通过自动化3D成像来推进临床前药物评估,以对脏器官进行高通量查
Haruka Oishi1, Nahid Tabibzadeh1,2, Ryuji Morizane1,2,3
1Nephrology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States of America.
Biofabrication
|March 28, 2024
概括
自动化3D成像和机器学习使得使用脏器官进行高通量药物查. 这种方法成功地确定了毒药物和治疗药物,推动了病研究.
科学领域:
- 生物技术是生物技术.
- 药物发现 药物发现 药物发现
- 再生医学是一种再生医学.
背景情况:
- 传统的二维细胞培养缺乏人类组织的复杂性,限制了药物发现.
- 来自多能干细胞的器官模仿人类的结构和细胞类型.
- 由于结构复杂性,当前的有机体模型在高通量选中面临着挑战.
研究的目的:
- 开发一种自动化管道,用于使用脏器官的高通量药物查.
- 在3D脏模型中评估药物的毒性和治疗潜力.
- 建立一个新的查平台,用于病研究.
主要方法:
- 从人类多能干细胞生成器官.
- 完全自动化的3D成像和机器学习用于形态分析.
- 对有机体暴露于西斯来建模急性损伤和药物查.
主要成果:
- 自动化分析准确地描述了脏片段特定的形态学.
- 从FDA批准的图书馆中识别了毒和治疗药物.
- 伊马替尼布显示出作为预防性治疗西斯普拉丁诱导的损伤的潜力.
结论:
- 自动化3D成像和机器学习克服了有机体查的局限性.
- 这个平台可以在3D脏结构中进行高通量毒性和治疗评估.
- 开发的管道推动了对脏疾病的药物发现.
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