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CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery Collaborations
The document discusses the Collaborative Drug Discovery (CDD) Vault, a software platform used for drug discovery collaborations, offering features for data management, visualization, and integration with public datasets. It highlights case studies in Chagas disease and tuberculosis, demonstrating how machine learning models and the CDD Vault were utilized to screen and validate compounds for potential treatments. The summary emphasizes CDD Vault's role in fostering collaboration, ensuring data security, and facilitating innovative approaches in drug discovery research.
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CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery Collaborations
- 1. CDD: VAULT, CDD:VISION AND CDD: MODELS FOR DRUG DISCOVERY COLLABORATIONS SEAN EKINS1,2 ANNA COULON-SPEKTOR1, KELLAN GREGORY1, CHARLIE WEATHERALL1, KRISHNA DOLE1, ANDREW MCNUTT1, PETER NYBERG1, TOM GILLIGAN1, XIAO BA1, BARBARA HOLTZ1, SYLVIA ERNST1, FRANK COLE1, MARC NAVRE1, ALEX M. CLARK3 AND BARRY A. BUNIN1 1 COLLABORATIVE DRUG DISCOVERY, 1633 BAYSHORE HIGHWAY, SUITE 342, BURLINGAME, CA 94010, USA; 2 COLLABORATIONS IN CHEMISTRY, 5616 HILLTOP NEEDMORE ROAD, FUQUAY-VARINA, NC 27526, USA; 3 MOLECULAR MATERIALS INFORMATICS, INC., 1900 ST. JACQUES #302, MONTREAL H3J 2S1, QUEBEC, CANADA
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- 3. CDD- Over adecade of drug discovery collaborations SaaS Easy to use Used by Academia Industry, Biotech Private Selective collaboration 100’s of published datasets
- 4. Enterprise Capabilities Web Interface,Management Tools, Integration, Customizable Drug Discovery Data Mining Search, Visualization, Presentation Chemical Intelligence Chemical Drawing, Registration, Property Calculators, Structure Search, SAR Tools Collaborative Environment Controlled Access, Data Privacy, Security, Community Free Public Data Access Screening Data, Compound Data CDD Vault Features
- 5. • Online Zendesk •CDD Models • CDD Vision • Integration of CDD Public, ChemSpider, Zinc, and PubChem. Benefits of CDD Vault
- 6. Budget Sensitive Startupor Academic Scientists Won’t lose data Get better results Easy to trial, set up, configure, be trained and GO! ex-Big Pharma Scientist Familiar with Registration/SAR Software Nimble Save $$$ with modern cloud solution Relax – data migration is a snap! Big Collaborations funded by Pharma, NIH, Foundations (PPP) Control exactly which data you share with others Relax – security is built in Foster interactions between biologists and chemists Passed Big Pharma & NIH FISMA audits (CDD does not own IP) The CDD Vault “Value Proposition”
- 7. • About 7million 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. Hotez et al., PLoS Negl Trop Dis. 2013 Oct 31;7(10):e2300 Chagas Disease
- 8. • Used publicChagas HTS data from Broad inst. • Created Machine learning models – validated • Used to screen multiple datasets of drugs and natural products • Selected compounds for testing • Testing in vitro • Testing in vivo Chagas Disease – Machine Learning
- 9. Comparing Diversity Screeningvs Machine learning SCREENING HIT SELECTION HIT CONFIRMATION (dose-response) SUITABLE FOR IN VIVO Confirmed In Vivo Efficacy 100,000 cpds (diversity lib) 2,000 cpds Hits (~2%) 1,000 cpds Hit- conf. (~50%) 20 cpds tested in vivo (~2%) 1 cpd with >80% efficacy (<5%) 99 cpds 17 cpds Hits (17%) 14 Hit-conf. (82%) 5 cpds tested in vivo (35%) 2 cpd with >80% efficacy (40%) Historical Data CDD-UCSD Project Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878
- 10. 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):e0003878
- 11. Sharing Chagas invitro and in vivo data in CDD Vault Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878 CDD and UCSD used Vault to securely share data In vitro and in vivo data captured Screening and dose response dataWork provided starting point for a phase II and phase I grant (submitted)
- 12. TB Project overview PhaseI STTR – Proof of concept of mimic strategy Phase II STTR – Expand mimic strategy and validation of phase I hits
- 13. streptomycin (1943) para-aminosalicyclic acid(1949) isoniazid (1952) pyrazinamide (1954) cycloserine (1955) ethambutol (1962) rifampicin (1967) Globally ~$500M in R&D /yr Multi drug resistance in 4.3% of cases Extensively drug resistant increasing incidence one new drug (bedaquiline) in 40 yrs TB key points
- 14. Tested >350,000 moleculesTested ~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 1 million, 1 million more to go TB Alliance + Japanese pharma screens R43 LM011152-01
- 15. Over 8 yearsanalyzed 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
- 16. Examples of CDDVault used for STTR
- 17. Examples of CDDVault used for STTR Computationally searched >80,000 molecules – and used Bayesian models for filter - narrowed to 842 hits -tested 23 compounds in vitro (3 picked as inactives), lead to 2 proposed as mimics of D- fructose 1,6 bisphosphate Sarker et al., Pharm Res 2012, 29: 2115-2127 a. b. 1R41AI088893-01
- 18. 5 active compoundsvs 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
- 19. • BAS00521003/ TCMDC-125802reported 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, 2013R43 LM011152-01 Taking a compound in vivo identifies issues
- 20. Optimizing the triazineseries as part of this project, improve solubility and show in vivo efficacy 1U19AI109713-01
- 21. Copyright © 2013All Rights Reserved Collaborative Drug Discovery MM4TB: 25 organizations New Old Neuroscience Kinetoplastid Drug Development Consortium
- 22. MM4TB • Provide CDDVault • Vault Support • Cheminformatics support to project • Example using CDD Vault to share docking data for Topo I project • Dock compounds in homology model of Mtb Topo I then import data in CDD
- 23. Complete inhibition of TopoI at 100nM MIC 60 – 250 uM MM4TB – Topo I Godbole et al., Antimicrob Agents Chemother 59:1549-57, 2015.
- 24. MM4TB – TopoI • Mtb Topo I docking identified new inhibitors – collaboration With Nagaraja group in India - Amsacrine Godbole et al., Biochem Biophys Res Comm 446:916-20, 2014.
- 26. CDD VISION Data takenfrom CDD Vault and utilized in CDD Vision Backend formed using immutable and Crossfilter.js, binding layer uses d3.js and jQuery, Rendering uses d3.js and Pixi.js
- 27. Launching CDD Vision CDDVISION
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- 35. Plots and Tables CDDVISION
- 36. Multiple plots, differentsizes CDD VISION
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- 44. Adding to a NewCollection CDD VISION
- 45. Adding to an ExistingCollection CDD VISION
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- 48. MoDELS RESIDE INPAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE How do we share them? How do we use Them?
- 49. 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
- 50. Predictions for theInhA target: (a) the ROC curve with ECFP_6 and FCFP_6 fingerprints; (b) modified Bayesian estimators for active and inactive compounds; (c) structures of selected binders. For 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 are inactive. In the app we used ECFP_6 fingerprints Building Bayesian models for each target in TB Mobile Clark et al., J Cheminform 6:38 2014
- 51. TB Mobile Ekins etal., 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
- 52. Single point data> 300K molecules Uses Bayesian algorithm and FCFP_6 fingerprints Clark et al., J Cheminform 6:38 2014
- 53. “Beautifully Simple” andequally fast to apply
- 54. Using AZ-ChEMBL datafor CDD Models
- 55. • Human microsomal intrinsicclearance • Rat hepatocyte intrinsic clearance
- 56. Clark et al.,JCIM 55: 1231-1245 (2015) Exporting models from CDD
- 57. Clark et al.,JCIM 55: 1231-1245 (2015)9R44TR000942-02 Open Models in MMDS
- 58. 9R44TR000942-02 Composite Models –Binned Bayesians Clark et al., Submitted 2015
- 59. Summary • Accessible software •Used widely in academia and industry • Leader in collaboration and security • Grown steadily through sales and grants • Dedicated sales in Europe, Asia • Coming soon: ELN • CDD provides integrated software for drug discovery
- 60. Jair Lage deSiqueira-Neto Joel Freundlich Peter Madrid Robert Reynolds Carolyn Talcott Malabika Sarker EU FP7 funding MM4TB NIH NIAID NIH NLM NIH NCATS Bill and Melinda Gates Foundation (Grant#49852) sean.ekins ekinssean@yahoo.com collabchem Acknowledgments and contact info