将药物基因组学和化学信息学与多种疾病表型相结合,以进行细胞类型引导的药物发现
Arda Halu1,2, Sarvesh Chelvanambi3, Julius L Decano3
1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA. arda.halu@channing.harvard.edu.
Genome medicine
|January 20, 2025
概括
帕托普蒂康是一种基于网络的新方法,通过整合药物基因组和化学信息数据来优先考虑药物. 这种方法通过考虑细胞类型特异性影响和疾病背景来增强药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 药物发现 药物发现
背景情况:
- 大规模的药物基因组资源,如连接地图 (CMap),有助于计算药物发现.
- 现有的CMap方法缺乏关于药物的生物活性和跨疾病的基因组影响的特异性.
- 为了解决这些局限性,引入了一种新的基于网络的统计方法 - - Pathopticon.
研究的目的:
- 开发一个细胞类型特定的药物优先级平台.
- 将药物基因组数据与化学信息和疾病信息相结合.
- 提高药物发现和重新定位的效率和成功率.
主要方法:
- 使用基于量子的实例Z分数共识 (QUIZ-C) 构建细胞类型特定的基因药物干扰网络.
- 通过将基因药物网络与疾病特征 (Enrichr) 和化学信息数据 (ChEMBL) 整合来计算病变类型一致性得分 (PACOS).
- 使用接收器运行特征曲线 (AUROC) 下的面积进行基准测试性能,并使用qPCR验证预测.
主要成果:
- QUIZ-C产生了拓上不同的,细胞类型特定的网络,富含已知的药物点.
- 在预测准确度方面,Pathopticon的表现优于化学信息学,网络和深度学习方法.
- 预测的药物表现出化学结构的多样性,而Pathopticon指导了血管疾病的体外实验.
结论:
- 帕托普蒂康为特定细胞类型的药物发现和重新定位平台提供了一个可行的蓝图.
- 该框架整合了药物基因组学和化学信息学,以提高药物开发效率.
- 这种方法对推进治疗开发具有广泛的影响.
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