A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development
Open accessAbstract
BACKGROUND: Adverse drug effects (ADEs) in children are common and may result in disability and death, necessitating post-marketing monitoring of their use. Evaluating drug safety is especially challenging in children due to the processes of growth and maturation, which can alter how children respond to treatment. Current drug safety-signal-detection methods do not account for these dynamics. METHODS: We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages. FINDINGS: We used dGAMs on a database of 264,453 pediatric adverse-event reports and found 19,438 ADEs signals associated with development and validated these signals against a small reference set of pediatric ADEs. Using our approach, we can hypothesize on the ontogenic dynamics of ADE signals, such as that montelukast-induced psychiatric disorders appear most significant in the second year of life. Additionally, we integrated pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression, such as CYP2C18 and CYP27B1, are associated with pediatric ADEs. CONCLUSIONS: We curated KidSIDES, a database of pediatric drug safety signals, for the research community and developed the Pediatric Drug Safety portal (PDSportal) to facilitate evaluation of drug safety signals across childhood growth and development. FUNDING: This study was supported by grants from the National Institutes of Health (NIH).
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Previous efforts in constructing KidSIDES focused on extracting ADE signals from spontaneous reporter databases while controlling for developmental stages.
“Our previous efforts in constructing a database for pediatric ADEs, KidSIDES, focused on extracting ADE signals from spontaneous reporter databases while controlling for developmental stages11.”
From OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models (2025) · cites this paper as [11]
This analysis recapitulates general trends in enriched occurrence of specific classes of ADEs suggested by prior relevant research efforts.
“This analysis recapitulates general trends in the enriched occurrence of specific classes of ADEs suggested by prior relevant research efforts11,27.”
From OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models (2025) · cites this paper as [11]
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Cited by
Sorted by citations
Tanaka, Y., Chen, H. Y., Belloni, P., Gisladottir, U., Kefeli, J., Patterson, J., Srinivasan, A., Zietz, M., Sirdeshmukh, G., Berkowitz, J., et al. (2025) · Med · doi:10.1016/j.medj.2025.100642 · Cited by 17 · see citing works
Self-citation
- Authors
- Giangreco, N. P., & Tatonetti, N. P.
- Affiliations
- Columbia University (US)
- Corresponding author
- Tatonetti, Nicholas P.
- Venue
- Med
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 3
- Issue
- 8
- Pages
- 579-595.e7
- ISSN
- 2666-6340 (ISSN-L)
- PMC
- PMC9378670
- OpenAlex
- W4283398051
- Cited by
- 16
- References
- 1
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 3
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Med (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01GM107145, U54CA209997)
- National Library of Medicine
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Complementary and alternative medicine › Complementary and Alternative Medicine Studies
- Keywords
- drug safety signals, child growth and development, drug metabolism genes, CYP2C18, CYP27B1, montelukast, mental illness, post-marketing surveillance, adverse event reports
- MeSH terms
- Child, Family, Humans, Databases, Factual, Adverse Drug Reaction Reporting Systems, Growth and Development, Drug-Related Side Effects and Adverse Reactions
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