Computational Identification of FDA-Approved Drugs as STAT3 Dimerization Inhibitors for Therapeutic Applications

초록

The dysregulation of STAT3 signaling is implicated in the progression of numerous diseases, including cancer and autoimmune disorders, making it a critical target for therapeutic intervention. Given its multifaceted involvement in multiple disease pathways, targeting STAT3 presents a broad therapeutic opportunity. This study employs an integrative computational approach combining pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations, and free energy calculation to identify and evaluate potential STAT3 dimerization inhibitors. FDA approved drug library was used as it offers significant advantages, including established safety profiles, known pharmacokinetics, and faster clinical translation. The library was screened using a receptor-ligand interaction pharmacophore model, followed by molecular docking and MD simulations to assess stability and interaction persistence within the STAT3 dimerization site. gmxMMPBSA analysis further validated the binding affinities of top-ranked compounds, providing detailed insights into their energetic profiles. Among the compounds, Tavalisse, ADP, and Vipivotide tetraxetan emerged as the most promising candidates, demonstrating exceptional binding stability, favorable interaction energies, and significant pharmacophore match scores. Additionally, the confirmatory triplicate runs of these compounds further emphasize the stable binding against the SH2 domain of STAT3. Moreover, this study also highlights the critical interaction profile of the screened compounds, forming hydrogen bonds, hydrophobic interactions, and maintaining ligand-receptor complexes. These findings offer a robust framework for the development of selective and potent STAT3 dimerization inhibitors, paving the way for therapeutic advancements against STAT3-mediated disorders.

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