Slides from the Pistoia Alliance Debates webinar on data sharing in pharma R&D.

1. 4 December, 2015
Sharing data with my co-opetition
A Pistoia Alliance Debates webinar
Chaired by Richard Lingard, Dotmatics

2. This webinar is being recorded

3. ©PistoiaAlliance
Panellists
34 December, 2015
Richard Lingard, Head of Commercial Operations, Dotmatics has a chemistry background and
has been working in the life sciences industry for over two decades, focusing on the business side of
drug discovery technology and services. Richard is responsible for the expansion and running of the
commercial and customer-facing teams as well as building customer and partner relationships and
executing company strategy at Dotmatics. Richard has enjoyed a number of international commercial
roles at research supporting organisations: Thomson Reuters and Biovia, as well as research
organisations like Argenta Discovery and Merck & Co Inc. As a Management & Chemical and
Sciences graduate of UMIST he is still pleased to be using his degree in everyday working life.
Chris Waller received his Ph.D. in Medicinal Chemistry and Natural Products from the University of
North Carolina in Chapel Hill in 1992. After a post-doctoral fellowship under the direction of Dr.
Garland Marshall at Washington University in St. Louis, Dr. Waller began his career and has held a
variety of positions in academic, government, biotech, and large pharmaceutical company sectors. In
2012, Dr. Waller joined Merck where he currently holds the position of Executive Director, MRLIT
Modeling Platforms. Dr. Waller was a founding board member of the Pistoia Alliance, serves as an
advisor to the Medicines for Malaria Venture and the Bill and Melinda Gates Foundation, serves on the
Board of Visitors for the School of Pharmacy at the University of North Carolina-Chapel Hill where he
has held an Adjunct Professor position since 2001, and is a frequent advisor to the FDA, NIH, NAS,
IOM, and EPA. Dr. Waller has over 70 peer-reviewed publications and numerous chemistry and
technology patents.
Martin Romacker is Principal Scientist in Data and Information Architecture and Terminologies at
Roche Innovation Center Basel. Primary focus is definition and application of Data Standards to
facilitate data federation and answering of complex scientific queries. Current activities include
Terminology Management, Semantic Engineering, Scientific Data Integration/Curation, Text Mining
and Information Retrieval/ Search Technologies. Previously a Senior Knowledge Engineering
Consultant at Novartis. More than 20 years of practice in knowledge management. PhD in
Computational Linguistics from University of Freiburg, Germany.

4. dotmatics
knowledge solutions
...
Sharing Data with my Co-opetition
Richard Lingard
SVP Global Commercial Operations
Dotmatics Ltd
Richard.lingard@dotmatics.com
Thanks to Rob Brown, VP Global Informatics

5. dotmatics
knowledge solutions
.. .
Budget - Reductions in Force in
Science and IT
Portfolio - Changing ratio of
Biologics to Small Molecules
Execution - Outsourcing and
external collaboration
Methodology - Translational
Medicine, Data Science, Omics…
The Changing Business of Research…
Patent cliffs and innovation deficit have driven life sciences to new business models

6. dotmatics
knowledge solutions
.. .
• Transformation has already happened
– Top 10 Pharma dropped 30K employees between 2013
and 2014
– 907 Biologics in Development in 2013 (out of approx
3400 total)
– In 2014 total external R&D spend across top pharma was
greater than internal investment
• Is it working?
– Strategically: More approvals? Bigger pipeline?
Increased profitability?
– Tactically: Success in projects? ROI?
• How to increase the ROI and odds of success?
– Organizational, cultural, scientific…
Changed Has Happened…Is it Working?
What can informatics do to help support successful change?

7. dotmatics
knowledge solutions
.. .
What Can Informatics do to Increase
Success?
Mordac the Preventer

8. dotmatics
knowledge solutions
.. .
• Support cost constrained research IT
– Lower TCO
– Increase agility (& customer satisfaction)
• Support resource constrained scientific
teams
– Integrated end-to-end workflows for efficiency
– Self service decision support (data and
science) for innovation
• Support the new research portfolio
– Biologics & mixed entities
• Support the new scientific methods
– Translational science
• Support the new organization
– Systems for collaboration
OR…informatics systems can…

9. dotmatics
knowledge solutions
.. .
• Demand from the business
– Change has happened and now has to show
ROI
• Enabling technology
– New technologies exist that weren’t
available before
Why Now?

10. dotmatics
knowledge solutions
.. . Transformative Technology
https://www.gartner.com/doc/2049315/nexus-forces-social-mobile-cloud
Reduced TCO
Agility, scalability and elasticity
Access anywhere
Real-time communication in
any location
Informed Decisions

11. dotmatics
knowledge solutions
.. .
• Demand from the business
– Change has happened and now has to
show ROI
• Enabling technology
– New technologies exist that weren’t
available before
• (Growing) acceptance from legal and
management
• Scientific informatics vendor landscape
is changing
– COTS fully integrated suites are now
available
– Lab informatics, analytics and collaboration
– Chemistry and biologics
Why Now?

12. dotmatics
knowledge solutions
.. . “Next Generation” Integrated Informatics?
Capability Legacy (Today) Next Generation
EndUser
Lab Workflow Multiple applications, custom
integration, manual processes, ad-hoc
requests
Fully integrated workflow for individual and
across groups
Data access Multiple sources, multiple
applications, “exact” searches
All relevant data in one interface (internal
and external), new methods of search
Collaboration Manual, ad-hoc, work-arounds Team collaboration apps , team publishing
on data, objects, analytics, KPIs
IT
Deployment Thick clients – high TCO
On-premise installations
Interactive web clients lowers TCO
Simple deployment Enables hosted
environments
Development
environment
Proprietary scripting languages
Coding by IT or vendor services
Custom versions
Configuration not customization
Rapid application extension
Webservice integration
Single code base for ease of upgrade

13. dotmatics
knowledge solutions
.. . The Effect of Data Silos
Compound#
Time
This result
On this
compound
Cannot influence the design
of these compounds
Data silos

14. dotmatics
knowledge solutions
.. . Federated Data Access

15. dotmatics
knowledge solutions
.. . Self Service Analytics and Visualization
Progressive
Disclosure
Aggregated Data
All Data Types

16. dotmatics
knowledge solutions
.. . Workflow - EXAMPLE: Sample Manager
Workflow
Experiment
Request
Monitor
Automate

17. dotmatics
knowledge solutions
.. . Integrated Informatics Solutions
Design
ReportAnalyze
Test
Make

18. dotmatics
knowledge solutions
.. .
• Submit single or
multiple tasks to be
performed on one
or multiple samples
• Manage workflow,
timing, resource
allocation
• Request and
schedule in-house
or CRO tasks
• Track compound or
sample progression
Request and Track

19. dotmatics
knowledge solutions
.. . Dashboards and KPIs

20. dotmatics
knowledge solutions
.. . Now Add External Partners….
Provision Spin Down
Design
ReportAnalyze
Test
Make
How to exchange scientific data?How to communicate
across the project members?
How to spin
up new
projects or
partners
quickly?
How to
safeguard IP
and distribute
it to the
partners?
How to track project status
and work schedules?
How to maximize the efficiency
of the virtual research team?
How to make project decisions
collaboratively across partners?

21. dotmatics
knowledge solutions
.. . The Maturity Curve Disconnect
*The De-Evolution of Informatics – Scientific Computing Oct 2012 – Michael Elliott
IT is struggling to catch up with
the business
• Only 2 of top 20 pharma
reported that they have a
comprehensive
externalization data
management strategy
30% of biopharma R&D spend beyond
company boundaries
• 20% YoY growth on externalized
research c.f. overall spending is flat
• 30-50% of discovery work through
partner alliances
• Payment on milestones not on work
units (molecules, assays)

22. dotmatics
knowledge solutions
.. . Collaboration – State of the Art?
Cambridge Healthtech Media Group – 2012 – 310 Qualified Respondants

23. dotmatics
knowledge solutions
.. . Characteristics of a Collaboration Solution
Secure Scientific Data
Storage and Exchange
•project oriented
•granular access control
Cloud Implementation
•no local installation
•little infrastructure required at
partners – fast to spin up/down
•no VPN issues
Comprehensive
Application Suite
•highly configurable to required
workflows
•small molecule and/or biologics
research projects
Real Time
•communication and
collaboration
•project management and
tracking, CRO metrics
Full Audit Trail
•management/distribution of IP

24. dotmatics
knowledge solutions
.. .
Scientific Project Data Repository
Scientific Search & Browse
Molecules, Biologics, Experiments, Assays,
Samples, Reagents, Images
Scientific Collaboration
Document Exchange
Scientific Document Indexing
Social Commentary
Project Scheduling and Status
Requesting
Request Status Tracking
Project Progress Tracking
Cloud Collaboration Platform
Administration
Users & Groups
Projects, Data Types

25. dotmatics
knowledge solutions
.. . Cloud Collaboration Solution
Provision Spin Down
Design
ReportAnalyze
Test
Make

26. dotmatics
knowledge solutions
.. . Role, row and data type security
Example: Logged in as an sponsor scientist, 2369 records available to query and browse
Logged in as a CRO/collaborative scientist, 131 records available
to query and browse

27. dotmatics
knowledge solutions
.. . Social Communication
(Non-Scientific) Trello Board Scientific Collaboration Board

28. dotmatics
knowledge solutions
.. .
Modern informatics systems can help
increase the success of changes occurring
in today’s research environments
• Do more with less in science and IT
• Support collaboration
• Support new research methodologies
and portfolios
Summary
Sharing Data with my Co-opetition

29. Value Driven from “Horizontal” Capabilities Is A Core
Differentiator of the MRL IT Strategy
TM IT PCD IT Clinical IT GRA ITCORE IT
Scientific Modeling Platform
(Provide Cross-domain Modeling Capabilities)
Scientific Information Management Platform
(Provide Cross-domain Data Integration and Delivery)
R&D Labs
Platform
Registration
Management
Platform
Integrated
Development
Platform
Genomics
Platform
Real World
Evidence
Platform
Domain Specific
Applications
Domain Specific
Applications
Domain Specific
Applications
Domain Specific
Applications
Domain Specific
Applications
Collaborative User Experience
(Provide Cross-domain Workflow Support)

30. Open Cell Line Registry for Public Cell Lines
Sharing and Jointly Maintaining a Registration System
Martin Romacker, Principal Scientist
Data and Information Architecture and Terminologies
Pharma Research and Early Development Informatics
Roche Innovation Center Basel
Pistoia Alliance Webinar “Sharing Data with my Co-opetition”, 3rd December 2015

31. Cell Line Curation
A typical setting
• Roche has a domain master for Cell Lines called RNCB
(Roche Non-Clinical Biorepository)
• Genentech has a domain master for Cell Lines called gCELL
• Loading of cell based exploratory studies from ArrayExpress into the Roche
tranSMART equivalend called UDIS (Understanding Disease Informatics
Systems) – Data in ArrayExpress were provided by Genentech
• Collaboration between Roche and Genentech – Genentech offering a superset
of the ArrayExpress data to Roche (all public cell lines)
• Inconsistencies between data sets at all levels: cell line names and annotations
such as disease, tissue type etc
Curation of the same cell lines many times
No synchronization between registration system
Need for a shared and open Cell Line Registry covering all public cell
lines

32. Cell Line Curation
A typical issue
• Excellent and comprehensive work
• No central repository to store results
• No central repository for annotation
• No central repository for maintenance

33. Cell Line Registry
Benefits
• Saving Time and Money
– Each cell line needs to be curated only once
– Region specific biohazard levels for each cell streamlining safety approval process
• Improving Quality
– Data curation quality is known and standardized
– Curation rules and tools implemented
– Cell line miss-idenfication/ contamination can be minimized (STR profiles)
• Supporting Research
– Coverage of use cases provided by business
– Easy identification and selection of cell lines streamlining experimental design process
– Open access for everybody (including tools, API etc)
• Supporting Cell Line Suppliers
– Easier access for customers to cell line catalogues
– Market transparency can support business strategy of suppliers
– Suppliers have full access to tools and annotations
Source: Cell Line Registry WG

34. Cell Line Registry
Foundation for Data Science
• Use cases collected from the Working Group – hands on approach (selection)
– What is the biohazard classification of a cell lines X in a given geographic region?
– Retrieve all cell lines with chromosomal rearrangement on long arm of chromosome 16
– Retrieve all cell lines which can be grown on medium X and which derive from liver
– Retrieve all cell lines that are derived from a patient with a mutation in the NGLY1 gene
– Retrieve all cell lines with a given gene fusion
– Retrieve correct cell line for a misspelled entry
– All cell lines expressing gene X including the parental cell line
– Histologic and molecular characterization of given tumor cell
– Retrieve cell lines stably transfected to express gene X under an inducible promoter
– Retrieve cell lines that have relatively normal karyotypes
– Find all cell lines whose creation was funded by NIAID grants
– Find cell lines derived from male homo sapiens over the age of 75
Prioritization of use cases to determine required Metadata elements

35. Cell Line Registry
Developing Metadata Model
• Collect all metadata descriptors currently in use at the WG partners
(this is clearly pre-competitive)
• Align metadata descriptors (which descriptors are shared possibly having
different names) and enumerable value domains (code lists, terms lists)
• Provide a list of all cell lines vendors and collect all metadata descriptors, cross
check these descriptors with the ones of the consortium partners
• Identify gaps in the merged metadata model (WG partners and vendors) based
on the prioritized use cases defined by the WG partners
• Compare harmonized metadata model with existing terminological and
ontological resources (Cell Line Ontology, OBI, CCONT etc.)
• Include new metadata descriptors for cell line registration if required to finalize
metadata model

36. Cell Line Registry
Investigation of Ontologies for Metadata Definition and Capture

37. Cell Line Registry
Covering Use Cases
• What is the biohazard classification of a cell lines X in a
given geographic region?
Source: Work done by Angeli for the WG

38. Cell Line Registry
Curation Platform and Knowledge Representation
• Curation Process
– Open cell line registry - single point of reference for all public cell lines
– Cell line vendors should also be part of the registry platform by
synchronizing and linking their catalogues
– Information architecture capable of linking different processes
• Curation process of the cell line registry itself
• Maintenance and update of procurement catalogues of vendors
• Synchronization with cell line registry system of all project partners
• Semantic Technologies for Knowledge Representation
– Provision of standards for knowledge representation
– Facilitating integration of existing resources for data capture/ annotation
– Simple extension of model if proprietary metadata elements needed
– Simple extension of content for proprietary cell lines

39. Cell Line Registry
Conclusions
• Public cell lines constitute a finite and still tractable universe
• No standards for cell line naming, no consistency across repositories
(both internal and external repositories)
• Permanent re-curation of same entities, lack of a single point of truth to
capture evolving science (eg STR analysis, gene fusions)
• Opportunity for creation of a shared open Cell Line Registry
• Pre-Work done by the Working Group, results will be exposed to a
larger community in 2016 to get more feedback and to extend the WG
• Final objective: establishing a sustainable approach to data sharing
very likely using semantic technologies and collaborative co-opetitive
maintenance of a public Cell Line Registry

40. Acknowledgements
(alphabetic order)
• Andreas Thielemann, Veit Uelshofer (Merck KGaA)
• Angeli Moeller (Thomson Reuters)
• Melissa Haendel, Matthew Brush (Oregon Health & Science
University)
• Nicole Washington (Lawrence Berkeley National Laboratory)
• Philippe Rocca-Serra (University of Oxford e-Research Centre)
• Stephanie Kueng, Said Aktas, Satu Nahkuri, Joachim Rupp
(Roche Innovation Center Basel)
• Tom Quaiser, Jan Kuentzer (Roche Innovation Center Penzberg)
In Italics: Members of the Cell Line Registry WG

41. Doing now what patients need next

42. Panellist discussion

43. Audience Q&A
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