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Cited by 564

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Nanoparticle-assisted Combination Therapies for Effective Cancer Treatment

Hu, Che-Ming Jack, Aryal, Santosh, Zhang, Liangfang·2010·Therapeutic Delivery

Abstract

Combination chemotherapy and nanoparticle drug delivery are two areas that have shown significant promise in cancer treatment. Combined therapy of two or more drugs promotes synergism among the different drugs against cancer cells and suppresses drug resistance through distinct mechanisms of action. Nanoparticle drug delivery, on the other hand, enhances therapeutic effectiveness and reduces side effects of the drug payloads by improving their pharmacokinetics. These two active research fields have been recently merged to further improve the efficacy of cancer therapeutics. This review article summarizes the recent efforts in developing nanoparticle platforms to concurrently deliver multiple types of drugs for combination chemotherapy. We also highlight the challenges and design specifications that need to be considered in optimizing nanoparticle-based combination chemotherapy.

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Metadata

Authors
Hu, C.-M. J., Aryal, S., & Zhang, L.
Venue
Therapeutic Delivery
Published
2010-08
Volume
1
Issue
2
Pages
323-334
ISSN
  • 2041-5990 (ISSN-L)
  • 2041-6008
OpenAlex
W2005433498
Cited by
564
References
3
FWCI
3.91
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nanoparticle-assisted Combination Therapies for Effective Cancer Treatment. https://app.cochat.ai/papers/10.4155/tde.10.13
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Hu, C.-M. J., Aryal, S., & Zhang, L. (2010). Nanoparticle-assisted Combination Therapies for Effective Cancer Treatment. Therapeutic Delivery. https://doi.org/10.4155/tde.10.13

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.