Evidence map›Paper›PMID 39085589›Full record

ArticleDrug safety2024

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

George A Neyarapally, Leihong Wu, Joshua Xu, Esther H Zhou, Oanh Dang, Joann Lee, Dharmang Mehta, Rochelle D Vaughn, Ellen Pinnow, Hong Fang

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

George A Neyarapally *Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA. George.neyarapally@fda.hhs.gov.ORCID http://orcid.org/0009-0003-6975-787X
Leihong Wu *Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, USA.
Joshua XuDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, USA.
Esther H ZhouOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Oanh DangOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Joann LeeOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Dharmang MehtaOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Rochelle D VaughnOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Ellen PinnowOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research (CDER), FDA, Silver Spring, MD, USA.
Hong FangOffice of Scientific Coordination, National Center for Toxicological Research (NCTR), FDA, Jefferson, AR, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.

objectiveOur objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.

resultsOur validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.

conclusionThe LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

Indexed as

Artificial IntelligenceDrug LabelingDrug-Related Side Effects and Adverse ReactionsProduct Surveillance, PostmarketingUnited States Food and Drug AdministrationAdverse Drug Reaction Reporting SystemsHumansUnited States

Identifiers

PMID39085589
PMCPMC11554693

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.