A cationic amino acid polymer nanocarrier synthesized in supercritical CO2 for co-delivery of drug and gene to cervical cancer cells

赫拉 姜黄素 纳米载体 癌细胞 化学 细胞凋亡 细胞 药物输送 材料科学 生物物理学 生物化学 癌症 生物 有机化学 遗传学
作者
K. Kavya,Stella Vargheese,Shruti Shukla,Imran A. Khan,Debasish Kumar Dey,Vivek K. Bajpai,Kavitha Thangavelu,Raju Vivek,R.T. Rajendra Kumar,Young‐Kyu Han,Yun Suk Huh,Yuvaraj Haldorai
出处
期刊:Colloids and Surfaces B: Biointerfaces [Elsevier BV]
卷期号:216: 112584-112584 被引量:22
标识
DOI:10.1016/j.colsurfb.2022.112584
摘要

The present study was undertaken to investigate the ability of a drug curcumin-loaded polymer to inhibit the growth of cervical cancer cells by enhancing the anti-cancer efficiency of curcumin. We synthesized poly(methacryloyl beta-alanine) (PMBA) as a nanocarrier by radical polymerization in supercritical CO2. The results showed that the curcumin encapsulated and folic acid (FA)-treated PMBA (Poly@Cur-FA) for 24 h activated the reactive oxygen species-mediated programmed cell death machinery in HeLa cells. This remarkable effect of Poly@Cur-FA treatment was visualized using different fluorescent probes, which demonstrated that the Poly@Cur-FA treatment disrupted the cell membrane, as also supported by scanning electron microscopy observations. The effect of Poly@Cur-FA dispersion on the cells was observed under a transmission electron microscope. Further, the HeLa cells were treated with the polymer encapsulated curcumin and Bcl2 siRNA (Pol-Cur-siRNA) for 24 h, which effectively suppressed the Bcl2 and simulated the autophagic pathway. This co-delivery system was designed to inhibit curcumin efflux and can enhance the treatment efficacy by targeting multiple signaling pathways, including cell cycle, apoptotic, and autophagic pathways. Collectively, the Pol-Cur-siRNA system appears to offer an efficient combinational therapeutic strategy that might overcome the problems associated with the chemosensitivity against the standard synthetic anti-cancer drugs. To support the experimental data, an artificial neural network model was developed to foresee the drug and gene release behaviors.
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