MDA7: A novel selective agonist for CB 2 receptors that prevents allodynia in rat neuropathic pain models

M. Naguib, P. Diaz, J. J. Xu, F. Astruc-Diaz, S. Craig, P. Vivas-Mejia, D. L. Brown

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


Background and purpose: There is growing interest in using cannabinoid type 2 (CB 2) receptor agonists for the treatment of neuropathic pain. In this report, we describe the pharmacological characteristics of MDA7 (1-[(3-benzyl-3-methyl-2,3-dihydro-1-benzofuran-6-yl)carbonyl]piperidine), a novel CB 2 receptor agonist. Experimental approach: We characterized the pharmacological profile of MDA7 by using radioligand-binding assays and in vitro functional assays at human cannabinoid type 1 (CB 1) and CB 2 receptors. In vitro functional assays were performed at rat CB 1 and CB 2 receptors. The effects of MDA7 in reversing neuropathic pain were assessed in spinal nerve ligation and paclitaxel-induced neuropathy models in rats. Key results: MDA7 exhibited selectivity and agonist affinity at human and rat CB 2 receptors. MDA7 treatment attenuated tactile allodynia produced by spinal nerve ligation or by paclitaxel in a dose-related manner. These effects were selectively antagonized by a CB 2 receptor antagonist but not by CB 1 or opioid receptor antagonists. MDA7 did not affect rat locomotor activity. Conclusion and implications: MDA7, a novel selective CB 2 agonist, was effective in suppressing neuropathic nociception in two rat models without affecting locomotor behaviour. These results confirm the potential for CB 2 agonists in the treatment of neuropathic pain.

Original languageEnglish
Pages (from-to)1104-1116
Number of pages13
JournalBritish Journal of Pharmacology
Issue number7
StatePublished - Dec 2008


  • Allodynia
  • CB
  • Cancer
  • Cannabinoid
  • Chemotherapy
  • Hyperalgesia
  • MDA7
  • Neuropathic pain


Dive into the research topics of 'MDA7: A novel selective agonist for CB 2 receptors that prevents allodynia in rat neuropathic pain models'. Together they form a unique fingerprint.

Cite this