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Cited by 80Pharmacovigilance and Adverse Drug ReactionsBiomedical Text Mining and OntologiesPharmaceutical Practices and Patient Outcomes

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA

Open access
Wu, Leihong, Ingle, Taylor, Liu, Zhichao, Zhao-Wong, Anna, Harris, Stephen, Thakkar, Shraddha, Zhou, Guangxu, Yang, Junshuang +5 more·2019·BMC Bioinformatics
DOI 10.1186/s12859-019-2628-5PMC PMC6419320
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Abstract

BACKGROUND: Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. RESULTS: This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). CONCLUSIONS: This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

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  • Use of standardized terminologies like MedDRA for AE extraction often led to missing some critical AEs because the FDA does not mandate MedDRA terminology in labeling.

    “some studies applied standardized terminologies like MedDRA for AE extraction, but this often led to missing some critical AEs, as the FDA does not mandate the use of MedDRA terminology in labeling [6, 19].”

    From Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025) · cites this paper as [6]

    The Discussion says FDA-approved drug labeling need not use MedDRA terminology and that differently worded ADRs could be missed by a MedDRA-based query. It does not establish that this often happens or that the missed events are critical. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [18]Harrison J, Zhao-Wong A. Working with MedDRA to improve data standards. Good Clinical Practice Journal. 2006.

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Authors
Wu, L., Ingle, T., Liu, Z., Zhao-Wong, A., Harris, S., Thakkar, S., Zhou, G., Yang, J., Xu, J., Mehta, D., Ge, W., Tong, W., & Fang, H.
Affiliations
National Center for Toxicological Research (US)
Corresponding author
  • Tong, Weida
  • Fang, Hong
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2019-03
Volume
20
Issue
S2
Pages
97-97
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-019-2628-5
PMC
PMC6419320
OpenAlex
W2921465693
Cited by
80
References
43
Citations by year
2026: 6 · 2025: 8 · 2024: 11 · 2023: 10 · 2022: 5 · 2021: 12 · 2020: 4 · 2019: 24
FWCI
8.33
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Energy
  • U.S. Food and Drug Administration
  • Oak Ridge Institute for Science and Education
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
Keywords
adverse drug reactions, serious adverse drug reactions, FDA drug labeling, MedDRA, black box warning, drug safety, new drug applications, text mining, hierarchical clustering, neurological disorders, vascular disease, drug abuse, substance abuse, respiratory depression, regulatory documents
MeSH terms
Drug Labeling, Humans, Adverse Drug Reaction Reporting Systems, Data Mining, Drug-Related Side Effects and Adverse Reactions

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