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更新和应用深度学习模型用于预测多发性硬化症患者使用的药物之间的相互作用
Michael Hecker1, Niklas Frahm1, Uwe Klaus Zettl1
1Division of Neuroimmunology, Department of Neurology, Rostock University Medical Center, Gehlsheimer Str. 20, 18147 Rostock, Germany.
Pharmaceutics
|January 26, 2024
概括
这项研究使用深度学习来预测多发性硬化症患者的药物-药物和药物-食物相互作用,为许多人确定了重大风险. 这些发现为更安全的药物管理提供了见解,并为患有多发性硬化症的患者提供了饮食建议.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 医学中的人工智能
背景情况:
- 患有多发性硬化症 (MS) 的患者通常需要多种药物,增加药物相互作用 (DDI) 和不良事件的风险.
- 了解这些相互作用对于管理患者安全和治疗疗效至关重要.
研究的目的:
- 使用深度神经网络 (DNN) 预测多发性硬化症患者的潜在药物相互作用 (DDI) 和药物食物相互作用 (DFI).
- 为了确定与常见的MS药物和饮食成分相关的特定风险.
主要方法:
- 一个深度学习模型,DeepDDI,通过DrugBank的广泛的DDI数据和FooDB的结构性食品数据进行了更新.
- 该模型分析了627名多发性硬化症患者的药物计划,以预测对对的DDI和DFI.
- 使用深度神经网络 (DNN) 模型,利用化学结构信息进行预测.
主要成果:
- 更新的DeepDDI模型实现了高精度 (92.2%的验证,92.1%的测试).
- 在81.2%的MS患者中,预测至少有一次DDI.
- 在克拉德里宾和芬戈利莫德的使用者中分别发现了出血和心肌梗塞的重大风险. 发现了evobrutinib的众多相互作用.
结论:
- 深度学习通过利用化学结构相似性,有效地预测MS患者的DDI和DFI.
- 该研究提供了关于潜在不良药物效应的关键信息,建议使用替代药物,并为MS患者提供饮食指导.
- 这种方法提高了个性化药物管理和多发性硬化症护理中的患者安全.
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