Using Machine Learning Models Based on Phenotypic Data to Discover New Molecules For neglected diseases
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The document discusses the utilization of machine learning models to identify new therapeutic molecules for neglected diseases, including tuberculosis, Chagas disease, and Ebola. It highlights collaborations and extensive screening of large datasets to discover potential drug candidates and improve upon existing treatment strategies. The emphasis is on urgent needs for effective treatments and the incorporation of open data to enhance drug discovery processes.
![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
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Chloroquine
Pyronaridine
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Tilorone
Untreated control
Log Conc. (M)%EbolaInfection
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
Ekins et al., F1000Res 4:1091, (2015)](https://image.slidesharecdn.com/acsbigdatasessiontalksdrevisedslides-160318040226/85/Using-Machine-Learning-Models-Based-on-Phenotypic-Data-to-Discover-New-Molecules-For-neglected-diseases-25-320.jpg)
Using Machine Learning Models Based on Phenotypic Data to Discover New Molecules For neglected diseases
- 2. Using Machine Learning Models Based on Phenotypic Data to Discover New Molecules for Neglected Diseases Sean Ekins Collaborative Drug Discovery, Inc., Burlingame, CA. Collaborations Pharmaceuticals, Inc. Fuquay Varina, NC. Collaborations in Chemistry, Inc. Fuquay Varina, NC. Wikipedia
- 3. Machine Learning Examples • Data is BIG for neglected diseases • 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 • Other diseases, emerging diseases?
- 4. Neglected Disease Drug Discovery An urgent need for new therapeutics http://www.mm4tb.org/
- 5. 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
- 6. 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 ~350,000 accessible TBDA screened over 1 million, 1 million more to go TB Alliance + Japanese pharma screens R43 LM011152-01
- 7. 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
- 8. 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
- 9. • 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
- 10. Modeling and mapping Mouse in vivo data Mouse TB model data over 70 yrs 784 training and 60 test set Extended earlier study J Chem Inf Model. 2014 Apr 28;54(4):1070-82
- 11. Optimizing the triazine series as part of this project, improve solubility and show in vivo efficacy 1U19AI109713-01
- 12. 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
- 13. • Modeled data with over 300,000 cpds but focused on smaller set • 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
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. 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
- 19. 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 a phase II and phase I grant (submitted) N N HN N N OH Cl O CH 3 Broad group missed this cpd
- 20. 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
- 21. 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
- 22. 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
- 23. 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. Ekins et al., F1000Res 4:1091, (2015)
- 24. Discovery Studio pseudotype Bayesian model B Discovery Studio EBOV replication model Good Bad Good Bad Ekins et al., F1000Res 4:1091, (2015)
- 25. 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 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 Ekins et al., F1000Res 4:1091, (2015)
- 26. Making Ebola models available • From data published by others …to proposing target • 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 • Models in MMDS http://molsync.com/ebola/ 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
- 27. 1000’s of models from • 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
- 28. 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
- 29. PolyPharma a new free app for drug discovery
- 30. #ZikaOpen Image by John Liebler
- 31. Proposed workflow for rapid drug discovery against Zika virus Ekins S, Mietchen D, Coffee M et al. F1000Research 2016, 5:150 (doi: 10.12688/f1000research.8013.1)
- 32. HOMOLOGY MODELS FOR ZIKA Models developed with SWISS-MODEL Will dock millions of compounds vs these models Ekins et al., F1000Research 5:275 (2016)
- 33. Ekins S, Mietchen D, Coffee M et al. 2016 F1000Research 2016, 5:150 (doi: 10.12688/f1000research.8013.1) Compounds and chemical libraries suggested for testing against Zika virus
- 34. • Data is out there to produce models for neglected diseases • Also modeled Marburg, Lassa, Dengue.. • Computational and experimental collaborations with open data have lead to : – New hits and leads – New IP – New grants for collaborators • Even Ebola had enough data to build models and suggest compounds to test in 2014 • Zika = starting from scratch – no data – need to use other approaches • Make findings open and published immediately • Need for easier facilities to test compounds • Challenges still – sharing and accessing information / knowledge • How do we prepare for the next BIG ONE Conclusions
- 35. Alex Clark Jair Lage de Siqueira-Neto Joel Freundlich Peter Madrid Robert Davey Megan Coffee Robert Reynolds Nadia Litterman Christopher Lipinski Christopher Southan Antony Williams Carolyn Talcott Malabika Sarker Steven Wright Mike Pollastri Ni Ai Barry Bunin and all colleagues at CDD Acknowledgments and contact info ekinssean@yahoo.com collabchem
- 36. ZIKAOPEN ACKNOWLEDGMENTS Tom Stratton Priscilla L. Yang
- 37. Software on github Models can be accessed at • http://molsync.com/bayesian1 • http://molsync.com/bayesian2 • http://molsync.com/transporters • http://molsync.com/ebola/