Self-Assembled Supramolecular Ribbon-Like Structures Complexed to Single Walled Carbon Nanotubes as Possible Anticancer Drug Delivery Systems.
Anna JagusiakKatarzyna ChłopaśGrzegorz ZemanekMalgorzata Jemiola-RzeminskaBarbara PiekarskaBarbara StopaTomasz PańczykPublished in: International journal of molecular sciences (2019)
Designing an effective targeted anticancer drug delivery method is still a big challenge, since chemotherapeutics often cause a variety of undesirable side effects affecting normal tissues. This work presents the research on a novel system consisting of single walled carbon nanotubes (SWNT), dispersed with Congo Red (CR), a compound that forms self-assembled ribbon-like structures (SRLS) and anticancer drug doxorubicin (DOX). SWNT provide a large surface for binding of planar aromatic compounds, including drugs, while CR supramolecular ribbon-like assemblies can be intercalated by drugs, like anthracycline rings containing DOX. The mechanism of interactions in SWNT-CR-DOX triple system was proposed based on electrophoretic, spectral, Dynamic Light Scattering and scanning electron microscopy analyzes. The profile of drug release from the investigated system was evaluated using dialysis and Differential Scanning Calorimetry. The results indicate that ribbon-like supramolecular structures of CR bind to SWNT surface forming SWNT-CR complexes which finally bind DOX. The high amount of nanotube-bound CR greatly increases the capacity of the carrier for the drug. The high capacity for drug binding and possible control of its release (through pH changes) in the analyzed system may result in prolonged and localized drug action. The proposed SWNT-CR-DOX triple system meets the basic criteria that justifies its further research as a potential drug carrier.
Keyphrases
- drug delivery
- electron microscopy
- walled carbon nanotubes
- high resolution
- adverse drug
- gene expression
- magnetic resonance imaging
- chronic kidney disease
- emergency department
- risk assessment
- computed tomography
- machine learning
- magnetic resonance
- quantum dots
- transcription factor
- binding protein
- artificial intelligence
- end stage renal disease