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Cited by 205

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Nanoparticles for Tumor Targeted Therapies and Their Pharmacokinetics

Wang, Jianqiu, Sui, Meihua, Fan, Weimin·2010·Current Drug Metabolism

Abstract

Various types of nanoparticles, such as liposomes, polymeric micelles, dendrimers, superparamagnetic iron oxide crystals, and colloidal gold, have been employed in targeted therapies for cancer. Both passive and active targeting strategies can be utilized for nano-drug delivery. Passive targeting is based on the enhanced permeability and retention (EPR) effect of the vasculature surrounding tumors. Active targeting relies on ligand-directed binding of nanoparticles to receptors expressed by tumor cells. Release of loaded drugs from nanoparticles may be controlled in response to changes in environmental condition such as temperature and pH. Biodistribution profiles and anticancer efficacy of nano-drugs in vivo would be different depending upon their size, surface charge, PEGylation and other biophysical properties. This review focuses on the recent development of nanoparticles for tumor targeted therapies, including physicochemical properties, tumor targeting, control of drug release, pharmacokinetics, anticancer efficacy and safety. Future perspectives are discussed as well.

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Metadata

Authors
Wang, J., Sui, M., & Fan, W.
Venue
Current Drug Metabolism
Published
2010-02-01
Volume
11
Issue
2
Pages
129-141
ISSN
1389-2002 (ISSN-L)
OpenAlex
W2128654928
Cited by
205
References
7
FWCI
6.47
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nanoparticles for Tumor Targeted Therapies and Their Pharmacokinetics. https://app.cochat.ai/papers/10.2174/138920010791110827
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Wang, J., Sui, M., & Fan, W. (2010). Nanoparticles for Tumor Targeted Therapies and Their Pharmacokinetics. Current Drug Metabolism. https://doi.org/10.2174/138920010791110827

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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.