CDD Extends Gates Grant

The Bill & Melinda Gates Foundation recently extended its previous grant to CDD to create a novel database to spark collaborative efforts to discover more effective drugs against tuberculosis (TB).

Over the past two years, the CDD TB database (CDD TB DB) has integrated the efforts of academic, non-profit, government and corporate laboratories distributed across the globe to accelerate their efforts to discover new therapies against this deadly disease.

As the project now moves into its third year of support, it is a good time to highlight some of the achievements of the first two years. CDD has:

• Secured participation of top scientists and research staff from more than 58 key labs focused on TB drug discovery comprising over 20 distinct scientific collaborative projects. CDD TB DB has become an integral part of the workflow of most of these groups, enabling them to better understand their data and exploit their finite resources more effectively.
• Facilitated collaborations between multiple laboratories requiring the secure exchange of both experimental and computational data pertaining to TB, enabling them to selectively compare their results, avoid duplicate work, and formulate better future research priorities. These collaborations have allowed previously disjointed efforts to begin to coalesce into the foundation of a “virtual pharmaceutical organization.”
• Secured the participation of four of the largest global pharmaceutical companies in support of TB drug discovery and facilitated the exchange of data between them and the academic and other non-profit research groups through both open and secure private channels.
• Digitized more than 300,000 unique molecules and well over 300,000 TB assay datapoints with MIC, IC50, and/or Tox data from published, patented, and community sources; curated them within the CDD TB DB; and published them for anyone to view and mine freely via CDD Public [hyperlink], a free service that CDD hosts for the benefit of the entire research community.
• Enabled the participating research groups to exploit sophisticated data analysis strategies that they did not otherwise have the capability to execute, tailored to the needs of each group. Examples have included identifying potential target mechanisms by computational methods, searching for potential compounds to test with pharmacophore models, focusing priorities on desirable molecular properties including ADME, rendering complex statistical analyses of datasets, and providing advanced visualization capabilities.
• Contributed substantially to the progress of all of the pilot user groups by helping to shape the selection of compounds and direction of research for further investigation.

Some of CDD’s most advanced features have debuted within this TB community. For example, one of the TB labs is using CDD’s software to manage a portfolio of distributed projects, much as a major pharmaceutical company does internally. CDD is now deploying this feature, dubbed “Projects,” to all users.

Participants in the CDD TB DB project have praised its value:

• Tanya Parish, Infectious Disease Research Institute: “CDD is much more than a data archive. It is really our workhorse for all our research, and it will be more important as we ramp up additional collaborations.”
• Joshua Odingo, Infectious Disease Research Institute: “CDD has been a gold mine!”
• Joel Freundlich, Texas A&M: “CDD has quite simply been the best software tool that we have leveraged in our TB drug discovery programs.”
• Todd Gruber, NIH: CDD allows “me to make fast decisions about which compounds to continue to study…especially in the search for novel scaffolds. CDD has been invaluable for handling the massive quantities of data.”

We are looking forward to extending the CDD TB DB project for an additional three years with continuing support from the B&MGF, for which we are grateful.

In the course of this project, CDD has published several papers relevant to TB, neglected disease and related work with pharmaceutical collaborators. All these papers are available from CDD on request.

1. Ekins S, Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, In Press, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/21129975
2. Ekins S and Williams AJ, Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs, MedChemComm, 1:325-330, 2010.
   http://pubs.rsc.org/en/Content/ArticleLanding/2010/MD/C0MD00129E
3. Ekins S, Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol BioSyst, 6: 2316-2324, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/20835433
4. 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.
   http://www.ncbi.nlm.nih.gov/pubmed/20693417
5. Ekins S and Williams EJ, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold?, Drug Disc Today, 15; 812-815, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/20732447
6. Ekins S, Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/20683645
7. Ekins S. and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/20107873
8. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol BioSyst, 6: 840-851, 2010.
   http://www.ncbi.nlm.nih.gov/pubmed/20567770
9. Bingham A and Ekins S Competitive collaboration in the pharmaceutical and biotechnology industry. Drug Discov Today. 14: 1079-1081, 2009.
   http://www.ncbi.nlm.nih.gov/pubmed/19835979
10. 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 Discov Today. 2009 Mar;14(5-6):261-70.
    http://www.ncbi.nlm.nih.gov/pubmed/19231313