精密药物再定位 (PDR):患者级建模和预测,将基础知识图与生物库数据图相结合
Çerağ Oğuztüzün1, Zhenxiang Gao2, Hui Li2
1Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA; Department of Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA.
Journal of biomedical informatics
|February 14, 2025
概括
精密药物重定向集成个体患者数据与知识图表,以发现个性化疗法. 多基因风险评分显著改善了阿尔茨海默病等疾病的药物优先级.
科学领域:
- 生物医学信息学 生物医学信息学
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
背景情况:
- 药物重用加速治疗的发展,但与个体患者的变异性作斗争.
- 个性化医疗需要整合患者特定的数据,以进行量身定制的药物发现.
研究的目的:
- 引入一个精确药物重定位 (PDR) 框架,用于单一患者的解决方案.
- 通过将个人数据与生物医学知识图集在一起,实现个性化药物发现.
主要方法:
- 开发了一个框架,将英国生物银行数据 (多基因风险评分,生物标志物,病史) 与生物医学知识图集在一起.
- 用阿尔茨海默病作为案例研究,将患者特定的模型与使用链接预测的基础模型进行比较.
- 通过患者药物历史和文献审查评估候选药物.
主要成果:
- 该PDR框架保持了强大的预测能力,多基因风险评分显著影响了药物优先级 (科恩的d=1.05).
- 废除研究证实了多基因风险评分 (PRS) 的关键作用.
- 患者特异型模型确定了基础模型遗漏的新药候选者,通过药物历史和与遗传特征一致的文献进行验证.
结论:
- 通过将患者特异性数据与知识图集集成,证明了精确药物重定向的有希望的方法.
- 突出了多基因风险评分在个性化复杂疾病药物发现方面的潜力.
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