Mining Online Communities and Social Networks for Safety Signals

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Mining Online Communities and Social Networks for Safety Signals

About PerficientPerficient is the leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience,business optimization, and industry solutions.

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Rodney LemeryDirector, Safety and PharmacovigilancePerficient20+ Years in Life SciencesBS in BiotechnologyMPH in International EpidemiologyPhD in Epidemiology

Overview of adverse drug reactions and signal detection

Regulatory climate surrounding social media usage in pharmacovigilance

Summary of literature on digital frameworks for using social media data in pharmacovigilance

Limitations and challenges in using these digital frameworks for using social media data in pharmacovigilance

Next stepsAgenda

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Unintended, harmful response suspected to be caused by the drug taken under normal circumstances (Lee, 2006)In the U.S. alone, ADRs are estimated to account for ~100,000 deaths annually (Lazarou, Pomeranz & Corey, 1998) Overview of Adverse Drug Reactions and Signal Detection

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Signals are considered to be both previously unknown associations and new aspects about an already known association (Harmark, et. Al., 2016)Overview of Adverse Drug Reactions and Signal Detection

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Overview of Adverse Drug Reactions and Signal Detection

One qualitative way to evaluate the signals we receive, is using the SNIP methodology:StrengthNewnessImportancePrevention

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According to a WHO publication (2002), the changing face of pharmacovigilance includes the following:Improve patient care Improve public health and safety Contribute to the risk/benefitPromote understanding of pharmacovigilance to the publicWHO. (2002). The Importance of Pharmacovigilance. Safety Monitoring of Medicinal Products. Geneva: World Health Organization.Overview of Adverse Drug Reactions and Signal Detection

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Overview of Adverse Drug Reactions and Signal Detection

Signals originate from clinical and post marketed data with limitations specific to each of these areas:Clinical TrialsTend to be smallNot diverseDemographics (race, gender etc.)ComorbiditiesConcomitant productsPost MarketingSpontaneous reporting systemsUnder-reporting *Electronic Health/Medical RecordsSocial Media**

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Current Regulatory Climates for Use of Social Media in PharmacovigilanceFDANo regulatory requirements specific to mining social mediaGuidance on analyzing patient reported outcomesFDASIA (2012) and the release of a strategic plan that emphasizes innovative collection and analysis of post-market dataEMAGVP guideline (2012)In 2014 Module VI updated and mandates regularly screening of websites under its controlThe same GVP stipulates that it is considered good practice for the MAH to monitor external sites such as patient support or special diseases group sites When made aware, the GVP suggests ADRs be handled in the same manner as a spontaneous reportIn 2016 Module VI has been issued in DRAFT and changes the definition of a identifiable reporterRequires qualification (ie. physician, nurse, patient etc.) and only one of the following:Name, address, phone

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NON-REGULATORY Supporting InitiativesStrengthening Collaboration for Operating Pharmacovigilance in Europe (SCOPE) Raise awareness of national reporting systems for AE reporting by consumers in Europehttp://www.scopejointaction.eu/Innovative Medicines Initiative (IMI) funded the WEB-RADR projectConduct scientific research into the use of social media networks and to develop dedicated applications (Apps) for reporting ADRs to the National Competent Authorities in Europehttp://web-radr.eu/Current Regulatory Climate for Useof Social Media in Pharmacovigilance

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Summary of Literature on Digital Frameworks for Using Social Media Data in PharmacovigilanceA recent meta-analysis of 22 studies published in the literature summarized the efforts and characteristics of social media pharmacovigilance activities and provided a comprehensive framework for conducting this type of research in the future (Sarkera, Ginn, Nikfarjama, et.al., 2015)

Sarkera, A., Ginn, R., Nikfarjama, A., OConnora, K., Smithc, K., Jayaramanb, S., Upadhayab, T., Gonzaleza, G.. (2015). Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54; pp. 202212

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

Table 2 Identified Sources for the 22 Studies

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

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Summary of Literature on Digital Frameworks for Using Social Media Data in PharmacovigilanceTable 3 Coding of Event Terms to Various Lexicons

Some studies used phonetic spelling dictionaries to try and ensure proper identification of medicinal products.

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

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.wide awake

.it feels like the Sahara desert in my mouth.

I take it for diarrhea. While another may say, Had to stop treatment, it was causing diarrhea.

Well played tysabri...kicking butt #nosleep

This cipro is totally "killing" my tummy .. hiks..

Over-eaten again just before bed. Stuffed. Good chance I will choke on my own vomit during sleep. I blame #Olanzapine #timetochange #bipolarSummary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

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Summary of Literature on Digital Frameworks for Using Social Media Data in PharmacovigilanceEven this rather robust model doesnt incorporate the act of evaluating and potentially reporting on the identified ADR through regulatory channels.

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Summary of Literature on Digital Frameworks for Using Social Media Data in PharmacovigilanceWe are proposing an augmentation to this framework that would allow an organization to evaluate the quality of the identified ADR and assess its reportability to a regulatory authority or partner.Freifeld, Brownstein, Menone, et. Al. (2014) coined the phrase Proto-AE to explain identifiable event terms in social media that had not been confirmed as actual adverse drug reactions.

Freifeld, C.C., Brownstein, J.S., Menone, C.M., Bao, W., Filice, R., Kass-Hout, T., and Dasgupta, N.. (2014). Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter. Drug Safety 37:343350

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Summary of Literature on Digital Frameworks for Using Social Media Data in PharmacovigilanceWe suggest the term proto-AE could be a useful identifier to relate the pre-reporting terms selected through the ADR identification process.

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

Carbonell, P., Mayer, M.A., Bravo, A.. (2015). Exploring brand-name drug mentions on Twitter for pharmacovigilance. Studies in Health Technology and Informatics. 210:55-9.Freifeld, C.C., Brownstein, J.S., Menone, C.M., Bao, W., Filice, R., Kass-Hout, T., and Dasgupta, N.. (2014). Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter. Drug Safety 37:343350

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Summary of Literature on Digital Frameworks for Using Social Media Data in Pharmacovigilance

Sarkera, A., Ginn, R., Nikfarjama, A., OConnora, K., Smithc, K., Jayaramanb, S., Upadhayab, T., Gonzaleza, G.. (2015). Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54; pp. 202212

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Limitations and Challenges in Using These Digital Frameworksfor Social Media Data in PharmacovigilanceETHICAL CONCERNSUse of identifiable data like geocode location on posting, username and other potentially personally identifiable informationNeglect of under-represented members of the online community; less computer literate, lack access to the internet, or have their social media usage censored

CHALLENGESADRs may be referred to using creative idiomatic expressions or terms not found within existing medical lexicons (.it feels like the Sahara desert in my mouth.)The informal nature of social media results in a prevalence of poor grammar, spelling mistakes, abbreviations and slangDifferentiate between indication and adverse eventDrugs may be described by their brand names, active ingredients, colloquialisms or generic drug terms (e.g. antibiotic)

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Next Steps General Support

Perficient can assist with general strategy in implementing a methodology for social media monitoring and reportingSupport the design and conduct analysis of a social media targeted project (by active substance or event of interest)Use of innovative technology to augment the social media framework your company currently uses

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QuestionsType your question into the chat box

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Thank You

ReferencesCarbonell, P., Mayer, M.A., Bravo, A.. (2015). Exploring brand-name drug mentions on Twitter for pharmacovigilance. Studies in Health Technology and Informatics. 210:55-9.Chokor, A., Sarker, A., Gonzalez, G. (2016). Mining the Web for Pharmacovigilance: the Case Study of Duloxetine and Venlafaxine. Masters project report retrieved on November 2, 2016 from https://arxiv.org/abs/1610.02567Duh, M.S., Cremieux, P., Van Audenrode, M., Vekeman, F., Karner, P., Zhang, H., and Greenberg, P. (2016). Can social media data lead to earlier detection of drug-related adverse events? Pharmacoepidemiology and Drug Safety, ePubForrow, S., Campion, D. M., Herrinton, L. J., Nair, V. P., Robb, M. A., Wilson, M., & Platt, R. (2012). The organizational structure and governing principles of the Food and Drug Administration's MiniSentinel pilot program. Pharmacoepidemiology and drug safety, 21(S1), 12-17.Freifeld, C.C., Brownstein, J.S., Menone, C.M., Bao, W., Filice, R., Kass-Hout, T., and Dasgupta, N.. (2014). Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter. Drug Safety 37:343350Hrmark, L., Raine, J., Leufkens, H., Edwards, I. R., Moretti, U., Sarinic, V. M., & Kant, A. (2016). Patient-Reported Safety Information: A Renaissance of Pharmacovigilance?.Drug safety,39(10), 883-890.Hazell, L., Shakir, S.A.. (2006). Under-reporting of adverse drug reactions : a systematic review. Drug Safety. 29(5):pp. 385-96.Lazarou, J, Pomeranz, B.H., Corey, P.N.. (1998). Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 279(15):pp. 1200-5.Lengsavath, M., Dal Pra, A., de Ferran, A. M., Brosch, S., Hrmark, L., Newbould, V., & Goncalves, S. (2016). Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance An Overview of the Regulatory Landscape.Therapeutic Innovation & Regulatory Science, 2168479016663264.OConnor, K., Pimpalkhute, P., Nikfarjam, A., Ginn, R., Smith, K. L., & Gonzalez, G. (2014). Pharmacovigilance on Twitter? Mining Tweets for Adverse Drug Reactions. AMIA Annual Symposium Proceedings, 924933.Topaz, M., Lai, K., Dhopeshwarkar, N., Seger, D.L., R., Saadon, Goss, F., Rozenblum, R., Zhou, L.. (2015). Clinicians Reports in Electronic Health Records Versus Patients Concerns in Social Media: A Pilot Study of Adverse Drug Reactions of Aspirin and Atorvastatin Drug Safety

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