Repurposing T‑614 for Gout: An In Silico Framework Integrating Network Pharmacology, Mendelian Randomization, and...

Background: Gout, an inflammatory arthritis driven by urate crystal deposition, still lacks therapies that are effective and well-tolerated. T-614 (iguratimod), an immunomodulatory drug approved for rheumatoid arthritis, has shown promise in Gout, but its molecular and immunogenetic mechanisms remain unclear. Methods: We assessed the druggability and pharmacokinetic properties of T-614 using SwissADME and ADMET Lab 2.0. Network pharmacology identified overlapping targets between T-614 and Gout and constructed a STRING-based protein-protein interaction network with GO/KEGG enrichment. A two-sample Mendelian randomization (MR) analysis of 731 immune-cell traits and Gout (FinnGen R9) identified causal immunophenotypes. Molecular docking, 100 ns molecular dynamics (MD) simulations, and MM/GBSA calculations were carried out for AKT1 (with docking support for TNF and IL1B). Public single-cell RNA sequencing (scRNA-seq) data of peripheral blood mononuclear cells were analyzed to profile cell-type-specific expression of core targets. Results: T-614 displayed favorable oral drug-likeness (QED = 0.641), high predicted plasma protein binding (PPB ≈ 94.22%), and limited blood-brain barrier penetration. AKT1, TNF, and IL1B were prioritized as putative hub targets by network-based analyses in PI3K/AKT, MAPK, and TNF signaling. Docking and representative MD/MM-GBSA analyses supported a thermodynamically favorable and dynamically stable binding mode AKT1/T-614 complex. MR identified 29 immune-cell traits genetically associated with Gout susceptibility, providing population-level causal inference and highlighting proinflammatory CD16⁺ monocytes and dysfunctional CD25⁺ Tregs, and scRNA-seq confirmed expression of AKT1, TNF, and IL1B in these Gout-relevant immune subsets. Conclusion: This integrative in silico framework suggests that T-614 could structurally engage multiple immunomodulator targets relevant to Gout and generates genetically and cell-type-informed hypotheses that require biochemical, cellular, and in vivo validation.