Evidence map›Paper›PMID 41121456›Full record

ArticleRespirology (Carlton, Vic.)2026

Global Disproportionality Analysis of Adverse Event Reports on Interstitial Lung Diseases for Cancer-Targeted Therapies, 1968-2024.

Jinyoung Jeong, Hyunjee Kim, Sooji Lee, Jiyoung Hwang, Lee Smith, Ho Geol Woo, Jaehyeong Cho, Dong Keon Yon

Abstract read
In one paragraph

Article in Respirology (Carlton, Vic.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Jinyoung JeongDepartment of Medicine, Kyung Hee University College of Medicine, Seoul, South Korea.
Hyunjee KimCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
Sooji LeeDepartment of Medicine, Kyung Hee University College of Medicine, Seoul, South Korea.
Jiyoung HwangDepartment of Medicine, Kyung Hee University College of Medicine, Seoul, South Korea.
Lee SmithCentre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK.
Ho Geol WooCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
Jaehyeong ChoCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
Dong Keon YonDepartment of Medicine, Kyung Hee University College of Medicine, Seoul, South Korea.ORCID 0000-0003-1628-9948

Funding

Ministry of Health & Welfare, South Korea RS-2025-02220492Ministry of Science and ICT, South Korea IITP-2024-RS-2024-00438239Ministry of Science and ICT, South Korea RS-2024-00509257
6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe increasing complexity of interstitial lung disease (ILD) related to cancer-targeted monoclonal antibodies (mAbs) has emerged as a significant clinical concern. Thus, this study aimed to investigate reporting signals of four ILD subtypes detected with cancer-targeted mAbs.

methodsThis global pharmacovigilance study conducted disproportionality analyses to detect signals of ILD subtypes reported with cancer-targeted mAbs. ILD subtypes were classified into eight categories according to previous studies, of which four with a low number of reports were excluded. Five cancer-targeted monoclonal antibodies (VEGF/VEGFR, CD20, PD-1/PD-L1, HER2, and EGFR inhibitors) were included according to ATC code (L01F). Reporting signals were evaluated using reporting odds ratio (ROR) with 95% CI and information component (IC) with IC

resultsInterstitial pneumonitis and pulmonary fibrosis showed significant disproportionate reporting signals across all drugs. Notably, interstitial pneumonitis showed significant signals with EGFR inhibitors (ROR, 47.46 [95% CI, 44.67-50.42]; IC, 5.47 [IC

conclusionsAlthough causal inference cannot be drawn, this global disproportionality study highlights reporting signals between cancer-targeted mAbs and ILD subtypes, underscoring the importance of strengthening adverse event reporting systems before and after mAb administration.

Indexed as

Antibodies, MonoclonalLung Diseases, InterstitialMolecular Targeted TherapyNeoplasmsAdverse Drug Reaction Reporting SystemsFemaleHumansImmune Checkpoint InhibitorsMalePharmacovigilanceAntibodies, MonoclonalImmune Checkpoint Inhibitorscancer‐targeted monoclonal antibodiesinterstitial lung diseaseinterstitial pneumoniapharmacovigilancepulmonary fibrosis

Identifiers

PMID41121456
PMCPMC12865513

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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.