整合人口层面和基于细胞的签名,用于药物重新定位
Chunfeng He1,2, Yue Xu1,2, Yuan Zhou3
1The Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, 310058, HangZhou, China.
Bioinformatics (Oxford, England)
|September 10, 2025
概括
我们开发了转录基因知情逆转距离 (TReD),这是一种用于药物重新定位的新框架. TReD利用遗传证据和分子签名来识别COVID-19和阿尔茨海默氏症等疾病的潜在治疗方法.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物重新定位为确定现有药物的新治疗用途提供了一种具有成本效益的策略.
- 具有强有力的人类遗传证据的药物在临床试验和FDA批准中显示出更高的成功率.
- 目前的药物重新定位方法通常集中在单个基因上,限制了更广泛的分子特征的探索.
研究的目的:
- 引入一种新的框架,即转录组信息的逆转距离 (TReD),用于有效的药物重新定位.
- 使用高维规范空间整合疾病特征和药物反应概况.
- 在疾病特定的基于细胞的查中量化候选药物的治疗逆转潜力.
主要方法:
- 开发了转录组信息逆转距离 (TReD) 框架.
- 嵌入疾病特征和药物反应概况到一个高维规范空间中.
- 应用TReD来识别COVID-19,2型糖尿病和阿尔茨海默病的候选药物.
主要成果:
- 确定了36种潜在的药物候选物用于COVID-19,16种用于2型糖尿病,11种用于阿尔茨海默病.
- 对已识别的候选人取得了显著的文献支持:69%的COVID-19,31%的2型糖尿病和64%的阿尔茨海默病.
- 发现7个COVID-19和3个阿尔茨海默病候选人已经在临床试验中.
结论:
- 该TReD框架为药物重新定位提供了一种基于遗传学的方法.
- 将人口级别的签名与基于细胞的查相结合,可以加速治疗策略的开发.
- 这一框架有可能极大地加快新疗法的发现速度.
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