Making it open- putting cheminformatics to use against the Ebola virus

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

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

It started with

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

Boosting views – Oct 2014

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,

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

Mean ± SD molecular properties calculated in CDD Vault using ChemAxonsoftware 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

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)

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.

• Two-pore channels required for viral entry into hostcells (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

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

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

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

Effect of drug treatment on infection with Ebola-GFPExperiment still in process

Compound EC50 (mM)

Chloroquine 10

Mol 1 0.27

Mol 2 Not determined

Mol 3 0.67

Mol 1Mol 2Mol 3

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.

http://molsync.com/ebola/

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

Acknowledgments

• Megan Coffee

• Joel Freundlich

• Nadia Litterman

• Christopher Lipinski

• Christopher Southan

• Alex Clark

• Peter Madrid

• Robert Davey

• Jean-Claude Bradley