Prediction on the risk population of idiosyncratic adverse reactions based on molecular docking with mutant proteins
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Hongbo Xie1,*, Diheng Zeng1,*, Xiujie Chen1,*, Diwei Huo2,*, Lei Liu1, Denan Zhang1, Qing Jin1, Kehui Ke1 and Ming Hu1
1Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, PR China
2The 2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, PR China
*These authors contributed equally to this work
Xiujie Chen, email: firstname.lastname@example.org
Denan Zhang, email: email@example.com
Keywords: drug-induced liver injury, personalized medicine, homology-modeling, molecular simulation, risk population prediction
Received: July 06, 2017 Accepted: September 20, 2017 Published: October 05, 2017
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 In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity.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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