Similarity-based profiling of pharmacovigilance data for drug safety pattern discovery
- 1College of Pharmacy and Research Institute for Drug Development, Pusan National University Busan 46241 Korea
- 2Graduate School of Data Science, Pusan National University Busan 46241 Korea
- 3Biomedical Research Institute, Pusan National University Hospital Busan 49241 Korea
Abstract
Spontaneous adverse event (AE) reporting systems enable large-scale pharmacovigilance but are typically analyzed as isolated drug–event pairs. Here, we constructed a drug-drug similarity network at an ingredient-level, using 968,966 reports from the Korea Adverse Event Reporting System (KAERS; 2020–2024). After preprocessing, 382,530 reports involving 1,058 ingredients and 3,749 AE events in MedDRA preferred terms (PTs) were retained. Significant ingredient–event signals were identified using proportional reporting ratio, reporting odds ratio, χ², and information component. Pairwise similarity between ingredients was quantified using a hypergeometric test based on shared significant PTs with a false discovery rate ingredients and 1,111 PTs, resulting in a network of 150 ingredients and 1,267 edges. Community detection revealed modules that recapitulated known pharmacological classes, including antineoplastic agents and contrast media, and exhibited clinically coherent safety profiles. Notably, cross-class clustering, including statins with anti-infective and anti-inflammatory agents, suggested shared downstream biological effects beyond primary indications. These findings demonstrate that a signal-based drug similarity network derived from spontaneously reported data can capture clinically meaningful safety patterns and reveal latent relationships across therapeutic classes, thereby providing a scalable approach to pharmacovigilance and hypothesis generation.
Related
- Distinct pattern of reported adverse event with SGLT2 inhibitors in heart failure: A pharmacovigilance study using VigiBase data
- AI-Empowered Multimodal Data Integration and Knowledge Discovery for Pharmacovigilance
- Comparative Safety Profiles of Sacituzumab Govitecan and Datopotamab Deruxtecan: A Real-World Pharmacovigilance Study
- Safety Profile of Tirzepatide in Real‐World Clinical Practice: A Pharmacovigilance Study Using the FAERS Database
- Safety of eribulin: A real-world study based on a pharmacovigilance database
BibTeX
@article{park2026similaritybased,
title = {{Similarity-based profiling of pharmacovigilance data for drug safety pattern discovery}},
author = {Seongjae Park and SeongJin Wi and Heeseon Jo and Yuseong Kwon and Nakyung Jeon and Haeseung Lee},
journal = {The Korean Journal of Physiology \& Pharmacology : Official Journal of the Korean Physiological Society and the Korean Society of Pharmacology},
year = {2026},
month = nov,
doi = {10.4196/kjpp.26.080},
url = {https://doi.org/10.4196/kjpp.26.080},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Similarity-based profiling of pharmacovigilance data for drug safety pattern discovery
AU - Seongjae Park
AU - SeongJin Wi
AU - Heeseon Jo
AU - Yuseong Kwon
AU - Nakyung Jeon
AU - Haeseung Lee
PY - 2026
DA - 2026/11/01
JO - The Korean Journal of Physiology & Pharmacology : Official Journal of the Korean Physiological Society and the Korean Society of Pharmacology
DO - 10.4196/kjpp.26.080
UR - https://doi.org/10.4196/kjpp.26.080
AB - Spontaneous adverse event (AE) reporting systems enable large-scale pharmacovigilance but are typically analyzed as isolated drug–event pairs. Here, we constructed a drug-drug similarity network at an ingredient-level, using 968,966 reports from the Korea Adverse Event Reporting System (KAERS; 2020–2024). After preprocessing, 382,530 reports involving 1,058 ingredients and 3,749 AE events in MedDRA preferred terms (PTs) were retained. Significant ingredient–event signals were identified using proportional reporting ratio, reporting odds ratio, χ², and information component. Pairwise similarity between ingredients was quantified using a hypergeometric test based on shared significant PTs with a false discovery rate ingredients and 1,111 PTs, resulting in a network of 150 ingredients and 1,267 edges. Community detection revealed modules that recapitulated known pharmacological classes, including antineoplastic agents and contrast media, and exhibited clinically coherent safety profiles. Notably, cross-class clustering, including statins with anti-infective and anti-inflammatory agents, suggested shared downstream biological effects beyond primary indications. These findings demonstrate that a signal-based drug similarity network derived from spontaneously reported data can capture clinically meaningful safety patterns and reveal latent relationships across therapeutic classes, thereby providing a scalable approach to pharmacovigilance and hypothesis generation.
SN - 1226-4512
SN - 2093-3827
ER -