Pharmacointeraction network models predict unknown drug-drug interactions.
Pharmacointeraction network models predict unknown drug-drug interactions.
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DOI：
10.1371/journal.pone.0061468
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Reis BY
中科院分区：
文献类型：
--
作者：
Cami A;Manzi S;Arnold A;Reis BY
药物-药物相互作用（ddi）可导致严重和潜在致命的不良事件。近年来，一些药物由于相互作用相关的不良事件（ae）而退出市场。目前检测DDI的方法依赖于在上市后阶段积累足够的临床证据——这是一个漫长的过程，通常需要数年时间，在此期间，许多患者可能遭受DDI的不良影响。可能的药物-药物ae组合的极大组合空间进一步阻碍了检测方法。因此，实际需要一种预测工具，可以提前数年识别潜在的ddi，使药物安全专业人员能够更好地优先考虑其有限的调查资源并采取适当的监管行动。为了满足这一需求，我们描述了预测性药物相互作用网络（PPINs）——一种通过利用所有已知ddi的网络结构以及药物和ae的其他内在和分类特性来预测未知ddi的新方法。我们从2009年一个被广泛使用的药物安全数据库的快照中构建了一个856种药物的DDI网络，并利用它来开发预测未来DDI的PPIN模型。我们将仅基于这些2009年数据预测的ddi与同一数据库的2012年快照中新报告的ddi进行了比较。使用标准的多变量方法组合预测因子，PPIN模型的AUROC（受试者工作特征曲线下面积）为0.81，特异性为90%，灵敏度为48%。对ddi严重程度的分析显示，该模型对“禁忌症”ddi的预测最有效（AUROC = 0.92），对“轻度”ddi的预测效果较差（AUROC = 0.63）。这些结果表明，基于网络的方法可以用于预测未知的药物-药物相互作用。
Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.
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影响因子：
3.9
作者：
Ekins, S
通讯作者：
Ekins, S
影响因子：
3.9
作者：
Lu, Chuang;Hatsis, Panos;Balani, Suresh K.
通讯作者：
Balani, Suresh K.
影响因子：
4.3
作者：
Chang RL;Xie L;Xie L;Bourne PE;Palsson BØ
通讯作者：
Palsson BØ
DOI：
10.1038/nrg2918
发表时间：
2011-01
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
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影响因子：
64.8
作者：
Clauset, Aaron;Moore, Cristopher;Newman, M. E. J.
通讯作者：
Newman, M. E. J.