Handle URI:
http://hdl.handle.net/10754/601419
Title:
Trusted Allies with New Benefits: Repositioning Existing Drugs
Authors:
Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
The classical assumption that one drug cures a single disease by binding to a single drug-target has been shown to be inaccurate. Recent studies estimate that each drug on average binds to at least six known and several unknown targets. Identifying the “off-targets” can help understand the side effects and toxicity of the drug. Moreover, off-targets for a given drug may inspire “drug repositioning”, where a drug already approved for one condition is redirected to treat another condition, thereby overcoming delays and costs associated with clinical trials and drug approval. In this talk, I will introduce our work along this direction. We have developed a structural alignment method that can precisely identify structural similarities between arbitrary types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets for these drugs. Our method combines information from several sources including sequence independent structural alignment, sequence similarity, drug-target tissue expression data, and text mining. In a cross-validation study, we used iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Conference/Event name:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology
Issue Date:
25-Jan-2016
Type:
Presentation
Appears in Collections:
Computational Bioscience Research Center (CBRC); KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology, January 2016; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGao, Xinen
dc.date.accessioned2016-03-16T12:53:41Zen
dc.date.available2016-03-16T12:53:41Zen
dc.date.issued2016-01-25en
dc.identifier.urihttp://hdl.handle.net/10754/601419en
dc.description.abstractThe classical assumption that one drug cures a single disease by binding to a single drug-target has been shown to be inaccurate. Recent studies estimate that each drug on average binds to at least six known and several unknown targets. Identifying the “off-targets” can help understand the side effects and toxicity of the drug. Moreover, off-targets for a given drug may inspire “drug repositioning”, where a drug already approved for one condition is redirected to treat another condition, thereby overcoming delays and costs associated with clinical trials and drug approval. In this talk, I will introduce our work along this direction. We have developed a structural alignment method that can precisely identify structural similarities between arbitrary types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets for these drugs. Our method combines information from several sources including sequence independent structural alignment, sequence similarity, drug-target tissue expression data, and text mining. In a cross-validation study, we used iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments.en
dc.titleTrusted Allies with New Benefits: Repositioning Existing Drugsen
dc.typePresentationen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.conference.dateJanuary 25-27, 2016en
dc.conference.nameKAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnologyen
dc.conference.locationKAUST, Thuwal, Saudi Arabiaen
kaust.authorGao, Xinen
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