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多刺激响应型纳米粒子的精准癌症治疗理性设计。

Rational Design of Multi-Stimuli-Responsive Nanoparticles for Precise Cancer Therapy.

机构信息

Suzhou Key Laboratory of Macromolecular Design and Precision Synthesis, Jiangsu Key Laboratory of Advanced Functional Polymer Design and Application, State and Local Joint Engineering Laboratory for Novel Functional Polymeric Materials, College of Chemistry, Chemical Engineering and Materials Science, ‡Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases, College of Pharmaceutical Sciences, and §School of Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, and School of Radiation Medicine and Protection, Soochow University , Suzhou 215123, China.

出版信息

ACS Nano. 2016 Jun 28;10(6):5947-58. doi: 10.1021/acsnano.6b01296. Epub 2016 Jun 10.

Abstract

Stimuli-responsive nanoparticles with target capacity are of great interest in drug delivery for cancer therapy. However, the challenge is to achieve highly smart release with precise spatiotemporal control for cancer therapy. Herein, we report the preparation and properties of multi-stimuli-responsive nanoparticles through the co-assembly of a 3-arm star quaterpolymer with a near-infrared (NIR) photothermal agent and chemotherapeutic compound. The nanoparticles can exhibit NIR light/pH/reduction-responsive drug release and intracellular drug translocation in cancer cells, which further integrate photoinduced hyperthermia for synergistic anticancer efficiency, thereby leading to tumor ablation without tumor regrowth. Thus, this rational design of nanoparticles with multiple responsiveness represents a versatile strategy to provide smart drug delivery paradigms for cancer therapy.

摘要

具有靶向能力的刺激响应性纳米粒子在癌症治疗的药物输送中具有重要意义。然而,挑战在于实现具有精确时空控制的高度智能释放,以用于癌症治疗。在此,我们通过 3 臂星形四聚体与近红外(NIR)光热剂和化疗药物的共组装,报告了多刺激响应性纳米粒子的制备和性质。这些纳米粒子可以在癌细胞中表现出近红外光/ pH/还原响应性药物释放和细胞内药物转运,进一步整合光诱导的高热治疗以实现协同抗癌效率,从而导致肿瘤消融而无肿瘤再生长。因此,这种具有多种响应性的纳米粒子的合理设计代表了一种用于癌症治疗的智能药物输送范例的通用策略。

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