Paclitaxel (PTX) is one of the first-line drugs for prostate cancer (PC) treatment. However, the poor water solubility, inadequate specific targeting ability, multidrug resistance, and severe neurotoxicity are far from being fully resolved, despite diverse PTX formulations in the market, such as the gold-standard PTX albumin nanoparticle (Abraxane) and polymer micelles (Genexol-PM). Some studies attempting to solve the multiple problems of chemotherapy delivery fall into the trap of an extremely complicated formulation design and sacrifice druggability. To better address these issues, this study designed an efficient, toxicity-reduced paclitaxel-ginsenoside polymeric micelle (RPM). With the aid of the inherent amphiphilic molecular structure and pharmacological effects of ginsenoside Rg5, the prepared RPM enhances the water solubility and active targeting of PTX, inhibiting chemotherapy resistance in cancer cells. Moreover, the polymeric micelles demonstrated favorable anti-inflammatory and neuroprotective effects, providing ideas for the development of new clinical anti-PC preparations.
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http://dx.doi.org/10.1021/acs.molpharmaceut.4c00204 | DOI Listing |
Chin Herb Med
October 2024
College of Chinese Medicinal Materials, Jilin Agricultural University, Changchun 130118, China.
J Ginseng Res
July 2024
Korean Medicine Application Center, Korea Institute of Oriental Medicine (KIOM), Dong-gu, Daegu, Republic of Korea.
J Ethnopharmacol
October 2024
Skin Health and Cosmetic Development & Evaluation Laboratory, China Pharmaceutical University, Nanjing, 211198, Jiangsu, China. Electronic address:
Mol Pharm
July 2024
Medical Research Institute, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China.
Sci Rep
May 2024
Department of Education, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Chronic Heart Failure (CHF) is a significant global public health issue, with high mortality and morbidity rates and associated costs. Disease modules, which are collections of disease-related genes, offer an effective approach to understanding diseases from a biological network perspective. We employed the multi-Steiner tree algorithm within the NeDRex platform to extract CHF disease modules, and subsequently utilized the Trustrank algorithm to rank potential drugs for repurposing.
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