A pH-Sensitive nanodrug based on ortho ester-coupled ibuprofen and doxorubicin for synergistic treatment of tumors
Source abstract
Abstract
Nano-drug delivery system (nDDS) that could be used for combination cancer therapy were expected to improve the chemotherapy effect of doxorubicin (DOX) and reduce the risk of cancer metastasis. Ibuprofen (IBU) could reduce local inflammation and inhibit metastatic cancer. In this work, a novel pH-sensitive nanocarrier (IBU-OE NPs) was prepared by crosslinking IBU with ortho ester (OE) and further by O/W emulsion solvent evaporation method, which possessed uniform diameter (~200 nm) with high DOX loading efficiency (drug loading content of ~14.24%, drug loading efficiency of ~83.50%). The DOX-loaded NPs (IBU-OE@DOX NPs) could improve the stability and solubility of DOX and IBU in healthy tissues (pH ~7.5), while achieved persistent release of DOX and IBU in cancer cells (pH 6.5-6.9). The release amount of DOX and IBU reached to 75.44% and 70.43% at pH 5.0 within 120 h. In vitro experiments revealed that the DOX of IBU-OE@DOX NPs could be internalized into human breast cancer cell lines (MCF-7), human pulmonary carcinoma cell lines (A549), and human liver carcinoma cell lines (HepG2), exhibited synergistic vitro cytotoxicity and efficient cell migration inhibition effects, while exhibited low cytotoxicity in mouse fibroblast cell lines (L929). Besides, the nanoparticles could efficiently accumulate and penetrate to MCF-7 multicellular spheroids, and showed better growth inhibition than free DOX. Thus, this pH-sensitive IBU dimer could be of great significance as potential drug carrier to improve the efficiency of drug synergistic delivery and drug permeation in cancer combined treatment. Graphical Abstract
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoplatforms for cancer theranostics · Nanoparticle-Based Drug Delivery · Supramolecular Self-Assembly in Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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