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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Molecular Biology Organization
2012
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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. |
format | Online Article Text |
id | pubmed-3421442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | European Molecular Biology Organization |
record_format | MEDLINE/PubMed |
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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