Self-Reinforced Cancer Targeting (SRCT) Depending on Reciprocally Enhancing Feedback between Targeting and Therapy

Jing-Jie Ye, Wuyang Yu, Bo-Ru Xie, Ke Li, Miao-Deng Liu, Xue Dong, Zhao-Xia Chen, Jun Feng, Xian-Zheng Zhang

ACS NANO(2022)

引用 3|浏览27
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摘要
Conventional cancer targeting methodology needs to be reformed to overcome the intrinsic barriers responsible for poor targeting efficiency. This study describes a concept of self-reinforced cancer targeting (SRCT) by correlating targeting with therapy in a reciprocally enhancing manner. SRCT is achieved on the basis of two prerequisites: (1) target molecules have to be expressed on cancer cell membranes but not on normal cells, and (2) notably, their expression on cancer cells must be actively upregulated in response to cellular attack by cancer treatments. As a proof-of-concept, a GRP78-targeting nanovehicle for chemotherapy was designed. Resultant data showed that chemotherapeutic drugs could effectively elevate GRP78 expression on the plasma membranes of cancer cells while having minimal influence on normal cells. DOX pretreatment of cancer cells and tumor tissues can greatly increase the targeting efficacy and therapeutic performance of the prepared GRP78-targeting nanomedicine while somewhat disfavoring the nontargeting counterpart. In vivo and in vitro results demonstrated that this GRP78-targeting nanomedicine could accurately target cancer cells to not only implement chemotherapy but also induce GRP78 upregulation on cancer cells, eventually benefiting continuous cancer-cell-targeted attack by the nanomedicines remaining in the blood circulation or administered in the next dose. The GRP78-targeting nanomedicine displays much better antitumor performance compared with the nontargeting counterpart. SRCT is expected to advance cancer-targeted therapy based on the positive dependency between targeting and therapeutic modalities.
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关键词
GRP78 protein,cancer-targeting chemotherapy,cancer cell surface receptor,self-reinforced cancer targeting,reciprocally enhancing feedback
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