ACS talk 28 march

1. Collaborative Computational
Technologies for Biomedical
Research: An Enabler of More Open
Drug Discovery
Sean Ekins, Ph.D., D.Sc.
Collaborations in Chemistry,
Fuquay-Varina, NC.
Antony J. Williams, Ph.D.,
Royal Society of Chemistry,
Wake Forest, NC.

2. In the long history of human kind (and
animal kind, too) those who have learned
to collaborate and improvise most
effectively have prevailed.
Charles Darwin

3. Time for Open Drug Discovery?
• Pharma Companies spend >$50 billion annually on
R&D
• How much historical data/knowledge/information is in
the public domain? And where is it?
• How much generated data is truly competitive?
• Pre-competitive and public domain data could deliver
high value to drug discovery
– Data mining
– Model-building
– Integrating into in-house and online systems
Is Open Drug Discovery a better way?

4. A Starting Point For a New Era?
How to do
it better?
Openness
What can we
do with
software to
facilitate it ?
Make it Open
We have tools
but need
integration The future is more
Open interfaces collaborative and Open
• Groups involved traverse the spectrum from pharma, academia, not for
profit and government
• More free, open technologies to enable biomedical research
• Pre-competitive organizations, consortia..

5. Some Definitions
Open Innovation
Open innovation is a paradigm that assumes that firms can and should use external ideas as well as
internal ideas, and internal and external paths to market, as the firms look to
advance their technology
Chesbrough, H.W. (2003).
Open Innovation: The new imperative for creating and profiting from technology.
Boston: Harvard Business School Press, p. xxiv
Collaborative Innovation
A strategy in which groups partner to create a product - drive the efficient allocation of R&D
resources. Collaborating with outsiders-including customers, vendors and even competitors-a
company is able to import lower-cost, higher-quality ideas from the best sources in the world.
Open Source
While open source and open innovation might conflict on patent issues,
they are not mutually exclusive, as participating companies can donate their patents
to an independent organization, put them in a common pool or grant
unlimited license use to anybody. Hence some open source initiatives
can merge the two concepts

6. • All pharmas have similar high level business processes efforts
• Is there any competitive advantage?
• In informatics?
• www.pistoiaalliance.org - companies and vendors
• Agree on the pre-competitive space
• Shift from software to services: e.g. sequence services
• Sequence Squeeze Competition for next generation
sequencing compression algorithm with $15K prize

7. Collaboration and Openness is Key
Major collaborative grants in EU: Framework, Innovative Medicines Initiative…NIH
moving in same direction
Cross-continent collaboration CROs in China, India etc. – Pharma’s in US / Europe
More industry – academia collaboration and ‘not invented here’ a thing of the past
More efforts to go after rare and neglected diseases -Globalization and
connectivity of scientists will be key
Current pace of change in pharma may not be enough.
Need to rethink how we use all technologies & resources…

8.  Improved Quality of data is essential
 Open PHACTS : IMI funded public-private partnership
between European Community and EFPIA
 Freely accessible for knowledge discovery and verification.
 Data on small molecules
 Pharmacological profiles
 ADMET data
 Biological targets and pathways
 Proprietary and public data sources.

9. Where Should We Draw The Pre-competitive Boundary?
Usually on tools Jackie Hunter has suggested Why not make everything upto
and after Target ID and Validation development precompetitive
technologies for
early drug Chapter 4 of book.. e.g. share ADME/Tox data so
discovery everyone understands failures for
a class of compounds?
Share ADME/Tox Models
Gupta RR, et al., Drug Metab Dispos,
38: 2083-2090, 2010

10. Could All Pharmas Share Their Data and Models?
Allergan Bayer
Merk KGaA
Merck Lilly
Pfizer
Could combining Lundbeck
models give
greater coverage Roche BI
of ADME/ Tox
chemistry space Novartis
and improve
predictions? GSK
AZ
BMS

11. Data, Models and Software Becoming More Accessible- Free,
Pre-competitive and Open Efforts - Collaboration

12. A Complex Ecosystem Of Collaborations
A New Business Model?
IP IP
Molecules, Models, Data Molecules, Models, Data
Inside Company Inside Academia
Shared
IP
Collaborators Collaborators
Molecules, Models, Data Molecules, Models, Data
IP IP
Inside Foundation Inside Government
Collaborators Collaborators
Bunin & Ekins DDT 16: 643-645, 2011
Collaborative platform/s

13. Example ; Collaborative Drug Discovery Platform
• CDD Vault – Secure web-based place for private data – private by default
• CDD Collaborate – Selectively share subsets of data
• CDD Public – Public data sets
• Unique to CDD – simultaneously query your private data, collaborators’
data, & public data, Easy GUI
www.collaborativedrug.com

14. Tools for Open Science
• Blogs
• Wikis
• Databases
• Journals
• What about Twitter, Facebook, could these be
used for social collaboration in science?

15. Tools for Open Science
Name Website Function
myExperiment http://www.myexperiment.org/ Workflows, communities
DIYbio http://diybio.org/ Community for do it yourself biologists
Protocol online http://protocol-online.org/ Biology protocols
Open wetware http://openwetware.org/wiki/Main_Page Materials, protocols and resources
Open Notebook science http://onschallenge.wikispaces.com/ Crowdsourced science challenge – initially
challenge on solubility measurement
UsefulChem project http://usefulchem.wikispaces.com/ One scientist’s open notebook
Laboratree http://laboratree.org/pages/home Science networking site
Science Commons http://sciencecommons.org/ Strategies and tools faster, efficient web-

16. Tools for Open Science
The Evolution of the e-lab Notebook
• Blogs - Will more scientists blog about work in the future?
• Wikis - more coming a way to track work and build databases
Scientists will use apps for science Apps connect to databases for content
• Apps become e-lab notebooks
• Journals - will more people create their own “journal” ?
• Combine all content - collaborative lab notebook

17. Mobile Apps for Drug Discovery:
Could They Facilitate Open Science?
Could pharma’s biggest
failing have been giving
everyone a PC?
Get the scientist out of their
office and back to the bench
Appify data – make
cheminformatics tools useful
Tablet better than phone?
Williams et al Chapter 28 Williams et al DDT 16:928-939, 2011

18. Open Drug Discovery Teams
A free App to collate social media
Saves hash tags on a topic
Chemistry aware
A new way to share links & info.
Access open knowledge
An alternative lab notebook
http://slidesha.re/GzVSPr
See Pfizer open innovation & rare disease vision
http://dl.dropbox.com/u/14511423/VRU.pptx

19. Crowdsourcing: Power law for ChemSpider
• ChemSpider Rank-
frequency plot
• Curation a = 1.4
• Depositions a = 1.5
• Slope is a measure of
contribution by whom
• Driven by very active
minority
• Power laws vary by
crowdsourcing type
Robin Spencer in Chapter 28
• How can we engage more contributors?

20. Drug Discovery Network
Could our Pharma R&D look like this
Massive collaboration networks – software
enabled. We are in “Generation App”.
Crowdsourcing will have a role in R&D. Drug
discovery possible by anyone with “app access”
Could apps improve crowdsourcing?
Ekins & Williams, Pharm Res, 27: 393-395, 2010.

21. Getting Chemists and Biologists to Collaborate?
• “Need them to be open minded for research direction”
• “A collaborator is not a means to their ends”
• “In a good collaboration “hypotheses” are viewed as temporary
starting points”
• “Take ownership and responsibility for research success and failure”
Victor Hruby – Chapter 7
• Ethics: effective communication, clear goals, shared and defined
responsibility for writing and publishing
McGowan et al Chapter 8
• Collaboration can be hampered by materials transfer agreements and
patents – need to standardize – use creative commons
• Wilbanks Chapter 9

22. The Need for Standards for Collaborative Technologies
• 1270 – standard size for bread loaves – Freiberg Germany
• We need standards for assay descriptions, structure representation, how data is
stored, data cleaning etc.
Standard name Website
The Open Biological and Biomedical Ontologies (OBO) http://www.obofoundry.org/
The Ontology for Biomedical Investigators (OBI) http://obi-ontology.org/page/Main_Page
The Functional Genomics Data Society (MGED) http://www.mged.org/index.html
Minimum Information About a Microarray Experiment (MIAME) http://www.mged.org/Workgroups/MIAME/miame.html
The Minimum Information About a Bioactive Entity (MIABE) http://www.psidev.info/index.php?q=node/394
Minimum Information for Biological and Biomedical Investigators (MIBBI) http://www.mibbi.org/index.php/MIBBI_portal
Minimum Information for Publication of real time QT-PCR data (MIQE) http://www.gene-quantification.de/miqe-press.html
• 2012 – standard for collaborative software?
• Ekins et al Chapter 13

23. Open Science: What is needed?
• Open tools – need good validation studies many
developed with no support
• Support scientists making data open (e.g. Bradley) Open
Science
• Support companies/groups promoting software for
data sharing needs
You!
• Lobby grant providers to require that grantees
deposit data in public domain. Make data quality a
criterion for funding
• Engage the community to help create what they
want. Rewards and recognition? - MORE
collaboration can benefit us all
• Give unemployed chemists another route to
discovery – materials, drugs, technologies

24. Open Science: The Landscape
• Currently few scientists practice ONS – so we need to change this
• Missing an open database system for storing/sharing data globally
• Commercial versions exist
• Currently few Open journals – cost may be prohibitive to many
• How do we measure scientists contributions via Open Science?
• The next generation are more likely to use collaborative software
• BIG DATA is on the way

25. Three Disruptive Strategies for Removing Drug
Discovery Bottlenecks
Disruptive Strategy #1: NIH mandates
minimum data quality standards, strict timeline
for data submission, and open accessibility for
all data generated by publicly funded research.
Disruptive Strategy #2: Reboot the industry by
extending the notion of “pre-competitive”
collaboration to encompass later stages of
research to allow public private partnerships
to flourish. The role of large pharma is late
stage development and branding.
Wikipedia vs Encyclopedia
Disruptive Strategy #3: FDA takes a proactive
role in making available relevant clinical data
Could open drug discovery
that will help to bridge the “valley of death”.
disrupt traditional drug
discovery?
Ekins et al: Submitted 2012

26. Fund and find the right Ensure quality of molecule structures
researchers with and data in ChemSpider
CollaborationFinder
Collaborative Informatics Could Disrupt Pharmaceutical Research
Selectively share with collaborators to Openly share findings with other
retain IP with CDD researchers and public in ODDT
Ekins et al: Submitted 2012

27. Maybe Darwin would have been a biohacker,
citizen scientist, open scientist, collaborative
scientist…
Would he have been able to disrupt drug
discovery?

28. Thank You
Book chapter Authors
Santosh Adayikkoth, Renée JG Arnold, O.K. Baek, Anshu
Bhardwaj, Alpheus Bingham, Jean-Claude Bradley, Samir
K. Brahmachari, Vincent Breton, A. Bunin, Christine
Chichester, Ramesh V. Durvasula, Gabriela Cohen-Freue,
Rajarshi Guha, Brian D. Zhiyu He, David Hill, Moses M.
Hohman, Zsuzsanna Hollander, Victor J. Hruby, Jackie
Hunter, Maggie A.Z. Hupcey, Steve Koch, George A.
Komatsoulis, Falko Kuester, Andrew S.I.D Lang., Robert
Porter Lynch, Lydia Maigne, Shawnmarie Mayrand-Chung,
Garrett J. McGowan, Matthew K. McGowan, Richard J.
McGowan, Barend Mons, Mark A. Musen, Cameron
Neylon, Christina K. Pikas, Kevin Ponto, Brian Pratt, Nick
Lynch, David Sarramia, Vinod Scaria, Stephan Schürer,
Jeff Shrager, Robin W. Spencer, Ola Spjuth, Sándor
Szalma, Keith Taylor, Marty Tenenbaum, Zakir Thomas,
Tania Tudorache, Michael Travers, Chris L. Waller, John
Wilbanks, Egon Willighagen, Edward D. Zanders
&
Mary P. Bradley, Alex M. Clark

29. Email: ekinssean@yahoo.com
Twitter: collabchem
Blog: http://www.collabchem.com/
Slideshare: http://www.slideshare.net/ekinssean
Email: williamsa@rsc.org
Twitter: ChemConnector
Blog: www.chemconnector.com
Slideshare: www.slideshare.net/AntonyWilliams
Many thanks to our collaborators