TB Mobile: Appifying Data on Antituberculosis Molecule                       Targets Sean Ekins1, 2 , Alex M. Clark3, Mala...
TB facts    Tuberculosis Kills 1.6-1.7m/yr (~1 every 8 seconds)    1/3rd of worlds population infected!!!!    Multi dru...
~ 20 public datasets for TBIncluding Novartis data on TB hits>300,000 cpdsPatents, Papers Annotated by CDDOpen to browse b...
Fitting into the drug discovery                  processEkins et al,Trends inMicrobiology19: 65-74, 2011
Predicting the target/s for small molecules                  Pathway analysis                  Binding site similarity to ...
Dataset Curation: TB molecules and target information  database connects molecule, gene, pathway and literatureMulti-step ...
TB molecules and target information database connects       molecule, gene, pathway and literature
Why not create an App for TB?                                         Exposure to huge audience                          ...
TB content in Open Drug Discovery Teams (ODDT)Sharing information and molecules openly – useful experience fordeveloping T...
TB Mobile layout on iPhone and Android    iPhone                  Android
TB Mobile Molecule Detail and LinksiPhone                 Android
TB Mobile Similarity Searching in the app  iPhone                   Android
TB Mobile – Filtering and Sharing Functions Each molecule can be copied to the clipboard then opened with other apps (e.g....
TB Mobile – Filtering and Sharing FunctionsData can also be filtered by target name, pathway name,essentiality and human o...
Process used to evaluate TB Mobile   Draw structures either in app or paste from    other apps e.g. MMDS   TB Mobile ran...
Results for pyridomycin on iPad
14 First line drugs active against Mtb evaluated in TB Mobile app and the top 3 molecules shown     Confirms all in TB Mob...
May suggest additional potential targets for known drugsPyrazinamide - activated to pyrazinoic acid may haveseveral target...
Molecules active against Mtb evaluated in TB Mobile app to illustrate a workflow we have curated an additional set of 20 m...
Molecules active against Mtb evaluated in TB Mobile app
Using TB Mobile app with     recent GSK TB hitsBallel et al.,Fueling Open-Source drug discovery: 177 small-molecule leads ...
TB Mobile – poster on                        http://goo.gl/UTTH0
TB Mobile – Is on iTunes and Google play                    and it is FREEhttp://goo.gl/vPOKS                             ...
TB mobile – find out more at www.scimobileapps.com                 http://goo.gl/Goa4e
TB Mobile – Is on the Pistoia Alliance App Catalog                     Connectivity with other apps from                  ...
Paper publishedhttp://goo.gl/7fGFW
What next ?   Update with more data   Add a weighting or scoring function to account    for heavily populated targets  ...
Benefits of creating TB Mobile   Exposure of CDD content from collaboration with    SRI   More visibility for brand in n...
Acknowledgments   2R42AI088893-02 “Identification of novel therapeutics for tuberculosis    combining cheminformatics, di...
You can find me @...                                               CDD Booth 205PAPER ID: 13433PAPER TITLE: “Dispensing pr...
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TB Mobile: Appifying data on antituberculosis molecule targets

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TB Mobile: Appifying data on antituberculosis molecule targets

  1. 1. TB Mobile: Appifying Data on Antituberculosis Molecule Targets Sean Ekins1, 2 , Alex M. Clark3, Malabika Sarker4, Carolyn Talcott4, Barry A. Bunin2 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. 1 2 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA. 3 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal Quebec, Canada H3J 2S1 4 SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA. .
  2. 2. TB facts Tuberculosis Kills 1.6-1.7m/yr (~1 every 8 seconds) 1/3rd of worlds population infected!!!! Multi drug resistance in 4.3% of cases Extensively drug resistant increasing incidence No new drugs in over 40 yrs until Bedaquiline Drug-drug interactions and Co-morbidity with HIV Increase in HTS phenotypic screening 1000’s of hits no idea of target Use of computational methods with TB is rare Ekins et al, Trends in Microbiology 19: 65-74, 2011
  3. 3. ~ 20 public datasets for TBIncluding Novartis data on TB hits>300,000 cpdsPatents, Papers Annotated by CDDOpen to browse by anyone http://www.collaborativedrug. com/register
  4. 4. Fitting into the drug discovery processEkins et al,Trends inMicrobiology19: 65-74, 2011
  5. 5. Predicting the target/s for small molecules Pathway analysis Binding site similarity to Mtb proteins Docking Bayesian Models - ligand similarity
  6. 6. Dataset Curation: TB molecules and target information database connects molecule, gene, pathway and literatureMulti-step process1.Identification of essential in vivo enzymes of Mtb involved intensive literaturemining and manual curation, to extract all the genes essential for Mtb growth invivo across species.2.Homolog information was collated from other studies.3.Collection of metabolic pathway information involved using TBDB.4.Identifying molecules and drugs with known or predicted targets involvedsearching the CDD databases for manually curated data. The structures anddata were exported for combination with the other data.5.All data were combined with URL links to literature and TBDB and deposited inthe CDD database.Over 700 molecules in dataset Sarker et al., Pharm Res 2012, 29, 2115-2127.
  7. 7. TB molecules and target information database connects molecule, gene, pathway and literature
  8. 8. Why not create an App for TB?  Exposure to huge audience with “smart phones”  Make science more accessible = >communication  Hardware is powerful  Mobile – take a phone into field and do science more readily than a laptopWilliams et al DDT 16:928-939, 2011  Bite size chunk of program
  9. 9. TB content in Open Drug Discovery Teams (ODDT)Sharing information and molecules openly – useful experience fordeveloping TB Mobile Mol Inform. 2012 Aug;31(8):585-597
  10. 10. TB Mobile layout on iPhone and Android iPhone Android
  11. 11. TB Mobile Molecule Detail and LinksiPhone Android
  12. 12. TB Mobile Similarity Searching in the app iPhone Android
  13. 13. TB Mobile – Filtering and Sharing Functions Each molecule can be copied to the clipboard then opened with other apps (e.g. MMDS, MolPrime, MolSync, ChemSpider, and from these exported via Twitter or email) or shared via Dropbox.
  14. 14. TB Mobile – Filtering and Sharing FunctionsData can also be filtered by target name, pathway name,essentiality and human ortholog
  15. 15. Process used to evaluate TB Mobile Draw structures either in app or paste from other apps e.g. MMDS TB Mobile ranks content Take a screenshot of results Compare to published data Annotate results, tabulate
  16. 16. Results for pyridomycin on iPad
  17. 17. 14 First line drugs active against Mtb evaluated in TB Mobile app and the top 3 molecules shown Confirms all in TB Mobile and retrieved
  18. 18. May suggest additional potential targets for known drugsPyrazinamide - activated to pyrazinoic acid may haveseveral targets e.g. FAS I and others
  19. 19. Molecules active against Mtb evaluated in TB Mobile app to illustrate a workflow we have curated an additional set of 20 molecules published since 2009 that have activity against Mtb and were identified by HTS or other methods
  20. 20. Molecules active against Mtb evaluated in TB Mobile app
  21. 21. Using TB Mobile app with recent GSK TB hitsBallel et al.,Fueling Open-Source drug discovery: 177 small-molecule leads against tuberculosisChemMedChem 2013.11 hits from GSK may be targeting a limitedarray of targets.TB Mobile biased towards those with largernumbers of molecules.GSK353069A looks like a dhfr inhibitor.No experimental verification of these predictionsCompound availability is however unclear.
  22. 22. TB Mobile – poster on http://goo.gl/UTTH0
  23. 23. TB Mobile – Is on iTunes and Google play and it is FREEhttp://goo.gl/vPOKS http://goo.gl/iDJFR
  24. 24. TB mobile – find out more at www.scimobileapps.com http://goo.gl/Goa4e
  25. 25. TB Mobile – Is on the Pistoia Alliance App Catalog Connectivity with other apps from Molecular Materials Informatics
  26. 26. Paper publishedhttp://goo.gl/7fGFW
  27. 27. What next ? Update with more data Add a weighting or scoring function to account for heavily populated targets Expand beyond the similarity measure Add algorithms to predict activity Could we appify data for other diseases/ targets
  28. 28. Benefits of creating TB Mobile Exposure of CDD content from collaboration with SRI More visibility for brand in new places Experiment in small database with focus on content delivery A functional app to reach scientists that may not have cheminformatics or bioinformatics training
  29. 29. Acknowledgments 2R42AI088893-02 “Identification of novel therapeutics for tuberculosis combining cheminformatics, diverse databases and logic based pathway analysis” from the National Institute of Allergy And Infectious Diseases. (PI: S. Ekins) The CDD TB has been developed thanks to funding from the Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”).
  30. 30. You can find me @... CDD Booth 205PAPER ID: 13433PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational andstatistical analyses”April 8th 8.35am Room 349PAPER ID: 14750PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug DiscoveryUsing Bayesian Models”April 9th 1.30pm Room 353PAPER ID: 21524PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources andtools”April 9th 3.50pm Room 350PAPER ID: 13358PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets”April 10th 8.30am Room 357PAPER ID: 13382PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-providedrepurposing candidates”April 10th 10.20am Room 350PAPER ID: 13438PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery”April 10th 3.05 pm Room 350

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