Inhibitors of Endocannabinoid-Metabolizing Enzymes Reduce Precipitated Withdrawal Responses in THC-Dependent Mice
Journal Title: The AAPS Journal - Year 2009, Vol 11, Issue 2
Abstract
Abstinence symptoms in cannabis-dependent individuals are believed to contribute to the maintenance of regular marijuana use. However, there are currently no medications approved by the FDA to treat cannabis-related disorders. The only treatment currently shown consistently to alleviate cannabinoid withdrawal in both animals and humans is substitution therapy using the psychoactive constituent of marijuana, Δ9-tetrahydrocannabinol (THC). However, new genetic and pharmacological tools are available to increase endocannabinoid levels by targeting fatty acid amide hydrolase (FAAH) or monoacylglycerol lipase (MAGL), the enzymes responsible for the degradation of the endogenous cannabinoid ligands anandamide and 2-arachidonoylglycerol, respectively. In the present study, we investigated whether increasing endogenous cannabinoids levels, through the use of FAAH (−/−) mice as well as the FAAH inhibitor URB597 or the MAGL inhibitor JZL184, would reduce the intensity of withdrawal signs precipitated by the CB1 receptor antagonist rimonabant in THC-dependent mice. Strikingly, acute administration of either URB597 or JZL184 significantly attenuated rimonabant-precipitated withdrawal signs in THC-dependent mice. In contrast, FAAH (−/−) mice showed identical withdrawal responses as wild-type mice under a variety of conditions, suggesting that the absence of this enzyme across the development of dependence and during rimonabant challenge does not affect withdrawal responses. Of importance, subchronic administration of URB597 did not lead to cannabinoid dependence and neither URB597 nor JZL184 impaired rotarod motor coordination. These results support the concept of targeting endocannabinoid metabolizing enzymes as a promising treatment for cannabis withdrawal.
Authors and Affiliations
Joel E. Schlosburg, Brittany L. A. Carlson, Divya Ramesh, Rehab A. Abdullah, Jonathan Z. Long, Benjamin F. Cravatt, Aron H. Lichtman
Keywords
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