journal article·2018-10

Optimizing the alginate coating layer of doxorubicin-loaded iron oxide nanoparticles for cancer hyperthermia and chemotherapy

Thi Thu Huong Le, Thuc Quang Bui, Thi Minh Thi Ha, Mai Huong Le, Hong Nam Pham, Phuong Thu Ha

Journal of Materials Science· Volume 53· Issue 19
Publisher page

Abstract

Chemotherapy in cancer treatment usually leads to serious side effects on patients due to the unselectiveness and high toxicity on normal cells of cancer drugs. Loading cancer drugs into nano-platforms could be an alternative approach to effectively deliver drugs to tumors and reduce toxic exposure on healthy cells. In this work, we synthesized drug delivery nano-systems based on Fe3O4 nanoparticles (obtained from co-precipitation reaction) which could provide targeting of drugs to the tumor sites by an external magnetic field. Also, the magnetic nanoparticles (MNPs) could generate heat to kill cancer cells at a certain temperature range. The systems were designed for loading anticancer agent doxorubicin by using alginate-coated iron oxide MNPs. It was found that the loading was achieved by complex formation of doxorubicin and the alginate layer. Various concentrations of alginate solutions produced different sizes as well as drug loading capacities of the nanoparticles. The highest loading content of 18.96% achieved at the alginate concentration of 4 mg ml−1, corresponding to the mass ratio of alginate to Fe3O4 of around 1:2. The magnetic properties, especially the inductive heating effect of the nanoparticles, along with the impact of the systems on tumor cells were investigated. The results proved that the nanoparticles can serve as a good drug delivery system, in terms of both effective hyperthermia and chemotherapy.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Characterization and Applications of Magnetic Nanoparticles · Electrohydrodynamics and Fluid Dynamics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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