Estrone-Conjugated PEGylated Liposome Co-Loaded Paclitaxel and Carboplatin Improve Anti-Tumor Efficacy in Ovarian Cancer and Reduce Acute Toxicity of Chemo-Drugs

Huan Tang & Jin Pei et al. · 2022-07-07

Ovarian cancer is the most lethal gynecologic malignancy. The combination of paclitaxel (PTX) and carboplatin (CBP) is the first-line remedy for clinical ovarian cancer. However, due to the limitations of adverse reaction and lacking of targeting ability, the chemotherapy of ovarian cancer is still poorly effective. Here, a novel estrone (ES)-conjugated PEGylated liposome co-loaded PTX and CBP (ES-PEG-Lip-PTX/CBP) was designed for overcoming the above disadvantages. ES-PEG-Lip-PTX/CBP was prepared by film hydration method and could recognize estrogen receptor (ER) over-expressing on the surface of SKOV-3 cells. The characterizations, stability and in vitro release of ES-PEG-Lip-PTX/CBP were studied. In vitro cellular uptake and its mechanism were observed by fluorescence microscope. In vivo targeting effect in tumor-bearing mice was determined. Pharmacokinetics and biodistribution were studied in ICR mice. In vitro cytotoxicity and in vivo anti-tumor efficacy were evaluated on SKOV-3 cells and tumor-bearing mice, respectively. Finally, the acute toxicity in ICR mice was explored for assessing the preliminary safety of ES-PEG-Lip-PTX/CBP. Our results showed that ES-PEG-Lip-PTX/CBP was spherical shape without aggregation. ES-PEG-Lip-PTX/CBP exhibited the optimum targeting effect on uptake in vitro and in vivo. The pharmacokinetics demonstrated ES-PEG-Lip-PTX/CBP had improved the pharmacokinetic behavior. In vitro cytotoxicity showed that ES-PEG-Lip-PTX/CBP maximally inhibited SKOV-3 cell proliferation and its IC ES-PEG-Lip-PTX/CBP was successfully prepared with an optimal physicochemical and ER targeting property. The data of pharmacokinetics, anti-tumor efficacy and safety study indicated that ES-PEG-Lip-PTX/CBP could become a promising therapeutic formulation for human ovarian cancer in the future clinic.
Authors
Huan Tang, Yizhuo Xie, Ming Zhu, Juan Jia, Rui Liu, Yujia Shen, Yucui Zheng, Xin Guo, Dongfanghui Miao, Jin Pei