In the evolving landscape of pharmacovigilance (PV), market authorization holders (MAHs) face mounting pressure to integrate real-world data (RWD) into their safety monitoring processes. This shift is propelled by a combination of regulatory initiatives and the democratization of artificial intelligence (AI), which together eliminate the feasibility of delaying RWD implementation.
Regulatory Momentum: A Call to Action
Regulators have long been ahead of industry in the use of RWD in PV. Several key moments illustrate the commitment of agencies like the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) to leverage RWD for enhanced drug safety.
- The passage of the FDA Amendments Act of 2007 by the United States Congress, which called for the establishment of an Active postmarked Risk Identification and Analysis (ARIA) system, specifically mentioned RWD.
- In May 2008, the FDA launched the Sentinel Initiative to create a national electronic system that monitors the safety of FDA-regulated medical products, allowing for rapid analysis of RWD from diverse sources. As part of the initiative, FDA created the Real-World Evidence Data Enterprise (RWE-DE) that is focused on realizing the value of longitudinal electronic health records (EHRs) linked with claims data, enabling the FDA to expand drug safety analyses for certain questions that were not possible to study with claims data alone. A paper published in September 2024 outlines typical use cases for the RWE-DE.
- FDA’s assessment of TreeScan, an AI-based tool that utilizes RWD to detect safety signals, was initiated in 2021. By validating the use of real-world evidence (RWE), the FDA reinforces the necessity for MAHs to incorporate RWD into their PV frameworks. Such assessments demonstrate the regulatory body’s expectation that safety monitoring should evolve alongside emerging technologies.
- The EMA’s DARWIN (Data Analytics for Real-World Insights in the EU) initiative currently holds RWD for 130 million patients, contributed by 40 data partners, further strengthening the trend. DAWRIN aims to utilize RWD in a systematic way to support regulatory decisions, highlighting the importance of incorporating real-world insights into the drug development lifecycle. As these regulatory bodies increasingly prioritize RWD, MAHs find the window to create their RWD strategies rapidly closing.
The Democratization of AI: A Transformative Force
While regulators continue to march forward with RWD, the democratization of AI is transforming how organizations approach data analysis and decision-making in PV. Key milestones in AI development, such as the release of transformer models like BERT in 2018, have revolutionized natural language processing (NLP), enabling more efficient extraction and analysis of information from unstructured data sources like clinical notes and patient reports.
The launch of ChatGPT in 2020 marked another pivotal moment, showcasing how AI can facilitate real-time interactions and data interpretation, making it easier for stakeholders to access crucial safety information. Importantly, the widespread availability of foundational models has lowered the bar to entry. No longer do organizations need millions of dollars and a team of AI experts—smaller players are now empowered to embrace this trend.
As large language models gain attention, their potential for automating routine tasks in PV becomes increasingly evident, freeing up human resources to focus on more complex safety evaluations. Additionally, the rise of AI-powered automation tools has significantly enhanced the ability of organizations to build custom AI models, once again lowering the bar.
How does all of this relate to RWD?
Simply put, AI relies on data; the more robust the data source, the deeper the insights AI can provide. With powerful tools available for analyzing large datasets with minimal investments, MAHs who lag in adopting these technologies will soon find themselves at a disadvantage.
TriNetX RWD in PV: A Significant Advantage for MAHs
The limitations of Individual Case Safety Reports (ICSRs) are well-documented, particularly regarding underreporting and insufficient data. In many instances, this makes it challenging to fully understand the causes of adverse events (AEs). However, RWD provides a more robust data source for analysis.
A recent example involves the signal related to semaglutide, which was reported in late 2023. The EMA announced concerns about cases of suicidal ideation and self-harm among patients using semaglutide. In response, a study utilizing RWD from the TriNetX network was conducted and published in January 2024, which found no association between the drug and suicidal ideation.
Notably, in April 2024, the EMA reviewed the study and concurred with its findings, concluding that no updates to the product labeling were warranted.
PV Strategy: Why Immediate Action is Crucial
The future of PV is here, driven by regulatory moves and the power of AI. MAHs can no longer afford to postpone their RWD strategies. As the tools and expectations evolve, those who embrace this change will not only enhance drug safety but also solidify their position in an increasingly data-driven healthcare landscape.
The message is clear: Developing a robust RWD strategy is essential for safety in this new era; TriNetX is leading the charge.
TriNetX’s EVIDEX® platform makes the most complex and demanding safety signal detection and safety signal management requirements easier by refining signals through analytics supported by the power of TriNetX’s fully integrated, comprehensive, global RWD and evidence.
Discover how TriNetX is empowering data-driven decisions and ways we can future proof your PV strategy, together.
About Elizabeth Smalley
Elizabeth brings more than 10 years of experience in product development, including data analytics, AI, and driving PV innovation to her role as Vice President, Pharmacovigilance Market Strategy at TriNetX. She routinely presents on PV strategy, signal detection, and RWD and leads customer advisory boards on innovation across the PV landscape.
