Computational Evaluation of New 2-Aminothiazole Derivatives as Anti-Inflammatory Agents

Abstract

Inflammation is a biological process implicated in numerous chronic diseases, such as rheumatoid arthritis, psoriasis, and inflammatory bowel disease. Dysregulated inflammatory signaling is primarily mediated by Tumor Necrosis Factor-alpha (TNF-α), which is identified as a major contributor to the progression of these conditions. This present work aims to discover a potential anti-inflammatory agent using structural and ligand-based in silico approaches. A set of 144 substituted 2-aminothiazole derivatives was designed by substituting the R1 and R2 positions to optimize their anti-inflammatory potential. Using ligand-based pharmacophore modeling followed by 3D-QSAR, 55 active compounds were selected based on predicted IC50 values. Subsequent ADMET analysis confirmed that the drug-likeness properties of 30 compounds were substantial, and they were subjected to molecular docking against the TNF-α protein (PDB ID: 2AZ5) alongside the standard drug meloxicam. Docking frrrrrrrresults exhibited a strong binding score for several derivatives, among them compound-8b (2-((4-hydroxyphenyl)amino)-5-(7-methylbenzo[d][1,3]dioxol-5-yl)-5,6-dihydrobenzo[d]thiazol-7(4H)-one) emerged as the lead candidate with a significant binding score compared to the standard drug. Additionally, molecular dynamics (MD) simulation results for compound-8b demonstrated superior stability and pharmacokinetic profiles compared to the standard drug, highlighting its potential to efficiently block TNF-α-mediated inflammatory pathways and to evaluate its efficacy as an anti-inflammatory agent.