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Using In Silico Tools in Repurposing Drugs for Neglected and Orphan Diseases

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Presentation given at the AAPS meeting 2016 in Denver in a session on repurposing. I describes several recent published studies and unpublished work.

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Using In Silico Tools in Repurposing Drugs for Neglected and Orphan Diseases

  1. 1. Using In Silico Tools in Repurposing Drugs for Neglected and Orphan Diseases Wednesday, 11/16 2.05-2.50 pm Sean Ekins, Ph.D., D.Sc.
  2. 2. Repurposing examples • Viagra – hypertension – erectile dysfunction – pulmonary arterial hypertension • Thalidomide – sedative – multiple myeloma • Ropinrole - Parkinson’s – Restless legs – sexual dysfunction
  3. 3. How to Repurpose • Screen libraries of approved drugs In vitro In silico
  4. 4. Finding Promiscuous Old Drugs 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. 5. Laboratories past and present Lavoisier’s lab 18th C Edison’s lab 20th C Author’s lab 21th C + Network of global collaborators
  6. 6. Chagas Disease • About 7 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. 7. T. cruzi C2C12 cells 6-8 days 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. 8. • Dataset from PubChem 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. 9. Bayesian Machine Learning Models - 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. 10. Synonyms Infection Ratio EC50 (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. 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. 12. 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 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. 13. 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
  14. 14. 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
  15. 15. 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 calculate the ROC for the models generated
  16. 16. 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) 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. 17. Discovery Studio pseudotype Bayesian model B Discovery Studio EBOV replication model Good Bad Good Bad F1000Research, 4:1091, 2015
  18. 18. 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
  19. 19. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE Can we make repurposing models available?
  20. 20. Open Extended Connectivity Fingerprints 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. 21. Open models in MMDS Clark et al., JCIM 55: 1231-1245 (2015) 9R44TR000942-02
  22. 22. ChEMBL 20 • Skipped targets 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. 23. What do 2000 ChEMBL 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. 24. PolyPharma a new free iOS app for drug discovery
  25. 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. 26. DISEASED CELLS HEALTHY CELLS 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. 27. Chaperone therapy • JJB has 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. 28. Same approach, bigger disease: 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. 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. 30. Can anyone do drug discovery & repurpose?
  31. 31. Repurpose using gene expression data https://clue.io/repurposing
  32. 32. Neglected and Rare Disease Drug Discovery Share urgent need for new therapeutics http://www.mm4tb.org/ http://www.phoenixnestbiotech.com/
  33. 33. Zika – what can we do? Image by John Liebler
  34. 34. Crowdsourcing Science Ekins, Perryman & Andrade PLoS Negl Trop Dis 10(10): e0005023
  35. 35. Homology models for Zika 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. 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. 37.  Identified 15 candidates 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. 38. • Minimal data for 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. 39. Joel Freundlich Jair Lage de 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. 40. Our Team Be a WCG 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. 41. Postdoc opening • 2yr funding • 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

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