| Summary of small molecule agonists of the kappa opioid receptor - BioAssay Summary Drug addiction is a disease originating in the central nervous system that produces compulsive behaviors despite the negative consequences that may result. Major addictive drugs of abuse include components of tobacco, opiates, marijuana, ethanol, cocaine, and derivatives of amphetamines. While the addictive behaviors produced by these substances may be generally similar, the drugs act at more .. |
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Target Depositor Specified Assays
Description: Data Source: Sanford-Burnham Center for Chemical Genomics (SBCCG) Source Affiliation: Sanford-Burnham Medical Research Institute (SBMRI, San Diego, CA) Network: NIH Molecular Libraries Production Centers Network (MLPCN) Grant Number: 1X01DA026208-01 Assay Provider: Dr. Lawrance Barak , Duke University, Durham NC Drug addiction is a disease originating in the central nervous system that produces compulsive behaviors despite the negative consequences that may result. Major addictive drugs of abuse include components of tobacco, opiates, marijuana, ethanol, cocaine, and derivatives of amphetamines. While the addictive behaviors produced by these substances may be generally similar, the drugs act at different receptor sites in the brain. Recent studies have shown that opioid receptors play a role regulating the addictive behaviors of other receptors that interact with illicit and legal substances of abuse. Opioid receptors are composed of multiple subtypes whose contributions to addictive behaviors are not fully delineated. Moreover, different compounds acting at a single receptor type may produce dissimilar behaviors due to variations in their chemical structures. Thus, both for delineating the biology and signaling ability of each receptor and for developing novel therapies, it would be desirable to identify multiple small molecule ligands that target a given receptor subtype. This entails expanding the chemical space about each of the receptors as variations in receptor conformations due to distinct ligand structures affect receptor signaling behaviors, and consequently physiological responsiveness. Morphine is a prime example of an opioid ligand with distinctive signaling properties. The specific aim of this project is to identify subtype specific small molecule agonists of the human kappa opioid receptor (KOR). These compounds can be optimized to provide the pharmacological means to precisely control the signaling of their specific target receptor subtype. This work thus provides the addiction field both novel research tools and potential therapeutic scaffolds. This AID summarizes SBCCG's probe development efforts to identify agonists of the kappa opioid receptor (KOR). Two potential probes have been identified and probe reports have been submitted describing compound screening. From the primary HTS and subsequent hit confirmation (AID 1777) several hit compounds were identified. Hits were clustered into scaffolds and SAR studies were performed on compounds from several scaffolds reconfirming potency (AID 2284). Agonist activity was further confirmed for these compounds via image-based high content assays (AID 2133, 2359). Compounds were further tested for selectivity against the related delta (AID 2343, 2370) and mu opioid receptors (AID 2344, 2352). Two novel scaffolds were identified as KOR agonists with CID5236771 (ML139) representing the potent and selective probe for chemical series 1 and CID44601470 (ML138) representing the potent and selective probe for chemical series 2. Details of protocols, compound structures, and results from the original assays can be found in PubChem at the respective AIDs listed below. Protocol Please see pertinent AIDs: 1777, 1966, 2133, 2284, 2343, 2344, 2352, 2359, 2370, 2492, 2493, 2497, 2500, 449737, 488822, 488824, 488833 Comment Probe molecules are defined as the positives of this assay and assigned a score of 100. Compounds SID87218782 and SID87334039 were identified as probe molecule. Result Definitions
Additional Information Grant Number: 1X01DA026208-01 Data Table (Concise) Classification
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