Flux-coupled network pharmacology to identify AR–HIF1A–mTOR tri-axis as a mechanistic vulnerability in genitourinary (GU) cancers.

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Background: Advanced genitourinary cancers are driven by intertwined androgen receptor (AR) signaling, HIF-1α-mediated hypoxia adaptation, and extensive metabolic rewiring, with mTOR serving as a critical convergence node. These pathways exhibit profound bidirectional crosstalk; AR fuels glycolysis and lipogenesis, hypoxia modulates AR activity while enforcing Warburg metabolism, and sustained mTORC1 signaling amplifies anabolic programs and therapy resistance, yet their integrated regulatory and metabolic dynamics have not been systematically modeled at the genome scale. Methods: Metabolic fluxes were inferred using Recon3D-based FBA; AR/HIF-1α/mTOR regulatory circuitry was reconstructed via dynamic Bayesian inference and integrated with crosstalk dynamics using logic-based differential equations (LogicODEs). Candidate repurposing compounds were identified and ranked by integrated 3D pharmacophore screening, high-throughput molecular docking, and all-atom molecular dynamics refinement in explicit solvent. Results: Flux simulations showed GU tumors shift toward fatty acid oxidation + glutamine fueling to sustain ATP under hypoxia. HIF1A activation tightened feedback on glycolytic enzymes, while AR amplified lipid biosynthesis genes (SCD1, ACACA). Network logic modeling revealed AR–HIF1A–mTOR as a tightly interdependent tri-axis where disrupting one destabilizes others. Computational drug screening identified ursolic acid and baicalein as multi-axis disruptors binding AR-LBD, HIF1A-PAS, and mTOR active sites. MD predicted stable tri-target occupation. Conclusions: Mechanistic-computational modeling identifies the AR–HIF1A–mTOR tri-axis as a vulnerable system in GU cancers. Multi-target natural compounds can collapse this regulatory architecture.