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

Abstract

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events.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 (ClinicalTrials.gov, 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.

Toward a complete dataset of drug–drug interaction information from publicly available sources

“…Studies have shown product labeling to be incomplete [19], [20] and one motivation for this activity is to extend previous pilot work on methods to address known limitations of the information source [21]. To do this, we examined cases where there existed an overlap between the PDDI datasets designed for NLP research with product labeling and other data sources.…”

Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays

“…This has been aided by the growing availability of consolidated databases. Besides HTS assays, new data streams can include text annotations automatically mined from biomedical literature (ChemoText [52]), product labels (SIDER) [53–55] and clinical notes [56,57]. …”

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study

“…The number of drugs that might need to be addressed (more than 16,000) and the complexity of the content in the SPLs make this a daunting task. Our experience in building NLP tools for the extraction of PDDI information [ 10 , 14 ] illustrated some of the difficulty and led us to the conclusion that some amount of manual involvement in the process was required.…”

“…In prior work, we developed a prototype system that used simple named entity recognition (NER) and Semantic Web Linked Data [ 9 ] to link claims about PDDIs from publicly available external resources to the Drug Interactions section of the product label [ 10 ]. Experiments found that our system linked at least one potentially novel interaction (ie, not mentioned in the label) to the Drug Interactions section of product labeling for 20 antidepressants.…”