Yu Hasun, Choo Sungji, Park Junseok, Jung Jinmyung, Kang Yeeok, Lee Doheon
Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
BMC Syst Biol. 2016 Jan 11;10 Suppl 1(Suppl 1):2. doi: 10.1186/s12918-015-0243-2.
Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.
We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with 'effect type', an integrated directed molecular network with 'effect type' and 'effect direction', and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.
We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main .
Our results showed that 'effect type' and 'effect direction' information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.
开发已批准药物的新用途，即药物重新定位，可以降低传统药物开发的成本和时间。基于网络的方法在该领域已展现出有前景的结果。然而，尽管在药物 - 靶点相互作用和分子途径中存在各种类型的相互作用，如激活或抑制，但大多数先前基于网络的研究都忽略了这些信息。
我们开发了一种新的计算方法，即疾病基因反向作用药物预测（PDOD），用于识别对疾病基因改变状态具有相反作用的药物。PDOD利用带有“效应类型”的药物 - 药物靶点相互作用、带有“效应类型”和“效应方向”的整合定向分子网络以及疾病患者中具有调控状态的疾病基因。利用这些信息，我们提出了一种评分函数，通过药物 - 药物靶点相互作用从药物到疾病的路径、分子途径中从药物靶点到疾病基因的最短路径以及疾病基因 - 疾病关联来发现可能恢复疾病基因改变状态的药物。
我们收集了药物 - 药物靶点相互作用、分子途径以及疾病中具有调控状态的疾病基因。PDOD应用于898种具有已知药物 - 药物靶点相互作用的药物和9种疾病。我们将PDOD预测已知治疗性药物 - 疾病关联的性能与先前方法进行了比较。PDOD优于其他先前未利用分子网络中方向信息的方法。此外，我们提供了一个简单的网络服务，研究人员可以在http://gto.kaist.ac.kr/pdod/index.php/main提交感兴趣基因及其改变状态，然后获得似乎对输入基因改变状态具有相反作用的药物。
我们的结果表明，基于网络的方法中的“效应类型”和“效应方向”信息可用于识别对疾病具有相反作用的药物。我们的研究可为基于网络的药物重新定位领域提供新的见解。
