Collaborative Computational Technologies for Biomedical Research: An Enabler Of More Open Drug Discovery
Upcoming SlideShare
Loading in...5

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



The current paradigm in the pharmaceutical industry is that products can only be created and developed by massive collaborative teams. Each company has to build their own costly R&D platforms and IT ...

The current paradigm in the pharmaceutical industry is that products can only be created and developed by massive collaborative teams. Each company has to build their own costly R&D platforms and IT infrastructure. Other research industries realized decades ago that they had to share data and methods because of cost. The pharmaceutical industry has been slow to realize this. Expanding beyond our recent book (Collaborative Computational Technologies for Biomedical Research) in which a growing number of technologies, consortia, precompetitive initiatives and complex collaboration networks are described, we suggest a more open drug discovery is being enabled by collaborative computational technologies. Academia however, is not training the next generation of scientists to practice open science or even collaborate, this represents challenges and opportunities. We will describe our observations and make recommendations that impact everyone from technology developers to granting agencies. This may enable future discoveries to be made outside traditional institutions.



Total Views
Views on SlideShare
Embed Views



2 Embeds 2 1 1



Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-NonCommercial LicenseCC Attribution-NonCommercial License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

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

  • 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.
  • In the long history of human kind (andanimal 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 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• Precompetitive organizations, consortia..
  • 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
  • • All pharmas have similar high level business processes efforts• Is there any competitive advantage?• in informatics?• - - 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 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 effort to go after rare and neglected diseases -Globalization and connectivityof 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 BoundaryUsually on tools Jackie Hunter has suggested Why not make everything uptoand after Target ID and Validation development precompetitivetechnologies forearly drug Chapter 4 of book.. e.g. share ADME/Tox data sodiscovery 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 PfizerCould combining Lundbeckmodels givegreater coverage Roche BIof ADME/ Toxchemistry space Novartisand improvepredictions? 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 CollaboratorsBunin & 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
  • 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 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 An example of one scientist’s open notebook Laboratree Science networking site Science Commons 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 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
  • 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 Pfizer open innovation & rare disease vision
  • 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 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.
  • 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) 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
  • 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 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 partnerships toflourish. The role of large pharma is late stagedevelopment and branding. WikipediaDisruptive Strategy #3: FDA takes a proactive Wikipedia vs Encyclopediarole in making available relevant clinical datathat 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 ResearchSelectively share with collaborators to Openly share findings with retain IP with CDD other researchers and public inEkins 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 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 ScientistsDB Logo by Kalliopi Monoyios
  • Email: ekinssean@yahoo.comTwitter: collabchemBlog: williamsa@rsc.orgTwitter: ChemConnectorBlog: www.chemconnector.comSlideshare: thanks to our collaborators