This document discusses the decentralization of biomedical research through open sharing of complex biological data. It describes Sage Bionetworks' approach of engaging diverse researcher communities to study problems too complex for any single institution. This involves empowering citizens to track their own health and contribute data to research. Examples show how decentralization is breaking down barriers between recruitment, data generation, and data analysis that previously occurred within institutional silos. Partnerships aim to accelerate research through open sharing of data across organizations.

1. brian m. bot | principal scientist |
2016 sep 28
sage bionetworks
clinical trials transformation initiative
a trend in biomedical research
| @BrianMBot
decentralization:

2. sage bionetworks
promote open systems, incentives, and norms
to redefine how complex biological data is
gathered, shared, and used
our approach
decentralization

3. sage bionetworks
engage diverse communities of researchers
around biological and analytical problems
too complex for a single institution
our focus
empower citizens to track their own health
and contribute deep phenotypic data to
research topics important to them
decentralization

4. decentralization
the process of redistributing or dispersing
functions, powers, people, or things away
from a central location or authority
wikipedia

5. pervasiveness of networked information
cloud infrastructure
democratization of research process
gov’t agencies and public pushing for > sharing
decentralization in biomedical research

6. recruitment
data generation
data analysis
institutional silos

7. recruitment data generation data analysis
institutional silosbreaking down

8. recruitment data generation data analysis
institutional silosbreaking down

9. crc subtyping consortium

10. A
B
C
D
E
F
1
2
3
4
5
6
expert team data subtype
crc subtyping consortium

11. A
B
C
D
E
F
1
2
3
4
5
6
...
expert team data subtype
crc subtyping consortium

12. crc subtyping consortium

13. doi:10.1038/nm.3967
crc subtyping consortium

14. analytical challenges
unbiased, consistent, and
rigorous method assessment
sampling of a space of
diverse methods

15. predict survival for
prostate cancer patients
analytical challenges

16. recruitment data generation data analysis
institutional silosbreaking down

17. recruitment data generation data analysis
institutional silosbreaking down

18. public / private partnership between NIH,
10 biopharmaceutical companies
and several non-profit organizations
accelerating medicines partnership
data generation data analysis|

19. ($ Millions)
Total
Project
Total NIH
Total
Industry
Alzheimer’s
Disease
129.5 67.6 61.9
Type 2
Diabetes
58.4 30.4 28
Rheumatoid
Arthritis
41.6 20.9 20.7
TOTAL 229.5 118.9 110.6
accelerating medicines partnership
data generation data analysis|

20. Target
Discovery
Target
Discovery
Target
Discovery
Target
Discovery
Target
Validation
Target
Validation
Target
Validation
Target
Validation
Shared
Information for
Target Identification
coordinate sharing of early-phase
target identification insights
accelerating medicines partnership
data generation data analysis|

21. AMP-AD
Collaborative
Workspace
Quarterly
Depositions
Broad/
RUSH
Mt.
Sinai
U Fl/
ISB/
Mayo
Emory
Sage
Other
Partners
Individual Partner Workspaces AMP-AD
Data Portal
Consortium Space
Public space
AMP-AD Synapse
Project Structure
accelerating medicines partnership
data generation data analysis|

22. recruitment data generation data analysis
institutional silosbreaking down

23. recruitment data generation data analysis
institutional silosbreaking down

24. Healthmobile

25. Healthm

26. insular health trackingmove beyond

27. nearly 200 million smart phone users in US
insular health trackingmove beyond

28. 2015 march

30. participant-centered consent
open source toolkit
http://sagebase.org/pcc

31. participant-centered consent
1. tiered information access by
participants
2. ‘pictorial’ dominant on first information
tier
3. text dominant on second information
tier
4. require perfect score on short
assessment

32. participant-centered consent

33. changeable by participant

34. first 6 months
16,585 participants consented
14,684 participants enrolled
9,520 agreed to ‘share broadly’
1,087 self reported PD diagnosis
recruitment
mPower

35. motor
initiation
gait/balance hypophonia memory
mPower
data generation
activities

36. six month data releasemPower
data generation

37. six month data releasemPower
task name
type of task and
schedule
unique
participants
unique tasks
demographics survey - once 6,805 6,805
MDS-UPDRS survey - monthly 2,024 2,305
PDQ8 survey - monthly 1,334 1,641
memory activity - t.i.d. 968 8,569
tapping activity - t.i.d. 8,003 78,887
voice activity - t.i.d. 5,826 65,022
walking activity - t.i.d. 3,101 35,410
data generation

38. Parkinson’s Disease Foundation
Eli Lilly
AstraZeneca
Apple
Verily
Intel
Infocepts
Posit Science
MIT
The Ohio State University
University of Otago
University of Texas Health Science Center
Istanbul Sehir University
University of Iowa
University of Virginia
University of Toronto
Johns Hopkins University
Vanderbilt University
University of Rochester
McGill University
Xi'an Jiaotong University
University of Washington
Harvard University
mHealth research communityParkinson
study data released before any primary publication
60+ independent ‘qualified researchers’
working towards building a PD research community

39. Sage Bionetworks joins The Scripps Research Institute (TSRI) for
PMI Cohort Program via Participant Technology Center (PTC)
2016 July 06
data generation

40. Sage Bionetworks joins The Scripps Research
Institute (TSRI) for PMI Cohort Program via
Participant Technology Center (PTC)
In collaboration with Scripps Participant Technologies Center (PTC):
• Sage Bionetworks will be responsible for the patient consent and
data governance, as well as the community outreach and
participant engagement efforts of the PTC
• Sage Bionetworks will also be engaged in the scientific and
engineering work to develop new methodologies for measuring
symptoms of health and disease, including developing symptom
measurements for phone, wearable, and other sensors
data generation

41. democratization of research
pervasiveness of networked information
cloud infrastructure
gov’t agencies and public pushing for > sharing
increasingly decentralized research ecosystem

42. brian m. bot
————————
principal scientist
community manager
sage bionetworks
brian.bot@sagebase.org
@BrianMBot
thank you