Smart pH-sensitive micelles based on redox degradable polymers as DOX/GNPs carriers for controlled drug release and CT imaging

摘要

An amphiphilic copolymer poly(epsilon-caprolactone)-ss-poly(2-(dimethylamino) ethyl methacrylate), PCL-SS-PDMAEMA, was designed and synthesized using ROP and ARGET ATRP methods. Dual stimulus responsive micelles were prepared by the self-assembly of PCL-SS-PDMAEMA. PDMAEMA could respond to acid conditions with protonation, followed by enhanced hydrophilicity and swelling of the micellar shell. In addition, the cleavable joint disulfide bond between the core and shell was disrupted when exposed to an abundance of the reductant reductive glutathione GSH, leading to the disassembly of the micellar structure. The smart response behavior can be used for intracellular controlled drug release in tumor cells. In terms of "theranostics" with higher therapy effect, the tool for tumor imaging and diagnose through computed tomography (CT) was considered with the loading of gold nanoparticles (GNPs). GNPs with good distribution were prepared by means of in situ reduction by PDMAEMA block and stabilized by the micelles. Polymeric micelles were used to load the anticancer drug doxorubicin (DOX) in the hydrophobic core and GNPs in the hydrophilic PDMAEMA shell. Subsequently, the micellar theranostics platform combining chemotherapy and CT diagnose was obtained. The pH- or redox-triggered drug release profiles suggesting that the DOX/GNPs-loaded micelles facilitated controlled release in response to different simulated microenvironments. Cellular uptake study was carried out, indicating that the micelles could be fast internalized within several hours. WITT assay showing significant inhibition against HepG2 and MCF-7 cells for the DOX/GNPs-loaded micelles. Finally, the in vitro CT imaging assay indicated the good CT diagnosis potential of DOX/GNPs-loaded micelles. The micelle simultaneously loaded with DOX and GNPs represent a promising theranostics platform for efficient cancer chemotherapy and diagnosis. (C) 2017 Elsevier B.V. All rights reserved.