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OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation
Achilleas Chytas1, George Gavriilides1, Anargyros Kapetanakis1
1Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Drug Safety
|February 8, 2025
Summary
A new data model, OpenPVSignal, transforms unstructured pharmacovigilance reports into a computable format. This enables easier integration of drug safety data into processing pipelines for enhanced drug safety surveillance.
Area of Science:
- Pharmacovigilance and Drug Safety
- Data Science and Knowledge Representation
- Computational Pharmacology
Background:
- Pharmacovigilance signal report (PVSR) documents offer crucial initial insights into drug-related adverse effects but are typically unstructured.
- The free-text format of PVSRs hinders their integration into data-processing pipelines for drug safety signal investigation and clinical decision-making.
Purpose of the Study:
- To introduce OpenPVSignal, a data model designed for publishing PVSRs in a computationally exploitable format.
- To ensure compliance with FAIR principles (Findable, Accessible, Interoperable, Reusable) for improved data access and reusability.
Main Methods:
- Conversion of pharmacovigilance signals from the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model.
- Development of a Knowledge Graph (KG) from the converted data.
- Technical validation by KG engineers and qualitative verification by medical and pharmacovigilance experts.
Main Results:
- A Knowledge Graph (KG) was successfully created from 101 PVSRs spanning 2011-2019.
- The resulting KG is openly available, facilitating broader access to structured pharmacovigilance data.
Conclusions:
- The developed Knowledge Graph (KG) is a valuable resource for data-processing pipelines.
- This KG can significantly support systems involved in drug safety activities and signal detection.
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