p4 c2011 slides ekins
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Partnering 4 cures meeting slides presented nov 7 201TRANSCRIPT
Collaborative Drug Discovery: An Alternative Business Model For Drug Discovery
Sean Ekins, M.Sc., Ph.D., D.Sc.Collaborations Director,
Collaborative Drug Discovery, Inc.
Introduction• 2003: Envisioned CDD
• 2004: Spun out of Lilly by Dr. Barry Bunin
• 2005: Eli Lilly co-invested in a syndicate with Omidyar Network and Founders Fund
• 2008: BMGF 2 year grant to support TB research ($1,896,923)
• 2010: STTR phase I with SRI TB – chem-bioinformatics integration ($150K)
• 2011: BMGF 3 year grant to support 3 academia: industry TB Collaborations (~$900,000)
• MM4TB 5 year EU Framework 7 funded project (Euro 249,700)• Bio-IT World Best Practices Award, Editors Choice• SBIR phase I ($150K)• 5 year NIH NIDA contract
Private and profitable
Overview: Sharing data and models to speed up drug discovery
Introduction
Collaboration 1. Pfizer - developing, validating and deploying open source ADME/Tox models
Collaboration 2. Tuberculosis research funded by the Bill & Melinda Gates Foundation,
Collaboration 3. European Commission FP7 funded More Medicines for Tuberculosis project
Collaboration 4. NIH funded STTR with the Stanford Research Institute International - TB drug discovery
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 integrationOpen interfaces
A core root of the current inefficiencies in drug discovery are due to organizations’ and individual’s barriers to collaborate effectivelyBunin & Ekins DDT 16: 643-645, 2011
• Groups involved traverse the spectrum from pharma, academia, not for profit and government• More free, open technologies to enable biomedical research• Precompetitive organizations, consortia..
How Can Collaborative Software Help?
• CDD Vault – Secure web-based place for private data – private by default
• CDD Collaborate – Selectively share subsets of data
• CDD Public –public data sets - Over 3 Million compounds, with molecular properties, similarity and substructure searching, data plotting etc
• Unique to CDD – simultaneously query your private data, collaborators’ data, & public data, Easy GUI
Overview of CDD
About CDDNetwork
Traction: thousands of leading researchers log into CDD today:
Academic customers: Harvard, Columbia, Johns Hopkins, UCSF (new assays)
Pharmas relationships: Pfizer, GSK, Novartis, Lilly (commercial partners)
Startups
Research institutes, Non profits NIH, BMGF, MM4TB etc
Neutral
Trusted for >7 years in the cloud
Moral high-ground due to years dedicated to neglected disease
Credible position
IP
CDD handles data corresponding to composition of matter & utility patents
Templates for rapid web-based transactions (IP corresponding to data)
CDD does not own IP
Collaboration 1. Needs Challenge..There is limited access to ADME/Tox data and models
needed for R&D
How could a company share data but keep the structures proprietary?
Sharing models means both parties use costly software
What about open source tools?
Collaborators had never considered this - So we proposed a study and Rishi Gupta generated models
Collaboration 1. Strategy Open algorithms, descriptors, closed data – can we unlock it?
Massive datasets 10’s- 100’s of thousands
We found open source = commercial tools
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010
CDK +fragment descriptors MOE 2D +fragment descriptorsKappa 0.65 0.67
sensitivity 0.86 0.86specificity 0.78 0.8
PPV 0.84 0.84
Collaboration 1. Opportunity
ADME/Tox Data crosses diseases
Potential to share models selectively with collaborators e.g. academics, neglected disease researchers
We used the proof of concept to submit an SBIR “Biocomputation across distributed private datasets to enhance drug discovery”
Develop prototype for sharing models securely- collaborate to show how combining data for TB etc could improve models
Phase II- develop a commercial product that leverages CDD
Collaboration 1. Future - Gain more by sharing more
Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions?
LundbeckPfizer
Merck
GSK
Novartis
Lilly
BMS
AllerganBayer
AZ
Roche BI
Merk KGaA
1. Spend less on data generation, descriptors and algorithms – use more open source – use models to help refine testing, external collaborators test your drugs
2. Selectively share data & models with collaborators and control access3. Have someone else host the models / predictions4. Predicting properties without the need to know the structures
Inside company
Collaborators
Databases, servers
Current investmentsSoftware >$1M/yrData >$10-100’s M/yr
Collaboration 2. 3 Academia/ Govt lab – Industry screening partnerships
CDD used for data sharing / collaboration – along with cheminformatics expertise
Previously supported larger groups of labs – many continued as customers
Collaboration 2. ~20 public datasets for TB
>300,000 cpds
Data used for models
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
Collaboration 3.
20 groups academia + AZ, Sanofi-Aventis, Tydock Pharma
Goal to discover drugs for TB
Use CDD to share data / collaboration – single vault
Bi annual face to face meetings
Collaboration 4. Phase I STTR - NIAID funded collaboration with Stanford Research International
Combining cheminformatics methods and pathway analysis
Used resources available to both to identify targets and molecules that mimic substrates
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)
POC took < 6mths
Submitted phase II STTR, Submitted manuscriptEkins et al,Trends in Microbiology Feb 2011
Ekins et al, Trends in Microbiology Feb 2011
a.
A complex ecosystem of collaborations: A new business model
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
SharedIP
Collaborative platform/s
Bunin & Ekins DDT 16: 643-645, 2011
Shown how CDD can help collaborations
Shown how Open source software could enable pharmas to share data as models
Develop complete platform for data and model sharing
Increase adoption of CDD
Shift to mobile future – collaboration Apps (MolSync + DropBox + MMDS = Share molecules as SDF files on the cloud = collaborate)
Help more groups discover potential drugs, faster
Current and Future Milestones
Williams et al, Drug Disc Today, 16:928-939, 2011
Off The Shelf Drug Discovery All pharmas have assets on shelf that reached clinic
Get the crowd to help in repurposing / repositioning these assets
How can software help?
- Create communities to test
- Provide informatics tools that are accessible to the crowd - enlarge user base
- Data storage on cloud – integration with public data
- Crowd becomes virtual pharma CROs and the “customer” for enabling services
Key PartnersCollaboration 1. Rishi Gupta, Chris Waller, Eric Gifford, Ted Liston, (Pfizer)
Collaboration 2. Joel Freundlich (Texas A&M), Gyanu Lamichhane (Johns Hopkins) and many others
Collaboration 3. Collaborators at MM4TB
Collaboration 4. Carolyn Talcott, Malabika Sarker, Peter Madrid, Sidharth Chopra (SRI International)
Additional software: ChemAxon, Accelrys
Funding: Bill and Melinda Gates Foundation, NIAID
Development : Colleagues at CDD
Users: CDD Customers
We need you to use collaborative tools and lobby pharma to share data securely – tell everyone!
We need the audience to collaborate – we can help
Summary Companies can share their data securely but rarely do
Open and commercials Tools could facilitate this
Neglected disease researchers could benefit
Discovery of hits and leads and ultimately drugs faster
Online platforms have made transactions easier
Could consortia of organizations allows similar efficiencies in drug discovery?
An ideal, web-based ecosystem would be inclusive and address both scientific and business inefficiencies in a systematic, technology driven manner.