The RxView Blog

We recently created two video's that help to explain the evolution of pharmacovigilance software and the value this evolution creates for an organization. See our Pharmacovigilance Software Overview for more information or schedule a personal demo of Evidex! 

I recently wrote an article for PharmExec called Pharmacovigilance Software is Having its “Salesforce” Moment. It discusses how the market for pharmacovigilance software is starting to catch up to other verticals through disruption by technological innovation. I argue that the barriers that were in place such as drug safety’s role as a cost center and the difficulty of accessing data are breaking down. And the result is that there are now choices in the pharmacovigilance software market that weren’t available just a few years ago.

After re-reading the article, I think there is an important, key point that got lost and could use further definition. The concept of the evolution of software from a “system of record”, to a “system of engagement”, to a “system of intelligence”, and why that evolution is so important.

Over the course of the last blog posts, the requirements of the four stages of the GVP Module IX Signal Management (GVP IX) process were discussed in great detail, along with specific ways in which Evidex Signal Management can help an organization to efficiently meet GVP IX requirements in a single, user-friendly, easy to implement platform built on a foundation of safety data and analytics.

After an initial review, a determination is made whether the signal is non-validated or that it is in fact a validated signal that needs prioritization and further assessment.

Signal validation is the process of confirming a new potentially causal association, or a new aspect of a known association, of a drug-event combination. When validating a signal, GVP IX recommends that the following elements should be considered:

Advera Health has long embraced the fact that safety signals can arise from a wide variety of data sources. The authors of GVP IX agree. The module specifies that common datasets include spontaneous reporting (MAH’s own database, national databases such as FDA Adverse Event Reporting System (FAERS), EudraVigilance, and VigiBase), active surveillance, studies, and scientific literature. All of these data sources should be considered depending on characteristics of the product and based on clinical judgement.

Good Pharmacovigilance Practices (GVP) are a set of measures put into practice in 2012 to facilitate the performance of pharmacovigilance in the European Union (EU). GVP is broken out into several modules that govern different aspects of pharmacovigilance processes. GVP Module IX - Signal Management (GVP IX) provides general guidance and requirements on scientific and quality aspects of signal management.

The guidelines apply to Marketing Authorization Holders (MAH) for medicines authorized by the European Medicines Agency (EMA) and EU Member States. However, in the absence of formal regulation on the process of signal management by other health authorities, such as FDA, these guidelines have become the de facto global standard.

There are several aspects of the GVP IX process:

In addition to asking questions around the value and validity of using social media monitoring for pharmacovigilance, we also often get questions around our thoughts on whether social media data are inherently biased. When I brought this question to the Booz Allen Epidemico data team, they educated me on the three dimensions of bias that come from different drug safety datasets: Patient reported outcomes, seriousness, and completeness. The graphic below shows how those three dimensions differ across social media, clinical trials, spontaneous reports, and EHR records.

One of the biggest fears that pharmacovigilance professionals have when it comes to using social media is that amount of work that it will create. How will you be able to take the time needed to "clean" the data and get it fit for analysis? Machines can be trained to identify posts with drug-adverse event combinations, but it is more difficult for them to be able to ensure the post is an accurate representation of an adverse event. Thus, Booz Allen's Epidemico team uses human curation to provide the most accurate data.