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Cited by 192

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

Peptide Targeted Lipid Nanoparticles for Anticancer Drug Delivery

Pearce, Timothy R., Shroff, Kamlesh, Kokkoli, Efrosini·2012·Advanced Materials

Abstract

Encapsulating anticancer drugs in nanoparticles has proven to be an effective mechanism to alter the pharmacokinetic and pharmacodynamic profiles of the drugs, leading to clinically useful cancer therapeutics like Doxil and DaunoXome. Underdeveloped tumor vasculature and lymphatics allow these first‐generation nanoparticles to passively accumulate within the tumor, but work to create the next‐generation nanoparticles that actively participate in the tumor targeting process is underway. Lipid nanoparticles functionalized with targeting peptides are among the most often studied. The goal of this article is to review the recently published literature of targeted nanoparticles to highlight successful designs that improved in vivo tumor therapy, and to discuss the current challenges of designing these nanoparticles for effective in vivo performance.

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Registry

Crossref · 10.1002/adma.201200832

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Metadata

Authors
Pearce, T. R., Shroff, K., & Kokkoli, E.
Venue
Advanced Materials
Publisher
Wiley
Published
2012-06-05
Volume
24
Issue
28
Pages
3803-3822
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2111436238
Cited by
192
References
7
FWCI
8.42
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Peptide Targeted Lipid Nanoparticles for Anticancer Drug Delivery. https://app.cochat.ai/papers/10.1002/adma.201200832
The paper
Pearce, T. R., Shroff, K., & Kokkoli, E. (2012). Peptide Targeted Lipid Nanoparticles for Anticancer Drug Delivery. Advanced Materials. https://doi.org/10.1002/adma.201200832

Data sources

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.