使用斑马鱼行为概况,寻找用于神经退行性疾病的药物改用候选者
Thaís Del Rosario Hernández1, Sayali V Gore1, Jill A Kreiling1
1Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA.
bioRxiv : the preprint server for biology
|September 25, 2023
概括
这项研究使用斑马鱼模型和人工智能来选潜在的阿尔茨海默病治疗方法的现有药物. 研究人员确定了64种神经退行性疾病的有前途的候选药物.
科学领域:
- 神经科学是一个神经科学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
背景情况:
- 对阿尔茨海默氏症等疾病而言,药物重新定位为新药设计提供了一个具有成本效益的替代方案.
- 斑马鱼幼虫模型对于研究认知功能和查疗法非常有价值.
- 卡尔西纽林抑制剂,如环素 (CsA),显示出预防阿尔茨海默病的潜力.
研究的目的:
- 确定FDA批准的药物,可以用于神经退行性疾病.
- 为了利用基于AI的新型行为分析工具进行高通量药物查.
- 发现调节斑马鱼幼虫行为的化合物,类似于氨酸抑制剂.
主要方法:
- 开发并利用Z-LaP追踪器,一个神经网络模型,来量化斑马鱼幼虫的行为.
- 使用Z-LaP追踪器对FDA批准的药物进行了高通量选.
- 为集群分析生成行为概况,以确定候选疗法.
主要成果:
- 通过行为分析和集群分析,确定了64种神经退行性疾病的候选治疗方法.
- 这项研究成功地选了一系列药物库,基于它们对斑马鱼幼虫认知行为的影响.
- 分析了行为数据,以寻找模仿氨酸抑制剂作用的化合物.
结论:
- 在斑马鱼中使用人工智能驱动的行为分析来重新定位药物是一种可行的策略,用于发现神经退行性疾病治疗方法.
- 鉴定了64种候选药物,需要对阿尔茨海默病和其他神经退行性疾病进行进一步的研究.
- 这种方法加快了对未满足医疗需求的疾病的潜在治疗方法的识别.
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