Online communities and social networks like Twitter and Facebook have become important real-world data repositories that can be leveraged by life sciences organizations to gain insight into the patient experience, as well as to identify potential safety issues related to drugs and devices – otherwise known as safety signal detection. Perficient’s director of safety and pharmacovigilance, Dr. Rodney Lemery, discussed the methods, benefits, and challenges involved with mining real-world data for adverse event drug reactions and other safety signals.

1. Mining Online Communities and
Social Networks for Safety Signals

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About Perficient
Perficient 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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Perficient Profile
Founded in 1997
Public, NASDAQ: PRFT
2015 revenue $473.6 million
Major market locations:
Allentown, Atlanta, Ann Arbor, Boston, Charlotte,
Chattanooga, Chicago, Cincinnati, Columbus, Dallas,
Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette,
Milwaukee, Minneapolis, New York City, Northern California,
Oxford (UK), Southern California, St. Louis, Toronto
Global delivery centers in China and India
3,000+ colleagues
Dedicated solution practices
~95% repeat business rate
Alliance partnerships with major technology vendors
Multiple vendor/industry technology and growth awards

4. Rodney Lemery
Director, Safety and Pharmacovigilance
Perficient
• 20+ Years in Life Sciences
• BS in Biotechnology
• MPH in International Epidemiology
• PhD in Epidemiology

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• 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 steps
Agenda

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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:
• Strength
• Newness
• Importance
• Prevention

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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/benefit
• Promote understanding of pharmacovigilance to
the public
WHO. (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 Trials
– Tend to be small
– Not diverse
• Demographics (race, gender etc.)
• Comorbidities
• Concomitant products
• Post Marketing
– Spontaneous reporting systems
• Under-reporting *
– Electronic Health/Medical Records
– Social Media**

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Current Regulatory Climates for
Use of Social Media in Pharmacovigilance
FDA
• No regulatory requirements specific to mining social media
• Guidance on analyzing patient reported outcomes
• FDASIA (2012) and the release of a strategic plan that emphasizes innovative collection and analysis of
post-market data
EMA
• GVP guideline (2012)
– In 2014 Module VI updated and mandates regularly screening of websites under its control
– The 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 report
– In 2016 Module VI has been issued in DRAFT and changes the definition of a identifiable reporter
• Requires qualification (ie. physician, nurse, patient etc.) and only one of the following:
• Name, address, phone

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

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Summary of Literature on Digital Frameworks for
Using Social Media Data in Pharmacovigilance
A 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., O’Connora, K., Smithc, K., Jayaramanb, S., Upadhayab, T., Gonzaleza, G.. (2015). Utilizing
social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54; pp. 202–212

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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 Pharmacovigilance
Table 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 #bipolar”
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
Even this rather robust model doesn’t 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 Pharmacovigilance
• We 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 37:343–350

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Summary of Literature on Digital Frameworks for
Using Social Media Data in Pharmacovigilance
We 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 37:343–350

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Summary of Literature on Digital Frameworks for
Using Social Media Data in Pharmacovigilance
Sarkera, A., Ginn, R., Nikfarjama, A., O’Connora, K., Smithc, K., Jayaramanb, S., Upadhayab, T., Gonzaleza, G.. (2015).
Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54; pp. 202–212

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Limitations and Challenges in Using These Digital Frameworks
for Social Media Data in Pharmacovigilance
ETHICAL CONCERNS
• Use of identifiable data like geocode location on posting, username and other potentially personally
identifiable information
• Neglect of under-represented members of the online community; less computer literate, lack access
to the internet, or have their social media usage censored
CHALLENGES
• ADRs 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 slang
• Differentiate between indication and adverse event
• Drugs 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 reporting
• Support 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

28. Questions
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30. Thank You

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References
• 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.
• 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/1610.02567
• Duh, 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, ePub
• Forrow, 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 Mini‐Sentinel 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 37:343–350
• Härmark, 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., Härmark, 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.
• O’Connor, 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, 924–933.
• Topaz, M., Lai, K., Dhopeshwarkar, N., Seger, D.L., R., Sa’adon, 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