超速电化学芯片和机器学习用于高通量,精确的抗癌药物查
Daniel S Doretto1,2, Paula C R Corsato2, Christian O Silva1,3
1Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil.
ACS sensors
|November 29, 2024
概括
这项研究引入了超密度电化学芯片和机器学习,用于高通量药物敏感性测试. 这种新方法准确地确定了癌细胞的活力,有助于临床前药物开发.
科学领域:
- 电化学 电化学 电化学
- 生物感应是一种生物感应.
- 机器学习在药物发现中的作用
背景情况:
- 电化学传感器为药物敏感性测试提供了潜力,但在吞吐量和准确性方面面临挑战.
- 缺乏合适的平台阻碍了临床前试验期间的候选药物分析,从而减缓了药物开发.
研究的目的:
- 开发一种高吞吐量,用户友好和准确的方法来确定2D瘤细胞活力,用于药物敏感性测定.
- 将超密度电化学芯片与机器学习 (ML) 集成,以克服目前药物查方面的局限性.
主要方法:
- 利用超密度电化学芯片和机器学习 (ML) 进行药物敏感性测试.
- 使用Ru(NH3) 63+和方波电压计 (SWV) 的细胞脱离电化学测试来监测细胞死亡.
- 评估了多克索鲁比辛对乳腺和结直肠癌细胞的影响,使用滴和微流体格式.
主要成果:
- 通过将快速SWV测量 (9秒) 与串行芯片分析相结合,实现了高通量分析.
- 通过基于ML的数据匹配,证明了精确的细胞活力 (98-104%) 和半最大致命度的确定.
- 在滴和微流体测试格式中验证了该方法.
结论:
- 电化学芯片和ML方法的结合使得准确和高通量药物敏感性测试成为可能.
- 这种方法显示了促进临床前药物查和开发的巨大潜力.
- 这些发现为电化学传感器在药物发现中的实际应用铺平了道路.
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