Synthesis, characterization, and evaluation of a magnetic molecular imprinted polymer for 5-fluorouracil as an intelligent drug delivery system for breast cancer treatment
Source abstract
Abstract
Magnetic carriers-based molecular imprinting has become one of the most interesting phenomena in the drug delivery field. In this study, we endeavor to devise an effective magnetic drug carrier via imprinting technology for 5-fluorouracil and investigate its anticancer activity toward breast cancer treatment. In this case, methacrylic acid and trimethylolpropane triacrylate were chosen for imprinting 5-fluorouracil on the magnetic fluorescent core. With this aim, fluorescein isothiocyanate was utilized as a fluorescent agent. FT-IR, EDX, XRD, VSM, FESEM, TEM, and BET analysis were used to characterize the synthesized carrier. According to adsorption studies, the adsorption of 5-fluorouracil was followed by Freundlich isotherm (QMAX: 121.6 mg/g) in a fast second-order kinetic model (6 min) in our proposed carrier. Afterward, based on the release studies the Higuchi dynamic model was suggested for leaching of 5-fluorouracil from the carrier. Eventually, the cytotoxicity effect of the projected carrier on MCF-10 and MCF-7 cell lines was investigated. In this case, the magnetic carrier has shown 61% cytotoxicity on cancer cells which were about 25 times greater than the pure drug, but it had no significant effect on normal cells. In all steps, the non-imprinted carrier was investigated as a comparison. This study has shown that our proposed magnetic nanocarrier can effectively be utilized in breast cancer treatment. The synthesis of magnetic molecular imprinted for 5-FU toward the breast cancer treatment eliminates the MDR of cancer cell toward the 5-FU.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Analytical chemistry methods development · Nanoparticle-Based Drug Delivery · Dendrimers and Hyperbranched Polymers
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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