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.
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.
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|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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