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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.
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
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


   There has to be a better way?
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
• Precompetitive organizations, consortia..
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
• 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 precompetitive space
• Shift from software to services
• e.g. sequence services

• Sequence Squeeze Competition for Next-gen sequencing
  compression algorithm $15K prize
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 effort 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…
 Improved Quality of data is essential

 Open PHACTS : 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.
Where Should We Draw The Precompetitive 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
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
Data, Models and Software Becoming More Accessible- Free,
      Precompetitive and Open Efforts - Collaboration
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
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
Tools for Open Science

•   Blogs
•   Wikis
•   Databases
•   Journals




• What about Twitter, Facebook, could these be
  used for social collaboration, science?
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/        An example of 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-
Tools for Open Science The Evolution of the e-lab
                   Notebook
  • Blogs - Will we see a shift as more scientists blog about
    work?
  • Wikis – creating more of these as 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 – more people create their own
  • Combine all content = collaborative lab notebook
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
Open Drug Discovery Teams
                                            A free app to collate social media
                                            Saves hashtags 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
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 v. active
                               minority
                             • Power laws vary by
                               crowdsourcing type

                             Robin Spencer in Chapter 28
• How can we engage more contributors?
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.
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
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
Open Science: What is needed?
•   Open tools – need good validation studies many
    developed with no support
                                                                Open
•   Support those scientists making data open (e.g. J.C.       Science
    Bradley)
                                                                needs
•   Support companies/groups promoting software for data         You!
    sharing

•   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 those that have been let go by industry another
    route to discovery – materials, drugs, technologies
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

• Need to educate the next generation on collaboration and
  collaborative software




• BIG DATA is on the way
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
Disruptive Strategy #3: FDA takes a proactive      Wikipedia vs Encyclopedia
role in making available relevant clinical data
that will help to bridge the valley of death.      Could open drug discovery
                                                   disrupt traditional drug
                                                   discovery?
  Ekins et al: Submitted 2012
Fund and find the right              Ensure quality of molecule structures
       researchers with                        and data in ChemSpider
      CollaborationFinder
 Collaborative Informatics Technologies Could Disrupt Pharmaceutical Research




Selectively share with collaborators to        Openly share findings with
          retain IP with CDD                 other researchers and public in
Ekins et al: Submitted 2012                              ODDT
Maybe Darwin would have been a biohacker,
citizen scientist, open scientist, collaborative
                   scientist…

  Would he have been able to disrupt drug
                discovery?
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           ScientistsDB Logo by Kalliopi Monoyios
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

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Collaborative Computational Technologies for Biomedical Research: An Enabler Of More Open Drug Discovery

  • 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. 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 There has to be 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 • Precompetitive 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 precompetitive space • Shift from software to services • e.g. sequence services • Sequence Squeeze Competition for Next-gen sequencing compression algorithm $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 effort 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 : 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 Precompetitive 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, Precompetitive 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, 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/ An example of 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 we see a shift as more scientists blog about work? • Wikis – creating more of these as 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 – more people create their own • 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 hashtags 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 v. 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 Open • Support those scientists making data open (e.g. J.C. Science Bradley) needs • Support companies/groups promoting software for data You! sharing • 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 those that have been let go by industry 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 • Need to educate the next generation on collaboration and 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 Disruptive Strategy #3: FDA takes a proactive Wikipedia vs Encyclopedia role in making available relevant clinical data that will help to bridge the valley of death. Could open drug discovery 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 Technologies Could Disrupt Pharmaceutical Research Selectively share with collaborators to Openly share findings with retain IP with CDD other researchers and public in Ekins et al: Submitted 2012 ODDT
  • 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 ScientistsDB Logo by Kalliopi Monoyios
  • 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