Discovery and preclinical validation of drug indications using compendia of public gene expression data.
Discovery and preclinical validation of drug indications using compendia of public gene expression data.
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DOI：
10.1126/scitranslmed.3001318
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发表时间：
2011-08-17
影响因子：
17.1
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Sirota M;Dudley JT;Kim J;Chiang AP;Morgan AA;Sweet-Cordero A;Sage J;Butte AJ
将已建立的药物化合物应用于新的治疗适应症，即所谓的药物重新定位，与传统药物开发相比具有几个优势，包括降低开发成本和缩短批准路径。最近的药物定位方法使用高通量的实验方法来评估化合物的潜在治疗质量。在这里，我们提出了一种系统的计算方法来预测新的治疗适应症，基于药物-疾病对中的分子特征的综合测试。我们综合了100种疾病的基因表达测量和164种药物化合物的基因表达测量，得出了这些药物的预测治疗潜力。我们展示了利用计算得出的治疗潜力恢复许多已知药物和疾病关系的能力，并预测了这些药物的许多新适应症。我们在实验上验证了抗溃疡药物西咪替丁作为治疗肺腺癌的候选药物的预测，并使用小鼠异种移植模型在体外和体内进行了验证。这种新的计算方法提供了一种新的和系统的方法来重新定位已有的药物来治疗广泛的人类疾病。
The application of established drug compounds to novel therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning employ high-throughput experimental approaches to assess a compound’s potential therapeutic qualities. Here we present a systematic computational approach to predict novel therapeutic indications based on comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds yielding predicted therapeutic potentials for these drugs. We demonstrate the ability to recover many known drug and disease relationships using computationally derived therapeutic potentials, and also predict many new indications for these drugs. We experimentally validated a prediction for the anti-ulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate both in vitro and in vivo using mouse xenograft models. This novel computational method provides a novel and systematic approach to reposition established drugs to treat a wide range of human diseases.
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影响因子：
11.5
作者：
Abbas, Sabiha;Bhoumik, Anindita;Ronai, Zeev A.
通讯作者：
Ronai, Zeev A.
影响因子：
46.9
作者：
Butte, AJ;Kohane, IS
通讯作者：
Kohane, IS
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
8.4
作者：
Fichtner, I;Slisow, W;Bibby, M
通讯作者：
Bibby, M
影响因子：
45.3
作者：
Del Rio, Maguy;Molina, Franck;Ychou, Marc
通讯作者：
Ychou, Marc