Talk at ACS fall meeting 2015

1. Making it Open – Putting Cheminformatics
to Use Against the Ebola Virus
Sean Ekins
Collaborations in Chemistry, Inc. Fuquay Varina, NC.
Collaborative Drug Discovery, Inc., Burlingame, CA.
Collaborations Pharmaceuticals, Inc. Fuquay Varina, NC.
The Growing Impact of Openness in Chemistry: A
Symposium in Honor of J.-C. Bradley
Wikipedia

2. 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

3. It started with

5. 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

6. Boosting views – Oct 2014

8. Pharmacophore based on 4 compounds
Ekins S, Freundlich JS and Coffee M 2014 [v2; ref status: indexed, http://f1000r.es/4wt]
F1000Research 2014, 3:277 (doi: 10.12688/f1000research.5741.2)
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,

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

10. Redocking VPL57 in 4IBI
Ekins S, Freundlich JS and Coffee M 2014 [v2; ref status: indexed, http://f1000r.es/4wt]
F1000Research 2014, 3:277 (doi: 10.12688/f1000research.5741.2)
• 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)

11. Docking FDA approved compounds in VP35 protein showing overlap with ligand (yellow) and 2D interaction
diagram
Ekins S, Freundlich JS and Coffee M 2014 [v2; ref status: indexed, http://f1000r.es/4wt]
F1000Research 2014, 3:277 (doi: 10.12688/f1000research.5741.2)
4IBI was used, 4IBI
ligand VPL57 shown in
yellow.
Amodiaquine (grey)
and 4IBI LibDock
score 90.80,
Chloroquine (grey)
LibDock score 97.82,
Clomiphene (grey)
and 4IBI LibDock
score 69.77,
Toremifene (grey) and
4IBI LibDock score
68.11

13. Mean ± SD molecular properties calculated in CDD Vault using ChemAxon
software for the 55 molecules with activity against the Ebola virus
* statistically significant p < 0.05 using the t-test and ANOVA. ** statistically significant p <
0.0001 using the t-test and ANOVA. Note data are skewed by SARA-133. When this molecule is
removed the mean values and significance data are shown in parentheses.
Molecular
weight
LogP H-bond
donors
H-bond
acceptors
Lipinski Rule
of 5
violations
pKa
Heavy atom
count
Polar Surface
Area
Rotatable
bond number
Undesirable
(n = 39)
508.49 ±
447.43
(438.66 ±
101.47)
3.75 ± 4.15
(4.35 ± 1.79)
2.38 ± 5.04
(1.60 ± 1.35)
6.33 ± 10.49
(4.68 ± 2.04)
0.69 ± 0.73
(0.63 ± 0.63)
7.45 ± 4.22
(7.34 ± 0.64)
35.79 ± 30.78
(31.00 ± 7.24)
104.03 ±
197.71
(72.78 ±32.23)
9.67 ± 14.07
(7.47 ± 3.29)
Desirable
(N = 16)
371.38 ±
107.47 (*)
1.22 ± 2.55* (**) 3.19 ± 1.94 (*) 5.25 ± 1.77 0.31 ± 0.48*
(*)
8.27 ± 3.33 26.06 ± 7.47
(*)
103.36 ± 32.81
(*)
6.37± 6.04

14. An example of small molecules active against Ebola virus data in the CDD Vault
Litterman N, Lipinski C and Ekins S 2015 [v1; ref status: awaiting peer review,
http://f1000r.es/523] F1000Research 2015, 4:38 (doi: 10.12688/f1000research.6120.1)

16. FDA approved drugs of most interest for
repurposing as potential Ebola virus
treatments.
Ekins S and Coffee M 2015 [v2; ref status: indexed, http://f1000r.es/554] F1000Research 2015,
4:48 (doi: 10.12688/f1000research.6164.2)
Chloroquine similarity in Approved Drugs mobile
app http://molmatinf.com/approveddrugs.html.

17. • Two-pore channels required for viral entry into host
cells (Sakurai, Y., et al., Science, 2015. 347(6225): p.
995-8.)
• seven small molecules (six actives and one
inactive) tested for inhibition of Ebola virus-GFP
infection tertiary amine tetrandrine (IC50 55nM)
• may define the structure activity relationship
• Creatd a common feature pharmacophore two
hydrogen bond donors and three hydrophobic
features
• Compared in Discovery Studio with the previously
published pharmacophore based on 4 Ebola virus
active compounds (RMSD 1.52Å).
• Amodiaquine fits (fit value 2.27)
• Searching 57 Ebola actives, retrieved 27 compounds
Two pore channel

19. Machine Learning
• 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

20. 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.

21. Discovery Studio pseudotype Bayesian model
B
Discovery Studio EBOV replication model
Good Bad
Good Bad

22. New Molecules scoring well with the Ebola Bayesian models
Mol 1 Mol 2 Mol 3
Discovery Studio
Replication model score 23.62 29.73 20.90
Discovery Studio
Pseudovirus model
score
17.16 22.25 17.73
Open Bayesian
Replication model score 1.01 1.63 1.31
Open Bayesian
Pseudovirus model
score
0.72 1.28 1.17

23. Effect of drug treatment on infection with Ebola-GFP
Experiment still in process
Compound EC50 (mM)
Chloroquine 10
Mol 1 0.27
Mol 2 Not determined
Mol 3 0.67
Mol 1
Mol 2
Mol 3

24. Making models available and more hits
• MMDS
• http://molsync.com/ebola/
• 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.

25. Conclusions
• Importance of social media for sparking open collaboration
• Considerable HTS screening efforts had not been explored – At least 4
screens to date.
• Computational analysis can suggested overlap in features / targets
– Four molecules may target VP35?
• Findings open and published immediately
• The need for creating a database of active compounds identified
– CDD Public Database
– Now likely over 200 hits published
– Proposed that the FDA approved drugs should be tested in vivo
• Need for more exhaustive use of models to propose compounds
• Identified 2 very active compounds out of 3 so far
• Machine learning models available – what other libraries to screen?
• Need to be prepared for next outbreak (apply to any virus, bacteria etc.)
– Suggested recommendations

26. Acknowledgments
• Megan Coffee
• Joel Freundlich
• Nadia Litterman
• Christopher Lipinski
• Christopher Southan
• Alex Clark
• Peter Madrid
• Robert Davey
• Jean-Claude Bradley