mining online communities and social networks for safety signals

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 ProfileFounded 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

Rodney LemeryDirector, Safety and PharmacovigilancePerficient• 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)


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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 publicWHO. (2002). The Importance of Pharmacovigilance. Safety Monitoring of Medicinal Products. Geneva: World Health Organization.


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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 dataEMA• 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 Useof Social Media in Pharmacovigilance

http://www.scopejointaction.eu/



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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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Table 2 – Identified Sources for the 22 Studies

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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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• “….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”


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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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• 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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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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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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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 PharmacovigilanceETHICAL 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

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 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

QuestionsType your question into the chat box

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Thank You

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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 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 37:343–350Härmark, 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

https://arxiv.org/abs/1610.02567