motri conference v2

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.

Outline

Introduction Collaborative Drug Discovery TB Collaborations and Drug Discovery Research

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

Typical Lab: The Data Explosion Problem & Collaborations

DDT Feb 2009

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

CDD: Single Click to Key Functionality

CDD: Mining across projects and datasets

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

Ekins et al,Trends in Microbiology

19: 65-74, 2011

Fitting into the drug discoveryprocess

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


G3: -1230843627


G4: 940811929


G5: 563485513


B1: 1444982751

0 out of 1158 good Bayesian Score: -3.135

B2: 274564616


B3: -1775057221 0 out of 982 good

Bayesian Score: -2.978

B4: 48625803


B5: 899570811


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 Dose response

Good

Bad


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


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


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


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


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

http://cheminf20.files.wordpress.com/2012/02/oddtsnap02.png

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.

motri conference v2

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