Molecular insights into volatile compounds from cocoa (Theobroma cacao L.) as potential multi-target therapeutic candidates for insulin resistance

Abstract

Volatile compounds from cocoa (Theobroma cacao L.) represent a relatively unexplored source of potential multi-target agents for insulin resistance. This study aimed to evaluate volatile compounds from various cocoa tissues as potential multi-target interventions in insulin resistance using an integrative in silico pharmacology approach. The study involved screening compounds based on drug-likeness, predicted bioactivity, toxicity, and membrane permeability, followed by network pharmacology, gene ontology and functional annotation, molecular docking, and molecular dynamics simulations. In this study, three key target proteins related to glucose regulation, lipid metabolism, and insulin signaling were used. From an initial set of 87 volatile compounds in cocoa leaves, pods, and seeds, 48 unique structures were curated for in silico evaluation. Stepwise screening identified 12 compounds meeting drug-likeness criteria, and four compounds were ultimately selected as the most promising candidates for further structural analysis: methyl decanoate, methyl octanoate, methyl 10-methylundecanoate, and lauric acid. Molecular docking analysis revealed target-dependent ligand-protein interactions, with methyl 10-methylundecanoate showing the most favorable predicted affinity for PTP1B and PPARA, with binding energies of -5.72 and -4.68 kcal/mol, respectively, while lauric acid showed the strongest affinity for HSD11B1, with a binding energy of -4.70 kcal/mol. Further molecular dynamics simulations demonstrated that the predicted protein-ligand complexes maintained structural stability. Overall, these findings suggest that cocoa-derived volatile compounds may serve as promising lead candidates for multi-target interventions in insulin resistance. However, further in vitro, in vivo, and target-specific validation studies are needed to confirm the biological relevance of these computational findings.