A panel of experts including Alexandre Passioukov, VP Translational Medicine at Pierre Fabre, Xose Fernandez, Chief Data Officer at Institut Curie, Abel Ureta-Vidal, CEO at Eagle Genomics share their first-hand experience of enabling translational research in pharmaceutical and biomedical organisations, and discuss the challenges around the establishment of streamlined, seamless data handling and governance to accelerate innovation.

1. Expert Panel on Data Challenges
in Translational Research
in Pharmaceutical and Biomedical Organisations
13.12.2017 – 3PM GMT

2. Xose Fernandez
Chief Data Officer
Panelists
Alexandre Passioukov
VP Translational Medicine
Abel Ureta-Vidal
Founder & CEO

3. Based in Cambridge, UK since 2008, on the Wellcome Genome Campus
AI-augmented knowledge discovery platform for Life Sciences R&D
• Human & animal health
• Personal care and cosmeceuticals
• Food and nutraceuticals
Delivering the innovation platform for the genomics era:
e[automateddatascientist]
to enable data driven decisions
to increase the success rate of innovation
About Eagle Genomics

4. Today: Significant inertia in biologists doing data-rich tasks
Data sources
meta data
summary
documents
protocols
experiment
al & clinical
reports
Concept entailment
pathways
patients
genes
supporting
evidence
drugs
Jennifer
Biologist

5. Capability 1:
ingest/curate/enrich
ability to ingest/curate/enrich
patient in a semi-automatic
manner and build a
catalog of patients
Capability 2:
prioritise/select
ability to prioritise/select the
most valuable/relevant
patient or groups of patients
Capability 3:
comparison analysis
ability to run complex
comparison analysis
between groups of
patients, using data
Capability 4:
visualise/pinpoint
ability to pinpoint the new
insight generated by the
analysis and assess its validity/
robustness/value
A translational medicine platform needs to have 4 main
capabilities:
In order to deliver its promise for new insight discovery:

6. e[automateddatascientist] in translational medicine
Framework applicable in other areas such as preclinical research, animal health, personal care, agri-tech
[to bedside] Interventional Observational
Application Therapeutic
targets
Diagnostic
biomarkers
Biological
mechanisms
Curate patient
data
Select
cohorts
Compare
cohorts
Pinpoint
insight
Patient data
[from bench]
Medical history Lab results
Biomolecular
assays
Images
Internal Collaborative
e[automateddatascientist]
e[curate]
Build
catalog
e[catalog] e[discover] e[discover]+e[nsembl]e[hive]
Capability 1 Capability 4Capability 3Capability 2
Drug
repurposing
…

7. Capability 1:
ingest/curate/enrich
value-based curation and
cataloguing
Capability 2:
prioritise/select
prioritisation selector
Capability 3:
comparison analysis
workflow management
system and analysis
pipeline builder
Capability 4:
visualise/pinpoint
insight navigator
&
genome browser
e[automateddatascientist]
Eagle’s platform - the e[automateddatascientist] has each capability embedded in
a module or a set of modules.

8. Main hurdles and challenges
• User experience for the specific business cases to drive adoption
• Interoperability and integration with existing systems
• Use and/or creation of standards ie FAIR principles (not a standard) for
smooth data/insight exchange
• Beyond finding relevant data, representing insight for systematic capture
• Federated data governance, involving cultural change

9. Data Challenges in Translational Research in
Pharmaceutical and Biomedical Organisations
Dr Xosé M Fernández
Chief Data Officer, Institut Curie
Institut Curie

10. 10
Ins$tut Curie - nom de l'éme+eur - Titre de
la présenta.on

11. 11
Current Status
Institut Curie
Service Transform
Health and social care services
are under extreme financial
pressure. Data is key and
underpin the capabilities that
will enable insight-based
service transformation.
Digital Maturity
Healthcare information systems
generate vast amounts of data
that must be used for enabling
high-value care.
Information ecosystem
Integration of data is an
increasingly critical goal.
Connecting the “system” and
enabling data to flow across
the organisation.
Strategic alignment
Our current health and social
care service model is changing
(e.g. new models of care)
moving away from traditional
mapping service type and
organisation.
Analysis capability
High-value and effective
service delivery and improved
outcomes cannot happen
without converting rich, high
quality, contextual data into
insight.

12. 12
Tackling some Data Challenges
Data creation, access, use and reuse
Promote sharing policies enshrined in institutional Data Management Plan
Metadata as a means of providing context for datasets, in order to
facilitate future discovery, access, aggregation, use and reuse of data
Support data creators to submit their data or other research materials to
a trusted and sustainable repository, for further curation and long-term
preservation, in line with documented collecting policies
Identification of digital objects, to facilitate discovery and linking of
datasets
Provide quality support and resources as a means of building capacity
and data skills within the Institut Curie.
Institut Curie

13. 13
Heterogeneous Data
Imaging
Radiographic imaging
2D/3D ultrasound
Electron microscopy
4D microscopy
Histopathology
Single-cell imaging…
60%
EHRs
Dossier médicale personnel
Dossier Communiquant en
Cancérologie
5%
Genomic data
Genomic platforms
France Médecine Génomique 2025
20%
Research data
Clinical trials
Phenotypes
LIMS
Multiple research databases…
15%
Institut Curie

14. 14
Information at your Fingertips
Institut Curie
*Continuum Soins Recherche
*

15. 15
Digital Ecosystem
Connecting different datasets requires work on
descriptors (critical time consuming step), as
historical data collections tend to be poorly
annotated.
METADATA
INFRASTRUCTURE
A central data hub hosting pseudo-anonymised
datasets in line with GDPR† requirements, with
links to raw data, as well as a ConSoRe instance
for EMR querying.
Suitable pilot project is the key for success, it
must combine diverse datasets (genomics,
transcriptomics, proteins, EHRs) with a team
keen to invest time to improve metadata.
PILOT PROJECT
Data Aggregation – How it works?
Institut Curie
SEMANTIC LAYER
Discoverability is guaranteed by APIs* which
aggregate pseudo-anonymised datasets
without revealing IDs hosted in secured space.
*Application programming interface
† General Data Protection Regulation

16. Institut Curie
Most sequencing will be happening at healthcare and not research
By 2025 over 60 million genomes
Data geographically distributed
Clinical data not interoperable
Healthcare not used to handle Terabytes or Exabytes
Technical knowhow currently in the research community
Secure access and governance
Human Genomics in Healthcare

17. Institut Curie
Main Challenges faced as Chief Data Officer
GDPR* as the main data challenge
Establish new governance (Curie Data Charter)
Outlining policies and procedures (Data Management Plan)
Data aggregation to consolidate digital ecosystem
Interoperability
Cultural change – Information as an asset
Translate data into insightful information
*General Data Protection Regulation

18. Eagle Genomics - Translational Webinar, Dec 13
Introduction – Dr A. Passioukov

19. KEY FIGURES
Holding
€30m
Dermatology
€113m
Ethics
€390m
Oncology
€174m
CDMO
€53m
Dermo-cosme$cs
€1,143m
Consumer
Health Care
€333m
FROM HEALTH TO BEAUTY

20. Data
Warehouse
Data Analytics
Knowledge
Base
Structured data Analysis Mechanistic
Insight
Genome
Etc..
Transcriptome
Proteome
Metabolome
Phenome
Reseach &
Dev.
global
network
Global Precision
medicine-based
healthcare system
Bioinformatics platform foundational for future healthcare

21. SEMI-VIRTUAL TM CONCEPT: COMMON DATABASE TO
3 INNOVATION UNITS (ONCO/DERMA/CNS)

22. 1. Use of cutting edge technologies (incl. digital);
2. Semi-virtual structure, with a common DataBase ‘agnostic’ of
therapeutic area;
3. Probability-of-Success (PoS) mindedness - focus on priority R&D
programs: CDx, go-no-go decisions;
4. Strong synergy with research: using patient data to generate
innovation & early inform IUs of winning options;
5. Exploration interfaces between IUs - identification of niches based on
molecular biology
Pierre Fabre TM: key principles

23. Stronger Together & with Others
Turn to External OpportuniCes/CollaboraCon
In-Silico Pla5orm to connect to external collabora.ons
Acquire Data
and/or
Generate Data
Data

24. BIG DIGITAL CHALLENGE FOR BIOPHARMA
1. Lack of vision for business value creation potential
2. Data access – data silos
3. Formats / Standards / Dictionaries in a heterogeneous “data lake”
4. Weak signals detection for competitive advantage

25. www.eaglegenomics.com
Panel discussion & Q&A

26. www.eaglegenomics.com
Thank you
info@eaglegenomics.com