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

Abstract

BackgroundBecause vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation.ObjectiveOur goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction.MethodsTwo experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty.ResultsOne of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts).ConclusionsThe results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader range of drug product labels. Preannotation of drug mentions may ease the annotation task. However, preannotation of PDDIs, as operationalized in this study, presented the participants with difficulties. Future work should test if these issues can be addressed by the use of better performing NLP and a different approach to presenting the PDDI preannotations to users during the annotation workflow.

An Innovative, Collaborative, and Strategic Approach to Proactively Evaluate and Update Drug Interactions Based on Prescribing Information of Newly Approved Medicinal Products

“…3 –5 This difficulty results from the need to initially recognize potential clinically significant toxicities (ie, AEs) reported as clinical signs, symptoms, or medical concepts and evaluate these AEs as consequences of a DDI. 3,4,6 The identification of DDIs between an established older drug and a newly approved product is particularly challenging because unless these older products are the representative drug probe for a particular drug class or a particular type of interaction (ie, ketoconazole or ritonavir representing CYP3A4 inhibitors and rifampin representing CYP3A4 inducers), additional PK simulation models and studies are not conducted to evaluate DDIs with newly authorized drug products. Drug manufacturers have to rely on reports of potential DDI-related toxicities from their postmarketing surveillance of postmarketing safety data, including those reported in the published literature, to recognize and evaluate new DDIs.…”

Reconciliation of patient/doctor vocabulary in a structured resource

“…56 In the long term, we plan to re-use data used to study the quality of life of patients with breast cancer and thus improve our processes similar to the one presented in previous work. 6 We could measure the impact of the resource, for example, on annotation 57,58 and classification tasks. 59 Similarly, we will apply our method to social media in English to extend existing CHV.…”