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Aptamer Nanomedicine for Cancer Therapeutics: Barriers and Potential for Translation
Abstract
Aptamer nanomedicine, including therapeutic aptamers and aptamer nanocomplexes, is beginning to fulfill its potential in both clinical trials and preclinical studies. Especially in oncology, aptamer nanomedicine may perform better than conventional or antibody-based chemotherapeutics due to specificity compared to the former and stability compared to the latter. Many proof-of-concept studies on applying aptamers to drug delivery, gene therapy, and cancer imaging have shown promising efficacy and impressive safety in vivo toward translation. Yet, there remains ample room for improvement and critical barriers to be addressed. In this review, we will first introduce the recent progress in clinical trials of aptamer nanomedicine, followed by a discussion of the barriers at the design and in vivo application stages. We will then highlight recent advances and engineering strategies proposed to tackle these barriers. Aptamer cancer nanomedicine has the potential to address one of the most important healthcare issues of the society.
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Metadata
- Authors
- Lao, Y.-H., Phua, K. K. L., & Leong, K. W.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-03-09
- Volume
- 9
- Issue
- 3
- Pages
- 2235-2254
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2046391187
- Cited by
- 259
- References
- 130
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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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.