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Collaborative Drug Discovery TB database (2011 Editors’ Choice Award Winner)
Sean Ekins
Collaborative Drug Discovery, Burlingame, CA.Collaborations in Chemistry, Fuquay Varina, NC.
Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ.
School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD.
CDD history• 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)
2012 : NIH picks CDD for Neuroscience Blueprint NetworkCDD securely hosts 140,000,000 datapoints in the cloud
Private and profitable
NetworkTraction: 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
NeutralTrusted for >8 years in the cloudMoral high-ground due to years dedicated to neglected diseaseCredible position
IPCDD handles data corresponding to composition of matter & utility patentsTemplates for rapid web-based transactions (IP corresponding to data)CDD does not own IP
About CDD
How to do it better?
What can we do with software to facilitate it ?
The future is more collaborative
We have tools but need integration
• Groups involved traverse the spectrum from pharma, academia, not for profit and government
• More free, open technologies to enable biomedical research• Precompetitive organizations, consortia..
A starting point for collaboration
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
Major collaborative grants in EU: Framework, IMI …NIH moving in same direction?
Cross continent collaboration CROs in China, India etc – Pharma’s in US / Europe
More industry – academia collaboration ‘not invented here’ a thing of the past
More effort to go after rare and neglected diseases -Globalization and connectivity of scientists will be key –
Current pace of change in pharma may not be enough.
Need to rethink how we use all technologies & resources…
Collaboration is everywhere
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 - Over 3 Million compounds, with molecular properties, similarity and substructure searching, data plotting etc
will host datasets from companies, foundations etc
vendor libraries (Asinex, TimTec, ChemBridge)
• Unique to CDD – simultaneously query your private data, collaborators’ data, & public data, Easy GUI
www.collaborativedrug.com
Tuberculosis Kills 1.6-1.7m/yr (~1 every 8 seconds) 1/3rd of worlds population infected!!!!
Multi drug resistance in 4.3% of cases Extensively drug resistant increasing incidence No new drugs in over 40 yrs Drug-drug interactions and Co-morbidity with HIV
Collaboration between groups is rare These groups may work on existing or new targets Use of computational methods with TB is rare Literature TB data is not well collated (SAR)
Funded by Bill and Melinda Gates Foundation
Applying CDD to Build a disease community for TB
~ 20 public datasets for TBIncluding Novartis data on TB hits
>300,000 cpds
Patents, PapersAnnotated by CDD
Open to browse by anyone
http://www.collaborativedrug.
com/register
Molecules with activity against
BMGF 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
CDD is a partner on a 5 year project supporting >20 labs and providing cheminformatics support
Already found hits for a TB target using docking www.mm4tb.org
More Medicines for Tuberculosis
Searching for TB molecular mimics; collaboration
Lamichhane G, et al Mbio, 2: e00301-10, 2011
Modeling – CDDBiology – Johns HopkinsChemistry – Texas A&M
Simple descriptor analysis on > 300,000 compounds tested vs TB
Dataset MWT logP HBD HBA RO 5Atom count PSA RBN
MLSMR
Active ≥ 90% inhibition at 10uM (N = 4096)
357.10 (84.70)
3.58 (1.39)
1.16 (0.93)
4.89 (1.94)
0.20 (0.48)
42.99 (12.70)
83.46 (34.31)
4.85 (2.43)
Inactive < 90% inhibition at 10uM (N = 216367)
350.15 (77.98)**
2.82 (1.44)**
1.14 (0.88)
4.86 (1.77)
0.09 (0.31)**
43.38 (10.73)
85.06 (32.08)
*4.91
(2.35)
TAACF-NIAID CB2
Active ≥ 90% inhibition at 10uM (N =1702)
349.58(63.82)
4.04(1.02)
0.98(0.84)
4.18(1.66)
0.19(0.40)
41.88(9.44)
70.28(29.55)
4.76(1.99)
Inactive < 90% inhibition at 10uM (N =100,931)
352.59(70.87)
3.38(1.36)**
1.11(0.82)**
4.24(1.58)
0.12(0.34)**
42.43(8.94)*
77.75(30.17)
**4.72
(1.99)
Novartis aerobic and anaerobic TB hits
Anaerobic compounds showed statistically different and higher mean descriptor property values compared with the aerobic hits (e.g. molecular weight, logP, hydrogen bond donor, hydrogen bond acceptor, polar surface area and rotatable bond number)
The mean molecular properties for the Novartis compounds are in a similar range to the MLSMR and TAACF-NIAID CB2 hits
Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011.
Bayesian Classification Models for TB
G1: 1704324327
73 out of 165 good Bayesian Score: 2.885
G2: -2092491099
57 out of 120 good Bayesian Score: 2.873
G3: -1230843627
75 out of 188 good Bayesian Score: 2.811
G4: 940811929
35 out of 65 good Bayesian Score: 2.780
G5: 563485513
123 out of 357 good Bayesian Score: 2.769
B1: 1444982751
0 out of 1158 good Bayesian Score: -3.135
B2: 274564616
0 out of 1024 good Bayesian Score: -3.018
B3: -1775057221 0 out of 982 good
Bayesian Score: -2.978
B4: 48625803
0 out of 740 good Bayesian Score: -2.712
B5: 899570811
0 out of 738 good Bayesian Score: -2.709
Good
Bad
active compounds with MIC < 5uM
Laplacian-corrected Bayesian classifier models were generated using FCFP-6 and simple descriptors. 2 models 220,000 and >2000 compounds
Ekins et al., Mol BioSyst, 6: 840-851, 2010
Bayesian Classification TB Models
Dateset (number of molecules)
External ROC Score
Internal ROC
Score Concordance Specificity Sensitivity
MLSMR All single point
screen (N = 220463) 0.86 ± 0 0.86 ± 0 78.56 ± 1.86 78.59 ± 1.94 77.13 ± 2.26
MLSMR dose response set
(N = 2273) 0.73 ± 0.01 0.75 ± 0.01 66.85 ± 4.06 67.21 ± 7.05 65.47 ± 7.96
Leave out 50% x 100
Ekins et al., Mol BioSyst, 6: 840-851, 2010
100K library Novartis Data FDA drugs
Additional test sets
Suggests models can predict data from the same and independent labs
Initial enrichment – enables screening few compounds to find actives
21 hits in 2108 cpds34 hits in 248 cpds1702 hits in >100K cpds
Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011.Ekins et al., Mol BioSyst, 6: 840-851, 2010
Bayesian Models Generated with kinase data [1] - - (blind testing of previous models showed 3-4 fold enrichment ) Models were built as described previously [2] 8.Data for single point screening (cut off for activity % inhibition at 10uM >or equal to 90%) 2.IC50 data Cut off for active = or equal to 5uM 3.IC90 data Cut off for active = or equal to 10uM and vero cell selectivity index greater or equal to 10. [1] Reynolds RC, et al. Tuberculosis (Edinburgh, Scotland) 2011 In Press.
[2] Ekins S, et al.,Mol BioSystems 2010;6:840-51.
Models with SRI kinase library data
Models with SRI kinase library data; refining data with cytotoxicity
Model 1 ROC XV AUC (N 23797) = 0.89Model 2 (N 1248) = 0.72Model 3 (N 1248) = 0.77
Leave out 50% x 100
Adding cytotoxicity data improves models
Dateset (number of molecules)
External ROC Score
Internal ROC Score Concordance Specificity Sensitivity
Model 1(N = 23797) 0.87 ± 0 0.88 ± 0 76.77 ± 2.14 76.49 ± 2.41 81.7 ± 2.96
Model 2(N = 1248) 0.65 ± 0.01 0.70 ± 0.01 61.58 ± 1.56 61.85 ± 8.45 61.30 ± 8.24
Model 3(N=1248) 0.74 ± 0.02 0.75 ± 0.02 68.67 ± 6.88 69.28 ± 9.84 64.84 ± 12.11
Original TB Models : refining data with cytotoxicity
Dateset (number of molecules)
External ROC Score
Internal ROC Score Concordance Specificity Sensitivity
MLSMR All single point screen
(N = 220463) 0.86 ± 0 0.86 ± 0 78.56 ± 1.86 78.59 ± 1.94 77.13 ± 2.26
MLSMR dose response set (N =
2273) 0.73 ± 0.01 0.75 ± 0.01 66.85 ± 4.06 67.21 ± 7.05 65.47 ± 7.96
NEW Dose resp and cytotoxicity (N = 2273) 0.82 ± 0.02 0.84 ± 0.02 82.61 ± 4.68 83.91 ± 5.48 65.99 ± 7.47
Ekins et al., Mol BioSyst, 6: 840-851, 2010
Single pt ROC XV AUC = 0.88Dose resp = 0.78Dose resp + cyto = 0.86
Leave out 50% x 100
Combining cheminformatics methods and pathway analysis Identified essential TB targets that had not been exploited Used resources available to both to identify targets and molecules that
mimic substrates Computationally searched >80,000 molecules - tested 23 compounds in
vitro (3 picked as inactives), lead to 2 proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 ug/ml)
POC took < 6mths - - Submitted phase II STTR, Submitted manuscript Still need to test vs target - verify it hits suggested target
Ekins et al,Trends in Microbiology Feb 2011
Phase I STTR - NIAID funded collaboration with Stanford Research International
Sarker et al 2011 submitted
Summary
Computational models based on Whole cell TB data could improve efficiency of screening
Collaborations get us to interesting compounds quickly
Availability of datasets enable analysis that could suggest simple rules
Understanding the chemical properties and characteristics of compounds = better compounds for lead optimization.
Additional prospective validation ongoing with IDRI, Southern Research Institute and UMDNJ using machine learning models - testing small numbers of compounds
UMDNJ – mined GSK malaria public data, scored with bayesian models – ordered from vendors
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
A new business model
Apps for collaborationODDT – Open drug discovery teamsFlipboard-like app for aggregating social media for diseases etc
Alex Clark, Molecular Materials Informatics, Inc
Williams et al DDT 16:928-939, 2011Clark et al submitted 2012Ekins et al submitted 2012
Acknowledgments Joel Freundlich (Texas A&M), Gyanu Lamichhane (Johns Hopkins) Carolyn Talcott, Malabika Sarker, Peter Madrid, Sidharth Chopra (SRI
International) MM4TB colleagues Chris Lipinski Takushi Kaneko (TB Alliance) Nicko Goncharoff (SureChem) Accelrys CDD – Barry Bunin
And collaborators from BMGF funded project (Clif Barry Lab, Carl Nathan Lab, Allen Casey, Robert Reynolds etc..)
Funding BMGF, NIAID.