Characterization of Betulinic Acid-Multiwalled Carbon Nanotubes Modified with Hydrophilic Biopolymer for Improved Biocompatibility on NIH/3T3 Cell Line.
Julia Meihua TanSaifullah BulloSharida FakuraziMohd Zobir HusseinPublished in: Polymers (2021)
The biocompatibility of carbon nanotubes (CNT) is fairly a challenging task for their applications in nanomedicine. Therefore, the objective of this research was to formulate four types of highly biocompatible betulinic acid-loaded biopolymer nanocomposites, namely chitosan-multiwalled carbon nanotubes (MWBA-CS), polyethylene glycol-multiwalled carbon nanotubes (MWBA-PG), Tween 20-multiwalled carbon nanotubes (MWBA-T2) and Tween 80-multiwalled carbon nanotubes (MWBA-T8). The physico-chemical properties of the modified nanocomposites were determined by Fourier transform infrared spectroscopy (FTIR), thermal analysis (TGA) and Raman spectroscopy, while the surface morphology of the resulting nanocomposites was studied using field emission scanning electron microscopy (FESEM). All data revealed that the external surface of MWBA nanocomposites was successfully coated with the respective polymer molecules through hydrophobic and electrostatic interactions with improved thermal profiles. The cell viability assay, which was performed on cultured normal embryonic mouse fibroblast cells, confirmed their excellent biocompatibility in phosphate-buffered saline aqueous media. Overall, our findings herein suggest that the synthesized biopolymer-coated MWBA nanocomposites are promising nanomaterials for drug delivery applications as they enhance the solubility and dispersibility of CNT with significantly reduced cytotoxic effect, especially in normal cells.
Keyphrases
- carbon nanotubes
- drug delivery
- induced apoptosis
- electron microscopy
- raman spectroscopy
- cell cycle arrest
- cancer therapy
- ionic liquid
- wound healing
- gold nanoparticles
- endoplasmic reticulum stress
- high throughput
- signaling pathway
- molecular dynamics simulations
- liquid chromatography
- hyaluronic acid
- big data
- endothelial cells
- solid state
- deep learning