Mechanism and optimization of CBD-mediated analgesic effects (Diversity Supplement)

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Project Summary Neuropathic pain is caused by damage to the somatosensory nervous system and the resulting pain has serious consequences to productivity, mood, and overall quality of life. Cannabis sativa has been used for thousands of years as a potent analgesic. However, its main cannabinoid component, delta-9-tetrahydrocannabinol (THC), has strong psychotropic side effects which restrict its usage for pain treatment. The second abundant constituent of cannabis, cannabidiol (CBD), can provide pain relief without major psychotropic effects, but its analgesic effects are not as potent as THC or cannabis. Interestingly, both clinical and preclinical studies suggest that administration of THC and CBD in a fixed ratio results in superior analgesic effects and avoids unwanted side effects. However, the mechanisms underlying the synergistic actions of THC and CBD remain unknown. The parent project to this research supplement aims to identify the neural mechanisms and provide optimization of CBD-mediated analgesic effects. The overall objective for this supplement is to investigate the neural mechanisms underlying the synergistic actions of THC and CBD in order to optimize analgesic potency and minimize psychotropic side effects. In Aim 1, we will determine if THC:CBD engages distinct neural circuit activation patterns compared to single drugs in a neuropathic pain mouse model. In Aim 2, we will determine if THC:CBD engages distinct dynamic coupling between calcium activities and endocannabinoid (eCB) signaling compared to single drugs. These proposed studies will contribute to the parent project’s overall premise of identifying the analgesic mechanisms and improving the therapeutical potentials of cannabinoids by adding an innovative and understudied research component. Revealing specific neural activation and eCB signaling changes induced by THC:CBD will advance not only mechanistic understanding of cannabis synergy, but also therapeutic target identification and optimization.