PathSynergy:一种深度学习模型,用于预测肝癌中药物协同作用
Fengyue Zhang1, Xuqi Zhao1, Jinrui Wei2
1Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, No. 100, East Daxue Road, Xixiangtang District, Nanning 530004, Guangxi, China.
Briefings in bioinformatics
|April 24, 2025
概括
一种名为PathSynergy的新模型,用于确定肝癌的协同药物组合,从而提高治疗效率. 这种方法加速了对抗耐药性的新疗法的发现,并提高了患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 肝癌是全球癌症死亡的主要原因之一.
- 晚期肝癌的5年生存率很差 (30%).
- 目前的一线向疗法 (sorafenib,lenvatinib) 面临耐药性问题,需要组合策略.
研究的目的:
- 开发一种新的预测模型,PathSynergy,用于识别对抗肝癌的协同药物组合.
- 克服传统,耗时和昂贵的药物协同作用发现方法的局限性.
主要方法:
- 综合药物特征,细胞系数据,药物向相互作用和信号通路.
- 使用图形神经网络和路径地图映射.
- 将PathSynergy的性能与分类,准确性和精度的基线模型进行了比较.
主要成果:
- 与基线模型相比,PathSynergy表现出优越的性能.
- 美国食品和药物管理局批准的六种药物 (pimecrolimus,topiramate,nandrolone decanoate,fluticasone propionate,zanubrutinib,levonorgestrel) 被确定与索拉芬尼布或伦瓦丁尼布具有协同作用.
- 这些对抗肝癌的协同药物组合首次得到了验证.
结论:
- 该PathSynergy模型提供了一种新的计算方法,用于发现协同作用的药物组合.
- 这种方法在药物发现和肝癌治疗的个性化医学中具有广泛的潜在应用.
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