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.

In recent years, the rapid development of “big data analytics” has created a surge within pharmaceutical companies to leverage data from the entire value chain to drive actionable insights. Although other departments within pharmaceutical companies have long been focused on utilizing data driven insights, modern pharmacovigilance departments are just starting to fully incorporate a data-and-analytics-first approach to signal detection, validation, and management.

In talking with current and prospective clients around Advera Health's partnership with Booz Allen's Epidemico social media monitoring data, we get asked a lot about not only the validity of the process Booz Allen goes through to cut through the "noise", but also about the value social media monitoring has for post-marketing drug safety surveillance and pharmacovigilance. In addition to highlighting the primer we put together, we also refer those interested to the numerous academic publications on the topic. Three core publications that Booz Allen has conducted in collaboration with regulators and industry serve as a starting point, and below is a summary of key points and links to the full publications. 

A study published Tuesday in the Journal of the American Medical Association found that people who take at least one of the 200 drugs that list depression as a side-effect are, in fact, more likely to be depressed. The study, which included 26,192 adults who participated in the National Health and Nutrition Examination Survey, does not prove a causal effect, but the association is striking. 

Modern pharmacovigilance departments today do more than process and review ICSRs. They are involved in the entire lifecycle of a drug, providing vital data points and analysis that inform everything from clinical trial design to commercial planning to post-market safety surveillance. In order to perform well, pharmacovigilance and safety science teams require access to an array of data and the analytics to make sense of those data. The insights gained from unique combinations of clinical and real world data drive signal detection, epidemiological research, and evidence generation, all of which support organization-wide decisions.

The problem is that drug safety insights are hard to obtain and teams often get caught in a "paralysis by analysis" trap.

Wow, it’s the end of February. I’m sure I’m not the only one who feels that the first two months of 2018 have flown by. Advera Health’s engineering, product, and analyst teams have been hard at work. Evidex’s back-end data architecture has been enhanced to keep up with our growing library of datasets and to ensure the analytics layered on all of the data are efficient and fast to meet client demands.