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1/17/2017
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January 23-25, 2017
Washington, DC
REAL WORLD
EVIDENCE AND PV: POTENTIAL
CONTRIBUTIONS TO DRUG
DEVELOPMENT
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RWE and Drug Safety:
A competitive approach
REAL WORLD EVIDENCE AND PV: POTENTIAL CONTRIBUTIONS TO DRUG DEVELOPMENT
Agenda
• Background
• Pressures driving change
• Deliverables
• RWD Future needs
• Q&A
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The world is changing…
BMS Highly Confidential
Heightened expectations of
manufacturers
Public, regulatory scrutiny with
penalties for inadequate compliance
Increasing AE data volume
Advanced, standardized reports and
assessments focused on benefit-risk
Increased active surveillance
Emergence of PV as applied
science
Novel analytics and method
development
Use of new and non-traditional
data sources (e.g., social media,
literature mining, big data)
Launch of FDA regulatory
science efforts
Growing power of regulators
New ability to launch queries without
MAH involvement
Authority to force label changes,
studies, REMS and effectiveness
assessments with severe non-
compliance penalties
Growing influence of regulators
beyond FDA and EMA
Externalization of safety data
Public availability of data
Large HA initiatives leading to new,
powerful sources of safety data (e.g.,
Sentinel)
3rd party (academic) analysis
increasing in volume & sophistication
Increasing pressure & requirements for new and enhanced
PV capabilities
RWD-RWE evolution
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Heightened Efforts to Proactively Address and Study Safety-Related Issues
FDA Guidance, 2008 / EU ADR Website / Mullard, Nature Reviews 2012 / Stang et al, Ann Int Med 2010, EMA Guideline 2012
RWE
RWE can serve multiple purposes in drug development
Signal
Detection & Evaluation
Risk/Benefit
Analysis
Medical
Genetics
Modeling
Regulatory
BiomarkersStudy Design
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RWE and Safety
Creating Tools for Enhanced Signal Generation
- Flexible ad-hoc capability for querying clinical data
- Graphical visualization of safety-related parameters
- Reporting rate calculations of spontaneous events
- Extended analyses of events of interest (e.g., DILI)
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Signal Detection Signal Refinement Signal Evaluation
Building on Disparate Data Sources
• Pre-Clinical
• Clinical
• Spontaneous Reports
• Literature
• Epidemiology studies
Hypothesis driven / Resource-Time
Intensive / More Convincing /
Testing the Anticipated
Identification of potential drug-
event associations using a
collection of methods
Process to evaluate the magnitude and
relevance of potential drug/outcome
associations in near-real time
Conduct of studies to more
definitively establish or refute
causality between drug/outcome
Developing a Signal Refinement Capability for Rapid Signal Evaluation
- Adaptation of Sentinel, OMOP, and other analytic methods
- Common data model development in collaboration with CORDS
- Library of readily accessible methods and case-definitions
- Capability to pool data across disparate data sources
- Development of internally validated methods
Adapted from: Racoosin, Active Surveillance Implementation Council Meeting #2, 2010 Bate, OMOP Symposium, 2011
Further Integrate RWD Insight And Strategize Regulatory Data Generation In The Earlier Space
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DiscoveryPre-
Clinical
Clinical
Phase I - III
Reg. Approval
& Post-Approval
• Identification of populations to be
enrolled in clinical development
process
• Defining natural history of disease
• Defining epidemiologic profile of
potential comparators
• Market segmentation and sizing
• Initiating pre-marketing risk
assessment
• Conducting natural history of disease
studies
• Planning of Risk Management Plan
(RMP) document
• Identification of patient reported
outcomes
• Contribute to pre-marketing risk
assessment
• Reviewing clinical trial protocols
• Evaluating safety signals
• Contributing to RMP document
• Supporting drug approval
submissions
• Evaluating safety signals
• Implementing and evaluating
risk mitigation strategies
• Supporting life cycle
management by providing data
for new indications and long
term outcome studies
Electronic
Medical
Record
Databases
Insurance
Claims
Records
Pharmacy
Claims
Records
Clinical /
Registry
Studies Survey
Findings
Explore And Evaluate Internal Or External Data Source Along Various Development Milestone
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Genomic
Data
Short term and long term RWD needs
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Natural
history study
Consortium
database /
Pt registry
EMR / insurance
claims data
• GE centricity (US)
• CPRD (UK)
• IMS Health (US, EU)*
• Rutgers PharmacoEpi*
• Patient Advocacy
• Organizations*
Natural history / RMP preparation• Predictive modeling around clinical
outcome measure
• Natural history study
• Event rate estimation
• Disease burden study
• Treatment burden study
PMR preparation• Evaluation of different visualization
technique*
• Absolute value and absolute
difference vs. change relative to
baseline
• Stratification, K-M, adjusted K-M
• EMR Linkage method*
PMR / PMC
And
REMS /
RMS
REMS / RMS
preparation
Feasibility and
method
evaluation of
different PMR
approach*
Gold standard: Prospective registry
managed by CRO (++++)
Alternate 2: patient reported clinical
outcome, EMR mining, online CRF
(+++)
Alternate 3: EMR mining, online
CRF (++)
Enhance PV through data mining +
event questionnaire (+)
REMS / RMS effectiveness
evaluation
Long term monitoring of safety
(primary), efficacy and PRO
(secondary)
Drug utilization study
Physician knowledge survey
Regulatory
Landscape
analysis*
Alternate 1: Prospective safety
registry by consortium (+++)
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Real World Data: Patient centric data
• Technology advancement will allow for the collection of
patient centric data
• Linking of pharmacy data with reasons for AEs
• Wearable data
• Social links
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Proactive Signal Detection: Social Media
• Rapid growth of electronically available health related information, and the ability to
process large volumes automatically, using natural language processing and machine
learning algorithms, have opened new opportunities for pharmacovigilance.
• Potential to assess patient safety in real world
• FDA testing social media data for data mining purposes
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6https://medwatcher.org/enterprise
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Summary
– External forces are driving data generation
– Safety data generation is uniquely position to support pricing and reimbursement decisions
– RWE generation needs to be strategically designed
– Alignment w development early
– RWD at strategic points in development and LCM will evolve strategy
– New technologies can be driving more efficient real world data collection to support pragmatic trials
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