Open Access Open Badges Research

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Richard D Boyce1*, John R Horn2, Oktie Hassanzadeh3, Anita de Waard4, Jodi Schneider5, Joanne S Luciano6, Majid Rastegar-Mojarad7 and Maria Liakata89

Author Affiliations

1 Department of Biomedical Informatics, University of Pittsburgh, Offices at Baum, 5607 Baum Blvd, Pittsburgh, PA, USA

2 Department of Pharmacy, University of Washington, Seattle, WA, USA

3 IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

4 Elsevier Labs, Jericho, VT, USA

5 Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland

6 Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY, USA

7 University of Wisconsin, Milwaukee, WI, USA

8 Department of Computer Science, Aberystwyth University, Wales, UK

9 Text mining group, EBI-EMBL, Hinxton, Cambridge, UK

For all author emails, please log on.

Journal of Biomedical Semantics 2013, 4:5  doi:10.1186/2041-1480-4-5

Published: 26 January 2013


Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market.

Regulatory science; Drug information services; Drug labeling; Linked data; Scientific discourse ontologies; Drug interactions; Pharmacokinetics; Treatment efficacy; Treatment effectiveness; Comparative effectiveness research