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

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Talk at ACS fall meeting 2015

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

  1. 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. 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. 3. It started with
  4. 4. 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
  5. 5. Boosting views – Oct 2014
  6. 6. 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,
  7. 7. Pharmacophores for EBOV VP35 generated from crystal structures in the protein data bank PDB.
  8. 8. 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)
  9. 9. 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
  10. 10. 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
  11. 11. 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)
  12. 12. 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.
  13. 13. • 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
  14. 14. 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
  15. 15. 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.
  16. 16. Discovery Studio pseudotype Bayesian model B Discovery Studio EBOV replication model Good Bad Good Bad
  17. 17. 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
  18. 18. 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
  19. 19. 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.
  20. 20. 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
  21. 21. Acknowledgments • Megan Coffee • Joel Freundlich • Nadia Litterman • Christopher Lipinski • Christopher Southan • Alex Clark • Peter Madrid • Robert Davey • Jean-Claude Bradley

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