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

1. 15 September, 2015
Analytics in pharma R&D
a Pistoia Alliance Debates webinar
chaired by Matt Jones

2. This webinar is being recorded

3. ©PistoiaAlliance
Agenda
Analytics in pharma R&D
315 September, 2015
• Introductions
• Analytics growth and recent trends: Matt Jones,
Tessella Analytics
• Insights of how big pharma is responding: Simon
Thornber, GSK R&D IT
• How a platform supplier is reacting - use cases
and examples: Eduardo Gonzalez, Perkin Elmer
BioInformatics Products Manager & Strategist
• Q&A
• Close

4. ©PistoiaAlliance
Panellists
Analytics in pharma R&D 415 September, 2015
Matt Jones has 16 years' experience of working in R&D IT
groups within the pharmaceutical industry. He joined GSK
(Glaxo Wellcome) following completion of his PhD in synthetic
organic chemistry and worked through various groups and
domains, from cheminformatics through to enterprise R&D IT
delivery, before leaving to join Tessella last year. Matt now works
as part of Tessella Analytics, working with our partners to
leverage analytics and bear down upon the challenges modern
R&D faces today.
Eduardo Gonzalez received his PhD in Molecular Genetics
from the University of Geneva in 1997 then joined GSK (Glaxo
Wellcome) in Geneva. He has extensive experience working
with a number of leading pharmaceutical and biotechnology
companies across Europe. Eduardo joined PerkinElmer
Informatics as Bioinformatics Product Manager in 2014, having
previously been Chief Technology Officer then Chief Strategy
Officer atIntegromics.
Simon Thornber has been at GSK for almost twenty years,
during which time he was worked in Bioinformatics,
Cheminformatics, and Research IT. His current role is 'director
of analytics' in R&D-IT, with responsibilities for driving innovation
in analytics and scientific computing.

5. Analytics
and
Pharma
R&D
Matt Jones

6. Tessella Analytics
Domain
Knowledge
Data
Handling
Skills
Math&
Statistics
Knowledge
Machine
Learning
250 staff
Over 30 years
of experience
Delivery of
1000s of data
analytics
projects
Tessella Analytics

7. Analytics - growth

8. Analytics – Trends
• Lots of (big) data
– Combination of external
and internal data
– And the diversity of
sources
• Visualisation and
explore relationships
– Dashboards and
interactive plots
• New insights from old
data
• Internet of Things
• … and wearables
• Rise of the data
scientist role
• Analytics as a service
• “No IT” solutions
• Cloud analytics
• …

9. Oversight Hindsight Insight Foresight
Not all Analytics are the Same
visual exploration
pattern finding
predictive models
optimal choice
manual automatic

10. Analytics Essentials
Speed Trust

11. R&D and the need for speed
• R&D is a unique environment
• Speed, Trust and Agility needed
• People need answer to questions
immediately
– Have rapidly changing priorities across a
number of scientific domains
– Direct and govern the next phase of work
– Can’t wait for months or even years

12. In R&D the right technology can start the
analytics opportunity. The right people with
the right skills are needed to deliver quick
solutions and keep the research cadence
going.
Analytics presents many opportunities and
challenges…
How is R&D responding?

13. Data Management Strategy.
• Data retention strategy.
– When storage costs can outstrip generation
costs, we need a very clear data retention
strategy.
• Data Blue Printing.
– How is data flowing through your processes,
and how is it informing decisions?
• Clear Data Standards.
– Which ones are you adopting, and how will
you translate between leading standards?
(Batch, Lot, Sample, Batch record, etc..)
• Roles like Chief Data Officer
becoming important.
– Company wide standards, sponsored from
the top.

14. Secondary Data Use
Public Data Published DataIn House Data Real World Data
Data Translation (Re-Formatting, Text Analytics, Normalisation) to produce
ANALYTICS READY DATA
Data Analysis
Partner Data

15. Bringing our software to their data.
Challenge:
•Data Sets can be too large to mirror.
•Data may not be permitted to leave a Partner organisation.
One Solution:
• Embassy Approach:
• Secured computing environment in the Partner organisation.
• GSK has set up one of these at the EBI.
• Firewall
• Intrusion detection
• Antivirus
• 2FA
• Encryption at rest
• VM management
• Software licensing
• Back-ups
• User Account management
• Support
• etc….

16. Complex calculations.
http://aws.amazon.com/solutions/case-studies/novartis/
One of the most extreme use cases is from Novartis who run complex insilico screening on
the cloud - the linked example shows how they ran a 10 million compound screen over
87,000 compute cores for 9 hours (39 CPU years of calculations), for a cost of $4,232
Need for a clear Information
protection strategy

17. 17
Agenda
• Data Science examples
• The biomedical industry vision
• Data re-use examples in the biomedical sector
◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation
◦ SciDB - Novartis
◦ The Cancer Genome Atlas (TCGA) - Roche
• Devices re-use new trend
• Potentiating a new role

18. 18
Data Science examples
• 2015
◦ To - Predicting novel therapeutic targets, novel biomarkers
- Genome sequencing
(genomic variants, gene expression patterns, etc…)
- Medical, pre-clinical & pathology imaging
- Electronic Health Records, Sensors…

19. 19
The biomedical industry vision
• Is it possible to predict new drug targets and patient responses
?

20. 20
Agenda
• Data Science examples
• The biomedical industry vision
• Data re-use examples in the biomedical sector
◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation
◦ SciDB - Novartis
◦ The Cancer Genome Atlas (TCGA) - Roche
• Devices re-use new trend
• Potentiating a new role
• Targeting Data Scientists

21. 21
The biomedical industry vision
• Translational Medicine is already transforming
how new therapies are discovered and developed
Patient Population Segregated Patient Population
IHC
FISH
Multiplex ELISA
NGS
GEA
CNV and
Translocations

22. 22
The biomedical industry vision
• Translational Medicine is already transforming
how new therapies are discovered and developed while
high-content omics technologies accelerate this trend
Patient Population Segregated Patient Population
IHC
FISH
Multiplex ELISA
NGS
GEA
CNV and
Translocations

23. 23
Agenda
• Data Science examples
• The biomedical industry vision
• Data re-use examples in the biomedical sector
◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation
◦ SciDB - Novartis
◦ The Cancer Genome Atlas (TCGA) - Roche
• Devices re-use new trend
• Potentiating a new role

24. 24
Data re-use examples in the biomedical sector
• Gene
Expression
Omnibus

25. 25
Data re-use examples in the biomedical sector
• “In database” computing

26. 26
Data re-use examples in the biomedical sector
• The Cancer Genome Atlas (TCGA)
◦ Predicting novel therapeutic targets, novel biomarkers…
◦ Example of public
genomic data mining
& leveraging
◦ TCGA
- Contains the main
genomic changes
in cancer
- >30 cancer types
- >45000 archives
- Size ~75 TB

27. 27
Data re-use examples in the biomedical sector
• The Cancer Genome Atlas (TCGA)
◦ Predicting novel therapeutic targets, novel biomarkers

28. 28
Agenda
• Data Science examples
• The biomedical industry vision
• Data re-use examples in the biomedical sector
◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation
◦ SciDB - Novartis
◦ The Cancer Genome Atlas (TCGA) - Roche
• Devices re-use new trend
• Potentiating a new role
• Targeting Data Scientists

29. 29
Devices re-use new trend
• Sensor analysis for improved trial designs with Kinect

30. 30
Devices re-use new trend
• At the interface
between
diagnostics
and
telemedicine

31. 31
Devices re-use new trend
• At the interface
between
diagnostics
and
telemedicine

32. 32
Agenda
• Data Science examples
• The biomedical industry vision
• Data re-use examples in the biomedical sector
◦ Gene Expression Omnibus (GEO) – Children’s Tumor Foundation
◦ SciDB - Novartis
◦ The Cancer Genome Atlas (TCGA) - Roche
• Devices re-use new trend
• Potentiating a new role

33. 33
Potentiating a new role
• Producing the data is not where the value resides
◦ Mining the data provides the valuable findings
◦ Algorithms are at the core of analytic capabilities
• Analytics potential value
◦ Find patterns/markers associated with a patient response
◦ Extract quantitative and discriminative information (faster) from sensors
• Technologies involved
◦ Computing closer to the data source
◦ “Translational” databases

34. 34
Potentiating a new role
• Challenge
◦ The complexity and size of the data, coupled to complex technologies limit the
opportunity for life science experts to explore and interpret the data
• A solution – Data Science
◦ Integrate the tools allowing to process the data and visualize the results
◦ Data Science combines strong scientific and disease domain expertise with
analytics capabilities to generate answers rather than information
• Educate “Data Scientists” to be able to use such integrated tools,
enabling them to perform advanced results exploration and queries
◦ For instance finding patients with similar patterns of mutations
in large genome-wide association studies databases

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Analytics in pharma R&D15 September, 2015

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