Dual-responsive mPEG-PLGA-PGlu hybrid-core nanoparticles with a high drug loading to reverse the multidrug resistance of breast cancer: An in vitro and in vivo evaluation
Source abstract
Abstract
In this study, monomethoxy (polyethylene glycol)-b-P (d,l-lactic-co-glycolic acid)-b-P (l-glutamic acid) (mPEG-PLGA-PGlu) nanoparticles with the ability to rapidly respond to the endolysosomal pH and hydrolase were prepared and the pH-sensitivity was tuned by adjusting the length of the PGlu segment. The mPEG5k-PLGA20k-PGlu (60) nanoparticles were specifically responsive to an endosomal pH of 5.0-6.0 due to the configuration transition of the PGlu segment and rapidly initiated chemical degradation after incubation with proteinase k for 10 min. Doxorubicin hydrochloride (DOX), used as a model drug, was easily encapsulated into nanoparticles and the DOX-loaded nanoparticles (DOX-NPs) exhibited a pH-dependent and enzyme-sensitive release profile in vitro. The dual sensitivity enabled the rapid escape of DOX-loaded nanoparticles from the endolysosomal system to target cellular nuclei, which resulted in increased cell toxicity against MCF/ADR resistant breast cancer cells and a higher cellular uptake than free DOX. In Vivo Imaging studies indicated that the nanoparticles could continuously accumulate in the tumor tissues through EPR effects and Ex vivo Imaging biodistribution studies indicated that DOX-NPs increased drug penetration into tumors compared with normal tissues. The in vivo antitumor activity demonstrated that DOX-loaded NPs had less body loss and a significant regression of tumor growth, indicating the increased anti-tumor efficacy and lower systemic toxicity. Therefore, this dual sensitive nanoparticle system may be a potential nanocarrier to overcome the multidrug resistance exhibited by breast cancer.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoparticle-Based Drug Delivery · Graphene and Nanomaterials Applications · Nanoplatforms for cancer theranostics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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