Using healthcare data for biomedical research

MAY 27, 2016

Kees van Bochove, CEO & Founder, The Hyve

Open Source u  Source code openly accessible and reusable for everyone

u  Enables pre-competitive collaboration: both academics and

industry can use and enhance it

u  Transparency: verification (scientific as well as IT security) can be

done by anyone, no ‘black box’

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The Hyve

u  Professionalsupportforopensourceso*wareforbioinforma1csandtransla1onal

researchso5ware,suchastranSMART,cBioPortal,i2b2,Galaxy,ADAMandOHDSI

MissionEnablepre-compe11vecollabora1oninlifescienceR&Dbyleveragingopensourceso*ware

Corevalues ShareReuseSpecialize

OfficeLoca5onsUtrecht,NetherlandsCambridge,MA,UnitedStates

ServicesSo5waredevelopmentDatascienceservicesConsultancyHos1ng/SLAs

Fast-growingStartedin201235peoplebynow

Interdisciplinary team

so5ware engineers, data scien1sts, project managers & staff; exper1se inbioinforma1cs,medicalinforma1cs,so5wareengineering,biosta1s1csetc.

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YoungCB meeting @ The Hyve

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New The Hyve offices being built at the Arthur van Schendellaan in Utrecht, The Netherlands

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3 Health Data Areas The Hyve is active in u  Translational Research Data

(‘Clinical & bioinformatics data’)

u  Population Health Data

(‘Real world data’)

u  Personal Health Data

(‘Wearable sensors data’)

Example project:

1.

TRANSLATIONAL RESEARCH DATA

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Center for Translational Molecular Medicine (CTMM) u  Public-private consortium

u  Dedicated to the development of Molecular

Diagnostics and Molecular Imaging technologies

u  Focusing on the translational aspects of molecular

medicine.

u  120 partners

u  universities, academic medical centers, medical

technology enterprises and chemical and

pharmaceutical companies.

u  Budget 300 M€

u  22 projects / research consortia

u  TraIT is the Translational Research IT project

supporting these projects with a joint IT infrastructure

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TraIT Consortium

Growing TraIT project team

TraIT data workflow Hospital (IT) Translational Research (IT)

data domains

clinical data

imaging data

experimental data

biobanking

integrated data translational analytics

workbench

HIS

PACS

LIS

Galaxy

tranSMART/ cohort explorer

R tranSMART/i2b2 datawarehouse

CBM-NL

OpenClinica

NBIA + AIM

e.g. PhenotypeDB, Annai Systems

e.g. Galaxy, Chipster

Samples (IT)

Pseudonymization

Public Data

BIMS

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TranSMART Platform: Scientific Function

CLINICAL GENETICS SENSORS IMAGING

DATA

UNDER STANDING BIOLOGY MEDICINE

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TranSMART Open Source History u  February 2012: J&J releases tranSMART as open

source on GitHub under GPL v3

u  December 2012: CTMM TraIT project decides to use

tranSMART as core infrastructure component

u  January 2013: IMI eTRIKS starts, uses tranSMART as

core infrastructure component

u  February 2013: kickoff of tranSMART Foundation, U.

Michigan publishes PostgreSQL port

u  March 2014: IMI EMIF kickoff, tranSMART is used as

data integration component

Contributors

Amsterdam, June 2013: tranSMART Workshop Attendees from 10 Pharma companies, 11 University Medical Centers and 12 IT companies

http://lanyrd.com/2013/transmart

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VUmc

Sanofi

Recombinant / Deloitte

University of

Michigan

Thomson Reuters Pfizer Astra

Zeneca CDISC

University of Luxembourg

Philips Johnson &

Johnson

The Hyve 70

Ann Arbor, Michigan, October 2014: Annual Meeting http://lanyrd.com/2014/transmart

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Bio IT World, Boston, April 2015 http://bit.ly/1R2N6uz

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TranSMART wins all the prizes: Best Show Award, Best Practices Award, Best Poster Award

Amsterdam, October 2015: Annual Meeting http://lanyrd.com/2015/transmart-foundation-annual-meeting/

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TranSMART: working with clinical data

cBioPortal for Cancer Genomics

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current community a.o.

Focus: intuitive exploratory analysis of the data

2.

POPULATION HEALTH DATA

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What Ewan Birney has to say about it … (GA4GH Leiden 2015)

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Time To Market: 11 – 18 years

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Neg

otia

tion

for R

eim

burs

emen

t 27

mem

ber S

tate

s

EMA

Fili

ng

Pre-

Clin

ical

Res

earc

h Cl

osed

& O

pen

Inno

vatio

n

Clinical Trials

EMA

App

rova

l for

Sal

e

HTA

App

rova

l

Phase 1 Phase 2 Phase 3

5,000 10,000

Compounds

250 Compounds

3 – 6 Years 6 – 7 Years

5 Therapies

1 Therapy

2 – 5 Years

Number of Patients/Subjects

20-100 100-500 1000-5000

Regulatory Review

Drug Discovery

Pre Clinical Testing

PhV Monitoring

Total Cost: $2 - $4 Billion USD

Sources: Drug Discovery and Development: Understanding the R&D Process, www.innovation.org;

CBO, Research and Development in the Pharmaceutical Industry, 2006;

Forbes, Matthew Herper, “The Truly Staggering Cost Of Inventing New Drugs”, February 10, 2012

Current EU “Patient Journey” is expensive and slow

New therapies don’t reach

patients until here

Phase 4 : $0.6B Drug Development : $2.6B

Secondary use of health data to enrich research

25 The value of healthcare data for secondary uses in clinical research and development — Gary K. Mallow, Merck, HIMSS 2012

1 2 3 4 5 6 7 8 9

1,000

10,000

100,000

1 million

Years

#Pa

tient

Exp

erie

nce

s /

Rec

ord

s

The “burning platform” for life sciences Pharma-owned highly controlled clinical trials data Clinical practice, patients, payers and providers own the data

Product Launch

R&D Phase IV

Challenge

Today, Pharma doesn’t have ready access to this data, yet insights for safety, CER and other areas are within this clinical domain, which includes medical records, pharmacy, labs, claims, radiology etc.

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Clinical Trials vs Observational Studies Clinical Trials Observational Studies

Study Design Controlled (hypothesis driven) “Real world” data

Sample Size Small (10.000 is large) Large (millions of people)

Endpoints Efficacy, safety Effectiveness, economic value

Statistics Descriptive statistics (e.g. ANOVA) Epidemiological modeling

Cost Expensive Not so expensive

Perspective Study population Society in general

To become the trusted European hub for health care data intelligence,

enabling new insights into diseases and treatments

EMIF vision

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Discover

Assess

Reuse

Data available through EMIF consortium

§  Large variety in “types” of data

§  Data is available from more than 53 million subjects from seven

EU countries, including

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

>40

mill

ion

MAAS

SDR

EGCUT

PEDIANET

SCTS

IMASIS

HSD

AUH

IPCI

ARS

SIDIAP

PHARMO

THIN

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Ap

pro

xim

ate

tota

l (c

umul

ativ

e)

num

be

r of s

ubje

cts

Available data sources in EMIF

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EMIF-Platform

EMIF-Available Data Sources; EXAMPLES

1K

2K

52K

400K

475K

2.8M

2.3M

10M

Status Jan 2016

3.6M

1.6M

1M

12M

6M

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OMOP & OHDSI - Overview

u  OMOP: Common Data Model for observational healthcare data:

persons, drugs, procedures, devices, conditions etc.

u  OHDSI: Large-scale analytics tools for observational data

An open source community, a.o. developing:

u  Tools to support the ETL / mapping process into OMOP (White Rabbit etc.)

u  Tools to perform analytics: e.g. Achilles for data profiling, Calypso for

feasibility assessment

www.omop.org

www.ohdsi.org

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OMOP Common Data Model v5.0

v  OMOP =

Observational

Medical

Outcomes

Partnership

v  CDM = Common

Data Model

v  SQL Tables

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OHDSI: Example Query Result

Slide from P. Ryan, Janssen

Re-use of healthcare data

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Prof. Johan van der Lei Erasmus MC University Medical Center

“We need to learn from experience and find ways to unite the large volumes of data in Europe. At

the end of the day, we are in this for better health care.”

Co-coordinator EMIF-Platform

EMIF-Platform

3.

PERSONAL HEALTH DATA

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Challenges in managing chronic diseases u  Health assessments via physician visits are time

limited and subjective, often not representative

for everyday life of the patient

u  The disease state can change a lot in between

visits, and important events are not visible to the

physician

But… it’s 2016: it’s now possible to objectively, remotely, and continuously

measure aspects of patient physiology, behavior and symptoms

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RADAR-CNS: Focus areas from diagnose & treat à predict & pre-empt

u  Epilepsy

u  Monitoring and predicting epileptic seizures

u  Multiple Sclerosis

u  Monitoring exacerbations and disease state

u  Depression

u  Monitoring for possible relapses, plan timely interventions

u  Predict bipolar state transitions

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Continuous Patient Assessment

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Potential digital biomarkers

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Preliminary Technology Stack

Analytics