Evidence map›Paper›PMID 41031721›Full record

ArticlePharmacotherapy2025

Identifying Pediatric Drug Safety Knowledge Gaps: An Integrated Approach Leveraging Real-World Data, a Biomedical Knowledge Base, and Postmarketing Surveillance Data.

Saurabh Rahurkar, Jiayi Ouyang, Pallavi Jonnalagadda, Xiaofu Liu, Shijun Zhang, Chien-Wei Chiang, Lei Wang, Aditi Shendre, Lang Li

Abstract read
In one paragraph

Article in Pharmacotherapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

9 authors.

Saurabh RahurkarDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0001-8597-3087
Jiayi OuyangDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0009-0008-9618-8226
Pallavi JonnalagaddaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-2322-3229
Xiaofu LiuDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-5721-091X
Shijun ZhangDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-4780-8416
Chien-Wei ChiangDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-0988-8478
Lei WangDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0003-1904-1737
Aditi ShendreDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0003-4123-7613
Lang LiDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-0746-1809

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development
6 · The paper itself

Abstract

backgroundDrug safety has historically been understudied in pediatric populations, rendering them "therapeutic orphans." Pediatric drug indications and dosages are often inferred by extrapolating safety, efficacy, and dosing data from adult studies, leading to widespread off-label use. However, this approach fails to account for age-specific differences in disease pathophysiology and developmental pharmacokinetics (PK). Despite evidence that adverse drug events (ADEs) manifest with greater severity in pediatric populations than in adults, fewer than 50% of drugs have been systematically studied for pediatric use. The lack of robust drug safety data may result in suboptimal or harmful treatment strategies.

methodsWe used a data-driven approach that integrated three databases -including Merative MarketScan claims, the Maternal and Pediatric Precision in Therapeutics (MPRINT) Knowledgebase (including 670,185 pediatric pharmacoepidemiology, PK, and clinical trial publications on 5062 drugs), the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, a postmarketing safety surveillance database), and FDA drug label data- to identify high-impact target. High-impact targets were defined as drugs that have a high prescription volume, limited safety evidence and high risk of serious ADEs.

resultsWith 229,550 prescriptions in MarketScan, only 9 studies, and almost 50 high risk serious ADEs benzonatate was identified as a high-impact drug of concern. Serious ADEs included seizure, death, and arrhythmia with proportional reportion ratios (PRRs) ranging from 4.3 to 477.8.

conclusionApproved in 1958, Benzonatate, a nonnarcotic antitussive agent has a limited safety evidence with only nine PE/PK publications in six decades. Moreover, it is frequently prescribed off-label for cough relief despite questionable effectiveness, and high-risk of serious ADEs. Our findings reveal a disconnect between clinical practice and suppporting safety evidence. As such, there is critical need to study the safety of this drug using emerging real-world data for real-world evidence. In summary, this study presents an approach that is systematic, objective, reproducible, and data driven to identify and prioritize drug-ADE combinations with limited evidence.

Indexed as

Drug-Related Side Effects and Adverse ReactionsKnowledge BasesProduct Surveillance, PostmarketingChildDatabases, PharmaceuticalDrug MonitoringHumansOff-Label UsePediatricsPharmacoepidemiologyUnited StatesUnited States Food and Drug Administrationadverse drug eventsdrug safetyreal‐world datareal‐world evidencetherapeutic orphans

Identifiers

PMID41031721
PMCPMC12530011

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.