Systems pharmacology, structural modeling, and experimental validation reveal retinoic acid as a modulator of...

Chronic Kidney Disease (CKD) lacks effective disease-modifying therapies. Retinoic acid (RA) exhibits immunomodulatory and antifibrotic properties; however, its molecular targets and structural mechanisms in CKD remain incompletely understood. We applied an integrative strategy combining network pharmacology, structure-based modeling, and in vivo validation. CKD-associated genes were collected from CTD, GeneCards, MalaCards, and TTD, while RA targets were predicted or curated from PharmMapper, SEA, CTD, and STITCH, followed by UniProt harmonization. Kidney-specific expression was ensured using Human Protein Atlas data (nTPM > 1), and disease-relevant targets were prioritized based on upregulation in GSE142025. Functional enrichment analyses were conducted using GO, KEGG, Reactome, and MSigDB Hallmark datasets (FDR q < 0.05). Protein-protein interaction networks were constructed via STRING and analyzed with CytoHubba to identify hub genes. Molecular docking, 100-ns GROMACS molecular dynamics simulations, and MM-GBSA binding free-energy analysis assessed the predicted stability of the RA-target complexes. Experimental validation was performed in a doxorubicin-induced CKD rat model using protein-level analysis. A total of 2,005 CKD genes and 507 RA targets were identified, with 252 overlapping genes, including 216 kidney-expressed targets. Transcriptomic prioritization yielded 86 upregulated targets enriched in immune-inflammatory signaling, apoptosis, and extracellular matrix remodeling. Network analysis identified five hub genes (AKT1, TP53, TNF, FN1, MMP9). Docking predicted moderate-to-strong binding affinities, particularly for MMP9 (- 9.8 kcal/mol). Molecular dynamics simulations indicated stable complexes, and MM-GBSA binding free energies ranked the hubs MMP9 > TNF > p53 > FN1 > AKT1 (- 26.4 to - 12.3 kcal/mol). In vivo, RA significantly modulated renal expression of all hub proteins. These findings identify RA as a context-dependent regulator of immune-fibrotic networks in CKD, with AKT1, TP53, TNF, FN1, and MMP9 as promising therapeutic targets.