P4 c2011 slides ekins


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Partnering 4 cures meeting slides presented nov 7 201

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P4 c2011 slides ekins

  1. 2. Collaborative Drug Discovery: An Alternative Business Model For Drug Discovery Sean Ekins, M.Sc., Ph.D., D.Sc. Collaborations Director, Collaborative Drug Discovery, Inc.
  2. 3. Introduction <ul><li>2003: Envisioned CDD </li></ul><ul><li>2004: Spun out of Lilly by Dr. Barry Bunin </li></ul><ul><li>2005: Eli Lilly co-invested in a syndicate with Omidyar Network and Founders Fund </li></ul><ul><li>2008: BMGF 2 year grant to support TB research ($1,896,923) </li></ul><ul><li>2010: STTR phase I with SRI TB – chem-bioinformatics integration ($150K) </li></ul><ul><li>2011: BMGF 3 year grant to support 3 academia: industry TB Collaborations (~$900,000) </li></ul><ul><ul><li>MM4TB 5 year EU Framework 7 funded project (Euro 249,700) </li></ul></ul><ul><ul><li>Bio-IT World Best Practices Award, Editors Choice </li></ul></ul><ul><ul><li>SBIR phase I ($150K) </li></ul></ul><ul><ul><li>5 year NIH NIDA contract </li></ul></ul><ul><li>Private and profitable </li></ul>
  3. 4. Overview: Sharing data and models to speed up drug discovery <ul><li>Introduction </li></ul><ul><li>Collaboration 1. Pfizer - developing, validating and deploying open source ADME/Tox models </li></ul><ul><li>Collaboration 2. Tuberculosis research funded by the Bill & Melinda Gates Foundation, </li></ul><ul><li>Collaboration 3. European Commission FP7 funded More Medicines for Tuberculosis project </li></ul><ul><li>Collaboration 4. NIH funded STTR with the Stanford Research Institute International - TB drug discovery </li></ul>
  4. 5. A Starting Point For A New Research Era? How to do it better? Openness What can we do with software to facilitate it ? Make it Open The future is more collaborative and Open We have tools but need integration Open interfaces A core root of the current inefficiencies in drug discovery are due to organizations’ and individual’s barriers to collaborate effectively Bunin & Ekins DDT 16: 643-645, 2011 <ul><li>Groups involved traverse the spectrum from pharma, academia, not for profit and government </li></ul><ul><li>More free, open technologies to enable biomedical research </li></ul><ul><li>Precompetitive organizations, consortia.. </li></ul>
  5. 6. How Can Collaborative Software Help? <ul><ul><li>CDD Vault – Secure web-based place for private data – private by default </li></ul></ul><ul><ul><li>CDD Collaborate – Selectively share subsets of data </li></ul></ul><ul><ul><li>CDD Public – public data sets - Over 3 Million compounds, with molecular properties, similarity and substructure searching, data plotting etc </li></ul></ul><ul><ul><li>Unique to CDD – simultaneously query your private data, collaborators’ data, & public data, Easy GUI </li></ul></ul>
  6. 7. Overview of CDD
  7. 8. About CDD <ul><li>Network </li></ul><ul><ul><li>Traction: thousands of leading researchers log into CDD today: </li></ul></ul><ul><ul><li>Academic customers: Harvard, Columbia, Johns Hopkins, UCSF (new assays) </li></ul></ul><ul><ul><li>Pharmas relationships: Pfizer, GSK, Novartis, Lilly (commercial partners) </li></ul></ul><ul><ul><li>Startups </li></ul></ul><ul><ul><li>Research institutes, Non profits NIH, BMGF, MM4TB etc </li></ul></ul><ul><li>Neutral </li></ul><ul><ul><li>Trusted for >7 years in the cloud </li></ul></ul><ul><ul><li>Moral high-ground due to years dedicated to neglected disease </li></ul></ul><ul><ul><li>Credible position </li></ul></ul><ul><li>IP </li></ul><ul><ul><li>CDD handles data corresponding to composition of matter & utility patents </li></ul></ul><ul><ul><li>Templates for rapid web-based transactions (IP corresponding to data) </li></ul></ul><ul><ul><li>CDD does not own IP </li></ul></ul>
  8. 9. Collaboration 1. Needs <ul><li>Challenge..There is limited access to ADME/Tox data and models needed for R&D </li></ul><ul><li>How could a company share data but keep the structures proprietary? </li></ul><ul><li>Sharing models means both parties use costly software </li></ul><ul><li>What about open source tools? </li></ul><ul><li>Collaborators had never considered this - So we proposed a study and Rishi Gupta generated models </li></ul>
  9. 10. Collaboration 1. Strategy <ul><li>Open algorithms, descriptors, closed data – can we unlock it? </li></ul><ul><li>Massive datasets 10’s- 100’s of thousands </li></ul><ul><li>We found open source = commercial tools </li></ul>Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
  10. 11. Collaboration 1. Opportunity <ul><li>ADME/Tox Data crosses diseases </li></ul><ul><li>Potential to share models selectively with collaborators e.g. academics, neglected disease researchers </li></ul><ul><li>We used the proof of concept to submit an SBIR “ Biocomputation across distributed private datasets to enhance drug discovery” </li></ul><ul><li>Develop prototype for sharing models securely- collaborate to show how combining data for TB etc could improve models </li></ul><ul><li>Phase II- develop a commercial product that leverages CDD </li></ul>
  11. 12. Collaboration 1. Future - Gain more by sharing more Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions? <ul><li>1. Spend less on data generation, descriptors and algorithms – use more open source – use models to help refine testing, external collaborators test your drugs </li></ul><ul><li>2. Selectively share data & models with collaborators and control access </li></ul><ul><li>3. Have someone else host the models / predictions </li></ul><ul><li>4. Predicting properties without the need to know the structures </li></ul>Models Inside company Collaborators Commercial Descriptors Algorithms In house data generation Data Databases, servers Current investments Software >$1M/yr Data >$10-100’s M/yr Lundbeck Pfizer Merck GSK Novartis Lilly BMS Allergan Bayer AZ Roche BI Merk KGaA
  12. 13. Collaboration 2. <ul><li>3 Academia/ Govt lab – Industry screening partnerships </li></ul><ul><li>CDD used for data sharing / collaboration – along with cheminformatics expertise </li></ul><ul><li>Previously supported larger groups of labs – many continued as customers </li></ul>
  13. 14. Collaboration 2. <ul><li>~20 public datasets for TB </li></ul><ul><li>>300,000 cpds </li></ul><ul><li>Data used for models </li></ul>100K library Novartis Data FDA drugs Suggests models can predict data from the same and independent labs Initial enrichment – enables screening few compounds to find actives Ekins and Freundlich, Pharm Res. 2011 Ekins et al., Mol BioSyst, 6: 840-851, 2010
  14. 15. Collaboration 3. <ul><li>20 groups academia + AZ, Sanofi-Aventis, Tydock Pharma </li></ul><ul><li>Goal to discover drugs for TB </li></ul><ul><li>Use CDD to share data / collaboration – single vault </li></ul><ul><li>Bi annual face to face meetings </li></ul>
  15. 16. Collaboration 4. <ul><li>Phase I STTR - NIAID funded collaboration with Stanford Research International </li></ul><ul><li>Combining cheminformatics methods and pathway analysis </li></ul><ul><li>Used resources available to both to identify targets and molecules that mimic substrates </li></ul><ul><li>Computationally searched >80,000 molecules - test 23 compounds in vitro, lead to 2 proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 mg/ml) </li></ul><ul><li>POC took < 6mths </li></ul><ul><li>Submitted phase II STTR, Submitted manuscript </li></ul>Ekins et al, Trends in Microbiology Feb 2011 Ekins et al, Trends in Microbiology Feb 2011 a.
  16. 17. A complex ecosystem of collaborations: A new business model Bunin & Ekins DDT 16: 643-645, 2011 Inside Company Collaborators Inside Academia Collaborators Molecules, Models, Data Molecules, Models, Data Inside Foundation Collaborators Molecules, Models, Data Inside Government Collaborators Molecules, Models, Data IP IP IP IP Shared IP Collaborative platform/s
  17. 18. <ul><li>Shown how CDD can help collaborations </li></ul><ul><li>Shown how Open source software could enable pharmas to share data as models </li></ul><ul><li>Develop complete platform for data and model sharing </li></ul><ul><li>Increase adoption of CDD </li></ul><ul><li>Shift to mobile future – collaboration Apps ( MolSync + DropBox + MMDS = Share molecules as SDF files on the cloud = collaborate) </li></ul><ul><li>Help more groups discover potential drugs, faster </li></ul>Current and Future Milestones Williams et al, Drug Disc Today, 16:928-939, 2011
  18. 19. Off The Shelf Drug Discovery <ul><li>All pharmas have assets on shelf that reached clinic </li></ul><ul><li>Get the crowd to help in repurposing / repositioning these assets </li></ul><ul><li>How can software help? </li></ul><ul><li>- Create communities to test </li></ul><ul><li>- Provide informatics tools that are accessible to the crowd - enlarge user base </li></ul><ul><li>- Data storage on cloud – integration with public data </li></ul><ul><li>- Crowd becomes virtual pharma CROs and the “customer” for enabling services </li></ul>
  19. 20. Key Partners <ul><li>Collaboration 1. Rishi Gupta , Chris Waller, Eric Gifford, Ted Liston, (Pfizer) </li></ul><ul><li>Collaboration 2. Joel Freundlich (Texas A&M), Gyanu Lamichhane (Johns Hopkins) and many others </li></ul><ul><li>Collaboration 3. Collaborators at MM4TB </li></ul><ul><li>Collaboration 4. Carolyn Talcott, Malabika Sarker , Peter Madrid, Sidharth Chopra (SRI International) </li></ul><ul><li>Additional software: ChemAxon, Accelrys </li></ul><ul><li>Funding: Bill and Melinda Gates Foundation, NIAID </li></ul><ul><li>Development : Colleagues at CDD </li></ul><ul><li>Users: CDD Customers </li></ul><ul><li>We need you to use collaborative tools and lobby pharma to share data securely – tell everyone! </li></ul><ul><li>We need the audience to collaborate – we can help </li></ul>
  20. 21. Summary <ul><li>Companies can share their data securely but rarely do </li></ul><ul><li>Open and commercials Tools could facilitate this </li></ul><ul><li>Neglected disease researchers could benefit </li></ul><ul><li>Discovery of hits and leads and ultimately drugs faster </li></ul><ul><li>Online platforms have made transactions easier </li></ul><ul><li>Could consortia of organizations allows similar efficiencies in drug discovery? </li></ul><ul><li>An ideal, web-based ecosystem would be inclusive and address both scientific and business inefficiencies in a systematic, technology driven manner. </li></ul>