To test the network, Reis and Cami integrated information from a commercially available drug safety database from Lexicomp with data on drug chemistry and information on drug and ADE taxonomy. They then took a snapshot of 809 drugs and 852 kinds of adverse events associated with those drugs in the Lexicomp data in 2005. Using the network model, they generated a list of predicted drug-ADE relationships and compared that list to a second snapshot of the Lexicomp database from 2010.
The researchers found the network model to be quite effective at predicting drug-ADE relationships that were absent in the 2005 snapshot but present in that from 2010. For instance, based only on data available in 2005, the model correctly identified 42 percent of the drug-ADE relationships that were subsequently discovered between 2006 and 2010, while correctly recognizing as false 95 percent of drug-ADE pairs that in the 2010 data were categorized as having no association.
"We think the approach holds real promise for strengthening efforts to identify and manage drug risks by helping drug safety practitioners predict high likelihood events and guide efforts to understand, avoid, and alleviate those events before they start appearing in patients," Cami said. "We're now working to extend these methods to incorporate additional sources of drug safety data and to promote their adoption in clinical drug safety practice."
"Today we rely mainly on post-marketing surveillance to identify unknown drug ADEs, especially with novel drug classes," said Shannon Manzi, PharmD, a pharmacist in the Children's Hospital Boston's Emergency Department and co-author on the study. "It would be impossible for drug companies to test every possibl
|SOURCE Children's Hospital Boston|
Copyright©2010 PR Newswire.
All rights reserved