Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.
Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.
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DOI：
10.1371/journal.pcbi.1002614
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发表时间：
2012
影响因子：
4.3
通讯作者：
Li L
中科院分区：
文献类型：
--
作者：
Duke JD;Han X;Wang Z;Subhadarshini A;Karnik SD;Li X;Hall SD;Jin Y;Callaghan JT;Overhage MJ;Flockhart DA;Strother RM;Quinney SK;Li L
药物相互作用（DDI）是药物不良事件的常见原因。在本文中，我们将文献发现方法与大型电子病历数据库方法分析相结合来预测和评估新型 DDI。我们根据已发表的体外药理学实验中鉴定的细胞色素 P450 (CYP) 代谢酶的底物和抑制剂，预测了 13197 种初始潜在 DDI。通过使用超过 800,000 名患者的临床存储库，我们将 DDI 的理论集缩小到患者实际服用的 3670 个药物对。最后，我们试图找出能够协同增加肌病风险的新组合。确定了 P 值小于 1E-06 的五对：氯雷他定和辛伐他汀（相对风险或 RR = 1.69）；氯雷他定和阿普唑仑（RR = 1.86）；氯雷他定和度洛西汀（RR = 1.94）；氯雷他定和罗匹尼罗（RR = 3.21）；以及异丙嗪和替加色罗 (RR = 3.00)。当同时服用时，与单独服用任何一种药物的预期附加肌病风险相比，每种药物对都显示出肌病风险显着增加。根据关于体外药物代谢和抑制效力的其他文献数据，预计氯雷他定和辛伐他汀以及替加色罗和异丙嗪分别通过 CYP3A4 和 CYP2D6 酶具有强 DDI。这种新的转化生物医学信息学方法不仅支持检测新的具有临床意义的 DDI 信号，还支持评估其潜在的分子机制。药物间相互作用是药物不良事件的常见原因。在本文中，我们开发了一种自动搜索算法，可以根据已发表的文献预测新的药物相互作用。然后，我们使用大型电子病历数据库，分析了同时使用这些潜在相互作用的药物与肌病作为药物不良事件的发生率之间的相关性。肌病包括一系列肌肉骨骼疾病，包括肌肉疼痛、无力和组织破坏（横纹肌溶解症）。我们的统计分析确定了 5 个药物相互作用对：（氯雷他定、辛伐他汀）、（氯雷他定、阿普唑仑）、（氯雷他定、度洛西汀）、（氯雷他定、罗匹尼罗）和（异丙嗪、替加色罗）。当同时服用时，与单独服用任何一种药物的预期附加肌病风险相比，每种药物对都显示出肌病风险显着增加。进一步的研究表明两种主要的药物代谢蛋白 CYP2D6 和 CYP3A4 与这五种药物对的相互作用有关。总的来说，我们的方法是稳健的，因为它可以整合所有已发表的文献、所有 FDA 批准的药物和非常大的临床数据集，以生成临床上显着相互作用的预测。然后可以在未来的基于细胞的实验和/或临床研究中进一步验证相互作用。
Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms. Drug-drug interactions are a common cause of adverse drug events. In this paper, we developed an automated search algorithm which can predict new drug interactions based on published literature. Using a large electronic medical record database, we then analyzed the correlation between concurrent use of these potentially interacting drugs and the incidence of myopathy as an adverse drug event. Myopathy comprises a range of musculoskeletal conditions including muscle pain, weakness, and tissue breakdown (rhabdomyolysis). Our statistical analysis identified 5 drug interaction pairs: (loratadine, simvastatin), (loratadine, alprazolam), (loratadine, duloxetine), (loratadine, ropinirole), and (promethazine, tegaserod). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Further investigation suggests that two major drug metabolism proteins, CYP2D6 and CYP3A4, are involved with these five drug pairs' interactions. Overall, our method is robust in that it can incorporate all published literature, all FDA approved drugs, and very large clinical datasets to generate predictions of clinically significant interactions. The interactions can then be further validated in future cell-based experiments and/or clinical studies.
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DOI：
10.1093/bioinformatics/btq382
发表时间：
2010-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Tari L;Anwar S;Liang S;Cai J;Baral C
通讯作者：
Baral C
影响因子：
6.7
作者：
通讯作者：
--
影响因子：
39.2
作者：
Stang, Paul E.;Ryan, Patrick B.;Woodcock, Janet
通讯作者：
Woodcock, Janet
影响因子：
3.9
作者：
Quinney, Sara K.;Zhang, Xin;Hall, Stephen D.
通讯作者：
Hall, Stephen D.
影响因子：
2.6
作者：
Schech, Stephanie;Graham, David;Shatin, Deborah
通讯作者：
Shatin, Deborah