As scientists in the life sciences we are trained to pursue singular goals around a publication or a validated target or a drug submission. Our failure rates are exceedingly high especially as we move closer to patients in the attempt to collect sufficient clinical evidence to demonstrate the value of novel therapeutics. This wastes resources as well as time for patients depending upon us for the next breakthrough. Edge Informatics is an approach to ameliorate these failures. Using both technical and social solutions together knowledge can be shared and leveraged across the drug development process. This is accomplished by making data assets discoverable, accessible, self-described, reusable and annotatable. The Open PHACTS project pioneered this approach and has provided a number of the technical and social solutions to enable Edge Informatics. A number of pre-competitive consortia and some content providers have also embraced this approach, facilitating networks of collaborators within and outside a given organization. When taken together more accurate, timely and inclusive decision-making is fostered.

1. Tom Plasterer, PhD.
Research & Development Information (RDI)
Director, US Cross-Science
Harnessing Edge Informatics to
Accelerate Collaboration in
BioPharma
WINNER

2. The US Cross-Science
Team in Research and
Development Information
(RDI) is a group of
informaticians,
mathematicians, project/
program managers,
developers, architects
dedicated to data
science—data discovery,
data reuse, data
harmonization, analytics
and self-describing data
or Smart Data
We strive to create
tangible digital and social
artefacts used to
accelerate delivering
medicines to patients and
improving their impact
once in the clinical setting.
These artefacts include
web-based software,
community-driven data
models, data sharing best
practices, data science
communities of practice
and strong advocacy of
Smart Data inside and out
of AstraZeneca.

3. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?

4. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?
‘A second concern held by some is that a
new class of research person will emerge
— people who had nothing to do with the
design and execution of the study but use
another group’s data for their own ends,
possibly stealing from the research
productivity planned by the data gatherers,
or even use the data to try to disprove
what the original investigators had posited.
There is concern among some front-line
researchers that the system will be taken
over by what some researchers have
characterized as “research parasites.”’
Dan Longo and Jeffrey Drazen, the deputy
editor and editor-in-chief, NEJM

5. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?
‘A second concern held by some is that a
new class of research person will emerge
— people who had nothing to do with the
design and execution of the study but use
another group’s data for their own ends,
possibly stealing from the research
productivity planned by the data gatherers,
or even use the data to try to disprove
what the original investigators had posited.
There is concern among some front-line
researchers that the system will be taken
over by what some researchers have
characterized as “research parasites.”’
Dan Longo and Jeffrey Drazen, the deputy
editor and editor-in-chief, NEJM

6. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?
‘A second concern held by some is that a
new class of research person will emerge
— people who had nothing to do with the
design and execution of the study but use
another group’s data for their own ends,
possibly stealing from the research
productivity planned by the data gatherers,
or even use the data to try to disprove
what the original investigators had posited.
There is concern among some front-line
researchers that the system will be taken
over by what some researchers have
characterized as “research parasites.”’
Dan Longo and Jeffrey Drazen, the deputy
editor and editor-in-chief, NEJM
‘The condescension implicit in
this statement is deeply
troubling. Drazen and Longo are
saying, essentially, that only the
people who originally collect a
data set can truly understand it,
and anyone else who wants to
take a look is a parasite.’
Steven Salzberg, JHU

7. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?
‘A second concern held by some is that a
new class of research person will emerge
— people who had nothing to do with the
design and execution of the study but use
another group’s data for their own ends,
possibly stealing from the research
productivity planned by the data gatherers,
or even use the data to try to disprove
what the original investigators had posited.
There is concern among some front-line
researchers that the system will be taken
over by what some researchers have
characterized as “research parasites.”’
Dan Longo and Jeffrey Drazen, the deputy
editor and editor-in-chief, NEJM
‘The condescension implicit in
this statement is deeply
troubling. Drazen and Longo are
saying, essentially, that only the
people who originally collect a
data set can truly understand it,
and anyone else who wants to
take a look is a parasite.’
Steven Salzberg, JHU

8. R&D | RDI
Sharing & Collaboration: Are you a Data Parasite?
‘A second concern held by some is that a
new class of research person will emerge
— people who had nothing to do with the
design and execution of the study but use
another group’s data for their own ends,
possibly stealing from the research
productivity planned by the data gatherers,
or even use the data to try to disprove
what the original investigators had posited.
There is concern among some front-line
researchers that the system will be taken
over by what some researchers have
characterized as “research parasites.”’
Dan Longo and Jeffrey Drazen, the deputy
editor and editor-in-chief, NEJM
‘The condescension implicit in
this statement is deeply
troubling. Drazen and Longo are
saying, essentially, that only the
people who originally collect a
data set can truly understand it,
and anyone else who wants to
take a look is a parasite.’
Steven Salzberg, JHU
‘But the science, data, and research
results are trapped in silos,
preventing faster progress and
greater reach to patients. It’s not
just about developing game-
changing treatments — it’s about
delivering them to those who need
them.’
Vice President Biden’s Blog

9. R&D | RDI
Public Research, Private Results?

10. R&D | RDI
Public Research, Private Results?

11. R&D | RDI
Public Research, Private Results?

12. R&D | RDI
Public Research, Private Results?
‘Payment of 32 dollars is just insane
when you need to skim or read tens
or hundreds of these papers to do
research. I obtained these papers
by pirating them. Later I found there
are lots and lots of researchers (not
even students, but university
researchers) just like me, especially
in developing countries. They
created online communities
(forums) to solve this problem.’
Alexandra Elbakyan
Sci-hub operator

13. R&D | RDI
Sharing Clinical Trial Results

14. R&D | RDI
Sharing Clinical Trial Results

15. R&D | RDI
Sharing Clinical Trial Results
Thousands of clinical trials have not
reported their results; some have not even
been registered.
Information on what was done and what
was found in these trials could be lost
forever to doctors and researchers, leading
to bad treatment decisions, missed
opportunities for good medicine, and trials
being repeated.
All trials past and present should be
registered, and the full methods and the
results reported.
We call on governments, regulators and
research bodies to implement measures to
achieve this.
AllTrials.Net Petition(2015)

16. R&D | RDI
Sharing Clinical Trial Results

17. R&D | RDI
Sharing Clinical Trial Results
• Maximize the benefits while minimizing
the risks of sharing clinical trial data
• Respect individual participants whose
data are shared
• Increase public trust in clinical trials and
the sharing of trial data
• Conduct the sharing of clinical trial data
in a fair manner
IOM Report: Sharing Clinical Trial Data:
Maximizing Benefits, Minimizing Risk
(2015)

18. R&D | RDI
Edge Informatics
Interfaces within the Drug Development Process
Target
Discovery
NGS Exome
analysis
Pathway
Analysis
Structure
Analysis
Lead
Discovery
RNAi
Assay
Development
HTS
Lead
Optimization
SAR
In vivo non-
human
testing
Exploratory
PK
Exploratory
Tox
Pre-Clinical
Development
GLP Tox
Formulation
ADME
PK
Efficacy
Clinical
Development
IND
Safety,
Tolerability
Phase I-III
Registration
NDA/BLA
MAA
Marketing &
Sales
PMR
REMS
PSUR
Observational
Research
Pathway
Enrichment
Disease
Contextualization
Seamless information connectivity (an EDGE) needed across domain
NODEs

19. R&D | RDI
Integration Quandary:
Content Does Not Combine Easily
Fit-for-Purpose to “Standards”
Models
Structured
Triplestores
Semi-StructuredUnstructured
Content
Lack of
Compatible
Containers →
the ”Plumbing
Problem”
Lack of
Compatible
Semantics→ the
”Meaning
Problem”

20. R&D | RDI
What’s Needed?
Linked Data!
LOD Cloud 2014Schmachtenberg, Bizer, Jentzsch and Cyganiak.
http://lod-cloud.net/

21. R&D | RDI
What’s Needed?
Linked Data!
LOD Cloud 2014Schmachtenberg, Bizer, Jentzsch and Cyganiak.
http://lod-cloud.net/
“Smart Data” means information that actually
makes sense.
Wired Magazine, April 2013

22. R&D | RDI
Thanks to: Eric Little, VP Data Science, Osthus
The Emergence of Smart Data
Standards Driven at Container Interfaces

23. R&D | RDI
Competitive Intelligence 360 (CI360) Approach
Flexibly Addressing Key Questions
23
Capture Business
Questions and
Sources
Domain Expert
Concept Map
Build Formal
Ontology
Challenge with
Linked Data
Examine with a
Faceted Browser
Share insights
with a Knowledge
Base

24. R&D | RDI
Capture Business Questions
Capture Business
Questions and
Sources

25. R&D | RDI
Translate Questions into Concepts
Domain Expert
Concept Map
“Where are the key clinical studies in NSCLC and who are the principle investigators?”

26. R&D | RDI
Challenge with Data
“Where are the key clinical studies in NSCLC and who are the principle investigators?”
(one example)
Challenge with
Linked Data
Source: https://clinicaltrials.gov/ct2/show/NCT02027428

27. R&D | RDI
Refine the Answer
Examine with a
Faceted Browser
“What are the open trials in metastatic breast cancer and what drugs are being tested?”

28. R&D | RDI
Share Insights as a Community
“Can a biomarker defined population be added to a trial record?”
Share insights
with a Knowledge
Base

29. R&D | RDI
Data FAIRport
To be Findable:
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data it describes
F4. (meta)data are registered or indexed in a searchable resource
To be Accessible:
A1. (meta)data are retrievable by their identifier using a standardized communications
protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where
necessary
A2. metadata are accessible, even when the data are no longer available
To be Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for
knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data
To be Reusable:
R1. meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards
Interoperability
Investment

30. R&D | RDI
Naming Things
Use Resolvable URIs
Interoperability
Investment
http://purl.uniprot.org/uniprot/P30453
http://www.uniprot.org/uniprot/P30453
http://purl.uniprot.org/uniprot/P30453.ttl

31. R&D | RDI
Describing Data
Reuse, Reuse, Reuse (build only if essential)
Interoperability
Investment

32. R&D | RDI
Describing Data
Reuse, Reuse, Reuse (build only if essential)
Interoperability
Investment

33. R&D | RDI
Describing Data
Reuse, Reuse, Reuse (build only if essential)
Interoperability
Investment

34. R&D | RDI
Finding Data
Vocabulary of Interlinked Datasets (VoID)
Interoperability
Investment

35. R&D | RDI
Cross-BioPharma Data Standardization Efforts
Interoperability
Investment

36. R&D | RDI
Open Data, Open Science Efforts
36

37. R&D | RDI
Open Data, Open Science Efforts
37

38. R&D | RDI
Open Data, Open Science Efforts
38

39. R&D | RDI
Open Data, Open Science Efforts
39

40. R&D | RDI
Open Data, Open Science Efforts
40

41. R&D | RDI
Open Data, Open Science Efforts
41

42. R&D | RDI
Get your plumbing right
• And your data won’t be stuck in a silo
Leverage working public solutions
• Don’t reinvent the wheel
Use Edge Informatics
• Consider handoffs—you don’t know how your data will be used in the
future
Invest in Data Stewardship
• Small tax to future-proof your efforts
Data and Collaboration ARE Business Assets