For pharmacovigilance decision makers, the need for structure, focus and strategic thinking is vital as evolving technologies, electronic medical records and the proliferation of patient experiences with medicines on social media all provide an often overwhelming amount of data. With more data to search, the process of sorting through the noise to find the signals will require more sophisticated tools and talent. But organizations who figure out how to effectively incorporate these data streams into their signal management environment will be able to make better, faster and more accurate decisions about the safety of their products.

Modern pharmacovigilance planning is full of questions: What makes a signal management process robust? What signal detection methods are available? How do you choose the most effective methods? When does quantitative signal detection methods add value to the early and accurate identification of new signals or changes in known adverse reactions? And would reviewing less conventional data sources — such as electronic medical records and registries — aid in increasing the effectiveness of a company’s pharmacovigilance program?

To answer some of these questions, we will moderate a session at this year’s annual DIA meeting on how to establish right-sized signal management systems. The session will address approaches for fit-for-purpose signal detection and management strategies for biopharmaceutical companies and look at what is happening in major markets around the world in terms of signaling innovation.  

Signal management in a connected world

The need for such right-sizing has never been greater, as regulatory bodies are using these data points to make important decisions about approvals and label claims. In 2015, approximately one third of the 102 confirmed signals managed by the European Medicine Agency’s (EMA’s) Pharmacovigilance Risk Assessment Committee (PRAC) resulted in changes to the product information. The prior year, 40 percent of all signals reviewed resulted in changes in labeling.

We are also seeing an increasing focus by inspectors on the signaling strategies organizations use, and the variety of sources that inform these decisions. Regulators expect strong and swift signal detection and analysis throughout a product’s lifecycle, and they will hold biopharma companies accountable for demonstrating the efficacy of their programs.

To have an effective signal management program in today’s connected world, biopharma companies have to look beyond the traditional qualitative signal detection, and take a more innovative approach to tracking and interpreting additional sources of data to make better, faster decisions. For example, biopharma companies may use zip codes attached to patient reported outcomes to identify geographic patterns related to reports of safety events tied to a specific medication. This can also be used in instances when a batch number for that product isn’t available. Electronic health records and other medical care data are also valuable sources of information to help companies identify or further investigate signals. There has also been a great deal of discussion about the use of social media data in the signal management process, and what obligation companies may have in response to a report on social media tied to a specific drug or therapy.

In such a complex data-driven environment, companies need a validated, audit-trailed system that can provide them with the holistic view of their risk management system. A reliable technology-based signal management system can make it easier to maintain compliance with health authority regulations. It also makes it easier to keep track of safety issues, and to compile data for inspections, audits, and Periodic Safety Update Report (PSUR) preparation.

How effective is your program?

When developing or assessing a signal management program, a biopharma company should be able to accurately answer the following questions – and be confident that the answers will meet the regulatory requirements for pharmacovigilance.

  1. Who makes signal management decisions?
  2. Where are signal management decisions and outcomes tracked?
  3. What are the data sources reviewed for signal detection?
  4. Is signal detection planned according to a strategy?
  5. What is the ratio between confirmed and unconfirmed signals for a given product or across products?
  6. Do you use templates to streamline each signal management step?
  7. Is there a clear link between signal management, PBRER preparation and RMP update?
  8. What tasks within the signal management process do you feel are most time-consuming? (Data review, report writing, documentation, tracking, inspections)


This post was co-authored by Deirdre McCarthy, Senior Benefit-Risk Management Director at Quintiles.