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An overview of active and passive targeting strategies to improve the nanocarriers efficiency to tumour sites
Abstract
Objectives: This review highlights both the physicochemical characteristics of the nanocarriers (NCs) and the physiological features of tumour microenvironment (TME) to outline what strategies undertaken to deliver the molecules of interest specifically to certain lesions. This review discusses these properties describing the convenient choice between passive and active targeting mechanisms with details, illustrated with examples of targeting agents up to preclinical research or clinical advances. Key findings: Targeted delivery approaches for anticancers have shown a steep rise over the past few decades. Though many successful preclinical trials, only few passive targeted nanocarriers are approved for clinical use and none of the active targeted nanoparticles. Herein, we review the principles and for both processes and the correlation with the tumour microenvironment. We also focus on the limitation and advantages of each systems regarding laboratory and industrial scale. Summary: The current literature discusses how the NCs and the enhanced permeation and retention effect impact the passive targeting. Whereas the active targeting relies on the ligand-receptor binding, which improves selective accumulation to targeted sites and thus discriminates between the diseased and healthy tissues. The latter could be achieved by targeting the endothelial cells, tumour cells, the acidic environment of cancers and nucleus.
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Metadata
- Authors
- Attia, M. F., Anton, N., Wallyn, J., Omran, Z., & Vandamme, T. F.
- Venue
- Journal of Pharmacy and Pharmacology
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-01
- Volume
- 71
- Issue
- 8
- Pages
- 1185-1198
- ISSN
- 0022-3573 (ISSN-L)
- 2042-7158
- OpenAlex
- W2943106036
- Cited by
- 1,085
- References
- 119
- FWCI
- 24.01
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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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.
