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

Lengsavath, Marina; Pra, Anne Dal; Ferran, Anne-Marie de; Brosch, Sabine; Härmark, Linda; Newbould, Victoria; Gonçalves, Susana Filipa

doi:10.1177/2168479016663264

Cited by 9 publications

(7 citation statements)

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“…Firstly, drug regulatory requirements in 182 countries/jurisdictions within and outside the EU, which were reviewed to gather information on existing guidance and legal requirements on social media and drug safety. The results were published in 2016, providing a baseline understanding of current requirements and gaps requiring further clarification [ 26 ]. Secondly, the EMA organised two large workshops with members of its Healthcare Professionals and Patients’ and Consumers’ Working Parties, the Pharmacovigilance Risk Assessment Committee (PRAC), representatives from Young People (Paediatric Committee), and medical ethics and data protection experts to discuss guiding principles as to how social media could facilitate the monitoring of the safety of medicines [ 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

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

Brosch¹,

Ferran

Newbould³

et al. 2019

Drug Saf

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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. Electronic supplementary material The online version of this article (10.1007/s40264-019-00811-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

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

Brosch¹,

Ferran

Newbould³

et al. 2019

Drug Saf

Self Cite

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“…), who have developed a series of regulatory guidances and launched initiatives in this direction across the entire value chain of pharmacovigilance [129]. Experts stress the need for strong and systematic processes for selection, validation and study implementation [130]. As practices and legal requirements vary across countries, the need for a concrete policy framework on the further use of social media as a new valid type of data sources for pharmacovigilance is emphasised [130].…”

Section: Regulatory Frameworkmentioning

confidence: 99%

“…Experts stress the need for strong and systematic processes for selection, validation and study implementation [130]. As practices and legal requirements vary across countries, the need for a concrete policy framework on the further use of social media as a new valid type of data sources for pharmacovigilance is emphasised [130]. Smith and Benattia [129] further stress the need for an internal revision of the form and function of pharmacovigilance within the biopharmaceutical industry.…”

Section: Regulatory Frameworkmentioning

confidence: 99%

“…Principal research area [62,63,86,[127][128][129][130]133] New regulatory paradigms for the incorporation of social data into pharmacovigilance systems for de novo signal detection or to complement and enrich primary data sources to better understand the strengths and limitations of social media in post-market safety surveillance and establish best practices [62]. With regard to the four points (key tasks, see Sect.…”

Section: Attributementioning

confidence: 99%

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Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

Pappa

Stergioulas

2019

Int J Data Sci Anal

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The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…Current EU regulations require marketing authorization holders to track and report only ADRs derived from SDM posts within their own website forums or otherwise brought to their attention [ 16 – 18 ]. While the current regulatory landscape does not mandate the pharmaceutical industry to review noncompany-controlled SDM (as in ‘active surveillance’), the regulator’s expectations to monitor such data sources may change if a viable solution to the problem of scaling is found.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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IntroductionThere 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.ObjectiveIn 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.MethodsWe 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.ResultsDuring 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (10.1007/s40264-018-0641-7) contains supplementary material, which is available to authorized users.

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

Cited by 9 publications

References 4 publications

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

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

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

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

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