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
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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.
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
* 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
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.
Ekins S, Freundlich JS and Coffee M, 2014 F1000Research 2014, 3:277
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)
Ekins S, Freundlich JS and Coffee M, 2014 F1000Research 2014, 3:277
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
Ekins S, Freundlich JS and Coffee M, 2014 F1000Research 2014, 3:277
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
• 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
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
• 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