Structure-based design of orthosteric and allosteric CCR2 inhibitors for potential IPF therapy

Idiopathic pulmonary fibrosis (IPF) is a fatal respiratory disease with an extremely poor prognosis. Our previous studies revealed that CCR2 (C-C chemokine receptor type 2) expression is significantly upregulated in bleomycin-induced pulmonary fibrosis in mice. Moreover, bioinformatics analysis indicated that higher CCR2 expression is associated with poorer prognosis in patients. Therefore, we proposed a novel dual-target intervention strategy focused on the orthosteric and allosteric binding sites of the CCR2 receptor. By integrating structure-based pharmacophore modeling, 3D-QSAR, common feature pharmacophore modeling, and large-scale virtual screening (covering 152 406 molecules), we successfully identified two candidate small molecules, compound 17 and compound 67, that exhibit high site selectivity. Molecular dynamics (MD) simulations, principal component analysis (PCA), and potential energy surface analyses <i>via</i> umbrella sampling confirmed that both compounds attain stable binding conformations at their respective target sites. MM/PBSA calculations revealed that compound 17 binds at the orthosteric site with a free energy of -30.91 kcal mol^-1, while compound 67 binds at the allosteric site with a free energy of -26.11 kcal mol^-1. Surface plasmon resonance (SPR) confirmed compound 17's direct binding to murine CCR2 (<i>K</i> _D = 3.46 μM), while co-administration with compound 67 synergistically enhanced binding affinity. Simultaneously, we analyzed the CCK8 results and found that both compounds 17, 67 and positive control nintedanib, exhibited a concentration-dependent increase in their inhibitory effects on pulmonary fibrosis. Furthermore, in a TGF-β-induced pulmonary fibrosis cell model, both compounds significantly reduced hydroxyproline and COL1A1 levels and upregulated ELN expression, with compound 17 exhibiting comparable antifibrotic efficacy to the positive control nintedanib. Collectively, our integrative computational-experimental approach reveals a therapeutic framework for precision-targeting CCR2-driven pathologies.