材料科学
磁性纳米粒子
药物输送
药品
靶向给药
纳米技术
阿霉素
纳米颗粒
生物医学工程
癌细胞
癌症
化疗
医学
药理学
外科
内科学
作者
Hyoryong Lee,Sukho Park
标识
DOI:10.1021/acsami.3c01087
摘要
Cancer is one of the diseases with high mortality worldwide. Various methods for cancer treatment are being developed, and among them, magnetically driven microrobots capable of minimally invasive surgery and accurate targeting are in the spotlight. However, existing medical magnetically manipulated microrobots contain magnetic nanoparticles (MNPs), which can cause toxicity to normal cells after the delivery of therapeutic drugs. In addition, there is a limitation in that cancer cells become resistant to the drug by mainly delivering only one drug, thereby reducing the treatment efficiency. In this paper, to overcome these limitations, we propose a microrobot that can separate/retrieve MNPs after precise targeting of the microrobot and can sequentially deliver dual drugs (gemcitabine (GEM) and doxorubicin (DOX)). First, after the proposed microrobot targeting, MNPs attached to the microrobot surface can be separated from the microrobot using focused ultrasound (FUS) and retrieved through an external magnetic field. Second, the active release of the first conjugated drug GEM to the surface of the microrobot is possible using near-infrared (NIR), and as the microrobot slowly decomposes over time, the release of the second encapsulated DOX is possible. Therefore, it is possible to increase the cancer cell treatment efficiency with sequential dual drugs in the microrobot. We performed basic experiments on the targeting of the proposed magnetically manipulated microrobot, separation/retrieval of MNPs, and the sequential dual-drug release and validated the performances of the microrobot through in vitro experiments using the EMA/FUS/NIR integrated system. As a result, the proposed microrobot is expected to be used as one of the methods to improve cancer cell treatment efficiency by improving the limitations of existing microrobots in cancer cell treatment.
科研通智能强力驱动
Strongly Powered by AbleSci AI