Predicting the frequencies of drug side effects
Predicting the frequencies of drug side effects
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DOI：
10.1038/s41467-020-18305-y
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发表时间：
2020-09-11
影响因子：
16.6
通讯作者：
Paccanaro, Alberto
中科院分区：
文献类型：
--
作者：
Galeano, Diego;Li, Shantao;Paccanaro, Alberto
药物风险效益评估的一个核心问题是确定人体副作用的频率。目前，在随机对照临床试验中通过实验确定频率。我们提出了一个机器学习框架，用于计算预测药物副作用的频率。我们的矩阵分解算法学习药物和副作用的潜在特征，这些特征既可再现又可生物学解释。我们展示了我们的方法在759种结构和治疗上不同的药物和994种来自所有人类生理系统的副作用上的有用性。我们的方法可以应用于任何药物，其中少数副作用的频率已被确定，以预测进一步的频率，但未确定的，副作用。我们表明，我们的模型是信息的生物学基础的药物活性：药物签名的各个组件相关的药物的不同解剖类别和特定的药物给药途径。
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug for which a small number of side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We show that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.