Academic/Small Company Collaborations for Rare and Neglected Diseases

Sean Ekins

Collaborations in Chemistry, Inc. Fuquay Varina, NC.

Wikipedia

Christina’s world – Andrew Wyeth

MOMA

RodinWilliam Kent - Peter the Wild Boy

Rare Diseases

Charcot-Marie-Tooth

Pitt-Hopkins

Kensington Palace

• In the USA -a rare disease affects less than 200,000 individuals, in aggregate, rare diseases affect 6-7% of the population

• In Europe – a disease or disorder is defined as rare when it affects less than 1 in 2000.

• impacting nearly 30 million Americans. • Eighty percent of these diseases have a genetic origin

F1000Res. 2015 Feb 26;4:53 F1000Res. 2014 Oct 31;3:261

DISEASED CELLS HEALTHY CELLS

Source: BioMarin

Sanfilippo Syndrome

Build up of Heparan sulfate in lysosomes leads to:

development and/or behavioral problems,

intellectual decline,

behavioural disturbance

hyperactivity,

sleep disturbance

develop swallowing difficulties and seizures

Immobility

Shortened lifespan usually <20

1. Replace enzyme with

Enzyme

Replacement

treatment

2. Gene therapy

3. Chaperone therapy

4. Substrate reduction

therapy

Sanfilippo Syndrome (MPS IIIC) - MPS IIIC

caused by genetic deficiency of heparan

sulfate acetyl CoA: a-glucosaminide N-

acetyltransferase, (HGSNAT).

Chaperone therapy

JJB has funded Dr. Joel Freundlich (Rutgers) to synthesize

analogs and Dr. Alexey Pshezhetsky (Univ Montreal) to

perform in vitro testing. Alexey discovered glycosamine as a

chaperone in 2009.

Glycosamine used to build a pharmacophore and search drug

databases for compounds for testing – updated as new

compounds tested

If you have similar compounds – please let us know…

Are there other rare diseases we could apply a generalizable

approach too?

glucosamineGlucosamine with

IIIC pharmacophore

Orphanet J Rare Dis. 2012 Jun 15;7:39

67.5

125

245

350

Value ($M)

Return on Investment = Priority Review Voucher

From FDA

When a rare pediatric disease or tropical disease

treatment is approved owner gets a Voucher

has value

Used Not UsedPrice Not

Disclosed

tropical tropical tropicalrare rare rare rare

Neglected Tropical Disease Examples

• 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

• Zika virus – Starting from scratch- what can we do?

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 2 million

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

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

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

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

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 grants (submitted) and further work

N

N

HN

N

N

OH

Cl

O

CH3

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

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,

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)

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

Ebola Pseudotype (actives = 41) 0.85 0.81 0.76 0.85 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

Effect of drug treatment on infection with Ebola-GFP

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

Data from Robert Davey, Manu Anantpadma and Peter Madrid

-8 -7 -6 -5 -4-10

0102030405060708090

100110

Chloroquine

Pyronaridine

Quinacrine

Tilorone

Untreated control

Log Conc. (M)%

Eb

ola

In

fecti

on

F1000Res Submitted 2015

Compound EC50 (mM) [95% CI] Cytotoxicity CC50 (µM)

Chloroquine 4.0 [1.0 – 15] 250

Pyronaridine 0.42 [0.31 – 0.56] 3.1

Quinacrine 0.35 [0.28 – 0.44] 6.2

Tilorone 0.23 [0.09 – 0.62] 6.2

Duplicate experiments

control

Ebola models• 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

• Lead to proposal for in vivo testing compound/s

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

Zika – what can we do?

Image by John Liebler

Proposed workflow for rapid drug discovery against Zika virus

Ekins S, Mietchen D, Coffee M et al. 2016 [version 1; referees: awaiting peer review]

F1000Research 2016, 5:150 (doi: 10.12688/f1000research.8013.1)

Homology models for Zika Proteins published months before first cryo-EM structure

Ekins S, Liebler J, Neves BJ et al. 2016 [version 1; referees: awaiting peer review] F1000Research 2016, 5:275 (doi: 10.12688/f1000research.8213.1)

Structures being used to dock molecules on:Selected ZIKV NS5 (A), FtsJ (B), HELICc (C), DEXDc (D), Peptidase S7 (E), NS1 (F), E Stem (G), Glycoprotein M (H), Propeptide (I), Capsid (J), and Glycoprotein E (K) homology models (minimized proteins) that had good sequence coverage with template proteins developed with SWISS-MODEL.

• Minimal data for using computational approaches for rare diseases

• Data available to produce models for neglected diseases

• modeled Lassa, Marburg, dengue viruses

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

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

– New IP

– New grants for collaborators

– Global collaborative project – Open Zika

• Zika is starting from no screening data, so need for several approaches

• Make findings open and publish immediately

• Need for facilities to test compounds

• Challenges still – sharing and accessing information / knowledge

• How do we prepare for next pathogen?

Conclusions

Joel FreundlichJair Lage de Siqueira-NetoPeter MadridRobert DaveyAlex ClarkAlex PerrymanRobert Reynolds

Megan CoffeeEthan PerlsteinNadia LittermanChristopher LipinskiChristopher SouthanAntony WilliamsMike PollastriNi AiBarry Bunin and all colleagues at CDDJill WoodAlexey Pshezhetsky

Acknowledgments and contact info

ekinssean@yahoo.com

collabchem

Using Pharmacophores

Tom Stratton

Priscilla L. Yang

PI = Carolina Horta Andrade, Ph.D. Co-PI Alexander Perryman, Ph.D.

Collaborators: