Multi-Enzyme Activity of MIL-101 (Fe)-Derived Cascade Nano-Enzymes for Antitumor and Antimicrobial Therapy

The clinical application of oncology therapy is hampered by high glutathione concentrations, hypoxia, and inefficient activation of cell death mechanisms in cancer cells. In this study, Fe and Mo bimetallic sulfide nanomaterial (FeS2@MoS2) based on metal-organic framework structure is rationally prepared with peroxidase (POD)-, catalase (CAT)-, superoxide dismutase (SOD)-like activities and glutathione depletion ability, which can confer versatility for treating tumors and mending wounds. In the lesion area, FeS2@MoS2 with SOD-like activity can facilitate the transformation of superoxide anions (O2-) to hydrogen peroxide (H2O2), and then the resulting H2O2 serves as a substrate for the Fenton reaction with FMS to produce highly toxic hydroxyl radicals (center dot OH). Simultaneously, FeS2@MoS2 has an ability to deplete glutathione (GSH) and catalyze the decomposition of nicotinamide adenine dinucleotide phosphate (NADPH) to curb the regeneration of GSH from the source. Thus it can realize effective tumor elimination through synergistic apoptosis-ferroptosis strategy. Based on the alteration of the H2O2 system, free radical production, glutathione depletion and the alleviation of hypoxia in the tumor microenvironment, FeS2@MoS2 NPS can not only significantly inhibit tumors in vivo and in vitro, but also inhibit multidrug-resistant bacteria and hasten wound healing. It may open the door to the development of cascade nanoplatforms for effective tumor treatment and overcoming wound infection. The bimetallic sulfide nano-materials (FeS2@MoS2) based on a metal-organic framework structure are prepared by calcination method. FeS2@MoS2 is multifunctional for tumor therapy and bacterial wound repair. It can eliminate tumor effectively through a synergistic apoptosis-ferroxide apoptosis strategy, and bind to amino groups on the surface of drug-resistant bacteria to effectively inhibit bacteria and accelerate wound healing.image