#signaldetection

Drug safety operations are becoming increasingly complex, with growing case volumes, evolving regulatory expectations, and the demand for faster, more consistent assessments. Traditional manual processes often struggle to keep up leading to delayed signal detection, operational fatigue, and compliance risks.

This visual highlights the top four challenges faced by pharmacovigilance and drug safety teams, and how AI-powered solutions are transforming global workflows:

✅ Data Overload - Thousands of safety reports, literature data, and case documents overwhelm internal teams and slow decision-making. AI automates data ingestion and triage, helping prioritize high-risk cases instantly.

✅ Inconsistent Case Assessment - Manual review can introduce variability and contribute to missed signals. AI-driven case evaluation supports more consistent, pattern-based safety assessments.

✅ Slow Literature Screening - Full-text review is time-intensive and delays signal detection. NLP-enabled screening accelerates identification of relevant safety content in seconds.

✅ Resource Fatigue & Burnout - Labor-intensive manual tasks increase workload and contribute to operational burnout. Automation reduces human effort so safety experts can focus on strategic tasks.

Effective drug monitoring requires the ability to detect and analyze safety trends across diverse global markets. A robust Global Pharmacovigilance strategy utilizes advanced Modern Safety Aggregate Signal Risk Management to identify emerging patterns in adverse event data. This proactive approach allows for the development of effective Risk Management Plans (RMP) and ensures that safety disclosures are always based on the most current clinical evidence, protecting both the patient and the integrity of the product.

False positives in signal detection can lead to unnecessary escalation and reduced confidence in safety decisions. A structured approach starting with statistical signals, followed by data review, clinical context validation, and final confirmation helps distinguish true risks from noise.

Key implementation timelines:

22 July 2025 – Regulation published in the Official Journal of the European Union

12 August 2025 – Core provisions, including EVDAS monitoring for signal detection, start to apply

12 February 2026 – Full regulation becomes applicable

EVDAS is the EudraVigilance Data Analysis System, an analytical tool used by the European Medicines Agency (EMA) to support pharmacovigilance and identify potential safety concerns with medicines in the EU. It analyses safety reports from the EudraVigilance database to detect potential safety signals, and EMA utilizes this system to support national competent authorities and marketing authorization holders in monitoring and evaluating drugs. 

COMMISSION IMPLEMENTING REGULATION (EU) 2025/1466 of 22 July 2025, which came into effect from the twentieth day following the publication of the revised IR 520/2012 i.e., from 12 August 2025.

Under the previous framework (Implementing Regulation (EU) 520/2012), only specific active substances were subject to mandatory EudraVigilance (EV) monitoring. The new regulation closes that chapter: the pilot list approach is abolished, and EVDAS becomes a compulsory data source for signal detection for every MAH operating in Europe and use it as an additional source of safety information to support their processes and enhance signals detected through other sources e.g., internal databases, literature, spontaneous reports.

An update to the Good Pharmacovigilance Practices (GVP) IX is scheduled to be implemented in Q2 2026, in alignment with the forthcoming requirements. However, MAH signal management process should reflect these changes with immediate effect.

Under the previous framework, MAHs were expected to consult EVDAS data primarily after the identification of a potential safety signal, using it as a confirmatory or supportive data source. With the introduction of Implementing Regulation (EU) 2025/1466, this approach fundamentally changes. EVDAS data must now be actively monitored throughout all stages of the signal management process, from early signal detection through validation, assessment, and prioritisation.

For MAHs, the time to act and ensure compliance is immediate:

MAHs with EU products authorised in Northern Ireland (NI) should monitor EudraVigilance (EVDAS), even if they are not authorised in an EU/EEA Member State.

MAH need to ensure they have trained personnel (safety physicians), access to EV analyses, and robust procedures to manage increased workload.

MAHs now need to mandatory review EVDAS data as part of their signal detection activities, of course it could be risk-based approach (based on product type, maturity of product, ongoing signal/risk etc.). This means, SOP should be accustomed to the approach.

Update their signal management standard operating procedures, signal detection templates, contracts/agreements and Pharmacovigilance System Master File (PSMF).

Ensure that all pharmacovigilance personnel receive appropriate training.

COD’s Capabilities in Facilitating MAH Compliance and Operations:

COD’s highly experienced signal management team already supports several MAHs globally, enabling companies to align their signal management processes with established industry best practices.

Support MAHs in updating their signal management SOPs to ensure alignment with the requirements of this Implementing Regulation.

Support MAHs in performing end-to-end signal management activities in line with the revised requirements, thereby strengthening preparedness for Regulatory Authority inspections

In the generation of various EVDAS line listings and their evaluation as part of the signal management process.

If signal management activities are outsourced to a subcontractor, COD can assist in performing audits to ensure compliance and quality oversight.

Get your Signal Management on Track!

Vigilare Biopharma Uses end-to-end Comprehensive, Compliant, User-friendly, Cost-effective advanced signal detection solutions for signal identification, assessment, tracking, and managing signals.

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The more frequently you look at data, the more noise you are disproportionally likely to get (rather than the valuable part called the signal); hence the higher the noise to signal ratio. And there is a confusion, that is not psychological at all, but inherent in the data itself. Say you look at information on a yearly basis, for stock prices or the fertilizer sales of your father-in-law’s factory, or inflation numbers in Vladivostock. Assume further that for what you are observing, at the yearly frequency the ratio of signal to noise is about one to one (say half noise, half signal) —it means that about half of changes are real improvements or degradations, the other half comes from randomness. This ratio is what you get from yearly observations. But if you look at the very same data on a daily basis, the composition would change to 95% noise, 5% signal. And if you observe data on an hourly basis, as people immersed in the news and markets price variations do, the split becomes 99.5% noise to .5% signal. That is two hundred times more noise than signal —which is why anyone who listens to news (except when very, very significant events take place) is one step below sucker.