NIH Rare Disease Day Poster
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NIH rare disease day meeting poster presented by Sean Ekins

NIH rare disease day meeting poster presented by Sean Ekins

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NIH Rare Disease Day Poster Document Transcript

  • 1. 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.AbstractRecent repurposing project tendering calls by the National Center for Methods and Results DiscussionAdvancing Translational Sciences (US) and the Medical Research Council We have previously described in detail the painstaking process to curate We have identified molecular structures for the majority of MRC and(UK) have included compound information and pharmacological data. the molecules [2]. 41 NCATS compounds and 12 MRC compounds NCATS compounds included in the industry provided repurposingHowever none of the internal company development code names were were identified with structures (see box). These molecules were then candidate datasets that were not available previously [2]. We haveassigned to chemical structures in the official documentation. We now tweeted from the Mobile Molecular DataSheet and can be readily demonstrated how mobile apps [3] and collaborative software [6]describe data gathering and curation to assign structures and in silico accessed in the Open Drug Discovery Teams mobile app ([3] Figure 1) can be used to share the molecules and data (Box 1) as well as toanalysis. We also describe how this data has been shared using Mobile These molecules were scored with three M. tuberculosis Bayesian suggest new uses for the compounds, currently under evaluation.apps and how collaborative software can facilitate target predictions by Models [4,5] and a malaria model developed in Discovery Studio All of these technologies and the datasets we have created areintegrating to public data sources. These efforts suggest potential new (Accelrys, San Diego CA). accessible now.uses for molecules that can be tested in vitro. The molecules have also been uploaded in a CDD vault [6] and If we are to enable in silico approaches to be used as used via an API to connect with similar molecules (>0.8 Tanimoto described here for repurposing candidates, it is important that suchIntroduction similarity) in ChEMBL. This has enabled us to predict potential targets efforts in future release the structures to prevent replication ofWe are currently seeing a shift towards drug repositioning or repurposing for the compounds (Figure 2) and provide links to these. efforts and errors [7]. Our ongoing work will aim to identify whether[1] as a strategy to find new uses for previously approved drugs and selected compounds have activity against neglected and rare“parked” or “off the shelf” molecules which have reached the clinic without diseases and accelerate this discovery process. Suchany safety signals but did not show efficacy against their intended primary computational tools may represent a disruptive strategy involvingdisease target. Both NCATS and MRC have released sets of compounds active collaboration [8].from drug companies without structures representing 56 and 22 smallmolecules, respectively [2]. We have attempted to collate these molecular References [1] Ekins S, Williams AJ, Krasowski MD and Freundlich JS, In silicostructures then use them with computational machine learning methods repositioning of approved drugs for rare and neglected diseases, Drug Discand similarity based methods to predict potential bioactivity. This has Today, 16: 298-310, 2011.potential for quick hypothesis testing and triage of ideas, as well ashighlighting potential molecules for rare and neglected diseases, in which [2] Southan C, Williams AJ and Ekins S, Challenges and Recommendations forthe resources are limited for testing. 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 [8] Ekins S, Waller CL, Bradley MP, Clark AM and Williams AJ, Four disruptive molecules, and via the API suggest potential targets in ChEMBL strategies for removing drug discovery bottlenecks, Drug Discovery Today, In with similarity based on the closest molecules associated with Press 2013. targets. Funding The CDD API was supported by Award Number 2R42AI088893-02 “Identification of novel therapeutics for tuberculosis combining We have also assessed the commercial availability of the cheminformatics, diverse databases and logic based pathway analysis” from molecules from vendors and added the information in CDD. To date we the National Institutes of Allergy and Infectious Diseases. have ordered 3 molecules predicted to have some M. tuberculosis activity and these include a known kinase inhibitor (Figure 3). These will Box 1. Data sources be tested in vitro against potential bacterial targets as described Compounds http://molsync.com/share/?ds=38 previously [4,5]. 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