Unveiling the antineoplastic potential of Rezafungin: An integrated computational framework for metronomic repurposing

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Cancer drug development faces major challenges, including high costs, long timelines, and multidrug resistance, creating a need for repurposing strategies. Here, we explored rezafungin (RF), a long-acting echinocandin antifungal, as a hypothesis-generating anticancer candidate using an integrated computational framework combining pharmacokinetic assessment, target prediction, kinome profiling, transcriptomic modeling, docking, molecular dynamics, and machine learning. The analyses suggest that RF may benefit from albumin-associated distribution and P-glycoprotein-mediated transport, while its long half-life and chemical stability may support metronomic dosing. SuperPred predicted TDP1 as the highest-probability target, with additional predicted interactions involving NF-κB, Cathepsin D, and HIF-1α. Kinome profiling indicated high selectivity and predicted activity against hematological cancer-associated and drug-resistant kinase variants. Transcriptomic analysis suggested coordinated suppression of DNA repair and metabolic regulators, consistent with a synthetic lethality hypothesis, whereas ferroptosis remained exploratory due to limited pathway support. Docking, binding-site analysis, molecular dynamics, and MM-PBSA provided structural support for stable interactions with TDP1 and HIF-1α, with known inhibitor controls strengthening the interpretation. Benchmarking against anidulafungin showed that each module of the pipeline produced coherent outputs for a related echinocandin scaffold. Overall, RF emerges as a mechanistically grounded, hypothesis-generating repurposing candidate for further experimental validation in cancer models.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.