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academic / small company collaborations for rare and neglected diseasesv2

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Talk at Science in the age of experience May 2016 - boston

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academic / small company collaborations for rare and neglected diseasesv2

  1. 1. Academic/Small Company Collaborations for Rare and Neglected Diseases Sean Ekins Collaborations in Chemistry, Inc. Fuquay Varina, NC. Wikipedia
  2. 2. 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
  3. 3. 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).
  4. 4. Chaperone therapy  JJB has funded Dr. Joel Freundlich (Rutgers) to synthesize analogs and Dr. Alexey Pshezhetsky (Univ Montreal) to perform in vitro testing. Alexey discovered glycosamine as a chaperone in 2009.  Glycosamine used to build a pharmacophore and search drug databases for compounds for testing – updated as new compounds tested  If you have similar compounds – please let us know…  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
  5. 5. 67.5 125 245 350 Value ($M) Return on Investment = Priority Review Voucher From FDA When a rare pediatric disease or tropical disease treatment is approved owner gets a Voucher has value Used Not Used Price Not Disclosed tropical tropical tropicalrare rare rare rare
  6. 6. Neglected Tropical Disease Examples • To discover new leads • Tuberculosis – from public data to open models to create IP • Chagas Disease - from public data to create new IP • Ebola virus – from little data to create open data and IP • Zika virus – Starting from scratch- what can we do?
  7. 7. Tuberculosis kills 1.6-1.7m/yr (~1 every 8 seconds) 1/3rd of worlds population infected!!!! streptomycin (1943) para-aminosalicyclic acid (1949) isoniazid (1952) pyrazinamide (1954) cycloserine (1955) ethambutol (1962) rifampicin (1967) Multi drug resistance in 4.3% of cases Extensively drug resistant increasing incidence one new drug (bedaquiline) in 40 yrs Tuberculosis
  8. 8. Tested >350,000 molecules Tested ~2M 2M >300,000 >1500 active and non toxic Published 177 100s 800 Bigger Open Data: Screening for New Tuberculosis Treatments How many will become a new drug? TBDA screened over 2 million TB Alliance + Japanese pharma screens R43 LM011152-01
  9. 9. Over 8 years analyzed in vitro data and built models Top scoring molecules assayed for Mtb growth inhibition Mtb screening molecule database/s High-throughput phenotypic Mtb screening Descriptors + Bioactivity (+Cytotoxicity) Bayesian Machine Learning classification Mtb Model Molecule Database (e.g. GSK malaria actives) virtually scored using Bayesian Models New bioactivity data may enhance models Identify in vitro hits and test models3 x published prospective tests ~750 molecules were tested in vitro 198 actives were identified >20 % hit rate Multiple retrospective tests 3-10 fold enrichment N H S N Ekins et al., Pharm Res 31: 414-435, 2014 Ekins, et al., Tuberculosis 94; 162-169, 2014 Ekins, et al., PLOSONE 8; e63240, 2013 Ekins, et al., Chem Biol 20: 370-378, 2013 Ekins, et al., JCIM, 53: 3054−3063, 2013 Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011 Ekins et al., Mol BioSyst, 6: 840-851, 2010 Ekins, et al., Mol. Biosyst. 6, 2316-2324, 2010, R43 LM011152-01
  10. 10. 5 active compounds vs Mtb in a few months 7 tested, 5 active (70% hit rate) Ekins et al.,Chem Biol 20, 370–378, 2013 1. Virtually screen 13,533-member GSK antimalarial hit library 2. Bayesian Model = SRI TAACF-CB2 dose response + cytotoxicity model 3. Top 46 commercially available compounds visually inspected 4. 7 compounds chosen for Mtb testing based on - drug-likeness - chemotype diversity GSK # Bayesian Score Chemical Structure Mtb H37Rv MIC (mg/mL) GSK Reported % Inhibition HepG2 @ 10 mM cmpd TCMDC- 123868 5.73 >32 40 TCMDC- 125802 5.63 0.0625 5 TCMDC- 124192 5.27 2.0 4 TCMDC- 124334 5.20 2.0 4 TCMDC- 123856 5.09 1.0 83 TCMDC- 123640 4.66 >32 10 TCMDC- 124922 4.55 1.0 9 R43 LM011152-01
  11. 11. • BAS00521003/ TCMDC-125802 reported to be a P. falciparum lactate dehydrogenase inhibitor • Only one report of antitubercular activity from 1969 - solid agar MIC = 1 mg/mL (“wild strain”) - “no activity” in mouse model up to 400 mg/kg - however, activity was solely judged by extension of survival! Bruhin, H. et al., J. Pharm. Pharmac. 1969, 21, 423-433. . MIC of 0.0625 ug/mL • 64X MIC affords 6 logs of kill • Resistance and/or drug instability beyond 14 d Vero cells : CC50 = 4.0 mg/mL Selectivity Index SI = CC50/MICMtb = 16 – 64 In mouse no toxicity but also no efficacy in GKO model – probably metabolized. Ekins et al.,Chem Biol 20, 370–378, 2013 Taking a compound in vivo identifies issues R43 LM011152-01
  12. 12. Optimizing the triazine series as part of this project, improve solubility and show in vivo efficacy 1U19AI109713-01
  13. 13. 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
  14. 14. 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
  15. 15. • 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
  16. 16. Model Best cutoff Leave-one out ROC 5-fold cross validation ROC 5-fold cross validation sensitivity (%) 5-fold cross validation specificity (%) 5-fold cross validation concordance (%) Dose response (1853 actives, 2203 inactives) -0.676 0.81 0.78 77 89 84 Dose response and cytotoxicity (1698 actives, 2363 inactives) -0.337 0.82 0.80 80 88 84 External ROC Internal ROC Concordance (%) Specificity (%) Sensitivity (%) 0.79 ± 0.01 0.80 ± 0.01 73.48 ± 1.05 79.08 ± 3.73 65.68 ± 3.89 5 fold cross validation Dual event 50% x 100 fold cross validation R41-AI108003-01 Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878
  17. 17. Good Bad Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878 T. cruzi Dose Response and cytotoxicity Machine Learning model features Tertiary amines, piperidines and aromatic fragments with basic Nitrogen Cyclic hydrazines and electron poor chlorinated aromatics R41-AI108003-01
  18. 18. 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
  19. 19. 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
  20. 20. 7,569 cpds => 99 cpds => 17 hits (5 in nM range) Infection Treatment Reading 0 1 2 3 4 5 6 7 Pyronaridine Furazolidone Verapamil Nitrofural Tetrandrine Benznidazole In vivo efficacy of the 5 tested compounds Vehicle Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878R41-AI108003-01
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. Pharmacophore based on 4 compounds Ekins S, Freundlich JS and Coffee M, 2014 F1000Research 2014, 3:277 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,
  25. 25. 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
  26. 26. 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.
  27. 27. Discovery Studio pseudotype Bayesian model B Discovery Studio EBOV replication model Good Bad Good Bad
  28. 28. 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 Data from Robert Davey, Manu Anantpadma and Peter Madrid -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 F1000Res Submitted 2015 Compound EC50 (mM) [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
  29. 29. Ebola models • Collaborated with lab to open up their screening data, build models, identified more active inhibitors • To date the most potent drugs and drug-like molecules • Still a need for a drug that could be used ASAP • Lead to proposal for in vivo testing compound/s More data continues to be published • We collated 55 molecules from the literature • 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.  Litterman N, Lipinski C and Ekins S 2015 F1000Research 2015, 4:38
  30. 30. Zika – what can we do? Image by John Liebler
  31. 31. Proposed workflow for rapid drug discovery against Zika virus Ekins S, Mietchen D, Coffee M et al. 2016 [version 1; referees: awaiting peer review] F1000Research 2016, 5:150 (doi: 10.12688/f1000research.8013.1)
  32. 32. Homology models for Zika Proteins published months before first cryo-EM structure Ekins S, Liebler J, Neves BJ et al. 2016 [version 1; referees: awaiting peer review] F1000Research 2016, 5:275 (doi: 10.12688/f1000research.8213.1) 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.
  33. 33. • Minimal data for using computational approaches for rare diseases • 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 • Challenges still – sharing and accessing information / knowledge • How do we prepare for next pathogen? Conclusions
  34. 34. Joel Freundlich Jair Lage de Siqueira-Neto Peter Madrid Robert Davey Alex Clark Alex Perryman Robert Reynolds Megan Coffee Ethan Perlstein Nadia Litterman Christopher Lipinski Christopher Southan Antony Williams Mike Pollastri Ni Ai Barry Bunin and all colleagues at CDD Jill Wood Alexey Pshezhetsky Acknowledgments and contact info ekinssean@yahoo.com collabchem Using Pharmacophores
  35. 35. Tom Stratton Priscilla L. Yang PI = Carolina Horta Andrade, Ph.D. Co-PI Alexander Perryman, Ph.D. Collaborators:

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