The Medicinal Chemistry Core (MCC;Core D) will provide chemistry resources complementary to those existing within the respective project teams. The MCC resource will distinguish itself by relying on extensive pharmaceutical medicinal chemistry experience melded with an academic foundation in antibacterials. Critically, the MCC staff have demonstrated in the pharmaceutical industry setting their ability to discover drug candidates by evolving small molecules to address a wide range of problems encountered during the process. This experience will be essential to: Project 1) the optimization of small molecules ofthe arylpropionyl trialkylphloroglucinol family as antibacterials. Project 2) the evolution of small molecule inhibitors of essential metabolic enzymes, such Pks13, in M. tuberculosis. Project 4) the solid-phase synthesis of non-ribosomally encoded peptides with significant potential as antibacterials, and Project 5) the discovery and optimization of small molecule antibacterials leveraging Bayesian computational models. These projects necessitate experience with a diverse range of chemistries as found in the description of each MCC member's background. Overall, the spectrum and depth ofthe MCC staffs chemistry background will prove critical to their ability to translate basic scientific discoveries within the individual projects to small molecules with significant potential for positively impacting the antibacterial clinical landscape to address the global health crisis.

Public Health Relevance

The need for new drugs to treat bacterial infections is beyond debate. The chemical, biological, and computational methodologies proposed by the projects to address this global health crisis are seamlessly interwoven with a chemistry core featuring extensive pharmaceutical medicinal chemistry experience to help translate basic discoveries toward small molecules with significant promise as novel antibacterial drugs

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
1U19AI109713-01
Application #
8655936
Study Section
Special Emphasis Panel (ZAI1)
Project Start
Project End
Budget Start
2014-04-25
Budget End
2015-03-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Rutgers University
Department
Type
DUNS #
City
Newark
State
NJ
Country
United States
Zip Code
07103
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Ekins, Sean; Madrid, Peter B; Sarker, Malabika et al. (2015) Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery. PLoS One 10:e0141076