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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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Proactive Signal Detection: Topics

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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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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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