Data-driven prediction of drug effects and interactions.
Data-driven prediction of drug effects and interactions.
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DOI：
10.1126/scitranslmed.3003377
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发表时间：
2012-03-14
影响因子：
17.1
通讯作者：
Altman RB
中科院分区：
文献类型：
--
作者：
Tatonetti NP;Ye PP;Daneshjou R;Altman RB
药物不良事件仍然是世界各地发病率和死亡率的主要原因。在药物获得临床使用批准之前，许多不良事件在临床试验期间未被发现。幸运的是，作为上市后监测的一部分，监管机构和其他机构保存了大量不良事件报告，这些数据库为从患者群体数据中研究药物效应提供了机会。然而，诸如伴随用药、患者人口统计学、患者病史和开药原因等混杂因素在自发报告系统中往往不具有特征，这些遗漏可能限制定量信号检测方法在分析此类数据时的使用。在这里，我们提出了一种自适应数据驱动的方法，用于在协变量未知或无法测量的情况下纠正这些因素，并将该方法与现有方法结合起来，使用三个测试数据集来改进药物效应分析。我们还提出了药物效应（Offsides）和药物相互作用副作用（Twosides）的综合数据库。为了证明这些新资源的生物学用途，我们利用它们来确定药物靶点，预测药物适应症，并发现药物类别的相互作用。然后，通过对电子病历的独立分析，我们证实了47种药物类别的相互作用（P < 0.0001）。我们的分析表明，选择性5 -羟色胺再摄取抑制剂和噻嗪类药物联合治疗与QT间期延长的发生率显著增加有关。我们的结论是，观察性临床数据中来自协变量的混杂效应可以在数据分析中得到控制，从而提高药物不良反应和相互作用的检测和预测。
Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.
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影响因子：
2
作者：
Noren, G. Niklas;Bate, Andrew;Edwards, I. Ralph
通讯作者：
Edwards, I. Ralph
影响因子：
5
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Cepeda, MS;Boston, R;Strom, BL
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Strom, BL
影响因子：
7.2
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Kuss, O.;Legler, T.;Boegermann, J.
通讯作者：
Boegermann, J.
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
9.9
作者：
Kuhn M;Campillos M;Letunic I;Jensen LJ;Bork P
通讯作者：
Bork P