Literature DB >> 23414686

Informatics confronts drug-drug interactions.

Bethany Percha1, Russ B Altman.   

Abstract

Drug-drug interactions (DDIs) are an emerging threat to public health. Recent estimates indicate that DDIs cause nearly 74000 emergency room visits and 195000 hospitalizations each year in the USA. Current approaches to DDI discovery, which include Phase IV clinical trials and post-marketing surveillance, are insufficient for detecting many DDIs and do not alert the public to potentially dangerous DDIs before a drug enters the market. Recent work has applied state-of-the-art computational and statistical methods to the problem of DDIs. Here we review recent developments that encompass a range of informatics approaches in this domain, from the construction of databases for efficient searching of known DDIs to the prediction of novel DDIs based on data from electronic medical records, adverse event reports, scientific abstracts, and other sources. We also explore why DDIs are so difficult to detect and what the future holds for informatics-based approaches to DDI discovery.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Mesh:

Year:  2013        PMID: 23414686      PMCID: PMC3808975          DOI: 10.1016/j.tips.2013.01.006

Source DB:  PubMed          Journal:  Trends Pharmacol Sci        ISSN: 0165-6147            Impact factor:   14.819


  51 in total

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Journal:  Drug Saf       Date:  2010-08-01       Impact factor: 5.606

2.  National Hospital Discharge Survey: 2007 summary.

Authors:  Margaret Jean Hall; Carol J DeFrances; Sonja N Williams; Aleksandr Golosinskiy; Alexander Schwartzman
Journal:  Natl Health Stat Report       Date:  2010-10-26

3.  Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels.

Authors:  N P Tatonetti; J C Denny; S N Murphy; G H Fernald; G Krishnan; V Castro; P Yue; P S Tsao; P S Tsau; I Kohane; D M Roden; R B Altman
Journal:  Clin Pharmacol Ther       Date:  2011-05-25       Impact factor: 6.875

4.  Fatal interaction between clarithromycin and colchicine in patients with renal insufficiency: a retrospective study.

Authors:  I F N Hung; A K L Wu; V C C Cheng; B S F Tang; K W To; C K Yeung; P C Y Woo; S K P Lau; B M Y Cheung; K Y Yuen
Journal:  Clin Infect Dis       Date:  2005-06-23       Impact factor: 9.079

5.  Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).

Authors:  Shawn N Murphy; Griffin Weber; Michael Mendis; Vivian Gainer; Henry C Chueh; Susanne Churchill; Isaac Kohane
Journal:  J Am Med Inform Assoc       Date:  2010 Mar-Apr       Impact factor: 4.497

6.  Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.

Authors:  Luis Tari; Saadat Anwar; Shanshan Liang; James Cai; Chitta Baral
Journal:  Bioinformatics       Date:  2010-09-15       Impact factor: 6.937

7.  Mining multi-item drug adverse effect associations in spontaneous reporting systems.

Authors:  Rave Harpaz; Herbert S Chase; Carol Friedman
Journal:  BMC Bioinformatics       Date:  2010-10-28       Impact factor: 3.169

8.  An onco-informatics database for anticancer drug interactions with complementary and alternative medicines used in cancer treatment and supportive care: an overview of the OncoRx project.

Authors:  Kevin Yi-Lwern Yap; En Yi Kuo; Jonathan Jun Jie Lee; Wai Keung Chui; Alexandre Chan
Journal:  Support Care Cancer       Date:  2009-09-09       Impact factor: 3.603

9.  A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents.

Authors:  Isabel Segura-Bedmar; Paloma Martínez; César de Pablo-Sánchez
Journal:  BMC Bioinformatics       Date:  2011-03-29       Impact factor: 3.169

10.  A side effect resource to capture phenotypic effects of drugs.

Authors:  Michael Kuhn; Monica Campillos; Ivica Letunic; Lars Juhl Jensen; Peer Bork
Journal:  Mol Syst Biol       Date:  2010-01-19       Impact factor: 11.429

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  46 in total

1.  Similarity-based modeling in large-scale prediction of drug-drug interactions.

Authors:  Santiago Vilar; Eugenio Uriarte; Lourdes Santana; Tal Lorberbaum; George Hripcsak; Carol Friedman; Nicholas P Tatonetti
Journal:  Nat Protoc       Date:  2014-08-14       Impact factor: 13.491

2.  Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.

Authors:  Feixiong Cheng; Zhongming Zhao
Journal:  J Am Med Inform Assoc       Date:  2014-03-18       Impact factor: 4.497

3.  The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Authors:  Santiago Vilar; George Hripcsak
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

4.  Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks.

Authors:  Ramakanth Kavuluru; Anthony Rios; Tung Tran
Journal:  IEEE Int Conf Healthc Inform       Date:  2017-09-14

5.  Computational methods and opportunities for phosphorylation network medicine.

Authors:  Yian Ann Chen; Steven A Eschrich
Journal:  Transl Cancer Res       Date:  2014-06-01       Impact factor: 1.241

6.  Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.

Authors:  Devendra Singh Dhami; Gautam Kunapuli; Mayukh Das; David Page; Sriraam Natarajan
Journal:  Smart Health (Amst)       Date:  2018-07-07

7.  Healthcare Costs Associated with Potentially Inappropriate Medication Prescribing Detected by Computer Algorithm Among Older Patients.

Authors:  Arnaud Pagès; Nadège Costa; Michaël Mounié; Philippe Cestac; Philipe De Souto Barreto; Yves Rolland; Bruno Vellas; Laurent Molinier; Blandine Juillard-Condat
Journal:  Drugs Aging       Date:  2022-05-24       Impact factor: 3.923

8.  Interaction network among functional drug groups.

Authors:  Minho Lee; Keunwan Park; Dongsup Kim
Journal:  BMC Syst Biol       Date:  2013-10-16

9.  Drug-drug interaction discovery and demystification using Semantic Web technologies.

Authors:  Adeeb Noor; Abdullah Assiri; Serkan Ayvaz; Connor Clark; Michel Dumontier
Journal:  J Am Med Inform Assoc       Date:  2017-05-01       Impact factor: 4.497

10.  Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.

Authors:  Matthew T Patrick; Redina Bardhi; Kalpana Raja; Kevin He; Lam C Tsoi
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

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