Predicted Immunomodulatory and Anti-Inflammatory Potential of Ficus carica Phytochemicals Targeting NF-kB and TNF-α Signaling Pathways: An In-Silico Investigation
Received: 10 May 2026; Revised: 29 May 2026; Accepted: 25 June 2026; Published: 29 June 2026
Abstract
Chronic inflammation and immune dysregulation are key contributors to the development and progression of metabolic diseases, including type 2 diabetes mellitus (T2DM). Natural phytochemicals capable of modulating inflammatory signaling pathways have attracted increasing interest as potential multi-target therapeutic agents. This study investigated the predicted immunomodulatory and antidiabetic potential of major Ficus carica leaf phytochemicals using an integrated in silico approach combining molecular docking, physicochemical characterization, pharmacokinetic analysis, and toxicity prediction. Six bioactive compounds (quercetin, kaempferol, chlorogenic acid, caffeic acid, rutin, and gallic acid) were evaluated against four key inflammatory and metabolic targets: NF-κB p50, TNF-α, DPP-4, and α-glucosidase. Molecular docking demonstrated that quercetin and kaempferol exhibited the strongest binding affinities toward both inflammatory and metabolic targets. Quercetin showed the highest affinity for NF-κB p50 (−7.39 kcal/mol) and DPP-4 (−7.00 kcal/mol), forming stable complexes through hydrogen bonds, hydrophobic interactions, π–π stacking, and electrostatic contacts. Physicochemical and pharmacokinetic analyses indicated favorable drug-likeness and acceptable aqueous solubility for most compounds. Toxicity prediction suggested low acute toxicity (classes 4–5) but indicated potential cardiotoxicity, cytotoxicity, and immunotoxicity, emphasizing the need for cautious interpretation. Overall, the predicted interactions with NF-κB and TNF-α support the potential immunomodulatory properties of Ficus carica phytochemicals, particularly quercetin and kaempferol, as promising multi-target candidates for inflammation-associated metabolic disorders. However, these computational findings remain hypothesis-generating and require validation through in vitro and in vivo studies to confirm their biological activity, safety, and therapeutic potential.