The Administrative Core (AdminCore) will be established as an integrated, priority-setting consortium involving multiple participating institutions with collaborative programmatic involvement from NIAID with the PD to advance the science and enhance potential product outcome. The operations and management function is designed to provide administrative support that facilitates scientific collaborations and efficient use of resources. To best serve the interest of the Center, the AdminCore will be centralized at the Public Health Research Institute of New Jersey Medical School-UMDNJ. The AdminCore will be overseen by the Center PD, who will be supported by a program administrator (50% FTE) and a financial director (15% FTE) to coordinate and manage all administrative support functions. To support the Center objectives, the AdminCore will organize regular meetings ofthe investigators, their collaborators (and subcontractors) and Scientific Cores. In addition the AdminCore will arrange for meetings ofthe Executive Committee and the annual meeting of the External Scientific Advisory Committee. The AdminCore will produce and submit the annual progress report to the NIH, and will coordinate research publications, meeting presentations of study results and any logistics related to travel. The CETR Administrative Core will have the following Specific Aims:1. To establish a highly efficient operations and management structur that provides essential oversight, guidance and support services to the CETR Program Leaders and integrates a Scientific Advisory Committee and the NIAID Program representative as strategic advisors. 2. To promote the flow of information, prioritization of compound development, and resource allocations by supporting regular reviews, and to manage regulatory issues and the preparation of interim and year-end reports. 3. To establish a product development strategy that addresses logistics for intellectual property filings and licensing opportunities and negotiations. 4. To coordinate publications and presentations of results arising from these studies.

Public Health Relevance

The Administration Core will provide oversight and guidance for the entire conduct ofthe CETR program by addressing all logistics related to the Program, fostering communication between multiple Program Leaders, Core directors, the Scientific Advisory Committee and the NIAID program officer. It will establish working priorities, objective reviews, and promote business development activities to support the Center goals.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Program--Cooperative Agreements (U19)
Project #
Application #
Study Section
Special Emphasis Panel (ZAI1)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Rutgers University
United States
Zip Code
Stratton, Thomas P; Perryman, Alexander L; Vilch├Ęze, Catherine et al. (2017) Addressing the Metabolic Stability of Antituberculars through Machine Learning. ACS Med Chem Lett 8:1099-1104
Lin, Wei; Mandal, Soma; Degen, David et al. (2017) Structural Basis of Mycobacterium tuberculosis Transcription and Transcription Inhibition. Mol Cell 66:169-179.e8
Lemetre, Christophe; Maniko, Jeffrey; Charlop-Powers, Zachary et al. (2017) Bacterial natural product biosynthetic domain composition in soil correlates with changes in latitude on a continent-wide scale. Proc Natl Acad Sci U S A 114:11615-11620
Awasthi, Divya; Freundlich, Joel S (2017) Antimycobacterial Metabolism: Illuminating Mycobacterium tuberculosis Biology and Drug Discovery. Trends Microbiol 25:756-767
Sukheja, Paridhi; Kumar, Pradeep; Mittal, Nisha et al. (2017) A Novel Small-Molecule Inhibitor of the Mycobacterium tuberculosis Demethylmenaquinone Methyltransferase MenG Is Bactericidal to Both Growing and Nutritionally Deprived Persister Cells. MBio 8:
Chu, John; Vila-Farres, Xavier; Inoyama, Daigo et al. (2016) Discovery of MRSA active antibiotics using primary sequence from the human microbiome. Nat Chem Biol 12:1004-1006
Ekins, Sean; Perryman, Alexander L; Clark, Alex M et al. (2016) Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). J Chem Inf Model 56:1332-43
Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean et al. (2016) Predicting Mouse Liver Microsomal Stability with ""Pruned"" Machine Learning Models and Public Data. Pharm Res 33:433-49
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