Transcript Figure 1. How the Open Drug Discovery Teams App can

Computational Approaches Used With Industry Provided Repurposing Candidates - Uses in Rare and Neglected Diseases
Sean Ekins1,2 , Christopher Southan3, Michael Travers1, Antony J. Williams4, Joel S. Freundlich5, 6, Barry A. Bunin1 and Alex M. Clark7
1 Collaborative
Drug Discovery, 1633 Bayshore Hwy, Suite 342, Burlingame, CA 94010, U.S.A., 2 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, U.S.A., 3 TW2Informatics Limited, Goteborg, 42166, Sweden, 4 Royal Society of
Chemistry, 904 Tamaras Circle, Wake Forest, NC 27587, USA. 5 Department of Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ – New Jersey Medical School, 185 South Orange Avenue Newark, NJ 07103, USA. 6 Department of Microbiology,
Immunology and Pathology, Colorado State University, 200 West Lake Street, CO 80523, USA. 6Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1.
Abstract
Recent repurposing project tendering calls by the National Center for
Advancing Translational Sciences (US) and the Medical Research Council
(UK) have included compound information and pharmacological data.
However none of the internal company development code names were
assigned to chemical structures in the official documentation. We now
describe data gathering and curation to assign structures and in silico
analysis. We also describe how this data has been shared using Mobile
apps and how collaborative software can facilitate target predictions by
integrating to public data sources. These efforts suggest potential new
uses for molecules that can be tested in vitro.
Introduction
We are currently seeing a shift towards drug repositioning or repurposing
[1] as a strategy to find new uses for previously approved drugs and
“parked” or “off the shelf” molecules which have reached the clinic without
any safety signals but did not show efficacy against their intended primary
disease target. Both NCATS and MRC have released sets of compounds
from drug companies without structures representing 56 and 22 small
molecules, respectively [2]. We have attempted to collate these molecular
structures then use them with computational machine learning methods
and similarity based methods to predict potential bioactivity. This has
potential for quick hypothesis testing and triage of ideas, as well as
highlighting potential molecules for rare and neglected diseases, in which
the resources are limited for testing.
Methods and Results
We have previously described in detail the painstaking process to curate
the molecules [2]. 41 NCATS compounds and 12 MRC compounds were
identified with structures (see box). These molecules were then tweeted
from the Mobile Molecular DataSheet and can be readily accessed in
the Open Drug Discovery Teams mobile app ([3] Figure 1) These
molecules were scored with three M. tuberculosis Bayesian Models [4,5]
and a malaria model developed in Discovery Studio (Accelrys, San
Diego CA).
The molecules have also been uploaded in a CDD vault [6] and
used via an API to connect with similar molecules (>0.8 Tanimoto
similarity) in ChEMBL. This has enabled us to predict potential targets
for the compounds (Figure 2) and provide links to these.
Discussion
We have identified molecular structures for the majority of MRC and
NCATS compounds included in the industry provided repurposing
candidate datasets that were not available previously [2]. We have
demonstrated how mobile apps [3] and collaborative software [6]
can be used to share the molecules and data (Box 1) as well as to
suggest new uses for the compounds, currently under evaluation.
All of these technologies and the datasets we have created are
accessible now.
If we are to enable in silico approaches to be used as
described here for repurposing candidates, it is important that such
efforts in future release the structures to prevent replication of
efforts and errors [7]. Our ongoing work will aim to identify whether
selected compounds have activity against neglected and rare
diseases and accelerate this discovery process. Such
computational tools may represent a disruptive strategy involving
active collaboration [8].
References
[1] Ekins S, Williams AJ, Krasowski MD and Freundlich JS, In silico
repositioning of approved drugs for rare and neglected diseases, Drug Disc
Today, 16: 298-310, 2011.
[2] Southan C, Williams AJ and Ekins S, Challenges and Recommendations for
obtaining chemical structures of industry provided repurposing candidates,
Drug Disc Today, 18: 58-70, 2013.
[3] Ekins S, Clark AM and Williams AJ, Open Drug Discovery Teams: A
Chemistry Mobile App for Collaboration, Mol Informatics, 31: 585-597, 2012.
[4] Ekins S, Reynolds RC, Kim H, Koo M-S, Ekonomidis M, Talaue M, Paget
SD, Woolhiser LK Lenaerts AJ, Bunin BA, Connell N and Freundlich JS. Novel
Bayesian leveraging bioactivity and cytotoxicity information for drug discovery,
Chem Biol, In Press 2013.
[5] Ekins S, Reynolds RC,, Franzblau SG, Wan B, Freundlich JS and Bunin BA.
Enhancing hit identification in Mycobacterium tuberculosis drug discovery using
validated dual-event Bayesian models, Submitted 2012
[6] Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S and Bunin B, Novel
web-based tools combining chemistry informatics, biology and social networks
for drug discovery, Drug Disc Today, 14: 261-270, 2009.
[7] Williams AJ, Ekins S and Tkachenko V, Towards a Gold Standard:
Regarding Quality in Public Domain Chemistry Databases and Approaches to
Improving the Situation, Drug Discovery Today,17 (13-14):685-701, 2012.
Figure 2. How CDD can be used to visualize the MRC and NCATS
molecules, and via the API suggest potential targets in ChEMBL
with similarity based on the closest molecules associated with
targets.
[8] Ekins S, Waller CL, Bradley MP, Clark AM and Williams AJ, Four disruptive
strategies for removing drug discovery bottlenecks, Drug Discovery Today, In
Press 2013.
Funding
We have also assessed the commercial availability of the
molecules from vendors and added the information in CDD. To date we
have ordered 3 molecules predicted to have some M. tuberculosis
activity and these include a known kinase inhibitor (Figure 3). These will
be tested in vitro against potential bacterial targets as described
previously [4,5].
The CDD API was supported by Award Number 2R42AI088893-02
“Identification
of
novel
therapeutics
for
tuberculosis
combining
cheminformatics, diverse databases and logic based pathway analysis” from
the National Institutes of Allergy and Infectious Diseases.
Box 1. Data sources
Compounds http://molsync.com/share/?ds=38
ODDT https://itunes.apple.com/us/app/oddt/id517000016?mt=8
CDD http://www.collaborativedrug.com
Figure 1. How the Open Drug Discovery Teams App can be used to
visualize molecules, in this case the MRC and NCATS compounds
tweeted from the Mobile Molecular DataSheet App.
Figure 2. Using CDD to visualize the MRC and NCATS molecules, the predictions from TB Bayesian models and commercial availability.
TW2Informatics Limited