines and T6 was active against the HepG2 cell line. Stronger binding energy was found by comparing MM-PBSA simulations with molecular docking, which suggests that compounds T3 and T5 have a better possibility of being effective compounds, in which T5 showed higher binding affinity. The results suggest that these compounds have the potential to develop effective COX-2 inhibitors as anti-cancer agents.

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.

Background: Breast cancer accounts for 6.9% of cancer-related fatalities and 11.7% of new cases globally, making it the most often diagnosed cancer. Primarily seen in breast cancer is hyperactivation of the AKT/PI3K/mTOR pathway. Key signalling protein in this mechanism, AKT is essential for cell survival, proliferation, therapeutic resistance, and cancer progression. Aim: This study aims to identify potent phytochemical derivatives from the ZINC Natural derivatives database through virtual screening using known standard drugs. It integrates the pharmacophore features of selective compounds to screen a natural compound library, identifying potential anticancer agents. Methods: A ligand-based pharmacophore model was initially developed and validated by using known potent phytoconstituents such as Berberine, Lycorine, Noscapine, and Haemanthidine (IC50 < 20 μM). A validated model having four key features was used to screen the ZINC Natural derivatives database and identified 612 hits with essential AKT inhibitory features and further subjected to molecular docking studies. ADMET analysis and pharmacokinetic properties were assessed for the top lead compounds. Results: Molecular docking studies found ZINC 20412215 was the most potent AKT inhibitor, with a docking score of -8.36 kcal/mol, than the standard drug Capivasertib (-8.07 kcal/mol). It exhibits better aqueous solubility, high gastrointestinal absorption, and a lower likelihood of CYP2D6-mediated metabolic interference compared to Capivasertib. It is non-hepatotoxic and cytotoxic, with potential metabolic interactions of the molecules. Conclusion: These findings revealed that the compound ZINC 20412215 is a potential lead for AKT-targeted breast cancer therapy. Further experimental validation is required to advance its development as an effective anticancer agent. Major Findings: The study identified novel AKT inhibitors from the ZINC natural compound library using pharmacophore modeling, docking, and ADMET analysis. Among them, ZINC 20412215 emerged as the most promising lead, combining strong binding affinity with favorable pharmacokinetic and toxicity profiles.