ict meets biowin - big data in biotechnologies par janssen
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Big data & RWD in pharma: big opportunities & big challenges
Bart Vannieuwenhuyse
Janssen Research & Development
16 December 2014
“The patients are waiting!”
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Dr. Paul Janssen
To eliminate disease through developing highly innovative medical solutions for people
around the world
Imagine a world where…
• Available patient-level data can be re-used to gain new insights on disease etiologies…
• Clinical R&D is optimized by 40% using existing patient level data…
• New treatments are effective in 95% of patients…
• Treatment failure is reduced by 60% with timely prediction and intervention…
• Risk/benefit profile of new therapies can be monitored continuously using naturalistic data…
All medical interventions are an opportunity to learn …
in the interest of better patient care
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Big Data Defined What is it ? Why is it different ?... Its Real World Data
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03.19.2012
http://www.extremetech.com/extreme/151086-minion-a-complete-dna-sequencer-on-a-usb-stick
Big data in pharma - London
Biomarkers – “lots of data”
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WGS RNAseq Mass Spec Imaging RT
Sensing
Big Data offers value to the pharma industry
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Real World Data (RWD) – working definition
• RWD is generated using data typically collected in usual health care settings. RWD is most commonly generated using a range of non-interventional (observational) studies, including: – Primary data collections such as registries collecting
prospective and/or retrospective data, or surveys collecting cross-sectional or retrospective information.
– Analyses of secondary data that includes (electronic) medical records, insurance claims data, and government databases which provide data typically used for retrospective analyses.
Challenges with re-use of patient level data
16-Dec-14
Data gaps • Missing data elements (e.g. outcomes) • Studies require details that may not be
routinely collected • Coding often only at first level (e.g.
ICD-9) therefore missing granularity • 80% of info stored as unstructured
data
Data quality • Longitudinal coherence • Coding for administrative reasons
(up – down coding) • Coding often months after patient
encounter • Data provenance – who entered the
data?
“Semantics” • Many standards – many versions • Complex care – many HCP’s
involved – many hand-overs • Need to pool data cross sites and
cross different countries • Pharma focused on CDISC
Privacy • Clearly a top priority • Different interpretations by country,
by region – complex
• TRUST
Big data in pharma - London
Successful example of data re-use for research
• RWE of ACHeI
> 2500 patient years of therapy documented in EHR-system
> 8 fold dataset compared to Cochrane
• Cost effective
Text mining derivation of service utilisation and costs. Created in one month
• Resembles cognitive decline curve derived from clinical trials
Cholinesterase inhibitors and Alzheimer’s disease
-1 0 1 2 316
1820
2224
time(years)
MM
SE
sco
re
S. Lovestone et al, unpublished 2012
Big data in pharma - London
16-Dec-14
The IMI is a unique Public-Private Partnership (PPP) between the pharmaceutical industry represented by the European Federation of
Pharmaceutical Industries and Associations (EFPIA) and the European Union represented by the European Commission.
Big data in pharma - London
Project overview
57 partners from 14 European countries
€56 million worth of resources Three projects in one Five year project
(2013 – 2017)
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ACADEMIC PARTNERS
SME PARTNERS EFPIA PARTNERS
PATIENT ORGANISATION
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Our vision
To be the trusted European hub for health care data intelligence, enabling new insights into diseases and treatments
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EMIF – Project objectives
EMIF: one project – three topics
1. EMIF-Platform: Develop a framework for evaluating, enhancing and providing access to human health data across Europe, to support the two specific topics below as well as research using human health data in general
2. EMIF-Metabolic: Identify predictors of metabolic complications in obesity, with the support of EMIF-Platform
3. EMIF-AD: Identify predictors of Alzheimer’s Disease (AD) in the pre-clinical and prodromal phase, with the support of EMIF-Platform
13 EMIF colloquium
A platform for modular extension
EMIF colloquium
EMIF
- M
etab
olic
EMIF
- AD
Data Privacy
Analytical tools
Semantic Integration
Information standards
Data access / mgmt
IMI Structure and Network
Research Topics
EMIF governance
Prev
entio
n al
gorit
hms
Pred
ictiv
e sc
reen
ing
Ris
k st
ratif
icat
ion
Call x Call x
Ris
k fa
ctor
ana
lysi
s
Patie
nt g
ener
ated
dat
a
TBD
EMIF
- Pl
atfo
rm
Metabolic CNS
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Data available through consortium
Large variety in “types” of data
Data is available from more than 53 million subjects from seven EU countries, including
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Primary care data sets
Hospital data
Administrative data
Regional record-linkage
systems
Registries and cohorts (broad
and disease specific)
Biobanks
>25,000 subjects in AD cohorts
>90,000 subjects in metabolic cohorts
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Expected benefits Enabling re-use of health data
– Scale: Access to large numbers of observational data (currently > 53 million subjects)
– Diversity: Datasets contain information on a wide range of patient aspects, clinical, omics, imaging…
– Depth: Datasets contain longitudinal patient information enabling time-series analyses
Delivering a working solution – “Compliance ready” solution: The framework will deploy state of the art security, legal and privacy tools
– Data harmonization: A “common data model” will allow efficient reuse across different data sources
– Analytical Methods: A range of analytical tools and methods will be made available
Doing relevant research – Disease insights: Combining different data types to gain new insights into disease etiologies
– Post-authorization studies: Monitoring of safety and efficacy of registered drugs
– Epidemiology: Studies requiring large population based dataset for signal detection
Further Information
EMIF is operating under IMI Grant Agreement n⁰115372
www.emif.eu Register for our newsletter!
EMIF general Bart Vannieuwenhuyse ([email protected]) Simon Lovestone ([email protected]) Johan van der Lei ([email protected])
EMIF-Platform Johan van der Lei ([email protected]) Annik Willems ([email protected])
EMIF-Metabolic Ulf Smith ([email protected]) Dawn Waterworth ([email protected])
EMIF-AD Pieter Jelle Visser ([email protected]) Johannes Streffer ([email protected])
Thank You
“The patients are waiting!”
Questions ?
Bart Vannieuwenhuyse
Janssen R&D
Senior Director Health Information Sciences
Big data in pharma - London 03.19.2012