journal article·2021-12

Sustainable amorphous Fenton nanosystem for visualization-guided synergistic tumor elimination

Jingjing Wang, Wei Zhang, Shi-Yu Lu, Wei Tang, Jie Hu, Wenting Yang, Qiuye Zhang, Xingyue Wang, Jun Deng, Yang Cao, Hui Liu

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Abstract

Abstract Chemodynamic therapy (CDT) shows great potential in tumor treatment, in which tumor cells are killed by hydroxyl radicals (·OH) generated by Fenton-catalysis-mediated hydrogen peroxide (H2O2) disproportionation. Fabricating a high-performance tumor microenvironment (TME)-responsive Fenton nanocatalyst and realizing sustainable ·OH production are essential for effective tumor elimination but still challenging. Herein, we reported a sustainable amorphous Fenton nanosystem (SAFN) for visualization-guided synergistic tumor therapy, highlighting the superiority of cascade catalysis and the sustainability of the Fenton nanosystem. The SAFN was fabricated by rational integration of a polydopamine (PDA) core, an amorphous ferric phosphate (FePi) payload, glucose oxidase (GOx) decoration, and a bull serum albumin coating. In an acidic TME, Fe(III) ions were released on demand from FePi and reduced to high-activity Fe(II) by PDA. The reducing capacity of PDA was stronger under acidic conditions, due to C=O exposure. Fe(II) and H2O2 are sustainably supplied by the PDA-reducing and GOx catalytic chemistry, achieving long-lasting ·OH production for enhanced CDT (ECDT). Localized hyperthermia can be generated by PDA under near-infrared laser irradiation for low-temperature photothermal therapy (LT-PTT) in a GOx-induced energy-shortage TME. In addition, a remarkable antitumor effect is achieved by this sustainable nanosystem through the synergistic effect of the starvation/LT-PTT/ECDT strategy.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoplatforms for cancer theranostics · Nanoparticle-Based Drug Delivery · Advanced Nanomaterials in Catalysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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