title: Predicting adverse drug events using pharmacological network models.
abstract: Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.
authors.affiliation: Children's Hospital Boston, Boston, MA 02115, USA. aurel.cami@childrens.harvard.edu
authors.forename: Aurel
authors.initials: A
authors.lastname: Cami
authors.forename: Alana
authors.initials: A
authors.lastname: Arnold
authors.forename: Shannon
authors.initials: S
authors.lastname: Manzi
authors.forename: Ben
authors.initials: B
authors.lastname: Reis
mesh_terms: D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D020407:Internet; D008962:Models, Theoretical