ArticleFrontiers in drug safety and regulation2025
Leveraging real-world data for safety signal detection and risk management in pre- and post-market settings.
Article in Frontiers in drug safety and regulation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Innovative strategies for early detection of cardiotoxicity: artificial intelligence and multi-modality collaborative models.Journal of thrombosis and thrombolysis · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolving regulatory landscape has increasingly recognized the value of real-world data (RWD) in enhancing drug safety surveillance across the clinical development lifecycle. Enabled by frameworks such as the FDA's Real-World Evidence (RWE) Programs and other international regulatory bodies, sponsors now have expanded opportunities to use RWD to detect, evaluate, and manage safety signals in both pre- and post-market settings. This paper examines how the integration of RWD, particularly through privacy-preserving record linkage (PPRL) methods like tokenization, can improve pharmacovigilance by enabling longitudinal safety monitoring while protecting patient privacy. Traditional safety surveillance methods, such as spontaneous adverse event reporting and aggregate signal detection, are limited by under-reporting and fragmented data sources. In contrast, linked RWD offers more comprehensive, patient-level insights into safety outcomes, including rare events, off-label use, and long-term risks. The paper outlines regulatory considerations for using de-identified, linked RWD in safety reporting, emphasizing the importance of clear protocols, IRB engagement, and legal compliance with HIPAA. It further highlights emerging best practices for integrating RWD into clinical development, such as early regulatory engagement and the incorporation of linked RWD-derived safety signals into risk management plans. Ultimately, we propose that leveraging linked RWD in a privacy-focused manner enables more proactive, scalable, and effective pharmacovigilance. This approach supports earlier detection of safety issues, enhances post-market follow-up, and promotes data continuity between trial and real-world settings, positioning RWD as a cornerstone of modern safety surveillance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.