Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance: An Overview of the Regulatory Landscape

Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project

The large-scale use of social media by the population has gained the attention of stakeholders and researchers in various fields. In the domain of pharmacovigilance, this new resource was initially considered as an opportunity to overcome underreporting and monitor the safety of drugs in real time in close connection with patients. Research is still required to overcome technical challenges related to data extraction, annotation, and filtering, and there is not yet a clear consensus concerning the systematic exploration and use of social media in pharmacovigilance. Although the literature has mainly considered signal detection, the potential value of social media to support other pharmacovigilance activities should also be explored. The objective of this paper is to present the main findings and subsequent recommendations from the French research project Vigi4Med, which evaluated the use of social media, mainly web forums, for pharmacovigilance activities. This project included an analysis of the existing literature, which contributed to the recommendations presented herein. The recommendations are categorized into three categories: ethical (related to privacy, confidentiality, and follow-up), qualitative (related to the quality of the information), and quantitative (related to statistical analysis). We argue that the progress in information technology and the societal need to consider patients' experiences should motivate future research on social media surveillance for the reinforcement of classical pharmacovigilance.

Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

INTRODUCTION

Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems.

AREAS COVERED

In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration.

EXPERT OPINION

The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.

Establishing a Framework for the Use of Social Media in Pharmacovigilance in Europe

The Innovative Medicines Initiative (IMI) WEB-RADR (Web-Recognising Adverse Drug Reactions) project looked at opportunities and challenges in using social media in pharmacovigilance as a rapidly evolving source of large, real-time data, which could provide new information on the actual use of medicines and potential safety issues. Two of the objectives were to develop principles for continuous monitoring of the safety of medicines without overburdening established pharmacovigilance systems and to propose a regulatory framework on the use of social media in pharmacovigilance. As a starting point, a review of existing legal requirements and regulatory guidance on social media use in pharmacovigilance was performed based on a survey conducted in 2014–2015. Furthermore, input from two large stakeholder workshops and evidence gathered from the research performed by WEB-RADR on the analysis of social media data were taken into consideration. Whilst analytical results of WEB-RADR indicated limited value of social media in detecting or confirming signals for a majority of the drugs studied, it is important to establish a regulatory framework for the use of social media in pharmacovigilance. Thus, the screening and reporting of suspected adverse reactions remains an important pillar in monitoring the safety and efficacy of medicines and the identification of new safety issues. Principles as to how social media can be used in pharmacovigilance are absolutely needed to provide clarity to patients, healthcare professionals, medicines regulators and the pharmaceutical industry.

Using social media in safety signal management: is it reliable?

Social media use is growing globally, with a reported 3 billion active users in 2017. This medium is used increasingly in a health setting by patients (and to a limited extent, healthcare professionals) to share experiences and ask advice on medical conditions as well as pharmaceutical products. In recent years, attention has turned to this huge, generally untapped, source of potential health information as a possible tool for pharmacovigilance, and in particular signal detection. In this article we explore some of the challenges of utilizing social media for safety signal detection and look at some of the pilot studies conducted to date in order to weigh the evidence for and against the utility of social media data in safety signal detection. After doing so we can conclude that the analysis of social media datasets has demonstrated a limited contribution to the signal detection and signal management process. The data available in social media can complement blind spots in traditional pharmacovigilance datasets and provide significant value for targeted investigations and studies such as those relating to abuse, misuse, use in pregnancy, and patient sentiments.

Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media

INTRODUCTION

There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective 'gold standards' beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM.

OBJECTIVE

In this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts.

METHODS

We used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset.

RESULTS

During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by the addition of an additional ML ICSR detector. On a blind test set of 2500 posts, the final model demonstrated a gKappa of 78% and an accuracy of 83%. In the time test, it took the final model 48 h to complete a task that would have taken an estimated 44,000 h for human experts to perform.

CONCLUSION

The results of this study indicate that an effective and scalable solution to the challenge of ICSR detection in SDM includes a workflow using an automated ML classifier to identify likely ICSRs for further human SME review.

Enabling social listening for cardiac safety monitoring: Proceedings from a drug information association-cardiac safety research consortium cosponsored think tank

This white paper provides a summary of the presentations and discussions from a think tank on "Enabling Social Listening for Cardiac Safety Monitoring" trials that was cosponsored by the Drug Information Association and the Cardiac Safety Research Consortium, and held at the White Oak headquarters of the US Food and Drug Administration on June 3, 2016. The meeting's goals were to explore current methods of collecting and evaluating social listening data and to consider their applicability to cardiac safety surveillance. Social listening is defined as the act of monitoring public postings on the Internet. It has several theoretical advantages for drug and device safety. First, these include the ability to detect adverse events that are "missed" by traditional sources and the ability to detect adverse events sooner than would be allowed by traditional sources, both by affording near-real-time access to data from culturally and geographically diverse sources. Social listening can also potentially introduce a novel patient voice into the conversation about drug safety, which could uniquely augment understanding of real-world medication use obtained from more traditional methodologies. Finally, it can allow for access to information about drug misuse and diversion. To date, the latter 2 of these have been realized. Although regulators from the Food and Drug Administration and the United Kingdom's Medicines and Healthcare Products Regulatory Agency participated in the think tank along with representatives from industry, academia, and patient groups, this article should not be construed to constitute regulatory guidance.