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The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Mucus-penetrating nanoparticles for drug and gene delivery to mucosal tissues

Open access
Lai, Samuel K., Wang, Ying-Ying, Hanes, Justin·2009·Advanced Drug Delivery Reviews

Abstract

Mucus is a viscoelastic and adhesive gel that protects the lung airways, gastrointestinal (GI) tract, vagina, eye and other mucosal surfaces. Most foreign particulates, including conventional particle-based drug delivery systems, are efficiently trapped in human mucus layers by steric obstruction and/or adhesion. Trapped particles are typically removed from the mucosal tissue within seconds to a few hours depending on anatomical location, thereby strongly limiting the duration of sustained drug delivery locally. A number of debilitating diseases could be treated more effectively and with fewer side effects if drugs and genes could be more efficiently delivered to the underlying mucosal tissues in a controlled manner. This review first describes the tenacious mucus barrier properties that have precluded the efficient penetration of therapeutic particles. It then reviews the design and development of new mucus-penetrating particles that may avoid rapid mucus clearance mechanisms, and thereby provide targeted or sustained drug delivery for localized therapies in mucosal tissues.

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Registry

Crossref · 10.1016/j.addr.2008.11.002

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Metadata

Authors
Lai, S. K., Wang, Y.-Y., & Hanes, J.
Venue
Advanced Drug Delivery Reviews
Publisher
Elsevier BV
Published
2009-02
Volume
61
Issue
2
Pages
158-171
ISSN
0169-409X (ISSN-L)
OpenAlex
W1994294415
Cited by
1,815
References
8
FWCI
2.06
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Mucus-penetrating nanoparticles for drug and gene delivery to mucosal tissues. https://app.cochat.ai/papers/10.1016/j.addr.2008.11.002
The paper
Lai, S. K., Wang, Y.-Y., & Hanes, J. (2009). Mucus-penetrating nanoparticles for drug and gene delivery to mucosal tissues. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2008.11.002

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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.