The CB2 Receptor as a Novel Therapeutic Target for Epilepsy Treatment

Epilepsy is characterized by repeated spontaneous bursts of neuronal hyperactivity and high synchronization in the central nervous system. It seriously affects the quality of life of epileptic patients, and nearly 30% of individuals are refractory to treatment of antiseizure drugs. Therefore, there is an urgent need to develop new drugs to manage and control refractory epilepsy. Cannabinoid ligands, including selective cannabinoid receptor subtype (CB1 or CB2 receptor) ligands and non-selective cannabinoid (synthetic and endogenous) ligands, may serve as novel candidates for this need. Cannabinoid appears to regulate seizure activity in the brain through the activation of CB1 and CB2 cannabinoid receptors (CB1R and CB2R). An abundant series of cannabinoid analogues have been tested in various animal models, including the rat pilocarpine model of acquired epilepsy, a pentylenetetrazol model of myoclonic seizures in mice, and a penicillin-induced model of epileptiform activity in the rats. The accumulating lines of evidence show that cannabinoid ligands exhibit significant benefits to control seizure activity in different epileptic models. In this review, we summarize the relationship between brain CB2 receptors and seizures and emphasize the potential mechanisms of their therapeutic effects involving the influences of neurons, astrocytes, and microglia cells. The unique features of CB(2)Rs, such as lower expression levels under physiological conditions and high inducibility under epileptic conditions, make it an important target for future research on drug-resistant epilepsy.

Automated summary

Epilepsy is characterized by repeated bursts of neuronal hyperactivity and synchronization in the central nervous system. Developing new drugs to manage and control refractory epilepsy, including cannabinoid ligands as novel candidates, is urgently needed. Cannabinoid ligands have shown significant benefits in controlling seizure activity in various animal models.