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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Tailor-Made Dual pH-Sensitive Polymer–Doxorubicin Nanoparticles for Efficient Anticancer Drug Delivery

Du, Jin-Zhi, Du, Xiao-Jiao, Mao, Cheng-Qiong, Wang, Jun·2011·Journal of the American Chemical Society

Abstract

Efficient delivery of therapeutics into tumor cells to increase the intracellular drug concentration is a major challenge for cancer therapy due to drug resistance and inefficient cellular uptake. Herein, we have designed a tailor-made dual pH-sensitive polymer-drug conjugate nanoparticulate system to overcome the challenges. The nanoparticle is capable of reversing its surface charge from negative to positive at tumor extracellular pH (∼6.8) to facilitate cell internalization. Subsequently, the significantly increased acidity in subcellular compartments such as the endosome (∼5.0) further promotes doxorubicin release from the endocytosed drug carriers. This dual pH-sensitive nanoparticle has showed enhanced cytotoxicity in drug-resistant cancer stem cells, indicating its great potential for cancer therapy.

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Metadata

Authors
Du, J.-Z., Du, X.-J., Mao, C.-Q., & Wang, J.
Venue
Journal of the American Chemical Society
Published
2011-10-17
Volume
133
Issue
44
Pages
17560-17563
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2054045589
Cited by
1,172
References
4
FWCI
28.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Tailor-Made Dual pH-Sensitive Polymer–Doxorubicin Nanoparticles for Efficient Anticancer Drug Delivery. https://app.cochat.ai/papers/10.1021/ja207150n
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Du, J.-Z., Du, X.-J., Mao, C.-Q., & Wang, J. (2011). Tailor-Made Dual pH-Sensitive Polymer–Doxorubicin Nanoparticles for Efficient Anticancer Drug Delivery. Journal of the American Chemical Society. https://doi.org/10.1021/ja207150n

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.