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Informatics Confronts Drug-drug Interactions

Overview
Specialty Pharmacology
Date 2013 Feb 19
PMID 23414686
Citations 64
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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.

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References
1.
Segura-Bedmar I, Martinez P, de Pablo-Sanchez C . A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents. BMC Bioinformatics. 2011; 12 Suppl 2:S1. PMC: 3073181. DOI: 10.1186/1471-2105-12-S2-S1. View

2.
Thummel K, Wilkinson G . In vitro and in vivo drug interactions involving human CYP3A. Annu Rev Pharmacol Toxicol. 1998; 38:389-430. DOI: 10.1146/annurev.pharmtox.38.1.389. View

3.
Francis L, Bonilla E, Soforo E, Neupane H, Nakhla H, Fuller C . Fatal toxic myopathy attributed to propofol, methylprednisolone, and cyclosporine after prior exposure to colchicine and simvastatin. Clin Rheumatol. 2007; 27(1):129-31. DOI: 10.1007/s10067-007-0696-9. View

4.
Chan A, Yap K, Koh D, Low X, Cheung Y . Electronic database to detect drug-drug interactions between antidepressants and oral anticancer drugs from a cancer center in Singapore: implications to clinicians. Pharmacoepidemiol Drug Saf. 2011; 20(9):939-47. DOI: 10.1002/pds.2167. View

5.
Preissner S, Kroll K, Dunkel M, Senger C, Goldsobel G, Kuzman D . SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Res. 2009; 38(Database issue):D237-43. PMC: 2808967. DOI: 10.1093/nar/gkp970. View