Optimisation of signal management in pharmacovigilance

Signal management is a fundamental aspect of pharmacovigilance, required by regulatory agencies worldwide. Drug manufacturers are under pressure to deliver complex, detailed safety analyses and to fulfil the expectations of a growing number of regulators. Expanding product portfolios and the increasing availability of data highlight the need for signal management solutions. A comprehensive approach is necessary to optimise signal detection and management efficiencies without compromising quality, and technology can offer the solution.

Designing a sound signal detection approach

Regulators such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have different signal management expectations. Module IX of the EMA’s Good Pharmacovigilance Practices (GVP) provides thorough recommendations on signal management processes that can be adopted to suit manufacturers’ product portfolios. Various methodologies are discussed and guidance on data source monitoring is also provided. 

The guidance recommends that a company’s applied methodology should take into consideration the characteristics of the product portfolio. Certain drugs have a much higher risk to the patient than others. These are often drugs that are new to the market, have a significant impact, or exist in a therapeutic area susceptible to adverse events.

Applying a risk-based approach to signal detection can help alleviate the burden of time-consuming signal detection searches across multiple sources, that often result in very little value. Risk-based signal detection can be achieved by grouping products into tiers for periodic monitoring. 

For example:

• Tier One: products with a new chemical entity or uncertain risk profile.
• Tier Two:  products with high public awareness or risk factors associated with product and class. 
• Tier Three: all other medications, including those that have been on the market for many years, have only a handful of adverse events per year, and really don’t have a significant impact. 

Once products have been grouped, a suitable periodicity of monitoring can be applied to each distinct category and searches performed within appropriate data sources. As an example, Tier One products may be looked at once every few weeks, whilst Tier Three products are monitored once every six months. Staggering the signal detection schedule and the frequency of monitoring can save time and free up resources to allow for more in-depth analysis to take place. 

Dependent on the volume, characteristics and nature of the data, a quantitative or qualitative signal detection approach may be employed. If the volume of data is high, it may be more pertinent to apply statistical methodologies to help prioritise potential signals for further analysis. The more robust signal detection approaches will comprise of multiple methodologies to be effective. Signal detection technology can support multiple methodologies within one solution, allowing companies the flexibility to pilot differing methods, and apply the most effective methods to their data sets. These methodologies tend to include statistical analysis and (grouped and individual) data review.

Overcoming common data management challenges

Many organisations struggle to collect, clean, and process all the data needed to identify and manage signals in internal and external sources. External safety data sits within numerous databases, such as the FDA Adverse Event Reporting System (FAERS), the Japan Adverse Drug Event Report (JADER) database, World Health Organization’s (WHO) Vigibase, and the EMA’s EudraVigilance system. Amongst these sources, there is vast overlap and duplication, due to the required exchange of information from companies to worldwide regulators.

One of the challenges typically experienced by drug manufacturers is preparing the data for signal detection and analysis, because there are such vast quantities of data of varying completeness and quality. To perform signal detection, many companies opt to generate and export listings of adverse events and detect disproportional reporting or changes in frequency. Following this initial detection, further data summaries or individual cases may be extracted to analyse the potential signal in further depth. 

To support these activities, signal management software can procure, structure, and display all the suitable data in one holistic solution to allow users to focus on data analysis, instead of spending time performing data retrieval activities. Companies may look to employ interactive data analysis and natural language processing to retrieve the information and then deploy appropriate algorithms to drill down and surface relevant issues through focused visualisations. Signal management professionals can benefit from these visual depictions of key information, which offer a welcome break from reviewing spreadsheets and listings.

Review of adverse event data and other important safety information is an important step in the assessment of a potential signal. It is imperative that companies track and document their approach and findings, to evidence their assessment. Tracking and documenting signals throughout the management process can be time-consuming, arduous, and complex, yet it’s essential for compliance purposes that companies can verify the implementation of thorough analysis and subsequent actions. 

Purpose-built software offers solutions to some of these challenges, enabling researchers to track the movement, priority, and status of signals throughout the process, whilst capturing their findings. Clear documentation is invaluable when it comes to presenting this information to regulators and other third parties with vested interest. 

Preparing for future success

Drug manufacturers can prepare for future success in signal management activities by considering the characteristics of their unique product portfolios to create an efficient, compliant, and optimised process. These include taking a risk-based approach to product categorisation, setting achievable compliance timelines for monitoring, utilising signal detection methodologies that are fit-for-purpose for the data source, and identifying the roles and responsibilities of each person involved in the investigation.

A clearly defined, multi-step, and well-documented process creates an audit-ready environment. Consistent signal evaluation and documentation methods comprising of standard templates and a standard decision process leaves little room for ambiguity. Full use of electronic capabilities such as standardised workflows and searchable storage can help to support comprehensive analysis and the re-use of concepts.

By implementing a holistic solution for end-to-end signal management, companies can leverage all the data sources at their disposal to detect and analyse signals that require investigation. They can clear the decks of all obstacles to achieve the full benefit of multi-modal signal management and the increased return on investment that it offers.

About the author

As product management lead for IQVIA’s Vigilance Signal, Stephanie Senn is responsible for driving the overall strategy for IQVIA’s signal detection and management solution. She is focused on identifying innovative opportunities to continuously improve the solution and support evolving requirements. Senn has eleven years of experience within safety and pharmacovigilance and worked within industry before joining IQVIA in 2018. She utilises her knowledge and first-hand experience in signal management to guide and support clients in the adoption of optimised, compliant signal processes. Senn obtained her Bachelor’s degree in Neuroscience from the University of Manchester and passed her Master’s degree in Drug Development Science with distinction at King’s College London.