TB Mobile: Appifying Data on Antituberculosis Molecule Targets

Sean Ekins1, 2 , Alex M. Clark3, Malabika Sarker4, Carolyn Talcott4, Barry A. Bunin2

1Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.

2Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.3Molecular Materials Informatics, 1900 St. Jacques #302, Montreal Quebec, Canada H3J 2S1

4SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.

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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

TB facts

Ekins et al,Trends in Microbiology

19: 65-74, 2011

~ 20 public datasets for TBIncluding Novartis data on TB hits >300,000 cpds

Patents, Papers Annotated by CDD

Open to browse by anyone

http://www.collaborativedrug.com/

register

Ekins et al,Trends in Microbiology

19: 65-74, 2011

Fitting into the drug discoveryprocess

Pathway analysisBinding site similarity to Mtb proteinsDockingBayesian Models - ligand similarity

Predicting the target/s for small molecules

Multi-step process

1.Identification of essential in vivo enzymes of Mtb involved intensive literature mining and manual curation, to extract all the genes essential for Mtb growth in vivo 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 involved searching the CDD databases for manually curated data. The structures and data were exported for combination with the other data.

5.All data were combined with URL links to literature and TBDB and deposited in the CDD database.

Over 700 molecules in dataset

Dataset Curation: TB molecules and target information database connects molecule, gene, pathway and literature

Sarker et al., Pharm Res 2012, 29, 2115-2127.

TB molecules and target information database connects molecule, gene, pathway and literature

Williams et al DDT 16:928-939, 2011

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 laptop

Bite size chunk of program

Why not create an App for TB?

TB content in Open Drug Discovery Teams (ODDT)

Mol Inform. 2012 Aug;31(8):585-597

Sharing information and molecules openly – useful experience for developing TB Mobile

iPhone Android

TB Mobile layout on iPhone and Android

TB Mobile Molecule Detail and Links

iPhone 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 Functions

Data can also be filtered by target name, pathway name, essentiality and human ortholog

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

Process used to evaluate TB Mobile

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 Mobile and retrieved

May suggest additional potential targets for known drugs

Pyrazinamide - activated to pyrazinoic acid may have several targets e.g. FAS I and others

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

Molecules active against Mtb evaluated in TB Mobile app

Ballel et al.,Fueling Open-Source drug discovery: 177 small-molecule leads against tuberculosis ChemMedChem 2013.

11 hits from GSK may be targeting a limited array of targets.

TB Mobile biased towards those with larger numbers of molecules.

GSK353069A looks like a dhfr inhibitor.

No experimental verification of these predictions

Compound availability is however unclear.

Using TB Mobile app with recent GSK TB hits

http://goo.gl/UTTH0

TB Mobile – poster on

http://goo.gl/vPOKS

http://goo.gl/iDJFR

TB Mobile – Is on iTunes and Google playand it is FREE

http://goo.gl/Goa4e

TB mobile – find out more at www.scimobileapps.com

TB Mobile – Is on the Pistoia Alliance App Catalog

Connectivity with other apps from Molecular Materials Informatics

Paper published

http://goo.gl/7fGFW

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

What next ?

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

Benefits of creating TB Mobile

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 205

PAPER ID: 13433PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statistical analyses”April 8th 8.35am Room 349

PAPER ID: 14750PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery Using Bayesian Models” April 9th 1.30pm Room 353PAPER ID: 21524

PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources and tools”April 9th 3.50pm Room 350PAPER ID: 13358

PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets”April 10th 8.30am Room 357

PAPER ID: 13382PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates”April 10th 10.20am Room 350

PAPER ID: 13438PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery”April 10th 3.05 pm Room 350