Identification of ABX-1431, a Selective Inhibitor of Monoacylglycerol Lipase and Clinical Candidate for Treatment of Neurological Disorders.
Justin S CisarOlivia D WeberJason R ClapperJacqueline L BlankmanCassandra L HenryGabriel M SimonJessica P AlexanderTodd K JonesR Alan B EzekowitzGary P O'NeillCheryl A GricePublished in: Journal of medicinal chemistry (2018)
The serine hydrolase monoacylglycerol lipase (MGLL) converts the endogenous cannabinoid receptor agonist 2-arachidonoylglycerol (2-AG) and other monoacylglycerols into fatty acids and glycerol. Genetic or pharmacological inactivation of MGLL leads to elevation in 2-AG in the central nervous system and corresponding reductions in arachidonic acid and eicosanoids, producing antinociceptive, anxiolytic, and antineuroinflammatory effects without inducing the full spectrum of psychoactive effects of direct cannabinoid receptor agonists. Here, we report the optimization of hexafluoroisopropyl carbamate-based irreversible inhibitors of MGLL, culminating in a highly potent, selective, and orally available, CNS-penetrant MGLL inhibitor, 28 (ABX-1431). Activity-based protein profiling experiments verify the exquisite selectivity of 28 for MGLL versus other members of the serine hydrolase class. In vivo, 28 inhibits MGLL activity in rodent brain (ED50 = 0.5-1.4 mg/kg), increases brain 2-AG concentrations, and suppresses pain behavior in the rat formalin pain model. ABX-1431 (28) is currently under evaluation in human clinical trials.
Keyphrases
- chronic pain
- quantum dots
- clinical trial
- pain management
- resting state
- fatty acid
- white matter
- neuropathic pain
- highly efficient
- cerebral ischemia
- endothelial cells
- emergency department
- visible light
- functional connectivity
- protein kinase
- anti inflammatory
- oxidative stress
- genome wide
- blood brain barrier
- multiple sclerosis
- cerebrospinal fluid
- spinal cord
- copy number
- binding protein
- subarachnoid hemorrhage
- combination therapy
- open label
- brain injury
- replacement therapy
- bioinformatics analysis
- double blind