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Acs collaborative computational technologies for biomedical research an enabler of more open drug discovery


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ACS talk 28 march

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Acs collaborative computational technologies for biomedical research an enabler of more open drug discovery

  1. 1. Collaborative Computational Technologies for BiomedicalResearch: 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. 2. In the long history of human kind (andanimal kind, too) those who have learned to collaborate and improvise most effectively have prevailed. Charles Darwin
  3. 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. 4. A Starting Point For a New Era?How to doit better?OpennessWhat can wedo withsoftware tofacilitate 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. 5. Some Definitions Open InnovationOpen 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. 6. • All pharmas have similar high level business processes efforts• Is there any competitive advantage?• In informatics?• - 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. 7. Collaboration and Openness is KeyMajor collaborative grants in EU: Framework, Innovative Medicines Initiative…NIHmoving in same directionCross-continent collaboration CROs in China, India etc. – Pharma’s in US / EuropeMore industry – academia collaboration and ‘not invented here’ a thing of the pastMore efforts to go after rare and neglected diseases -Globalization andconnectivity of scientists will be keyCurrent pace of change in pharma may not be enough.Need to rethink how we use all technologies & resources…
  8. 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. 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. 10. Could All Pharmas Share Their Data and Models? Allergan Bayer Merk KGaA Merck Lilly PfizerCould combining Lundbeckmodels givegreater coverage Roche BIof ADME/ Toxchemistry space Novartisand improvepredictions? GSK AZ BMS
  11. 11. Data, Models and Software Becoming More Accessible- Free, Pre-competitive and Open Efforts - Collaboration
  12. 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 CollaboratorsBunin & Ekins DDT 16: 643-645, 2011 Collaborative platform/s
  13. 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
  14. 14. Tools for Open Science• Blogs• Wikis• Databases• Journals• What about Twitter, Facebook, could these be used for social collaboration in science?
  15. 15. Tools for Open Science Name Website Function myExperiment Workflows, communities DIYbio Community for do it yourself biologists Protocol online Biology protocols Open wetware Materials, protocols and resourcesOpen Notebook science Crowdsourced science challenge – initially challenge on solubility measurement UsefulChem project One scientist’s open notebook Laboratree Science networking site Science Commons Strategies and tools faster, efficient web-
  16. 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. 17. Mobile Apps for Drug Discovery: Could They Facilitate Open Science?Could pharma’s biggestfailing have been givingeveryone a PC?Get the scientist out of theiroffice and back to the benchAppify data – makecheminformatics tools usefulTablet better than phone?Williams et al Chapter 28 Williams et al DDT 16:928-939, 2011
  18. 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 Pfizer open innovation & rare disease vision
  19. 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. 20. Drug Discovery NetworkCould our Pharma R&D look like thisMassive collaboration networks – softwareenabled. We are in “Generation App”.Crowdsourcing will have a role in R&D. Drugdiscovery possible by anyone with “app access”Could apps improve crowdsourcing? Ekins & Williams, Pharm Res, 27: 393-395, 2010.
  21. 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. 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) The Ontology for Biomedical Investigators (OBI) The Functional Genomics Data Society (MGED) Minimum Information About a Microarray Experiment (MIAME) The Minimum Information About a Bioactive Entity (MIABE) Minimum Information for Biological and Biomedical Investigators (MIBBI) Minimum Information for Publication of real time QT-PCR data (MIQE)• 2012 – standard for collaborative software?• Ekins et al Chapter 13
  23. 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. 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. 25. Three Disruptive Strategies for Removing Drug Discovery BottlenecksDisruptive Strategy #1: NIH mandatesminimum data quality standards, strict timelinefor data submission, and open accessibility forall data generated by publicly funded research.Disruptive Strategy #2: Reboot the industry byextending the notion of “pre-competitive”collaboration to encompass later stages ofresearch to allow public private partnershipsto flourish. The role of large pharma is latestage development and branding. Wikipedia vs EncyclopediaDisruptive Strategy #3: FDA takes a proactiverole in making available relevant clinical data Could open drug discoverythat will help to bridge the “valley of death”. disrupt traditional drug discovery? Ekins et al: Submitted 2012
  26. 26. Fund and find the right Ensure quality of molecule structures researchers with and data in ChemSpider CollaborationFinder Collaborative Informatics Could Disrupt Pharmaceutical ResearchSelectively share with collaborators to Openly share findings with other retain IP with CDD researchers and public in ODDTEkins et al: Submitted 2012
  27. 27. Maybe Darwin would have been a biohacker,citizen scientist, open scientist, collaborative scientist… Would he have been able to disrupt drug discovery?
  28. 28. Thank YouBook chapter AuthorsSantosh Adayikkoth, Renée JG Arnold, O.K. Baek, AnshuBhardwaj, Alpheus Bingham, Jean-Claude Bradley, SamirK. Brahmachari, Vincent Breton, A. Bunin, ChristineChichester, Ramesh V. Durvasula, Gabriela Cohen-Freue,Rajarshi Guha, Brian D. Zhiyu He, David Hill, Moses M.Hohman, Zsuzsanna Hollander, Victor J. Hruby, JackieHunter, Maggie A.Z. Hupcey, Steve Koch, George A.Komatsoulis, Falko Kuester, Andrew S.I.D Lang., RobertPorter Lynch, Lydia Maigne, Shawnmarie Mayrand-Chung,Garrett J. McGowan, Matthew K. McGowan, Richard J.McGowan, Barend Mons, Mark A. Musen, CameronNeylon, Christina K. Pikas, Kevin Ponto, Brian Pratt, NickLynch, David Sarramia, Vinod Scaria, Stephan Schürer,Jeff Shrager, Robin W. Spencer, Ola Spjuth, SándorSzalma, Keith Taylor, Marty Tenenbaum, Zakir Thomas,Tania Tudorache, Michael Travers, Chris L. Waller, JohnWilbanks, Egon Willighagen, Edward D. Zanders&Mary P. Bradley, Alex M. Clark
  29. 29. Email: ekinssean@yahoo.comTwitter: collabchemBlog: williamsa@rsc.orgTwitter: ChemConnectorBlog: www.chemconnector.comSlideshare: thanks to our collaborators