Sorafenib-Loaded Copper Peroxide Nanoparticles with Redox Balance Disrupting Capacity for Enhanced Chemodynamic Therapy against Tumor Cells.
Chunmei ChenYixin TanTing XuYihao SunSheng ZhaoYi OuyangYan ChenLiang HeXiaohong LiuHui LiuPublished in: Langmuir : the ACS journal of surfaces and colloids (2022)
Chemodynamic therapy (CDT) is a promising hydroxyl radical (•OH)-mediated tumor therapeutic method with desirable tumor specificity and minimal side effects. However, the efficiency of CDT is restricted by the pH condition, insufficient H 2 O 2 level, and overexpressed reductive glutathione (GSH), making it challenging to solve these problems simultaneously to improve the efficacy of CDT. Herein, a kind of polyvinylpyrrolidone-stabilized, sorafenib-loaded copper peroxide (CuO 2 -PVP-SRF) nanoparticle (NPs) was designed and developed for enhanced CDT against tumor cells through the synergetic pH-independent Fenton-like, H 2 O 2 self-supplying, and GSH depletion strategy. The prepared CuO 2 -PVP-SRF NPs can be uptaken by 4T1 cells to specifically release Cu 2+ , H 2 O 2 , and SRF under acidic conditions. The intracellular GSH can be depleted by SRF-induced system xc - dysfunction and Cu 2+ -participated redox reaction, causing the inactivation of GPX4 and generating Cu + . A great amount of •OH was produced in this reducing capacity-disrupted condition by the Cu + -mediated Fenton-like reaction, causing cell apoptosis and lipid hydroperoxide accumulation-induced ferroptosis. They display an excellent 4T1 cell killing outcome through the improved •OH production capacity. The CuO 2 -PVP-SRF NPs display elevated therapeutic efficiency of CDT and show good promise in further tumor treatment applications.
Keyphrases
- oxide nanoparticles
- drug delivery
- high glucose
- diabetic rats
- aqueous solution
- mental health
- cell death
- metal organic framework
- wastewater treatment
- hydrogen peroxide
- oxidative stress
- cancer therapy
- cell proliferation
- drug induced
- nitric oxide
- wound healing
- machine learning
- artificial intelligence
- stress induced
- big data
- bone marrow
- deep learning