This document describes the development of an app called TB Mobile that provides a mobile interface for accessing data on antituberculosis molecule targets. The app was designed to make scientific data more accessible on mobile devices and features a database connecting molecules, genes, pathways and literature related to tuberculosis. It allows users to browse datasets, search for similar molecules, and filter data. Evaluation showed it could successfully retrieve and rank known tuberculosis drugs and hits from high-throughput screens. The goal is to update the app with more data and improve its predictive capabilities to help accelerate tuberculosis drug discovery.

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. 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. ~ 20 public datasets for TB
Including Novartis data on TB hits
>300,000 cpds
Patents, Papers Annotated by CDD
Open to browse by anyone
http://www.collaborativedrug.
com/register

4. Fitting into the drug discovery
process
Ekins et al,
Trends in
Microbiology
19: 65-74, 2011

5. Predicting the target/s for small molecules
Pathway analysis
Binding site similarity to Mtb proteins
Docking
Bayesian Models - ligand similarity

6. Dataset Curation: TB molecules and target information
database connects molecule, gene, pathway and literature
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
Sarker et al., Pharm Res 2012, 29, 2115-2127.

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

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 laptop
Williams et al DDT 16:928-939, 2011  Bite size chunk of program

9. TB content in Open Drug Discovery Teams (ODDT)
Sharing information and molecules openly – useful experience for
developing TB Mobile
Mol Inform. 2012 Aug;31(8):585-597

10. TB Mobile layout on iPhone and Android
iPhone Android

11. TB Mobile Molecule Detail and Links
iPhone Android

12. TB Mobile Similarity Searching in the app
iPhone Android

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. TB Mobile – Filtering and Sharing Functions
Data can also be filtered by target name, pathway name,
essentiality and human ortholog

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. Results for pyridomycin on iPad

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. May suggest additional potential targets for known drugs
Pyrazinamide - activated to pyrazinoic acid may have
several targets e.g. FAS I and others

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. Molecules active against Mtb evaluated in TB Mobile app

21. Using TB Mobile app with
recent GSK TB hits
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.

22. TB Mobile – poster on
http://goo.gl/UTTH0

23. TB Mobile – Is on iTunes and Google play
and it is FREE
http://goo.gl/vPOKS
http://goo.gl/iDJFR

24. TB mobile – find out more at www.scimobileapps.com
http://goo.gl/Goa4e

25. TB Mobile – Is on the Pistoia Alliance App Catalog
Connectivity with other apps from
Molecular Materials Informatics

26. Paper published
http://goo.gl/7fGFW

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. 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. 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. You can find me @... CDD Booth 205
PAPER ID: 13433
PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and
statistical analyses”
April 8th 8.35am Room 349
PAPER ID: 14750
PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery
Using Bayesian Models”
April 9th 1.30pm Room 353
PAPER ID: 21524
PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources and
tools”
April 9th 3.50pm Room 350
PAPER ID: 13358
PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets”
April 10th 8.30am Room 357
PAPER ID: 13382
PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-provided
repurposing candidates”
April 10th 10.20am Room 350
PAPER ID: 13438
PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery”
April 10th 3.05 pm Room 350