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The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Hierarchical Targeting Strategy for Enhanced Tumor Tissue Accumulation/Retention and Cellular Internalization

Open access
Wang, Sheng, Huang, Peng, Chen, Xiaoyuan·2016·Advanced Materials

Abstract

Targeted delivery of therapeutic agents is an important way to improve the therapeutic index and reduce side effects. To design nanoparticles for targeted delivery, both enhanced tumor tissue accumulation/retention and enhanced cellular internalization should be considered simultaneously. So far, there have been very few nanoparticles with immutable structures that can achieve this goal efficiently. Hierarchical targeting, a novel targeting strategy based on stimuli responsiveness, shows good potential to enhance both tumor tissue accumulation/retention and cellular internalization. Here, the recent design and development of hierarchical targeting nanoplatforms, based on changeable particle sizes, switchable surface charges and activatable surface ligands, will be introduced. In general, the targeting moieties in these nanoplatforms are not activated during blood circulation for efficient tumor tissue accumulation, but re‐activated by certain internal or external stimuli in the tumor microenvironment for enhanced cellular internalization.

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Crossref · 10.1002/adma.201601498

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Metadata

Authors
Wang, S., Huang, P., & Chen, X.
Venue
Advanced Materials
Publisher
Wiley
Published
2016-06-03
Volume
28
Issue
34
Pages
7340-7364
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2413359062
Cited by
381
References
3
FWCI
20.17
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Hierarchical Targeting Strategy for Enhanced Tumor Tissue Accumulation/Retention and Cellular Internalization. https://app.cochat.ai/papers/10.1002/adma.201601498
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Wang, S., Huang, P., & Chen, X. (2016). Hierarchical Targeting Strategy for Enhanced Tumor Tissue Accumulation/Retention and Cellular Internalization. Advanced Materials. https://doi.org/10.1002/adma.201601498

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.