The rising global burden of inflammatory disorders highlights the urgent need for novel anti-inflammatory agents with improved efficacy and reduced adverse effects. Thiazolidin-2,4-dione (2,4-TZD), a five-membered heterocyclic scaffold, is recognized for its diverse biological activities, particularly its ability to regulate key inflammatory mediators. Structural hybridization of 2,4-TZD with dithiocarbamate (DTC) moieties represents a promising strategy to enhance anti-inflammatory potential through synergistic molecular interactions. In this study, a series of twenty-seven novel 2,4-thiazolidinedione–dithiocarbamate hybrid derivatives were rationally designed using computational approaches. Molecular docking studies were performed to predict binding affinity, and interaction patterns of the designed compounds against pro-inflammatory targets cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2). In addition, in silico evaluation of physicochemical properties, pharmacokinetic behavior, and toxicity profiles was carried out using established prediction tools. Drug-likeness was assessed by applying Lipinski’s Rule of Five and related criteria to ensure favorable oral bioavailability. Docking analysis revealed that several designed hybrids exhibited strong binding affinity and favorable interaction profiles with COX-1 and COX-2 enzymes. Based on binding affinity and poses of ligand–protein interactions, eleven compounds emerged as promising candidates. Among these, top six derivatives (R11, R12, R14, R15, R19, and R21) demonstrated superior binding affinities against COX enzyme. ADME (Absorption, distribution, metabolism and excretion) and toxicity predictions indicated acceptable ADME characteristics, along with a little or no risk of toxicity, supporting their suitability as lead-like molecules. The present computational investigation identifies novel 2,4-thiazolidinedione–dithiocarbamate hybrids with promising anti-inflammatory potential. The integrated docking and in silico pharmacokinetic evaluation provides a rational framework for lead identification and optimization. The selected top-ranked derivatives represent valuable candidates for future chemical synthesis and experimental validation, contributing to the development of safer and more effective TZD-based anti-inflammatory agents.
Sharma et al. (Fri,) studied this question.