The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.
Stimuli-responsive polymeric micelles for drug delivery and cancer therapy
Open accessAbstract
Polymeric micelles (PMs) have been widely investigated as nanocarriers for drug delivery and cancer treatments due to their excellent physicochemical properties, drug loading and release capacities, facile preparation methods, biocompatibility, and tumor targetability. They can be easily engineered with various functional moieties to further improve their performance in terms of bioavailability, circulation time, tumor specificity, and anticancer activity. The stimuli-sensitive PMs capable of responding to various extra- and intracellular biological stimuli (eg, acidic pH, altered redox potential, and upregulated enzyme), as well as external artificial stimuli (eg, magnetic field, light, temperature, and ultrasound), are considered as "smart" nanocarriers for delivery of anticancer drugs and/or imaging agents for various therapeutic and diagnostic applications. In this article, the recent advances in the development of stimuli-responsive PMs for drug delivery, imaging, and cancer therapy are reviewed. The article covers the generalities of stimuli-responsive PMs with a focus on their major delivery strategies and newly emerging technologies/nanomaterials, discusses their drawbacks and limitations, and provides their future perspectives.
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Metadata
- Authors
- Zhou, Q., Zhang, L., Yang, T., & Wu, H.
- Venue
- International Journal of Nanomedicine
- Publisher
- Informa UK Limited
- Published
- 2018-05
- Volume
- Volume 13
- Pages
- 2921-2942
- ISSN
- 1176-9114 (ISSN-L)
- 1178-2013
- OpenAlex
- W2803246957
- Cited by
- 408
- References
- 151
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- gold
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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.