Collaborative Drug Discovery Wins Phase II SBIR Grant to Develop Tools for Biocomputation across Distributed Private Data-sets to Enhance Drug Discovery
November 19, 2013
Burlingame, Calif.—Nov 19, 2013—Collaborative Drug Discovery (CDD), a provider of a web-based drug discovery informatics platform, announced they have been awarded a grant for a software development project focused on computational model and data sharing.
The Phase II Small Business Innovation Research (SBIR) grant from the National Center for Advancing Translational Sciences is part of a program to enable sharing of biological data.
“In phase I, we demonstrated that computational machine learning models for tuberculosis could be shared between laboratories and used to make predictions and select compounds for testing,” said Sean Ekins, Ph.D., D.Sc., Chief Scientific Officer at CDD. In collaboration with Joel Freundlich (Rutgers –New Jersey Medical School), Robert Reynolds (Southern Research Institute) and Allan Casey (Infectious Disease Research Institute) new active compounds were identified against whole cell Mycobacterium tuberculosis in vitro (1-5). Ekins added “This work followed our valuable collaboration with Pfizer in 2010, which also resulted in a paper describing the use of open source tools for computational ADME models (6). We realized then that there was an important opportunity to use CDD to host and selectively share computational models.”
The grant will build on CDD’s pioneering work of secure selective sharing of hosted data, and will be used to develop a new product for CDD.
The project described was supported by Award Number 9R44TR000942-02 from National Center for Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Advancing Translational Sciences or the National Institutes of Health.
About Collaborative Drug Discovery, Inc.
CDD (www.collaborativedrug.com) provides the most widely used web-based drug discovery informatics platform on the market. CDD Vault® is the secure, private industrial-strength database combining traditional drug discovery informatics (registration and SAR) with social networking capabilities. A freely available version of CDD Vault enables researchers to mine a unique aggregation of public information from a variety of scientific data providers.
Media Contacts: Barry Bunin, PhD, Collaborative Drug Discovery, (650) 204-3084, [email protected]llaborativedrug.com
1. Ekins S, Casey A.C, Roberts D, Parish T. and Bunin BA, Bayesian Models for Screening and TB Mobile for Target Inference with Mycobacterium tuberculosis, Submitted 2013.
2. Ekins S, Freundlich JS and Reynolds RC, Fusing dual-event datasets for Mycobacterium Tuberculosis machine learning models and their evaluation, J Chem Inf Model, In Press 2013.
3. Ekins S, Freundlich JS, Hobrath JV, White EL, Reynolds RC, Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery, Pharm Res, In Press 2013.
4. 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, PLOS ONE, 8(5): e63240, 2013.
5. 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. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery, Chem Biol, 20: 370-378, 2013.
6. Rishi R. Gupta, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S, Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010.