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Published on: December 15, 2010
Ultrasound-responsive nanocarriers for cancer therapy: Physiochemical features-directed design
Feijuan Yuan1, Dan Peng1, Man Lu1
1Department of Ultrasound Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Institute, Hospital & Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China; Department of Ultrasound Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Abstract:
The design of ultrasound (US)-responsive nanocarriers (URNs) guided by their physicochemical characteristics represents a pivotal strategy for advancing cancer therapy. By exploiting the intrinsic physicochemical properties of diverse nanocarriers, including liposomes, polymers, nanobubbles (NBs)/nanodroplets (NDs), inorganic composites, and metal-organic frameworks (MOFs), URNs achieve precise spatiotemporal control over drug delivery, enhanced tumor penetration, and synergistic therapeutic outcomes under US stimulation. Leveraging US-induced mechanical and thermal effects, URNs exhibit expanding multimodal capabilities, encompassing gene delivery, chemo-sonodynamic therapy, immunomodulation, and gas-mediated reprogramming of the tumor microenvironment (TME). Despite significant preclinical progress, the clinical translation of URNs requires addressing key challenges: (1) inconsistent pharmacokinetics and long-term biosafety profiles; (2) insufficient targeting efficiency due to tumor heterogeneity; (3) a lack of standardized US parameter protocols to balance cavitation and thermal effects; and (4) challenges in scalable manufacturing and quality control. This review systematically evaluates the structure-property-function correlations in URN design, analyzes the physicochemical determinants of acoustic responsiveness alongside current limitations, and proposes a roadmap that integrates computational modeling, stimuli-responsive materials, and artificial intelligence (AI)-assisted parameter optimization to advance next-generation URNs, ultimately outlining future directions for personalized oncology and clinical translation.
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