journal article·2020-04

Composite phospholipid-coated hollow mesoporous silica nanoplatform with multi-stimuli responsiveness for combined chemo-photothermal therapy

Yifan Wu, Junya Lu, Yuling Mao, Tongying Jiang, Qinfu Zhao, Siling Wang

Journal of Materials Science· Volume 55· Issue 12
Publisher page

Abstract

Chemo-photothermal therapy can improve the therapeutic effects of simple chemotherapy with the help of cytotoxic heat generated by photothermal therapy. In this paper, a novel nanoplatform based on hollow mesoporous silica (HMSN) was constructed, which could achieve multi-stimuli-responsive release through chemo-photothermal interaction. Cypate as a NIR photothermal agent was conjugated to the outer surface of HMSN by disulfide bonds. Due to the insoluble feature, Cypate imparted lipophilicity to the surface of HMSN, thereby promoting the strong encapsulation of complex phospholipid layer by hydrophobic interaction. The phospholipid layer hindered the premature release of the model drug doxorubicin (DOX), prolonged the circulation time in vivo, and improved the biocompatibility and stability in physiological environment. Under glutathione, acidic conditions and NIR irradiation, the release of DOX was significantly accelerated, indicating that DOX/HMSN-CyL exhibited redox/pH/NIR multi-stimuli in response to drug release. Cytotoxicity experiments have proved that combination therapy induced the maximum levels of cell killing. Furthermore, combination index of DOX/HMSN-CyL was 0.26, demonstrating the chemo-photothermal synergetic therapy. Multicellular tumor spheroid experiments were also performed, and the same trend was showed. In summary, this novel nanoplatform has broad application prospects as a drug delivery system for combination therapy.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

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

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

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