Prediction on the risk population of idiosyncratic adverse reactions based on molecular docking with mutant proteins.
Prediction on the risk population of idiosyncratic adverse reactions based on molecular docking with mutant proteins.
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基于突变蛋白分子对接预测特异质不良反应危险人群
DOI：
10.18632/oncotarget.21509
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发表时间：
2017-11-10
期刊：
影响因子：
--
通讯作者：
Hu M
中科院分区：
文献类型：
--
作者：
Xie H;Zeng D;Chen X;Huo D;Liu L;Zhang D;Jin Q;Ke K;Hu M
特异质药物不良反应是指在人群中罕见且不可预测的药物反应。这些反应通常发生在药物上市后，这意味着它们与人群的基因型密切相关。由于在药物开发过程中缺乏适当的测试模型，因此预测此类不良反应是一项重大挑战。在本研究中，我们选择了撤回的药物，因为它们被撤回的原因以及容易从哪些国家或地区获得。我们选择地乐洛尔及其手性药物（拉贝洛尔）作为替代药物，因为它们已因严重的肝毒性而从欧洲市场（英国）撤出。首先，我们从比较毒理学基因组学数据库（CTD）中检索并获得了地乐洛尔诱导的肝损伤相关蛋白，多药耐药蛋白1（MDR 1）。然后，我们在dbSNP数据库中检索并提取了477个MDR1上的非同义单核苷酸多态性（nsSNP）。其次，我们使用VarMod工具预测这些nsSNPs诱导的MDR1功能变化，从中提取出显著改变该蛋白功能的nsSNPs。第三，我们构建了这些变异蛋白的三维结构，并使用AutoDock进行对接研究，选择最佳模型来确定nsSNPs的位点。最后，我们使用来自1000个基因组计划的数据来验证风险SNP的优势人群分布。我们将相同的策略应用于上市后药物性肝损伤药物，以进一步测试我们方法的可行性。
Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process. In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity. First, we searched for and obtained the Dilevalol-induced- liver-injury related protein, multidrug resistance protein 1 (MDR1), from the Comparative Toxicogenomics Database (CTD). Then, we searched and extracted 477 non-synonymous single nucleotide polymorphisms (nsSNP) on MDR1 in the dbSNP database. Second, we used the VarMod tool to predict the functional changes of MDR1 induced by these nsSNPs, from which we extracted the nsSNPs that significantly change the functions of this protein. Third, we built the three-dimensional structures of those variant proteins and used AutoDock to perform a docking study, choosing the best model to determine the sites of nsSNPs. Finally, we used the data from the 1000 Genomes Project to verify the dominant population distribution of the risk SNP. We applied the same strategy to the post-marketing drug-induced liver injury drugs to further test the feasibility of our method.
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影响因子：
3
作者：
Winnenburg R;Shah NH
通讯作者：
Shah NH
影响因子：
7.3
作者：
Wang, RX;Lu, YP;Wang, SM
通讯作者：
Wang, SM
影响因子：
3
作者：
Trott, Oleg;Olson, Arthur J.
通讯作者：
Olson, Arthur J.
OAHG：用多级本体注释人类基因的综合资源。
影响因子：
4.6
作者：
Cheng, Liang;Sun, Jie;Zhou, Meng
通讯作者：
Zhou, Meng
DOI：
10.1002/jez.a.307
发表时间：
2006-09-01
影响因子：
2.8
作者：
Mattingly, C. J.;Rosenstein, M. C.;Boyer, J. L.
通讯作者：
Boyer, J. L.