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Cited by 565

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Polymeric nanoparticles for drug delivery to the central nervous system

Open access
Patel, Toral, Zhou, Jiangbing, Piepmeier, Joseph M., Saltzman, W. Mark·2012·Advanced Drug Delivery Reviews

Abstract

The central nervous system (CNS) poses a unique challenge for drug delivery. The blood-brain barrier significantly hinders the passage of systemically-delivered therapeutics and the brain extracellular matrix limits the distribution and longevity of locally-delivered agents. Polymeric nanoparticles represent a promising solution to these problems. Over the past 40 years, substantial research efforts have demonstrated that polymeric nanoparticles can be engineered for effective systemic and local delivery of therapeutics to the CNS. Moreover, many of the polymers used in nanoparticle fabrication are both biodegradable and biocompatible, thereby increasing the clinical utility of this strategy. Here, we review the major advances in the development of polymeric nanoparticles for drug delivery to the CNS.

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Crossref · 10.1016/j.addr.2011.12.006

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Metadata

Authors
Patel, T., Zhou, J., Piepmeier, J. M., & Saltzman, W. M.
Venue
Advanced Drug Delivery Reviews
Publisher
Elsevier BV
Published
2012-05
Volume
64
Issue
7
Pages
701-705
ISSN
0169-409X (ISSN-L)
OpenAlex
W1982471389
Cited by
565
References
61
FWCI
0.79
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Polymeric nanoparticles for drug delivery to the central nervous system. https://app.cochat.ai/papers/10.1016/j.addr.2011.12.006
The paper
Patel, T., Zhou, J., Piepmeier, J. M., & Saltzman, W. M. (2012). Polymeric nanoparticles for drug delivery to the central nervous system. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2011.12.006

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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.