INDI: a computational framework for inferring drug interactions and their associated recommendations
Abstract 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 pred...
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| Format: | Article |
| Language: | English |
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Springer Nature
2012-07-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/msb.2012.26 |
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| _version_ | 1850179765323956224 |
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| author | Assaf Gottlieb Gideon Y Stein Yoram Oron Eytan Ruppin Roded Sharan |
| author_facet | Assaf Gottlieb Gideon Y Stein Yoram Oron Eytan Ruppin Roded Sharan |
| author_sort | Assaf Gottlieb |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-169bc07ae6c447b1974ff1979afd6ee8 |
| institution | OA Journals |
| issn | 1744-4292 |
| language | English |
| publishDate | 2012-07-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-169bc07ae6c447b1974ff1979afd6ee82025-08-20T02:18:25ZengSpringer NatureMolecular Systems Biology1744-42922012-07-018111210.1038/msb.2012.26INDI: a computational framework for inferring drug interactions and their associated recommendationsAssaf Gottlieb0Gideon Y Stein1Yoram Oron2Eytan Ruppin3Roded Sharan4The Blavatnik School of Computer Science, Tel‐Aviv UniversityDepartment of Physiology & Pharmacology, Sackler School of Medicine, Tel‐Aviv UniversityDepartment of Physiology & Pharmacology, Sackler School of Medicine, Tel‐Aviv UniversityThe Blavatnik School of Computer Science, Tel‐Aviv UniversityThe Blavatnik School of Computer Science, Tel‐Aviv UniversityAbstract 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.https://doi.org/10.1038/msb.2012.26Cytochrome p450drug–drug interactionspharmacodynamic interactionspharmacokinetic interactionssimilarity‐based prediction |
| spellingShingle | Assaf Gottlieb Gideon Y Stein Yoram Oron Eytan Ruppin Roded Sharan INDI: a computational framework for inferring drug interactions and their associated recommendations Molecular Systems Biology Cytochrome p450 drug–drug interactions pharmacodynamic interactions pharmacokinetic interactions similarity‐based prediction |
| 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 | Cytochrome p450 drug–drug interactions pharmacodynamic interactions pharmacokinetic interactions similarity‐based prediction |
| url | https://doi.org/10.1038/msb.2012.26 |
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