治疗对住院风险的影响的系统估计作为药物重定向查方法
Costa Georgantas1, Jaume Banus, Roger Hullin
1Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, costa.georgantas@chuv.ch.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
这项研究引入了一种新的查方法,使用现实世界的数据,通过分析药物对住院风险的影响来识别药物重定向 (DR) 候选人. 它发现了7个显著的关联,包括现有药物的潜在新用途.
科学领域:
- 药理学和药理学流行病学
- 计算生物学 计算生物学
- 现实世界的证据研究研究.
背景情况:
- 药物再利用 (DR) 传统上依赖于生物和化学数据,经常忽视现实世界的观察见解.
- 现有的DR候选者识别的计算方法很少利用大规模的观测数据来发现新发现.
研究的目的:
- 开发和验证一个系统的查方法来测量药物对住院风险的影响,使用现实世界的数据.
- 通过分析药物疾病类别的关联,识别潜在的药物重用候选人.
主要方法:
- 利用来自英国生物银行的大规模观测数据,分析了6468种跨心血管,代谢和呼吸系统疾病的药物疾病对.
- 使用分类系统将药物和疾病分成功能类别.
- 结合因果推断和时间到事件建模来评估治疗对住院风险的影响,并设置参数以尽量减少虚假相关性.
主要成果:
- 在多次测试纠正后,确定了减少住院风险的7个统计学上显著的关联.
- 证实了已知的关联,并发现了对心脏选择性β-阻断剂和 thiazides 的潜在新应用.
- 发现了质子抑制剂副作用的证据,以及抗糖尿病药物的多种潜在关联.
结论:
- 提出的系统查方法有效地识别了使用现实数据的药物重定向候选人.
- 现实世界的数据在发现现有药物的新疗法应用方面具有重大实用性.
- 这项研究突出了药物重定向对心血管,代谢和呼吸系统疾病的潜力.
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