1.
Using In Silico Tools in Repurposing
Drugs for Neglected and Orphan
Diseases
Wednesday, 11/16 2.05-2.50 pm
Sean Ekins, Ph.D., D.Sc.

2.
Repurposing examples
• Viagra – hypertension –
erectile dysfunction –
pulmonary arterial
hypertension
• Thalidomide – sedative
– multiple myeloma
• Ropinrole - Parkinson’s
– Restless legs – sexual
dysfunction

3.
How to Repurpose
• Screen libraries
of approved
drugs
In vitro In silico

4.
Finding Promiscuous Old Drugs for New Uses
• 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
• Perhaps screen these first?
Ekins and Williams, Pharm Res 28(8):1785-91, 2011

5.
Laboratories past and present
Lavoisier’s lab 18th C Edison’s lab 20th C
Author’s lab 21th C
+ Network of
global
collaborators

6.
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

7.
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

8.
• 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

9.
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

10.
Synonyms Infection Ratio EC50 (µM) EC90 (µM) Hill slope
Cytotoxicity CC50
(µM)
Chagas mouse model (4
days treatment,
luciferase): In vivo
efficacy at 50 mg/kg bid
(IP) (%)
(±)-Verapamil
hydrochloride, 715730,
SC-0011762
0.02, 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
CH 3
O
N
N
+
N
O
O
–
O
O
O
N
+
O
O
–
N
H
N
NH2
O
In vitro and in vivo data for compounds selected
R41-AI108003-01

11.
7,569 cpds => 99 cpds => 17 hits (5 in nM range)
Infection Treatment Reading
0 1 2 3 4 5 6 7
In vivo efficacy of the 5 tested compounds
Pyronaridine Furazolidone Verapamil
Nitrofural Tetrandrine Benznidazole
Vehicle
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878R41-AI108003-01

12.
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 Theileria
Parasites tick-transmitted
R41-AI108003-01
Work provided starting point for grants (submitted) and further work
N
N
HN
N
N
OH
Cl
O
CH 3

13.
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

14.
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

15.
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

16.
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.
F1000Research, 4:1091, 2015

17.
Discovery Studio pseudotype Bayesian model
B
Discovery Studio EBOV replication model
Good Bad
Good Bad
F1000Research, 4:1091, 2015

18.
Effect of drug treatment on infection with Ebola-GFP
3 Molecules selected from MicroSource Spectrum virtual screen and tested in vitro
All of them nM activity
-8 -7 -6 -5 -4
-10
0
10
20
30
40
50
60
70
80
90
100
110
Chloroquine
Pyronaridine
Quinacrine
Tilorone
Untreated control
Log Conc. (M)
%EbolaInfection
F1000Research, 4:1091, 2015
Compound EC50 (uM) [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
R21 funding to test pyronaridine in the in vivo mouse

19.
MoDELS RESIDE IN PAPERS
NOT ACCESSIBLE…THIS IS
UNDESIRABLE
Can we make
repurposing models
available?

20.
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 service
Clark et al., J Cheminform 6:38 2014

21.
Open models in MMDS
Clark et al., JCIM 55: 1231-1245 (2015)
9R44TR000942-02

22.
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
http://molsync.com/bayesian2

23.
What do 2000 ChEMBL models
look like
Folding bit size
Average
ROC
http://molsync.com/bayesian2
Clark and Ekins, J Chem Inf Model. 2015 Jun 22;55(6):1246-60

24.
PolyPharma a new free iOS app for drug discovery

25.
Christina’s world – Andrew Wyeth
MOMA
Rodin
William 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

26.
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).

27.
Chaperone therapy
• JJB has funded Dr. Alexey Pshezhetsky (Univ Montreal) to perform in
vitro testing. Alexey discovered glycosamine as a chaperone in 2009.
• Glycosamine was used to build a pharmacophore and search drug
databases for compounds for testing – updated as new compounds
tested.
• Are there other rare diseases we could apply a generalizable approach
too?
glucosamine
Glucosamine with IIIC pharmacophore
Orphanet J Rare Dis. 2012 Jun 15;7:39

28.
Same approach, bigger disease: Alzheimer’s disease
α7 nAChR PAM pharmacophore
Galantamine (Yellow) and dihydrocodeine
mapped to the galantamine
pharmacophore
GSK published α7 nAChR PAM
pharmacophore (Capelli et al., 2010)
Models filtered FDA approved drugs to 160
molecules, 8 tested in vitro by Charles
River
EC50 values for Cpd 1 = 0.021 µM, Cpd 2 = 0.004 µM and PNU-120596 = 1.42 µM.
Work with Dr’s. McMurtray, Mathews, Chung and Diaz at LABioMed

29.
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?

30.
Can anyone do drug discovery & repurpose?

31.
Repurpose using gene expression
data
https://clue.io/repurposing

32.
Neglected and Rare Disease Drug Discovery
Share urgent need for new therapeutics
http://www.mm4tb.org/ http://www.phoenixnestbiotech.com/

33.
Zika – what can we do? Image by John Liebler

34.
Crowdsourcing Science
Ekins, Perryman & Andrade PLoS Negl Trop Dis 10(10): e0005023

35.
Homology models for Zika Proteins published months before first cryo-EM structure
Ekins S, Liebler J, Neves BJ et al. 2016 F1000Research 2016, 5:275
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.

36.
Timeline
Mid-May – Oct. 6, 2016:
 60,000 volunteers donated CPU time from ~ 240,000 devices
 >11,000 CPU years have been donated to OpenZika
 1.242 billion different docking jobs have been submitted
 207 binding sites on 138 different protein targets are involved
 2-5 different binding sites are targeted / protein
 6 million compounds are docked against each site
 11 million out of a new library of 38 million compounds have been prepared
for future docking experiments
 739 million docking results have been sent back to our server
 Currently visually inspecting the docking results against the NS3 helicase:RNA
complex  13 new candidates identified

37.
 Identified 15 candidates for
assays (from library of 7,628
approved drugs & clinical
candidates)
 These are predicted to bind the
(apo) ZIKV NS3 helicase (3 of
the 15 are shown above)
 After medicinal chemistry
inspection, we selected 8 to
order & assay (but 1 is too
expensive, and 1 is restricted by
the DEA)
 5 of the 6 we ordered passed
LC/MS quality control & will be
assayed at UCSD
1st candidates from OZ
have been identified
NS3 helicase (PDB ID 5jmt)

38.
• Minimal data for using computational approaches
• 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
Conclusions

39.
Joel Freundlich
Jair Lage de Siqueira-Neto
Peter Madrid
Robert Davey
Alex Clark
Alex Perryman
Robert Reynolds
Megan Coffee
Nadia Litterman
Christopher Lipinski
Christopher Southan
Antony Williams
Mike Pollastri
Ni Ai
Jill Wood
Alexey Pshezhetsky
Barry Bunin and all colleagues at CDD
Funding – NIH NCATS, NIAID
Acknowledgments and contact info
Dr’s. Aaron McMurtray, Paul Mathews, Julia Chung and Natalie Diaz
• Sean Ekins, Ph.D., D.Sc.
• Email collaborationspharma@gmail.com
• Phone 215-687-1320

40.
Our Team
Be a WCG volunteer and help our research!!! We need you! http://openzika.ufg.br
Carolina Andrade Alex Perryman
Rodolpho Braga Melina Mottin Roosevelt Silva Wim Degrave Ana Carolina Ramos João Herminio
Lucio Freitas Jr.Jair Lage Joel Freundlich

41.
Postdoc opening
• 2yr funding
• Help coordinate projects, identify new projects and
write grants/ papers
• Pharmaceutical or Chemisty or Biology PhD
• Able to work in US
• Based in Raleigh area NC