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INDI: a computational framework for inferring drug interactions and their associated recommendations

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction me...

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Autores principales: Gottlieb, Assaf, Stein, Gideon Y, Oron, Yoram, Ruppin, Eytan, Sharan, Roded
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Molecular Biology Organization 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3421442/
https://www.ncbi.nlm.nih.gov/pubmed/22806140
http://dx.doi.org/10.1038/msb.2012.26
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author Gottlieb, Assaf
Stein, Gideon Y
Oron, Yoram
Ruppin, Eytan
Sharan, Roded
author_facet Gottlieb, Assaf
Stein, Gideon Y
Oron, Yoram
Ruppin, Eytan
Sharan, Roded
author_sort Gottlieb, Assaf
collection PubMed
description Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.
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spelling pubmed-34214422012-08-17 INDI: a computational framework for inferring drug interactions and their associated recommendations Gottlieb, Assaf Stein, Gideon Y Oron, Yoram Ruppin, Eytan Sharan, Roded Mol Syst Biol Article Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike. European Molecular Biology Organization 2012-07-17 /pmc/articles/PMC3421442/ /pubmed/22806140 http://dx.doi.org/10.1038/msb.2012.26 Text en Copyright © 2012, EMBO and Macmillan Publishers Limited https://creativecommons.org/licenses/by-nc-sa/3.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial Share Alike 3.0 Unported License, which allows readers to alter, transform, or build upon the article and then distribute the resulting work under the same or similar license to this one. The work must be attributed back to the original author and commercial use is not permitted without specific permission.
spellingShingle Article
Gottlieb, Assaf
Stein, Gideon Y
Oron, Yoram
Ruppin, Eytan
Sharan, Roded
INDI: a computational framework for inferring drug interactions and their associated recommendations
title INDI: a computational framework for inferring drug interactions and their associated recommendations
title_full INDI: a computational framework for inferring drug interactions and their associated recommendations
title_fullStr INDI: a computational framework for inferring drug interactions and their associated recommendations
title_full_unstemmed INDI: a computational framework for inferring drug interactions and their associated recommendations
title_short INDI: a computational framework for inferring drug interactions and their associated recommendations
title_sort indi: a computational framework for inferring drug interactions and their associated recommendations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3421442/
https://www.ncbi.nlm.nih.gov/pubmed/22806140
http://dx.doi.org/10.1038/msb.2012.26
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