2013 03-21 etriks overview
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eTRIKS: A Knowledge Management Platform for Translational Research
Anthony Rowe, Janssen R&D On Behalf of eTRIKS
Challenge of Drug Development Complex Disease Phenotypes
2
Challenge of Drug Development Complex Disease Phenotypes
3
4
How do we stratify these complex phenotypes?
WGS RNAseq Mass Spec Imaging RT Sensing
Next Genera;on Pla?orms -‐> Data Explosion
Typical Pharma Biomarker Program
5
Discovery Preclinical Development Phase I Phase II Phase III
Biomarker Discovery
Biomarker Validation
Diagnostic Development
Ongoing Drug Development Programme
Associated Biomarker Programme
Challenges in running internal biomarker programs
• Study popula;on is defined by clinical development program – Does not provide a cross sec;onal view of the popula;on
– Does not enable early detec;on • Cost of running sufficiently powered Phase 0 studies is prohibi;ve
• How to overcome these challenges ?
Collaboration with Academia
Industry
Academia
Public Private
Consor;um
Sharing costs enables bigger studies
Organisa(on
Org 1
Org 2
Org 3
Org 4
Org 5
Org 6
Org 7
Organisa(on
Org 1
Innova(ve Medicines Ini(a(ve: Joining Forces in the Healthcare Sector
Ø Open collabora;on in public-‐private consor;a (data sharing, dissemina;on of results)
Ø “Non-‐compe;;ve” collabora;ve research for EFPIA companies
Ø Compe;;ve calls to select partners of EFPIA companies (IMI beneficiaries)
Key Concepts
Challenge 1: Fixed Budget over 5 Years
Science
Infrastructur
e
Challenge 2: Fixed Time Line
The value of data is long lived, virtual organisa;ons are not:
E.G Framingham Heart Study started in 1948
Who stewards the data when the consor;um ends?
Project Consor;um
Org 2
Org n
Org 1
Org 3
Org 4
5 Years
How do we provides a cost effec0ve model to provide a Knowledge management pla6orm to IMI and similar projects?
Sustainable Open Platform
Oct-‐2012 – Sept-‐2017
20M Euro
Translational Research Information and Knowledge Management Service
2B Euro Public Private Partnership The IMI Research Agenda Requires an Open Knowledge Management Infrastructure
2B Euro Public Private Partnership
Consortium of 16 Partners Academic/Pharma/Coordination/Standards
Academic Lead Development
EFPIA Lead
Coordination
Hosting
Analytics
Standards
Imperial College London
AstraZeneca Universite Du Luxembourg Sanofi
Roche Pfizer Merck Serono Lundbeck
Janssen IDBS Glaxo SmithKline Lilly
CNRS CDISC Biosci Consulting Bayer
• Start with a proven pla?orm, tranSMART • Deliverables reflec;ng demands of actual Efficacy and Safety projects
• Small consor;um • Limit funding in the first phase. • Explicit consor;um capabili;es & skills
Requirments of the call
Deliverables
Pla9orm: Building on open source TranSMART system a KM pla?orm for collabora;ve KM for IMI transla;onal projects
Services:
Support for IMI (& other EU) TR Studies re KM data services TR project KM consulta;on, cura;on support, historic data cura;on Pla?orm maintenance, enhancements & code control Administra;on, exploita;on support, training, awareness
Content: Populate with exis;ng and ac;ve TR Study Data
Clinical Study Data Pre-‐Clinical Study Data (e.g. in vivo) Biomarker data associated with Studies: ‘omics, gene;c, NGS, etc. Background knowledge (e.g. molecular pathway data, literature)
Standards: Development and adop;on of TR informa;on standards
Research: Research & Development of new analy;cs methods and tools
The TranSMART Pla?orm
The TranSMART Pla?orm
tranSMART is a knowledge management pla?orm that enables scien;sts to develop and refine research hypotheses by inves;ga;ng correla;ons between gene;c and phenotypic data, and assessing their analy;cal results in the context of published literature and other work.
• Data set Explorer: • Phenotypic data, such as demographics, clinical observa;ons, clinical trial outcomes, and adverse events
• High content biomarker data, such as gene expression, genotyping, pharmacokine;c and pharmaco-‐dynamics markers, metabolomics data, and proteomics data
• ‘Search’:
• Unstructured text-‐data, such as published journal ar;cles, conference abstracts and proceedings, and internal studies and white papers
• Reference data from sources such as MeSH, UMLS, Entrez, etc. • Metadata providing context about datasets, allowing users to assess the relevance of results delivered by tranSMART
TranSMART Screenshot
Work Packages
WP1
WP2
WP3
WP4
WP5
WP6
WP7
Platform Deployment
Platform Development
Data Standards
Curation and Analysis
Management and Sustainability
Community and Outreach
Ethics
CNRS/JPNV
Imperial/Sanofi/Pfizer
Roche/IDBS/Merck/CDISC
Luxembourg/Sanofi
AstraZeneca/BioSci ConsulJng
Janssen/BioSci ConsulJng
GSK/CNRS/Bayer/Sanofi Biosci Con
sulJng
(Collabo
ra(o
n M
anagem
ent)
WP Number WP Name WP Leads
Project Name Project Contact Therapeu(c Area Data Type Summary IMI Round IMI U-‐BIOPRED P Sterk Severe Asthma Clinical, Omics 1st
IMI OncoTrack D Henderson Colon Cancer Clinical, Next Genera;on Sequencing, Protein Arrays Cell-‐based Assays, Animal Models, Cancer Stem Cells
2nd
IMI ABI RISK D Sikkema Julie Davidson
Biopharmaceu;cal Risk Assessment
Clinical observa;ons, Legacy cohorts, Cell-‐based assays, Gene Expression, Long-‐term studies
3rd
IMI PREDECT J Hickman Prostate, Breast and Lung Cancer
Tissue Micro-‐Arrays, In Vitro Culture Models, GEMM Animal Models 2nd
IMI ND4BB K Brown Phil Gribbon
Comba;ng An;microbial Resistance
Pharmacology, In vivo, Clinical, omics 6th
MRC-‐ABPI RA-‐MAP J Issacs S Brockbank Rheumatoid Arthri;s Clinical, Omics Not IMI
IMI NEWMEDS K Stoller S Kapoor
Depression & Schizophrenia Clinical, Pre-‐Clinical 1st
IMI Predict-‐TB P Bordes G Davis Tuberculosis Clinical, Pre-‐Clinical PK/PD 3rd
Supported Project Pipeline at project start
What have we done in the first 6 months?
• Building the development community
• First supported project
• Public Server
6 month update
• ~50 Developers, 3 days in London, Feb 25-‐27 • June 2013 -‐ tranSMART 1.1
– Stable Postgres version – Data services
• Security • Export • Plugin framework
• September 2013 -‐ tranSMART 1.2 – Faceted Search – SOLR Indexing (unified search)
• TBD -‐ Research branch – Mongo Db – NGS
TM Hackathon/Tech Strategy
• Building the development community
• First supported project
• Public Server
6 month update
U-BIOPRED (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes)
→ a 5-year European project to understand more about severe asthma
#
Hypothesis#
The use of biomarker profiles comprised of various types of high-dimensional data, integrated with an innovative systems biology approach into distinct phenotype handprints, will enable significantly better prediction of therapeutic efficacy than single or even clustered biomarkers of one data type, and will identify novel targets.##
What UBIOPRED is producing: #
ü Large cohort & biobank of deeply phenotyped adult and paediatric patients#
#ü ‘Handprints’: stratification of severe asthma##ü Preclinical models more reflective of clinical
disease##ü A GMP viral challenge exacerbation model #
40#
210 members#
1.025 subjects#
1.500 variables#
175.000 samples#
3.000.000 data points#
• Building the development community
• First supported project – Next 5 projects being scoped
• Public Server -‐ TBA
6 month update
1. Ensure the legacy of project data/results 2. Facilitate dataset integra;on 3. Increase opera;onal efficiency 4. Establish a common set of standards
www.eTRIKS.org Linked In Discussion Group: eTRIKS Twiper @etriks1