After three decades of a national cocaine epidemic, and many clinical trials, there are no FDA-approved medications for this painful and costly addiction. Relapse rates remain stubbornly high, and can approach 80% at 6 months post- treatment. The poor translation from elegant preclinical (animal) studies to clinical benefit may be due, in part, to limited knowledge of the candidate medications' ability to engage relapse-relevant brain targets in humans -- prior to initiating large-scale clinical trials. The proposed project will address this critical knowledge gap, using NEURO-imaging (fMRI) tools, combined with hypothesis-driven NEURO-behavioral probes, to determine whether BP1.4979, a new candidate medication specifically targeting the dopamine D3 receptor, can impact relapse-relevant endophenotypes (e.g., cue-triggered activation of motivational circuitry; activation of inhibitory circuitry) at a dose under consideration for future clinical efficacy trials. D3 receptos have strong promise as addiction targets, but safe, D3-specific agents are very rare. Seventy-two imaging- eligible cocaine inpatients will be randomized either to the DA D3 partial agonist, BP1.4979 (30 mg), or to placebo. Prior to, and following, induction onto medication or placebo, the participants will be tested with our hypothesis-driven (brain, Specific Aim 1, and behavioral, Specific Aim 2) probes for reward (GO!) and inhibition (STOP). The over-arching hypothesis is that the DA D3-modulating medication will blunt the brain- behavioral response to our reward-related GO! probes, while potentially improving the brain-behavioral response to our STOP probes. Our design also offers the natural opportunity to explore (Exploratory Aim) whether the medication response on the brain-behavioral measures is related to individual genetic variants affecting (directly or indirectly) DA neurotransmission, and to cocaine use during a brief but informative relapse window following the inpatient stay. An experienced team, innovative probes, and a novel (previously unavailable) D3 medication are strengths of the proposal.

Public Health Relevance

Regionally, cocaine remains the top-ranked drug associated with hospital treatment admissions, and the second ranked cause of drug-related deaths. An effective, FDA-approved medication for this 30-year national epidemic will have an immediate, widespread impact on public health, reducing cocaine-related morbidity and mortality. Incorporating relapse-relevant brain-behavioral (NEURO) targets in medication development may accelerate the pace of discovery, improving, and saving, many lives.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA039215-02
Application #
9249538
Study Section
Special Emphasis Panel (ZRG1-RPIA-N (09)F)
Program Officer
Ramey, Tanya S
Project Start
2016-04-01
Project End
2020-01-31
Budget Start
2017-02-01
Budget End
2018-01-31
Support Year
2
Fiscal Year
2017
Total Cost
$567,810
Indirect Cost
$203,201
Name
University of Pennsylvania
Department
Psychiatry
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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Zheng, Qiang; Wu, Yihong; Fan, Yong (2018) Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Front Neuroinform 12:69
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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

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