Using the CDD Vault for MM4TB


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BIO2014 - Mining for gold - creating business opportunities from publicly funded collaborative research - session

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  • CDD is 10 yrs old
    SaaS is not new
    Being on cloud, personal cloud is not new
    Sharing data , collaborations is not new
    We were first, we helped build foundation
    What we do next is important and depends on you
  • Molecules and biology e.g. PubChem
    Molecules and analytical data e.g. ChemSpider
    These are massive..primarily look ups - not private ....not CDD

    Standalone, little integration and lots of errors
    Need to use caution
  • CDD has lots of public data
    How to expose high value data
    How to create addition high value datasets
    TB molecules and targets
    TB in vivo mouse data
    We developed a mobile app yrs before any of our competitors

    Easy use
    Expose user to data
    CDD brand recognition
    Start something that can grow
    App becomes way to fast prototype ideas
    Data visualization, output

  • Using the CDD Vault for MM4TB

    1. 1. Using the CDD Vault for MM4TB Sean Ekins PhD, DSc. CSO, Collaborative Drug Discovery, Inc Burlingame, CA
    2. 2. The Exploitation pathway… Torbjörn Ingemansson European Commission Sean Ekins Collaborative Drug Discovery, Inc. Claire Skentelbery European Biotechnology Network Bonny Harbinger National Institutes of Health
    3. 3. “by provisioning the right amount of storage and compute resources, cost can be significantly reduced with no significant impact on application performance”
    4. 4. CDD- a decade of drug discovery collaborations2004 - present SaaS Easy to use Used by Academia Industry, Biotech Private Selective collaboration 100’s of published datasets
    5. 5. NIH funded STTR collaboration Some collaborations outside also 1R41AI088893-01 2R42AI088893-02 Developing molecules for TB Using computational data mining New technologies for TB drug discovery
    6. 6. From Desktop to Mobile apps – making data accessible Clark et al., submitted 2014 Predict targets
    7. 7. Drug discovery is repetitive and there are 1000s of diseases Drug discovery is high risk Do we need robots or just smarter programs that discover the ideas we test?
    8. 8. 24 groups in this project use a single Participants from India, Russia, South Africa, Europe, USA
    9. 9. TB Drug Accelerator Another example of a big TB collaboration 7 Big Pharma and 4 academic institutes will open up targeted sections of their compound libraries and share data with each other. • Abbott • AstraZeneca • Bayer • Eli Lilly • GlaxoSmithKline • Merck • Sanofi • Infectious Disease Research Institute (IDRI) • NIH National Institute of Allergy and Infectious Diseases • Texas A&M University • Weill Cornell Medical College
    10. 10. The Big Picture • This did not happen overnight – 10 years to get here! • Collaboration and selective secure sharing is key – move ideas and data – to clinical compounds • Domain expertise in drug discovery • Bring scientists together globally • Grants can accelerate / catalyze collaboration • Grants can fund further technology development • Impact how we develop therapies