This application is to establish a U54 Medication Development Center of Excellence (MDCE). The proposed MDCE will be integrated within the umbrella of the Penn/VA Center for Studies of Addiction (CSA) and benefits greatly from that integration, permitting access to infrastructure and resources not generally available outside of a large research center. The MDCE has priority access to important resources: 1) the Berrettini genetics lab 2) the Center biostatistician; 3) a web-based Data Management Unit; and, 4) state-of the-art Brain Imaging. Our theme is the identification and comprehensive screening of innovative medications for the treatment of Cocaine Use Disorder (CUD). Our MDCE proposes to emphasize novel medications such as BP1.4979, a dopamine D3 partial agonist, the GABA B agonist, long acting baclofen and clavulanic acid (CLAV). In addition, we will improve our ability to identify efficacious new medications by identifying characteristics of CUD patients that may increase their likelihood of responding positively to a particular medication and by streamlining the identification of medication responders in clinical trials. The Administrative Core will coordinate and integrate a Human Laboratory and Genetics Pilot Program, an Education Core, a Biostatistics and Data Management Core and three research projects. The Human Laboratory and Genetics Pilot Program will employ neurobehavioral tasks that may aid in the detection of medication responders early in treatment and will explore pharmacogenetic interactions that could explain and potentially capitalize on heterogeneity in medication response. The research projects are arranged to allow for novel medications to be studied from safety through preliminary efficacy. In Project 1, we will administer cocaine to volunteers while receiving either BPI .4979 or CLAV to test for potential toxic interactions with cocaine, providing safety and preliminary efficacy data for these compounds. Project 2 will provide neurobehavioral and neuroimaging data to determine whether medications selected for study effectively engage the appropriate brain targets at the dosages proposed for study. Project 3 proposes to evaluate promising novel compounds in 12-week, placebo-controlled trials. By providing comprehensive screening of candidate medications and sequentially testing them, as described, we expect to rapidly identify effective medications that justify investment in the next level of development, multi-site efficacy trials.

Public Health Relevance

Cocaine Use Disorder (CUD) is a significant public health problem responsible for substantial medical, psychiatric, and economic costs. There are currently no medications approved for the treatment of CUD. The development of an effective pharmacotherapy for CUD has the potential to significantly impact the public health by addressing the needs of a sizable treatment population.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54DA039002-02
Application #
8925041
Study Section
Special Emphasis Panel (ZDA1)
Program Officer
Walton, Kevin
Project Start
2014-09-15
Project End
2019-07-31
Budget Start
2015-08-01
Budget End
2016-07-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Psychiatry
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145
Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226
Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Zheng, Qiang; Fan, Yong (2018) INTEGRATING SEMI-SUPERVISED LABEL PROPAGATION AND RANDOM FORESTS FOR MULTI-ATLAS BASED HIPPOCAMPUS SEGMENTATION. Proc IEEE Int Symp Biomed Imaging 2018:154-157
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Sun, Yan; Zhang, Yang; Zhang, Dai et al. (2018) GABRA2 rs279858-linked variants are associated with disrupted structural connectome of reward circuits in heroin abusers. Transl Psychiatry 8:138

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