Hyaluronic acid and carbon dots-gated hollow mesoporous silica for redox and enzyme-triggered targeted drug delivery and bioimaging

•Hollow mesoporous silica nanoparticles (HMSN) were used as a drug carrier.•Hyaluronic acid (HA) and PEI carbon dots (CDPEI) was grafted on HMSN via disulfide bonds.•The DOX-loaded HMSN-SS-CDPEI@HA had a high drug loading efficiency up to 30.5%.•DOX/HMSN-SS-CDPEI@HA showed redox/pH dual-responsive drug release.•DOX/HMSN-SS-CDPEI@HA showed a real-time imaging targeted drug delivery.

In this work, a redox and enzyme dual-stimuli responsive drug delivery system (DDS) with tracking function (HMSN-SS-CDPEI@HA) based on carbon dots capped hollow mesoporous silica nanoparticles (HMSN) has been developed for targeted drug delivery. The positively charged CDPEI nanoparticles prepared by polyethylenimine (PEI) were grafted on the pore openings of HMSN through disulfide bonds and were used as “gatekeepers” to trap the drugs within the hollow cavity. The hyaluronic acid (HA), a natural polysaccharide, was further grafted on the surface of HMSN to realize targeted drug delivery, controlled drug release and improved the stability. Doxorubicin (DOX) was chosen as a model drug due to its wide clinical application. In vitro drug release profiles demonstrated that DOX-loaded HMSN-SS-CDPEI@HA exhibited redox and enzyme dual-responsive drug release property. In addition, the prepared HMSN-SS-CDPEI@HA exhibited excellent fluorescent properties and biocompatibility. Confocal laser scanning microscope (CLSM) and flow cytometry (FCM) illustrated that HMSN-SS-CDPEI@HA exhibited a higher cellular uptake via the CD44 receptor-mediated endocytosis by CD44-receptor over-expressed A549 cells than NIH-3T3 (receptor-negative) cells, leading to higher cytotoxicity against A549 cells than NIH 3T3 cells. This work suggested an exploration of dual-stimuli responsive as well as real-time imaging targeted drug delivery system based on HMSN and the prepared HMSN-SS-CDPEI@HA could be a promising platform for cancer therapy.

Graphical abstractDownload high-res image (140KB)Download full-size image