机器学习工程纳米酶系统用于协同作用的抗瘤铁/亡疗法
Tianliang Li1, Bin Cao2, Tianhao Su1
1Materials Genome Institute, Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials, and Shanghai Engineering Research Center of Organ Repair, Shanghai University, Shanghai, 200444, China.
Small (Weinheim an der Bergstrasse, Germany)
|December 16, 2024
概括
这项研究引入了一种新型纳米酶系统 (FZH),通过诱导铁亡和亡来增强抗瘤疗法. 它利用机器学习来克服自,一种阻碍癌症治疗的保护机制.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 计算生物学 计算生物学
背景情况:
- 具有多酶活性的纳米酶通过与瘤微环境 (TME) 相互作用,显示出对抗瘤治疗的希望.
- 纳米酶诱导的保护性自会显著降低它们的治疗效果.
研究的目的:
- 开发一个有针对性的纳米酶系统 (FZH),增强协同作用的抗瘤ferroptosis和apoptosis治疗.
- 利用机器学习 (ML) 来优化纳米酶设计和发现新的治疗策略.
主要方法:
- 为了改进数据模式识别,开发了一种新的ML模型,即高斯过程回归的顺序倒向树分类器 (TCGPR).
- 贝叶斯优化算法用于在广泛的材料搜索空间内进行候选人选择.
- Fe-Arg-CDs@ZIF-8/HAD (FZH) 纳米酶系统被设计并评估其抗瘤作用.
主要成果:
- FZH纳米酶系统通过顺序响应TME,有效地发挥抗瘤作用,诱导级联反应以促进铁亡.
- 由FZH引起的内源性氧化 (NO) 升高直接杀死瘤细胞.
- FZH抑制了由氧化应激 (OS) 诱导的保护性自,从而增强了协同性铁/灭疗法.
结论:
- 为了改进基于纳米酶的瘤治疗,已经开发了一种整合ML,实验验证和生物应用的新策略.
- 通过克服治疗耐药性机制,FZH系统显示出具有重要的协同作用的抗瘤ferroptosis和apoptosis治疗潜力.
- 这项工作突显了基于机器学习的材料发现在推进癌症治疗方面的力量.
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