Slides for st judes

Collaboration in Pharmaceutical Research: From Neglected Diseases to ADME/Tox

Sean Ekins

Collaborations in Chemistry, Fuquay Varina, NC.Collaborative Drug Discovery, Burlingame, CA.

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.

In the long history of human kind (and animal kind, too) those who have learned to collaborate and improvise most

effectively have prevailed.

Charles Darwin

Outline

Introduction Collaborative Drug Discovery TB Collaborations and Drug Discovery Research Open ADME Models Repurposing FDA approved drugs The Future – Mobile Apps for Drug Discovery

Open Innovation Open innovation is a paradigm that assumes that firms can and should use external ideas as well as

internal ideas, and internal and external paths to market, as the firms look to advance their technology

Chesbrough, H.W. (2003). Open Innovation: The new imperative for creating and profiting from technology.

Boston: Harvard Business School Press, p. xxiv

Collaborative Innovation A strategy in which groups partner to create a product - drive the efficient allocation of R&D

resources. Collaborating with outsiders-including customers, vendors and even competitors-a company is able to import lower-cost, higher-quality ideas from the best sources in the world.

e.g. Innocentive, crowdsourcing

Open SourceCompanies can donate their patents to an independent organization, put them in a common

pool or grant unlimited license use to anybody.

e.g. GSK malaria data, Novartis TB data

Some Definitions

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 effectivelyBunin & 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

Hardware is getting smaller

1930’s

1980s

1990s

Room size

Desktop size

Not to scale and not equivalent computing power – illustrates mobility

Laptop

Netbook

Phone

Watch

2000s

Models and software becoming more accessible- free, precompetitive efforts - collaboration

Free tools are proliferating

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

CDD is a partner on a 5 year project supporting >20 labs and providing cheminformatics support 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 machine learning

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

Bayesian classification is a simple probabilistic classification model. It is based on Bayes’ theorem

h is the hypothesis or modeld is the observed datap(h) is the prior belief (probability of hypothesis h before observing any data)p(d) is the data evidence (marginal probability of the data)p(d|h) is the likelihood (probability of data d if hypothesis h is true) p(h|d) is the posterior probability (probability of hypothesis h being true given the observed data d)

A weight is calculated for each feature using a Laplacian-adjusted probability estimate to account for the different sampling frequencies of different features.

The weights are summed to provide a probability estimate

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


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

External 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] 1.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

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

Bayesian Classification TB Models


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, submitted 2011

Malaria data in CDD

> 22,000 compounds

Including datasets from Dr.

Guy’s group

Ekins, Hohman and Bunin in:Collaborative Computational Technologies for Biomedical Research, Edited by Sean Ekins, Maggie A. Z. Hupcey, Antony J. Williams.Published 2011 by John Wiley & Sons, Inc

Other datasets

http://www.slideshare.net/ekinsseanEkins S and Williams AJ, MedChemComm, 1: 325-330, 2010.

Analysis of malaria and TB datasets

Multiple antimalarial datasets

Ekins and Williams Drug Disc Today 15; 812-815, 2010 Ekins and Williams, MedChemComm, 1: 325-330, 2010.

Dataset MW logP HBD HBA Lipinski rule of 5 alerts

PSA (Å2) RBN

GSK data (N = 13,471) 478.2 ± 114.3 4.5 ± 1.6 1.8 ± 1.0 5.6 ± 2.0 0.8 ± 0.8 76.8 ± 30.0 7.2 ± 3.4

St Jude (N = 1524) 385.3 ± 71.2 3.8 ± 1.6 1.1 ± 0.8 4.9 ± 1.8 0.2 ± 0.4 72.2 ±29.3 5.2 ±2.3

Novartis (N = 5695) 398.2 ± 105.3 3.7 ± 2.0 1.2 ± 1.1 4.7 ± 2.1 0.4 ± 0.7 74.7 ± 37.9 5.6 ± 3.0

Johns Hopkins All FDA drugs (N = 2615)

349.1 ± 355.8 1.2 ± 3.4 2.4 ± 4.6 5.1 ± 5.5 0.3 ± 0.8 96.0 ±139.8 5.4 ± 9.6

Johns Hopkins Subset > 50% malaria inhibition at 96h (N = 165)

458.0 ± 298.6 2.2 ± 2.7 2.1 ± 3.4 5.4 ± 4.7 0.6 ± 0.9 90.6 ± 104.4 7.1 ± 7.7

Antimalarial drugs (N = 14)

341.6 ± 67.0 3.8 ± 1.6 1.8 ± 1.0 5.3 ± 1.5 0.2 ± 0.6 53.4 ± 21.2 5.8 ± 3.0

Screening hits in total are not ‘lead-like’ (MW < 350, LogP< 3) closest to ‘natural product lead-like’. Although GSK suggests that the compounds are “drug-like” the evidence for this is weak

Antimalarial Compound libraries and filter failures

Ekins and Williams Drug Disc Today 15; 812-815, 2010

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TB Compound libraries and filter failures

Filtering using SMARTs filters to remove thiol reactives, false positives etc at University of New Mexico (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter)

Ekins et al., Mol Biosyst, 6: 2316-2324, 2010

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Abbott Alerts

Pfizer Lint Alerts

GSK alerts

Correlation between the number of SMARTS filter failures and the number of Lipinski violations for different types of rules sets with FDA drug set from CDD (N = 2804)

Suggests # of Lipinski violations may also be an indicator of undesirable chemical features that result in reactivity

Correlations

Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011.

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

Active compounds vs Mtb and P. Falciparum have higher mean molecular weights and logP values

A high proportion of compounds fail the Abbott filters for reactivity when compared to drugs and antimalarials

Understanding the chemical properties and characteristics of compounds = better compounds for lead optimization.

St Jude and Novartis datasets should be screened vs Mtb as their property space is close to TB actives

Rare and Neglected disease researchers lack ADME/Tox insights

Could all pharmas share their data as models with each other?

Increasing Data & Model Access

Ekins and Williams, Lab On A Chip, 10: 13-22, 2010.

The big idea

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? Pfizer had never considered this - So we proposed a

study and Rishi Gupta generated models

What can be developed with very large training and test sets?

HLM training 50,000 testing 25,000 molecules

training 194,000 and testing 39,000

MDCK training 25,000 testing 25,000

MDR training 25,000 testing 18,400

Open molecular descriptors / models vs commercial descriptors

Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

Open source tools for modeling

Massive Human liver microsomal stability model

HLM Model with CDK and SMARTS Keys:

HLM Model with MOE2D and SMARTS Keys

# Descriptors: 578 Descriptors# Training Set compounds: 193,650

Cross Validation Results: 38,730 compounds

Training R2: 0.79

20% Test Set R2: 0.69

Blind Data Set (2310 compounds): R2 = 0.53RMSE = 0.367

Continuous Categorical:κ = 0.40Sensitivity = 0.16Specificity = 0.99PPV = 0.80Time (sec/compound): 0.252

# Descriptors: 818 Descriptors# Training Set compounds: 193,930

Cross Validation Results: 38,786 compounds

Training R2: 0.77

20% Test Set R2: 0.69

Blind Data Set (2310 compounds): R2 = 0.53RMSE = 0.367

Continuous Categorical: κ = 0.42Sensitivity = 0.24Specificity = 0.987PPV = 0.823Time (sec/compound): 0.303

PCA of training (red) and test (blue) compounds

Overlap in Chemistry space


RRCK Permeability and MDRC5.0 RRCK Permeability

C5.0 MDR

CDK descriptors Kappa = 0.47Sensitivity = 0.59Specificity = 0.93PPV = 0.67

Kappa = 0.62Sensitivity = 0.85Specificity = 0.77PPV = 0.83

MOE2D and SMARTS Keys

Kappa = 0.53Sensitivity = 0.64Specificity = 0.94PPV = 0.72(Baseline)

Kappa = 0.67Sensitivity = 0.86Specificity = 0.80PPV = 0.85(Baseline)

CDK and SMARTS Keys



Open descriptors results almost identical to commercial descriptors

Across many datasets and quantitative and qualitative dataSmaller solubility datasets give similar results

Provides confidence that open models could be viable

MDCK training 25,000 testing 25,000

MDR training 25,000 testing 18,400


Merck KGaA

Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions?

Lundbeck

Pfizer

Merck

GSK

Novartis

Lilly

BMS

Allergan Bayer

AZ

Roche BI

Merk KGaA

Model coverage of chemistry space

Next steps

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 Engage Pistoia Alliance to expand concept to many

companies – in progress

Open source software for molecular descriptors and algorithms Spend only a fraction of the money on QSAR Selectively share your models with collaborators and control access Have someone else host the models / predictions

The next opportunities for crowdsourcing…

Inside company

Collaborators

Current investments>$1M/yr

>$10-100’s M/yr

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 complex ecosystem of collaborations: A new business model

Finding Promiscuous Old Drugs for New Uses

Research published in the last six years - 34 studies - Screened libraries of FDA approved drugs against various whole cell or target assays.

1 or more compounds with a suggested new bioactivity

13 drugs were active against more than one additional disease in vitro

Finding Promiscuous Old Drugs for New Uses

109 molecules were identified by screening in vitro

Statistically more hydrophobic (log P) and higher MWT than orphan-designated products with at least one marketing approval for a common disease indication or one marketing approval for a rare disease from the FDA’s rare disease research database.

Created structure searchable databases in CDD

Data in publications is increasing but who is tracking it?

Ekins and Williams, Pharm Res, 28, 1785-1791, 2011.

2D Similarity search with “hit” from screening

Export database and use for 3D searching with a pharmacophore or other model

Suggest approved

drugs for testing - may also

indicate other uses if it is

present in more than one database

Suggest in silico hits for in vitro screening

Key databases of structures and bioactivity data FDA drugs

database

Repurpose FDA drugs in silico

Ekins S, Williams AJ, Krasowski MD and Freundlich JS, Drug Disc Today, 16: 298-310, 2011

Crowdsourcing Project “Off the Shelf R&D”

All pharmas have assets on shelf that reached clinic

“Off the Shelf R&D”

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

Tools for Open Science

• Blogs• Wikis• Databases• Journals

• What about Twitter, Facebook, could these be used for social collaboration, science?

2020: A Drug Discovery Odyssey

Could our Pharma R&D look like this

Massive collaboration networks – software enabled. We are in “Generation App”

Crowdsourcing will have a role in R&D. Drug discovery possible by anyone with “app access”

Ekins & Williams, Pharm Res, 27: 393-395, 2010.

Example of Social Collaboration in Science:Tweets, Blog Lead to The Green Solvents App

I attend seminar on solvent selection guide

I tweet during talk

Mobile App developer Alex Clark responds to twitter along with Antony Williams starts an email discussion about Green Chemistry apps

I blog that evening

3 days later an App is createdBy Alex

•Make science more accessible = >communication

•Mobile – take a phone into field /lab and do science more readily than on a laptop

•GREEN – energy efficient computing

•MolSync (+ DropBox) + MMDS = Share molecules as SDF files on the cloud = collaborate

Mobile Apps for Drug Discovery

Williams et al DDT 16:928-939, 2011

www.scimobileapps.com

How do you find scientific mobile Apps ?

Development of Wiki’s to track developments in tools..

Acknowledgments Rishi Gupta, Eric Gifford, Ted Liston, Chris Waller (Pfizer) Antony J. Williams (RSC) 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) Matthew D. Krasowski (University of Iowa) Alex Clark (Molecular Materials Informatics, Inc) Accelrys CDD – Barry Bunin Funding BMGF, NIAID. Everyone that has shared data in CDD..

Email: [email protected]

Slideshare: http://www.slideshare.net/ekinssean

Twitter: collabchem

Blog: http://www.collabchem.com/

Website: http://www.collaborations.com/CHEMISTRY.HTM

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