Activated Carbon-Enriched Electrospun-Produced Scaffolds for Drug Delivery/Release in Biological Systems.
Zhanna K NazarkinaAlena O StepanovaBoris P ChelobanovRen I KvonPavel A SimonovAndrey A KarpenkoPavel P LaktionovPublished in: International journal of molecular sciences (2023)
To vectorize drug delivery from electrospun-produced scaffolds, we introduce a thin outer drug retention layer produced by electrospinning from activated carbon nanoparticles (ACNs)-enriched polycaprolacton (PCL) suspension. Homogeneous or coaxial fibers filled with ACNs were produced by electrospinning from different PCL-based suspensions. Stable ACN suspensions were selected by sorting through solvents, stabilizers and auxiliary components. The ACN-enriched scaffolds produced were characterized for fiber diameter, porosity, pore size and mechanical properties. The scaffold structure was analyzed by scanning electron microscopy and X-ray photoelectron spectroscopy. It was found that ACNs were mainly coated with a polymer layer for both homogeneous and coaxial fibers. Drug binding and release from the scaffolds were tested using tritium-labeled sirolimus. We showed that the kinetics of sirolimus binding/release by ACN-enriched scaffolds was determined by the fiber composition and differed from that obtained with a free ACN. ACN-enriched scaffolds with coaxial and homogeneous fibers had a biocompatibility close to scaffold-free AC, as was shown by the cultivation of human gingival fibroblasts and umbilical vein cells on scaffolds. The data obtained demonstrated that ACN-enriched scaffolds had good physico-chemical properties and biocompatibility and, thus, could be used as a retaining layer for vectored drug delivery.
Keyphrases
- tissue engineering
- drug delivery
- high resolution
- electron microscopy
- cancer therapy
- induced apoptosis
- machine learning
- magnetic resonance
- electronic health record
- computed tomography
- magnetic resonance imaging
- deep learning
- single molecule
- artificial intelligence
- transcription factor
- cell death
- cell proliferation
- big data
- cell cycle arrest
- ionic liquid
- dual energy
- pet imaging
- walled carbon nanotubes