c&e news talk sept 16

Open Science for Rare and Neglected

Diseases

Sean Ekins

Collaborations Pharmaceuticals, Inc. Fuquay Varina, NC.

Collaborations in Chemistry, Inc. Fuquay Varina, NC.

Collaborative Drug Discovery, Inc., Burlingame, CA.

Phoenix Nest, Brooklyn, NY

Hereditary Neuropathy Foundation, New York, NY

Wikipedia

http://www.collaborationspharma.com/

http://www.collaborationspharma.com/

Open Science Closed Science

Objectives – The Big Picture

Demonstrate – open data - models – new leads – accessible to anyoneDescribe - experiences working on rare and neglected diseasesSuggest - What could be done to increase success in bringing to clinic

Inspire – others to help

Neglected and Rare Disease Drug Discovery Share urgent need for new therapeutics

http://www.mm4tb.org/ http://www.phoenixnestbiotech.com/

Laboratories past and present

Lavoisier’s lab 18th C Edison’s lab 20th C

Author’s lab 21th C

+ Network of global collaborators

Crowdfunding Science

There are no shortages of people, ideas, diseases perhaps only money…

Crowdfunding Rare Disease Science

Sanfilippo Syndrome - funds raised in ~ 2 years

Funding gene therapy research/ development for Sanfilippo Type A

Funding gene therapy research/ development / Chaperone research for

Sanfilippo Type C

Start a company

Enables you to apply for SBIR /STTR

grants

Fund research – help academics and

commercialize their work

Then use NIH TRND and NCATS and

other programs

http://goo.gl/k0o9Q0

Developing an enzyme replacement for MPS IIID with Dr. Patricia Dickson at LA BioMed

Idea + Data + Skills + Time = Discovery

Drug Discovery on a Shoestring

• What disease / target • do I want to work on?• Will it make a

difference?

• What data is there I can use?• What is the data quality?• Is it public or do I need to

reach out to a lab?

• What technology can I access?• Am I capable of following through?• Who can I get to help me?• Where do I find the right person/s?

• How do I fit it into my day job?• Is this an evening / weekend project?• What will have to give?

Having the big idea

• Driven by personal circumstance• You or family member/ friend has a disease

• You meet someone and they inspire you• Driven by surroundings

• You live in an area where disease is endemic

• You read/ hear something e.g. Twitter• You do it for the recognition / kudos • You want to give back

• None of the above• There are >7000 rare diseases – pick one!

"Rub al Khali 002" by Nepenthes

The chemistry/ biology data desert outside of pharma circa early 2000’s

Limited ADME/Tox data

Paucity of Structure Activity Data

Small datasets for modeling

Drug companies – gate keepers of information for drug discovery

"Oasis in Libya" by Sfivat

The growing chemistry/biology data Oasis outside of pharma circa 2015

Examples of using open data

• To discover new leads• Tuberculosis – from public data to open models to create IP

• Chagas Disease - from public data to create new IP

• Ebola virus – from little data to create open data and IP

• Making lots of machine learning models open

• Demonstrate collaborations

Tuberculosis kills 1.6-1.7m/yr (~1 every 8 seconds)

1/3rd of worlds population infected!!!!

streptomycin (1943)para-aminosalicyclic acid (1949)isoniazid (1952) pyrazinamide (1954)cycloserine (1955)ethambutol (1962)rifampicin (1967)

Multi drug resistance in 4.3% of cases

Extensively drug resistant increasing incidence

one new drug (bedaquiline) in 40 yrs

Tuberculosis

Tested >350,000 molecules Tested ~2M 2M >300,000

>1500 active and non toxic Published 177 100s 800

Bigger Open Data: Screening for New Tuberculosis Treatments

How many will become a new drug?

TBDA screened over 1 million, 1 million more to go

TB Alliance + Japanese pharma screens

R43 LM011152-01

Over 8 years analyzed in vitro data and built models

Top scoring molecules

assayed for

Mtb growth inhibition

Mtb screening

molecule

database/s

High-throughput

phenotypic

Mtb screening

Descriptors + Bioactivity (+Cytotoxicity)

Bayesian Machine Learning classification Mtb Model

Molecule Database

(e.g. GSK malaria

actives)

virtually scored

using Bayesian Models

New bioactivity data

may enhance models

Identify in vitro hits and test models3 x published prospective tests ~750

molecules were tested in vitro

198 actives were identified

>20 % hit rate

Multiple retrospective tests 3-10 fold

enrichment

NH

S

N

Ekins et al., Pharm Res 31: 414-435, 2014

Ekins, et al., Tuberculosis 94; 162-169, 2014

Ekins, et al., PLOSONE 8; e63240, 2013

Ekins, et al., Chem Biol 20: 370-378, 2013

Ekins, et al., JCIM, 53: 3054−3063, 2013

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

Ekins et al., Mol BioSyst, 6: 840-851, 2010

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

R43 LM011152-01

5 active compounds vs Mtb in a few months

7 tested, 5 active (70% hit rate)

Ekins et al.,Chem

Biol 20, 370–378,

2013

1. Virtually screen 13,533-member GSK antimalarial hit library

2. Bayesian Model = SRI TAACF-CB2 dose response + cytotoxicity model

3. Top 46 commercially available compounds visually inspected

4. 7 compounds chosen for Mtb testing based on

- drug-likeness- chemotype diversity

GSK #Bayesian

Score Chemical Structure

Mtb H37Rv MIC

(mg/mL)

GSK Reported

% Inhibition HepG2 @ 10 mM cmpd

TCMDC-123868 5.73 >32 40

TCMDC-125802 5.63 0.0625 5

TCMDC-124192 5.27 2.0 4

TCMDC-124334 5.20 2.0 4

TCMDC-123856 5.09 1.0 83

TCMDC-123640 4.66 >32 10

TCMDC-124922 4.55 1.0 9

R43 LM011152-01

• BAS00521003/ TCMDC-125802 reported to be a P.

falciparum lactate dehydrogenase inhibitor

• Only one report of antitubercular activity from 1969

- solid agar MIC = 1 mg/mL (“wild strain”)

- “no activity” in mouse model up to 400 mg/kg

- however, activity was solely judged by

extension of survival!

Bruhin, H. et al., J. Pharm. Pharmac. 1969, 21, 423-433.

.

MIC of 0.0625 ug/mL • 64X MIC affords 6 logs of

kill

• Resistance and/or drug

instability beyond 14 d

Vero cells : CC50 = 4.0

mg/mL

Selectivity Index SI =

CC50/MICMtb = 16 – 64

In mouse no toxicity but

also no efficacy in GKO

model – probably

metabolized.

Ekins et al.,Chem Biol 20, 370–378, 2013

Taking a compound in vivo identifies issues

R43 LM011152-01

Optimizing the triazine series as part of this project, improve solubility and show in

vivo efficacy

1U19AI109713-01

Chagas Disease

• About 7 million to 8 million people estimated to be infected worldwide

• Vector-borne transmission occurs in the Americas.

• A triatomine bug carries the parasite Trypanosoma cruzi which causes the disease.

• The disease is curable if treatment is initiated soon after infection.

• No FDA approved drug, pipe line sparse

Hotez et al., PLoS Negl Trop Dis. 2013 Oct 31;7(10):e2300

R41-AI108003-01

T. cruzi

C2C12 cells

6-8 days

infect

T. cruzi(Trypomastigote)

T. cruzi high-content screening assay

Plate containing

compounds

T.cruzi

Myocyte

Fixing & Staining

Reading

3 days

R41-AI108003-01

• Dataset from PubChem AID 2044 – Broad Institute data

• Dose response data (1853 actives and 2203 inactives)

• Dose response and cytotoxicity (1698 actives and 2363 inactives)

• EC50 values less than 1 mM were selected as actives.

• For cytotoxicity greater than 10 fold difference compared with EC50

• Models generated using : molecular function class fingerprints of maximum

diameter 6 (FCFP_6), AlogP, molecular weight, number of rotatable bonds,

number of rings, number of aromatic rings, number of hydrogen bond

acceptors, number of hydrogen bond donors, and molecular fractional polar

surface area.

• 5-fold cross validation or leave out 50% x 100 fold cross validation was used

to calculate the ROC for the models generated

T. cruzi Machine Learning models

R41-AI108003-01 Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

ModelBest

cutoff

Leave-one out

ROC

5-fold cross

validation ROC

5-fold cross

validation sensitivity

(%)

5-fold cross

validation

specificity (%)

5-fold cross

validation

concordance (%)

Dose response

(1853 actives,

2203 inactives)

-0.676 0.81 0.78 77 89 84

Dose response

and cytotoxicity

(1698 actives,

2363 inactives)

-0.337 0.82 0.80 80 88 84

External ROC Internal ROC

Concordance

(%)

Specificity

(%) Sensitivity (%)

0.79 ± 0.01 0.80 ± 0.01 73.48 ± 1.05 79.08 ± 3.73 65.68 ± 3.89

5 fold cross validation

Dual event 50% x 100 fold cross validation


Good Bad

Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

T. cruzi Dose Response and cytotoxicity Machine Learning model features

Tertiary amines, piperidines and aromatic fragments with basic Nitrogen

Cyclic hydrazines and electron poor chlorinated aromatics

R41-AI108003-01

Bayesian Machine Learning Models

- Selleck Chemicals natural product lib. (139 molecules);- GSK kinase library (367 molecules);- Malaria box (400 molecules);- Microsource Spectrum (2320 molecules);- CDD FDA drugs (2690 molecules);- Prestwick Chemical library (1280 molecules);- Traditional Chinese Medicine components (373 molecules)

7569 molecules

99 molecules


Synonyms Infection Ratio EC50 (µM) EC90 (µM) Hill slopeCytotoxicity CC50

(µM)

Chagas mouse model (4

days treatment,

luciferase): In vivo

efficacy at 50 mg/kg bid

(IP) (%)

(±)-Verapamil hydrochloride, 715730,

SC-00117620.02, 0.02 0.0383 0.143 1.67 >10.0 55.1

29781612, Pyronaridine 0.00, 0.00 0.225 0.665 2.03 3.0 85.2

511176, Furazolidone 0.00, 0.00 0.257 0.563 2.81 >10.0 100.5

501337, SC-0011777, Tetrandrine

0.00, 0.00 0.508 1.57 1.95 1.3 43.6

SC-0011754,Nitrofural 0.01, 0.01 0.775 6.98 1.00 >10.0 78.5*

* Used hydroxymethylnitrofurazone for in vivo study (nitrofural pro-drug)

Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

H3C

O

N

CH3

N

CH3

H3C

O

CH3

O

H3C

O

H3C

N

N

HN

N

N

OH

Cl

O

CH3

O

NN

+

N

O

O–

O

O

O

N+

O

O–

N

HN

NH2

O

In vitro and in vivo data for compounds selected

R41-AI108003-01

7,569 cpds => 99 cpds => 17 hits (5 in nM range)

Infection Treatment Reading

0 1 2 3 4 5 6 7

Pyronaridine Furazolidone Verapamil

Nitrofural Tetrandrine Benznidazole

In vivo efficacy of the 5 tested compounds

Vehicle

Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878R41-AI108003-01

Pyronaridine: New anti-Chagas and known anti-Malarial

EMA approved in combination with artesunate

The IC50 value 2 nM against the growth of KT1 and KT3 P. falciparum

Known P-gp inhibitor

Active against Babesia and TheileriaParasites tick-transmitted

R41-AI108003-01

Work provided starting point for a phase II and phase I grant (submitted)

2014-2015 Ebola outbreak

March 2014, the World Health Organization (WHO) reported a major Ebola outbreak in Guinea, a western African nation

8 August 2014, the WHO declared the epidemic to be an international public health emergency

I urge everyone involved in all aspects of this epidemic to openly and rapidly report their experiences and findings. Information will be one of our key weapons in defeating the Ebola epidemic. Peter Piot

Wikipedia

Wikipedia

https://en.wikipedia.org/wiki/Guinea

Madrid PB, et al. (2013) A Systematic Screen of FDA-Approved Drugs for Inhibitors of Biological Threat Agents. PLoS ONE 8(4): e60579. doi:10.1371/journal.pone.0060579

Chloroquine in mouse

Pharmacophore based on 4 compounds

Ekins S, Freundlich JS and Coffee M, 2014 F1000Research 2014, 3:277

amodiaquine, chloroquine, clomiphene

toremifene all are active in vitro

may have common features and bind

common site / target / mechanism

Could they be targeting proteins like viral

protein 35 (VP35)

component of the viral RNA polymerase

complex, a viral assembly factor, and an

inhibitor of host interferon (IFN) production

VP35 contributes to viral escape from host

innate immunity - required for virulence,

Pharmacophores for EBOV VP35 generated from crystal structures in the protein data bank PDB.


Redocking VPL57 in 4IBI

• The 4IBI ligand was removed from the structure and redocked.

• The closest pose (grey) was ranked 29 with RMSD 3.02A and LibDock score 86.62 when compared to the actual ligand in 4IBI (yellow)


Docking FDA approved compounds in VP35 protein showing overlap with ligand (yellow) and 2D interaction diagram

4IBI was used, 4IBI ligand VPL57 shown in yellow.

Amodiaquine (grey) and 4IBI LibDockscore 90.80,

Chloroquine (grey) LibDock score 97.82,

Clomiphene (grey) and 4IBI LibDockscore 69.77,

Toremifene (grey) and 4IBI LibDock score 68.11


Machine Learning for EBOV

• 868 molecules from the viral pseudotype entry assay and the EBOV replication assay

• Salts were stripped and duplicates removed using Discovery Studio 4.1 (Biovia, San

Diego, CA)

• IC50 values less than 50 mM were selected as actives.

• Models generated using : molecular function class fingerprints of maximum diameter 6

(FCFP_6), AlogP, molecular weight, number of rotatable bonds, number of rings,

number of aromatic rings, number of hydrogen bond acceptors, number of hydrogen

bond donors, and molecular fractional polar surface area.

• Models were validated using five-fold cross validation (leave out 20% of the database).

• Bayesian, Support Vector Machine and Recursive Partitioning Forest and single tree

models built.

• RP Forest and RP Single Tree models used the standard protocol in Discovery Studio.

• 5-fold cross validation or leave out 50% x 100 fold cross validation was used to

calculate the ROC for the models generated

Models

(training set 868 compounds)

RP Forest

(Out of bag

ROC)

RP Single Tree

(With 5 fold

cross validation

ROC)

SVM

(with 5 fold

cross validation

ROC)

Bayesian

(with 5 fold

cross validation

ROC)

Bayesian

(leave out

50% x 100

ROC)

Open

Bayesian

(with 5 fold

cross

validation

ROC)

Ebola replication (actives = 20) 0.70 0.78 0.73 0.86 0.86 0.82

Ebola Pseudotype (actives = 41) 0.85 0.81 0.76 0.85 0.82 0.82

Ebola HTS Machine learning model cross validation

Receiver Operator Curve Statistics.

Discovery Studio pseudotype Bayesian model

B

Discovery Studio EBOV replication model

Good Bad

Good Bad

Compound EC50 (uM)

Chloroquine 10

Mol 1 0.42

Mol 2 0.35

Mol 3 0.23

Effect of drug treatment on infection with Ebola-GFP

Compound EC50 (uM)

Chloroquine 6.9

Mol 1 0.23

Mol 2 0.19

Mol 3 0.52

3 Molecules selected from MicroSource Spectrum virtual screen and tested in vitroAll of them nM activity

Data from Robert Davey and Peter Madrid

Duplicate experiments

Making Ebola models available• From data published by others …to proposing target

• Collaborated with lab to open up their screening data, build models, identified more active inhibitors

• To date the most potent drugs and drug-like molecules

• Still a need for a drug that could be used ASAP

• Models in MMDS http://molsync.com/ebola/

More data continues to be published

• We collated 55 molecules from the literature

• A second review lists 60 hits– Picazo, E. and F. Giordanetto, Drug Discovery Today. 2015 Feb;20(2):277-86

• Additional screens have identified 53 hits and 80 hits respectively– Kouznetsova, J., et al., Emerg Microbes Infect, 2014. 3(12): p. e84.

– Johansen, L.M., et al., Sci Transl Med, 2015. 7(290): p. 290ra89.

Litterman N, Lipinski C and Ekins S 2015 F1000Research 2015, 4:38

http://molsync.com/ebola/

MoDELS RESIDE IN PAPERS

NOT ACCESSIBLE…THIS IS

UNDESIRABLE

How do we share them?

How do we use Them?

Model resources for ADME/Tox

Open Extended Connectivity Fingerprints

ECFP_6 FCFP_6• Collected,

deduplicated, hashed

• Sparse integers

• Invented for Pipeline Pilot: public method, proprietary details

• Often used with Bayesian models: many published papers

• Built a new implementation: open source, Java, CDK– stable: fingerprints don't change with each new toolkit release

– well defined: easy to document precise steps

– easy to port: already migrated to iOS (Objective-C) for TB Mobile app

• Provides core basis feature for CDD open source model serviceClark et al., J Cheminform 6:38 2014

Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b) modified Bayesian estimators for active and inactive compounds; (c) structures of selected binders.

For each listed target with at least two binders, it is first assumed that all of the molecules in the collection that do not indicate this as one of their targets are inactive.

In the app we used ECFP_6 fingerprints

Building Bayesian models for each target in TB Mobile

Clark et al., J Cheminform 6:38 2014

TB Mobile Vers.2

Ekins et al., J Cheminform 5:13, 2013

Clark et al., J Cheminform 6:38 2014

Predict targetsCluster molecules

http://goo.gl/vPOKS

http://goo.gl/iDJFR

CDD Models - Build model

9R44TR000942-02

Ames Bayesian model built using CDD Models showing ROC for 3

fold cross validation. Note only FCFP_6 descriptors were used

9R44TR000942-02

Exporting models from CDD

Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02

http://molsync.com/bayesian1


Open models in MMDS

Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02

ChEMBL 20

• Skipped targets with > 100,000 assays and sets with < 100 measurements

• Converted data to –log

• Dealt with duplicates

• 2152 datasets

• Cutoff determination

• Balance active/ inactive ratio

• Favor structural diversity and activity distribution

Clark and Ekins, J Chem Inf Model. 2015 Jun 22;55(6):1246-60

What do 2000 ChEMBL models look like

Folding bit size

AverageROC

http://molsync.com/bayesian2Clark and Ekins, J Chem Inf Model. 2015 Jun 22;55(6):1246-60


Nature Reviews Drug Discovery 9, 215–236 (1 March 2010)

Transporters modeled

Created models for

P-gp

OATPs

OCT1

OCT2

BCRP

hOCTN2

ASBT

hPEPT1

hPEPT2

NTCPMATE1,

MATE-2K

MRP4

Results for Bayesian model cross validation. 5-fold and Leave one out (LOO) validation with Bayesian models generated with Discovery Studio and Open Models implemented in the mobile app MMDS. * = previously

published

Ekins et al Drug Metab Dispos In Press 2015

Transporter models

R41-AI108003-01

Ekins et al Drug Metab Dispos In Press 2015

Transporter modelshttp://molsync.com/transporters

9R44TR000942-02 5R01DK058251-14

http://molsync.com/transporters

• Very few researchers chasing >7000 diseases

• NIH ORDR budget for 2015 estimated at $813M

• Relatively easy to treat. At the forefront of gene therapy resurgence

• Only miniscule clinical trials possible

• Incentives – exclusivity, vouchers

Rare disease biology not well knownAffects 10s- 1000s per disease

The Rare Disease Opportunity

Used Not Used

67.5

125

245

350

0

50

100

150

200

250

300

350

400

Novartis Janssen BioMarin KnightTherapeutics

Retrophin UnitedTherapeutics

VALUE ($M)

Tropical Tropical TropicalRare Rare Rare

According to statute, FDA's rare pediatric disease priority review voucher program is now slated to end after 17 March 2016. - See more at: http://www.raps.org/Regulatory-Focus/News/2015/03/18/21750/Pediatric-Priority-Review-Voucher-Program-Set-to-End-After-FDA-Approves-New-Drug/#sthash.j6XGLEXz.dpuf

Benefits of Tropical disease and rare pediatric disease priority review voucher program : The

golden ticket

Open Science can start with 140 characters

A Mobile App for Open Drug Discovery

A flipboard for science #ODDT

iOS only

Embraced by rare disease advocates

Getting people to share data openly is a challenge

Tweets saved indefinitely

Developed with Alex Clark

Open Drug Discovery Teams – brings data from Twitter and the internet together

Ekins et al., Mol Informatics, 31: 585-597, 2012

http://goo.gl/r9NP7p

• Virtually anyone can do this

• Data is out there to produce models for drug discovery

• Computational and experimental collaborations with open data have lead to :– New hits and leads

– New IP

– New grants for collaborators

• Even Ebola had enough data to build models and suggest compounds to test in 2014

• Make findings open and published immediately

• Huge opportunity to work on rare diseases

• Challenges still – sharing and accessing information / knowledge– Lack of trust in models

– Belief that you need super computers – when an app might be enough

– Barriers to sharing and collaboration

Conclusions

Alex ClarkJair Lage de Siqueira-NetoJoel FreundlichPeter MadridRobert DaveyMegan CoffeeEthan PerlsteinRobert ReynoldsNadia LittermanChristopher LipinskiChristopher SouthanAntony WilliamsCarolyn Talcott Malabika SarkerSteven Wright Mike PollastriNi AiBarry Bunin and all colleagues at CDD

Acknowledgments and contact info

[email protected]

collabchem

sean.ekins

Extra slide

PolyPharma a new free app for drug discovery