Document Detail


Detecting signals of drug-drug interactions in a spontaneous reports database.
MedLine Citation:
PMID:  17506784     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
AIMS: The spontaneous reports database is widely used for detecting signals of ADRs. We have extended the methodology to include the detection of signals of ADRs that are associated with drug-drug interactions (DDI). In particular, we have investigated two different statistical assumptions for detecting signals of DDI.
METHODS: Using the FDA's spontaneous reports database, we investigated two models, a multiplicative and an additive model, to detect signals of DDI. We applied the models to four known DDIs (methotrexate-diclofenac and bone marrow depression, simvastatin-ciclosporin and myopathy, ketoconazole-terfenadine and torsades de pointes, and cisapride-erythromycin and torsades de pointes) and to four drug-event combinations where there is currently no evidence of a DDI (fexofenadine-ketoconazole and torsades de pointes, methotrexade-rofecoxib and bone marrow depression, fluvastatin-ciclosporin and myopathy, and cisapride-azithromycine and torsade de pointes) and estimated the measure of interaction on the two scales.
RESULTS: The additive model correctly identified all four known DDIs by giving a statistically significant (P < 0.05) positive measure of interaction. The multiplicative model identified the first two of the known DDIs as having a statistically significant or borderline significant (P < 0.1) positive measure of interaction term, gave a nonsignificant positive trend for the third interaction (P = 0.27), and a negative trend for the last interaction. Both models correctly identified the four known non interactions by estimating a negative measure of interaction.
CONCLUSIONS: The spontaneous reports database is a valuable resource for detecting signals of DDIs. In particular, the additive model is more sensitive in detecting such signals. The multiplicative model may further help qualify the strength of the signal detected by the additive model.
Authors:
Bharat T Thakrar; Sabine Borel Grundschober; Lucette Doessegger
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Publication Detail:
Type:  Comparative Study; Journal Article     Date:  2007-05-15
Journal Detail:
Title:  British journal of clinical pharmacology     Volume:  64     ISSN:  0306-5251     ISO Abbreviation:  Br J Clin Pharmacol     Publication Date:  2007 Oct 
Date Detail:
Created Date:  2007-09-20     Completed Date:  2008-03-14     Revised Date:  2013-06-06    
Medline Journal Info:
Nlm Unique ID:  7503323     Medline TA:  Br J Clin Pharmacol     Country:  England    
Other Details:
Languages:  eng     Pagination:  489-95     Citation Subset:  IM    
Affiliation:
Drug Safety Risk Management, F. Hoffman-La Roche Ltd, Basel, Switzerland. bharat.thakrar@roche.com
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MeSH Terms
Descriptor/Qualifier:
Adverse Drug Reaction Reporting Systems
Drug Interactions / physiology*
Models, Chemical
Pharmaceutical Preparations / adverse effects*
Chemical
Reg. No./Substance:
0/Pharmaceutical Preparations
Comments/Corrections

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