TB Mobile: Appifying data on antituberculosis molecule targets
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. .
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
~ 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
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 Mtb proteins Docking Bayesian Models - ligand similarity
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.
TB molecules and target information database connects molecule, gene, pathway and literature
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
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
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. MMDS, MolPrime, MolSync, ChemSpider, and from these exported via Twitter or email) or shared via Dropbox.
TB Mobile – Filtering and Sharing FunctionsData can also be filtered by target name, pathway name,essentiality and human ortholog
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
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
May suggest additional potential targets for known drugsPyrazinamide - activated to pyrazinoic acid may haveseveral targets e.g. FAS I and others
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
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 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.
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
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
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”).
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