[PPT] - Pharmacovigilance & Social Media A new approach to Safety Signal PowerPoint Presentation

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A new approach to Safety Signal Detection: Potential & Issues

Karen Whitelock, Drug Safety Responsible, Novartis Pharma Australia Dr David Lewis, Global Head of Pharmacovigilance Melbourne, May 2015

Pharmacovigilance & Social Media

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Disclaimer

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The information within this presentation is based on the presenters expertise and experience and represents the views of the presenter for the purpose of this presentation.

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Overview: Pharmacovigilance of social media

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Social media landscape and pharmaceutical medicines
Regulations & guidelines for PV of digital media
AE reporting via Social Media
Overview of Novartis PV of digital media
WEB-RADR IMI project
Assessment of pharmacovigilance using social media
Mobile reporting of suspected adverse reactions
Digital Drug Safety Surveillance
Data Protection for Health Apps - Data Privacy

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Trends in Consumer Technology

Computer & smartphone evolution The digital media landscape...

Internet-based applications that allow for the creation and exchange of user-generated content

From: Chopra R - Pharmacovigilance & Digital Media (http://de.slideshare.net/rkc78834/pharmacovigilance-digital-media)

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Multiple threads of digital media

Electronic Health (eHealth) Using information and communication technologies for the provision of health related services (diagnosis, monitoring treatment) Telemedicine Delivery of health care at a distance using information and communication technologies consumer healthcare. Mobile Health (mHealth) Using mobile communication systems for the provision of health related services Digital Health The intersection of the digital revolution with consumer healthcare, includes genomics (use of gene chips to store a patient’s genetic identifiers and responses)

@Facebook 1.25b users @Twitter 302m users @YouTube >1b users @Pinterest 70m users

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Engaging with patients via digital media

Social media

Use to provide information & engage with stakeholders. Also valuable within the

rganisation to

encourage collaboration

Apps

An increasingly wide array in the market ranging from information

nly through to

sophisticated sensors and medical devices

Gamification

Applying game design techniques and mechanics to “real life” applications in order to make them more engaging

Wearables and smart sensors

So far, largely only wristbands and sensors but the potential is for much more

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Pharmaceutical industry use of digital media

MAHs main use of digital media is for marketing and sales

Sponsors are primarily using social media for commercial

purposes to distribute information about:

Medicines (to healthcare professionals and non-HCPs)
Diseases, and the treatment of disease
Company matters including announcements
To listen to patient and professional conversations about

marketed medicines, and not to support clinical research

A minority of companies use social and digital media for:
Patient engagement
Patient recruitment and retention within clinical trials

| Streamlining Multi-Centre Research May

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TGA:Monitoring the internet or digital media

1.2.2 Sponsors should regularly screen internet or digital media under their management or responsibility, for potential reports of suspected ARs.

includes digital media that is owned, paid for and/or controlled by the sponsor.
frequency of screening allows for valid ARs to be reported within reporting timeframe

based on the date the information was posted on the internet site/digital medium.

Sponsors may utilising their websites to facilitate collection of suspected ARs.
If a sponsor becomes aware of a suspected AR described

in non-company sponsored digital medium,

the report should be assessed to determine whether it qualifies for reporting.
Cases from the internet or digital media, the identifiability
f the reporter refers to the existence of a real person, that

is, it is possible to verify the contact details of the reporter

e.g., an email address under a valid format has been provided

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| Streamlining Multi-Centre Research May

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VI.B.1.1.4. Information on suspected adverse reactions from the internet or digital media MAHs should regularly screen internet or digital media under their management or responsibility, for potential reports of suspected ADRs. In this aspect, digital media is considered to be company sponsored if it is owned, paid for and/or controlled by the MAH.

The frequency of the screening should allow for potential

valid ICSRs to be reported to the competent authorities within the appropriate reporting timeframe

Marketing authorisation holders may also consider

utilising their websites to facilitate the collection of suspected ADRs

EEA Good Pharmacovigilance Practice (GVP) VI

PV guidance on digital media (July 2012) focuses on ICSRs

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Digital media and safety issues (GVP Module VI)

VI.B.1.1.4. Information on ADRs from digital media (ctd.)

If a MAH becomes aware of a report of suspected adverse reaction described in any non-company sponsored digital medium, the report should be assessed to determine whether it qualifies for reporting.

VI.C.2.2.6 Emerging safety issues

Good practice for the MAH to monitor special internet sites or digital media (e.g. patients’ support or special diseases groups)

Check if they describe significant safety issues which may

necessitate reporting in accordance with VI.C.2.2.6.

Frequency of the monitoring depends on the risks associated to

the medicinal product

| Streamlining Multi-Centre Research May

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MAH governance of the use of digital media

Most MAHs have developed controls governing use of digital media

Nearly all MAHs have developed guidelines to address

use of social media

General guidelines include:
Rules for discussing company business on personal sites,

how to set up a page or site, and privacy issues;

Guidelines concerning authorized and unauthorized uses by

personnel of social media;

Social Media Advisory Board-specified practices;
Guidelines for posting video online;
Directives that stipulate one-way communication between

personnel and patients involved in clinical studies;

Restrictions, e.g., for company business only.

| Streamlining Multi-Centre Research May

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AE reporting via social media

Source: White paper on Adverse Event Reporting by VisibleR , Oct. 2011, http://www.visibletechnologies.com/resources/white-papers/adverse-events/

All brand mentions 257,177 posts; 224 brands 100%

Filtered for relevance 24%

With AE terms

5%

Adverse Event reports 0.4% of all brand mentions 3.3% contain AE-specific data

Contain AE keywords 12,530 posts

3.3% mentions are case reports 1 in 7 = name & contact info

| Streamlining Multi-Centre Research May

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Overview of Novartis PV of digital media

Highlighting challenges of monitoring the different channels

Novartis Pharma Social Media Programmes Novartis Pharma Social Media Listening Programmes Novartis associate/vendor screening non-NVS controlled platforms Single Reports

AEs with 4 minimum criteria

Aggregate Reports

AEs with 2 minimum criteria

Single Case Safety Reports

Within 24 hours
Send to DSE in the country where AE occurred

(if UNK send to country where AE was received or reviewed (Local Programs) or Switzerland/US (Global Programs) Aggregate Reports

Monthly reports / End of programme
Send to country of the Programme Owner

Novartis associate/vendor screening NVS controlled platforms Single Reports

AEs with 4 minimum criteria
AEs with 2 minimum criteria

(Novartis product & ADR)

Single Case Safety Reports

Within 24 hours
Send to DSE in the country where

the AE took place (if UNK send to country where AE was received or reviewed (Local Programs) or Switzerland/US (Global Programs)

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IMI WEB-RADR research on social media

WEB-RAdR consortium will explore PV of social media

Hypothesis Data capture, collation, timely data mining and appropriate analysis can provide actionable intelligence in relation to protection of public health. Research can provide value to stakeholders including patients, healthcare professionals, regulators and to the pharmaceutical industry.

Emerging communication technology is changing the way people

interact with their healthcare providers and products

Large body of health care data is being generated in social media
Mobile technology creates an environment where people are constantly

connected to the Internet

The value of such data is not fully established
Mining and analysis of social media is an emerging science
Regulatory guidance is behind the emergence of new technology

| Streamlining Multi-Centre

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A brief history of Web-RADR over time...

Web-RADR is an IMI project involving academia, HAs and industry

New technologies in mobile devices & new apps are

leading to an evolutionary change in the extent, geographies and modes of use of the internet

Information gathering and sharing of experiences,
pinions and suggestions is becoming routine
Web-RADR is a ground-breaking EU

Innovative Medicines Initiative ( )

funded initiative to recommend

policies, frameworks, tools & methods

The project will explore the value of the new

developments to assess the insights versus traditional PV

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WEB-RAdR IMI project

Comprises experts from regulatory agencies, academia, WHO & industry

WEB-RADR consortium brings together expertise from

world-leading organisations in their fields

Project structured to enable EFPIA partners to participate

in all areas

WEB-RADR has the potential to shape the regulatory

framework of the future, thereby augmenting traditional pharmacovigilance

WEB-RADR offers the maximum possible benefits from a

regulatory, societal & scientific perspective

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WEB-RADR Consortium structure

EFPIA Applicant Consortium

Regulators & Patients

Novartis (Lead) J&J (Deputy) Sanofi-Aventis Amgen AstraZeneca UCB Bayer + GSK MHRA (PM) EMA Halmed Lareb Eurordis Universities of London, Liverpool & Gröningen WHO (UMC), Epidemico and SRDC

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Overview of Web-RADR work packages

Effective project management to link the work streams together

Mobile & social media usage patterns 2b

Regulatory framework WP 1

Scientific impact & new insights 4 Social media platform 2a Mobile ADR reporting app & safety comms 3a

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Spontaneous Reporting Electronic Paper based

ICSR quantity & appropriateness
Quality (VigiGrade)
Demography
Duplication (VigiMatch)
Signal generation
Qualitative Analysis
Integration

Comparisons

WEB-RAdR Scientific Impact Traditional pharmacovigilance vs. New

Adverse Drug Reaction Positive benefit risk assessment for each patient

Traditional model HCP-centric

Signal generation
Trustworthiness
Integration

App- based Social Media

WEB-RADR model Patient-centric

Identify risks & tolerability issues

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WEB-RAdR Pharmacovigilance of social media

Targeting advances beyond state of the art pharmacovigilance

Novel signal detection techniques
Indicators of completeness + quality
Advanced geographic recognition
Filtering capability using EMA IME
Filtering for health system interaction
International collaboration portal for all users
Data export compatibility
Multi-source spontaneous report comparisons
Indicators of completeness + quality (vigiGrade)

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Data quality indicators: healthcare vs patients

Assessment of Novartis data from SRs, literature and POPs

Initial and follow up all time Literature Spontane

us

Marketing Programs n % n % n % Formulation 181 13.3 TTO 132 9.7 Demographics 1169 86.2 Indication 936 69.0 Dosage 891 65.7 Action Taken 557 41.1 Duration 19 1.4 Medical History 867 63.9 Lab Data 710 52.4 Dechallenge 225 16.6 Rechallenge 19 1.4 Total no of cases 1356 Average score 4.86946903 4960 65.7 2838 37.6 7058 93.5 4606 61.0 5529 73.2 3389 44.9 2571 34.0 3587 47.5 1135 15.0 1059 14.0 69 0.9 7551 4.740077 1345 73.7 694 38.0 1802 98.8 1371 75.2 1591 87.2 863 47.3 873 47.9 835 45.8 194 10.6 155 8.5 11 0.6 1824 5.6579509

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Social media: caveats for patient safety

New developments will lead to new challenges for pharmacovigilance

MAHs must obey the law
MAHs must operate ethically
We must not cross the boundaries of the

doctor-patient relationship, or be misled by false data or spurious signals

But we should be encouraged by the
pportunities that digital media offer...

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Future of social media: From telling to engaging

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New apps and mobile technologies will continue to evolve

Best practices include:
Establish relationships through two-way dialogue and

human connections that encourage participation

Demonstrate patient and customer responsiveness

through timely, transparent responses and request feedback

Share content that makes the public want to interact

with the company and specific brands

Use of innovative strategies to leverage information

and drive online influence

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Digital Drug Safety Surveillance: Twitter

Monitoring pharmaceutical products in Twitter (Freifeld et al 2014)

Existing post-marketing adverse event surveillance

systems suffer from under-reporting and data processing lags

Social media services such as Twitter are seeing

increasing adoption, and patients are using them to describe adverse experiences with medical products

An analysis of 4,401 of these ‘posts with resemblance to

adverse events’ (‘Proto-AEs’) from Twitter found concordance with consumer-reported FDA Adverse Event Reporting System reports at the System Organ Class level

Further research is required to investigate this finding

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Identifying instances of Proto-AEs in the UK

Pilot study results exploring PV of social media

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Data collection scheme for Twitter & FAERS

Study to compare AE reporting via Twitter and patient reports to FDA

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Correlation by SOC proto-AEs in Twitter/FAERS

Rank order (log-log scale): Proto-AEs = posts with resemblance to AEs

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Social Media ADR Comparison to Pivotal Trial

Patient reporting can be of good quality and differs from HCP sources

Antibody abnormal* Herpes zoster (skin)* Swelling* Migraine* Paraesthesia* Weight decreased* Skin discomfort* Headache* Condition aggravated* AST increased Dyspepsia Vomiting Lymphopenia Albumin in urine Erythema Rash Nausea Diarrhea Pruritis Flushing Abdominal pain Social Media (n=42) 5 10 15 Pivotal Trial (Tx arm N=769) 10 20 30 40

dimethyl fumarate

Dec 1, 2013 through Jan 14, 2014, Twitter & Facebook drug:event pairs coded using MedDRA v16 ≥2% higher than placebo Trial Data: Tecfidera US PI

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Social media reports vs FAERS

Hypothesis generating for risk minimisation

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Note: Until methods for signal detection in social media and scientific analysis has been fully developed, refined and finalized, it is proposed that all data extracted from MedWatcher Social and initial findings will be treated as potential hypotheses for research purposes only.

Pilot Social Data Analysis for Drug List

Data used for refining algorithms (WP2A) and development of signal detection methods (WP2B) Data exported by MAHs for evaluation and analysis Data used for scientific impact evaluation (WP4)

Proposed Process Flow

Validate Findings from research >

new insights
n drug

safety profiles

Drug Use
Prescription

patterns

Patient

compliance

PV best

practices

Report to Health Authorities , Publication s (All) Signal Analysis and Risk Managem ent (MAHs) Share New insights with Patient Groups via Mobile (WP3A) (HAs)

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Key points: pharmacovigilance of social media

Secondary data use – contact with reporter not foreseen
Masking of pilot drugs for unbiased study
Anonymous background data for comparative analysis
Action after validation of process and signal algorithms
Data sharing between Work Packages during pilot
Use of findings to guide policy decisions
Publication of results after completion of research

Proposal is aligned with secondary use of data per GVP VI

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Relevance of Data Protection for Health-Apps

“The close interaction with the operating system allows apps to access significantly more data than a traditional internet browser.” “Apps are able to collect large quantities of data from the device (location data, data stored on the device by the user and data from the different sensors) and process these in order to provide new and innovative services to the end user.”

Personal data

“Any information relating to

an identified or identifiable natural person (data subject)

Health data (sensitive, personal)

Any personal data closely linked

to the health status of a person, such as genetic data or data on a person’s consumption of medicines

Personal Data in Apps

Automatically generated by the

device, (e.g. geolocation data, network settings, IP address)

Generated by the user through

apps (photos, notes, contact lists)

Generated by the apps (e.g.

browsing history)

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Data privacy - Who am I?

Age: 50-year-old
Gender: Male
Suspected ADR:

Respiratory arrest

Suspect drugs:

Diprivan, pethidine, alprazolam & sertraline

PMH: Low BMI, vitiligo, lupus
Outcome: Fatal

http://en.wikipedia.org/wiki/Death_of_Michael_Jackson

Occupation: Rockstar
Narrative: Patient was

being treated by his personal physician at his

mansion. Administration of

propofol led to respiratory arrest and paramedics were called to assist. The resulting court case saw the attending doctor found guilty of involuntary manslaughter.

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Who am I? Personal medical data

Age: 41 years
Gender: Male
Suspected ADR:

Possible drug interaction with alcohol

Suspect drugs:

Fluoxetine, tiapride, and albendazole

PMH: Alcoholism?
Outcome: Fatal
Occupation: Deputy Head of

Security, Ritz Hotel, Paris

Narrative: Patient was driving

a Mercedes at high speed in the Pont d’Alma tunnel in

Paris. He lost control of the

car and crashed, killing himself and two passengers.

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Summary and vision of the future...

Early challenges are being overcome, now we need to generate more substantive data!

Patients are increasingly aware of safety information
Rapid growth of apps & digital media sites as MAHs exploit
pportunities
Potential for 2-way interactions is hard to ignore, so mobile

reporting & listening capabilities will increase

MAHs must offer real benefits or advantages to patients,

and manage the risk of receiving AE reports

Important that pharmacovigilance is carefully managed
Web-RADR provides the opportunity to shape the future,

and to work with stakeholders to improve public health

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1995 2014

Social media management strategy

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2016 2016

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Questions?