Bigger Data to Increase Drug Discovery
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The document discusses the potential of utilizing bigger data in drug discovery to enhance the treatment of various diseases, including tuberculosis and Ebola, by integrating machine learning with large datasets. It emphasizes the importance of collaboration, data accessibility, and mobile applications in advancing research and developing effective drug candidates. It concludes with a call for larger small molecule screening datasets to better test machine learning algorithms' scalability.
Bigger Data to Increase Drug Discovery
- 1. Bigger Data to Increase Drug DiscoveryBigger Data to Increase Drug Discovery Sean EkinsSean Ekins Phoenix Nest, Inc., Brooklyn, NY. Collaborations in Chemistry, Inc., Fuquay Varina, NC. Collaborative Drug Discovery, Inc., Burlingame, CA. Collaborations Pharmaceuticals, Inc., Fuquay Varina, NC.
- 2. In a Perfect World… • All major diseases cured • All > 7000 rare diseases have treatments available • Neglected diseases are eradicated • Antibiotics, antivirals, vaccines developed to anticipate all future mutations • Drug resistance eradicated • All research coordinated globally • Government/individuals collaboration- discovers / fund all research • Billions of molecules will be available with data for different targets • All decisions will involve machine learning • Life expectancy is infinite
- 3. Big DATA
- 6. Ebola- related tweets in a 6 week period 2014 Robert Moore
- 7. Why ‘Bigger’ and not ‘Big’
- 8. Just a matter of scale? Drug Discovery’s definition of Big data Everyone else’s definition of Big data
- 9. What about Chemistry and Biology - Pharmacology X.0 • Data Sources • PubChem • ChEMBL • ToxCast over 1800 molecules tested against over 800 endpoints
- 12. But what about small data? • In some cases its all we have • In vivo data is not high throughput • Small data builds networks DATA V http://smalldatagroup.com/
- 13. The past • 1996 • Data from low throughput Drug-drug interaction studies • E.g. Ki values with CYP 3A4 • A drug company might have 10s of values • This data was used to build 3D QSAR, pharmacophores JPET, 290: 429-438, 1999
- 14. Hydrophobi c features (HPF) Hydrogen bond acceptor (HBA) Hydrogen bond donor (HBD) Observed vs. predicted IC50 r Acoustic mediated process 2 1 1 0.92 Tip-based process 0 2 1 0.80 Acoustic Tip based Generated with Discovery Studio Generated with Discovery Studio (Accelrys)(Accelrys) Cyan = hydrophobicCyan = hydrophobic Green = hydrogen bond acceptorGreen = hydrogen bond acceptor Purple = hydrogen bond donorPurple = hydrogen bond donor Each model shows most potent Each model shows most potent molecule mappingmolecule mapping How you dispense liquids may be important: insights from small dataHow you dispense liquids may be important: insights from small data PLoS ONE 8(5): e62325 (2013)
- 15. Ebola inhibitor Pharmacophore Ekins S, Freundlich JS and Coffee M F1000Research 2014, 3:277 Docking FDA approved compounds in VP35 protein showing overlap with ligand (yellow) Proposed amodiaquine, chloroquine, clomiphene toremifene Which all are active in vitro may have common features and bind common site / target A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus
- 16. The last 5 years -Present • 2010 • Data from high throughput screens at Pfizer • E.g. metabolic stability data ~200K compounds • This data was used to build machine learning models • 2015 • Could easily be double this amount Drug Metab Dispos, 38: 2083-2090, 2010
- 17. Ebola Machine Learning Models 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 Ekins, Freundlich, Madrid and Clark
- 19. Tuberculosis still kills 1.6-1.7m/yr (~1 every 8 seconds) 1/3rd of worlds population infected!!!! streptomycin (1943)streptomycin (1943) para-para-aminosalicyclic acid (1949)aminosalicyclic acid (1949) isoniazid (1952)isoniazid (1952) pyrazinamide (1954)pyrazinamide (1954) cycloserine (1955)cycloserine (1955) ethambutol (1962)ethambutol (1962) rifampicin (1967)rifampicin (1967) Multi drug resistance in 4.3% of casesMulti drug resistance in 4.3% of cases Extensively drug resistant increasingExtensively drug resistant increasing incidenceincidence 2 new drugs (bedaquiline, delamanid)2 new drugs (bedaquiline, delamanid) in 40 yrsin 40 yrs Tuberculosis – a big diseaseTuberculosis – a big disease
- 20. Tested >350,000 moleculesTested >350,000 molecules Tested ~2M 2M Tested ~2M 2M >300,000 >300,000 >1500 active and non toxic>1500 active and non toxic Published 177 100s Published 177 100s 800 800 Big Data: Screening for New Tuberculosis TreatmentsBig Data: Screening for New Tuberculosis Treatments How many will become a new drug? How do we learn from this big data? TBDA screened over 1 million, 1 million more to go TB Alliance + Japanese pharma screens
- 21. Over 8000 molecules with dose response data for Mtb in CDD Public from NIAID/SRI https://app.collaborativedrug.com/register
- 22. Over 6 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 ~750~750 molecules were testedmolecules were tested in vitroin vitro 198 actives were identified198 actives were identified >20 % hit rate>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,
- 23. 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 (µg/mL) GSK Reported % Inhibition HepG2 @ 10 µM 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
- 24. Filling out the triazine matrix using SARtable: A new kind of map Green = good activity, Red = bad; colored dots are predictions
- 25. No relationship between internal or external ROC and the number of molecules in the training set? PCA of combined data and ARRA(red) Ekins et al., J Chem Inf Model 54: 2157-2165 (2014) Internal and leave out 50%x100 ROC track each other External ROC less correlation Smaller models do just as well with external testing ~350,000
- 26. What matters most >70 years of TB mouse in vivo data – Mind the gap - 770 molecules MIND THE TB GAP Ekins et al., J Chem Inf Model 54: 1070-82, 2014 Ekins, Nuermberger & Freundlich DDT 19: 1279-1282, 2014
- 27. In vivo Machine Learning Models ROC 5 fold cross validation RP Forest RP Single Tree SVM Bayesian 3 /11 (27.2%) 4/11 (36.4%) 7/11 (63.6%) 8/11 (72.7%) External test set Ekins et al., J Chem Inf Model 54: 1070-82, 2014 RP Forest RP Single Tree SVM Bayesian 0.75 0.71 0.77 0.73
- 28. ow can we find the in vivo active compound e need a map..
- 29. >70 years of TB in vivo data Green = in vivo mouse active Empty = in vivo inactive Yellow = 2013-2015 data Uses Bayesian fingerprints and clustering by similarity Clark and Ekins - unpublished Clustering in vivo mouse TB dataHex plot
- 30. >70 years of TB in vivo data Green = in vivo mouse active Empty = in vivo inactive Yellow = 2013-2015 Clark and Ekins - unpublished Clustering in vivo mouse TB data Triazine surrounded by inactives Issues High Log P, poor solubility
- 31. How do we ‘increase drug discovery’? • Make data and models more accessible • Collaborate • Share – Create mobile apps • Encourage engagement from non scientists
- 32. MoDELSRESIDE IN PAPERS NOT ACCESSIBLE…THISIS UNDESIRABLE How do wesharethem? How do weuseThem?
- 33. • CDD Vision Uses Bayesian algorithm and FCFP_6 fingerprints Bayesian models Clark et al., J Cheminform 6:38 2014
- 34. Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6Predictions for the InhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b) modified Bayesian estimators for active and inactive compounds;fingerprints; (b) modified Bayesian estimators for active and inactive compounds; (c) structures of selected binders.(c) structures of selected binders. For each listed target with at least two binders, it is first assumed that all of theFor each listed target with at least two binders, it is first assumed that all of the molecules in the collection that do not indicate this as one of their targets aremolecules in the collection that do not indicate this as one of their targets are inactive.inactive. In the app we used ECFP_6 fingerprintsIn the app we used ECFP_6 fingerprints Building Bayesian models for each target in TB MobileBuilding Bayesian models for each target in TB Mobile Clark et al., J Cheminform 6:38 2014
- 35. TB Mobile Vers.2TB Mobile Vers.2 Ekins et al., J Cheminform 5:13, 2013 Clark et al., J Cheminform 6:38 2014 Predict targets Cluster molecules http://goo.gl/vPOKS http://goo.gl/iDJFR
- 36. Predictions for 2013-2015 in vivo molecules
- 37. Bayesian models added to mobile apps: MMDS
- 38. Bayesian models added to mobile apps: Approved drugs
- 39. Human Microsomal Intrinsic clearance Human protein binding Solubility pH 7.4 AZ dataset models >1000 molecules
- 40. Models from ChEMBL data http://molsync.com/bayesian2
- 41. What do 2000 ChEMBL models look like Folding bit size Average ROC http://molsync.com/bayesian2
- 42. Bigger datasets and model collections • Profiling “big datasets” is going to be the norm. • A recent study mined PubChem datasets for compounds that have rat in vivo acute toxicity data • This could be used in other big data initiatives like ToxCast (> 1000 compounds x 800 assays) and Tox21 etc. • Kinase screening data (1000s mols x 100s assays) • GPCR datasets etc (1000s mols x 100s assays) Zhang J, Hsieh JH, Zhu H (2014) Profiling Animal Toxicants by Automatically Mining Public Bioassay Data: A Big Data Approach for Computational Toxicology. PLoS ONE 9(6): e99863. doi:10.1371/journal.pone.0099863 http://127.0.0.1:8081/plosone/article?id=info:doi/1
- 43. • Data is at your fingertips instantly • labs add data to a massive corpus of knowledge • Instantly available to all • Algorithms for mining, prediction • Millions of models accessible • Making decisions on experiments needed and running them • Data visualization, exploration is real-time, updated • Data follows you Sean Ekins, a computational drug discovery consultant at Collaborations in Chemistry in North Carolina, is much more skeptical. He notes pharma companies have found hundreds of antimalaria compounds more potent than TNP-470 and says that he is not convinced Eve can do QSAR. He wants to see Eve go head-to-head with a real computational chemist. “Eve should go back to the Garden of Eden and leave drug discovery to scientists who know what they are doing,” Ekins says. How close are we?
- 44. • Computers and models do not replace scientists • A tool to help us sift through ideas quickly • Many examples have lead to leads • Bigger data not needed for good models • More data becoming public • Can model ADME, bioactivity and more • Collaboration and software is important • Mobile apps have useful cheminformatics features - aid anyone to do drug discovery • Models are compact < 1MB and portable • The age of model sharing is here Conclusions
- 45. Wanted • “Bigger” small moleculescreening datasets • Preferably > 500,000 – 1,000,000 moleculeswith data • To test how machinelearningAlgorithmsScale • Contact ekinssean@yahoo.com
- 46. Nadia Litterman, Krishna Dole and all at CDD, Megan Coffee, SRI, MM4TB and manyNadia Litterman, Krishna Dole and all at CDD, Megan Coffee, SRI, MM4TB and many others …Funding:others …Funding: Bill and Melinda Gates Foundation (Grant#49852)Bill and Melinda Gates Foundation (Grant#49852) 1R41AI088893-01,1R41AI088893-01, 2R42AI088893-02, R43 LM011152-01,2R42AI088893-02, R43 LM011152-01, 9R44TR000942-02, 1R41AI108003-01, 1U19AI109713-01, MM4TB, Software: BioviaMM4TB, Software: Biovia Freundlich Lab
Editor's Notes
- #15: You do not need big data to show fundamental observations