Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.
Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.
复制标题
使用药物的化学，生物学和表型特性对不良药物反应进行大规模预测。
DOI：
10.1136/amiajnl-2011-000699
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发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Xu H
中科院分区：
文献类型：
--
作者：
Liu M;Wu Y;Chen Y;Sun J;Zhao Z;Chen XW;Matheny ME;Xu H
药物不良反应是导致药物开发失败的主要原因之一。严重的ADR直到药物上市后阶段才被发现，往往会导致患者发病率。在药物的整个生命周期中，包括药物设计的早期阶段、临床试验的不同阶段和上市后的监测，都需要对潜在的ADR进行准确的预测。许多研究已经利用药物的化学结构或分子途径来预测ADRs。在这里，作者提出了一种基于机器学习的方法来预测ADR，方法是将药物的表型特征(包括适应症和其他已知的ADR)与药物的化学结构和生物学特性(包括蛋白质靶标和途径信息)相结合。进行了一项大规模研究，以预测832种批准药物的1385种已知ADR，并对这项任务的五种机器学习算法进行了比较。这项基于五次交叉验证的评估表明，支持向量机算法的性能优于其他算法。在三种类型的信息中，表型数据对ADR预测的信息量最大。当在基线化学信息中加入生物学和表型特征时，ADR预测模型在曲线下面积(从0.9054到0.9524)、精度(从43.37%到66.17%)和召回率(从49.25%到63.06%)都有显著的提高。最重要的是，所提出的模型成功地预测了与停用罗非昔布和西立伐他汀相关的不良反应。结果表明，药物的表型信息对ADR的预测是有价值的。此外，他们证明了结合化学、生物或表型信息的不同模型可以从批准的药物中建立，并且它们有可能在临床前和上市后阶段检测临床上重要的ADR。
Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.
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