A study conducted by researchers at the University of Illinois Urbana-Champaign used deep learning and large-scale computer simulations to identify structural differences in synthetic cannabinoid molecules. The findings published in ELife shed light on how synthetic cannabinoid molecules found in new psychoactive substances (NPS) bind to human brain receptors differently from natural cannabinoids.
NPS simulate the effects of cannabinoids, but they activate distinct signaling pathways. In particular, they trigger the “beta arrestin pathway” instead of the “G protein pathway.” The researchers believe this switch in signaling can produce more severe psychological effects. Study author Dr. Diwakar Shukla explained that “New psychoactive substances bind very strongly to cannabinoid receptors in the brain and are slow to unbind, making them difficult to observe and simulate in standard laboratory or computer experiments.” Therefore, the team used a simulation approach known as the Transition-Based Reweighting Method (TRAM). TRAM calculates the thermodynamics and kinetics of slow molecular processes.
The team used TRAM to observe slow molecular processes involved in the unbinding of NPS from cannabinoid receptors. They also relied on the Folding@Home platform, which allows millions of global volunteers to donate computing power. The team used these tools to run numerous simulations simultaneously, compile the results, and determine which simulations to run next based on algorithms. The research team shed light on the design of safer cannabinoid-based drugs with minimal or no negative side effects.
Revealing the NPS signal via pathways associated with more harmful effects can contribute to the design of new molecules that do not trigger these pathways. For example, researchers can develop cannabinoid formulations with compounds that bind less tightly or unbind more quickly from receptors, and minimize adverse side effects.
Sources: ELife, Eureka News Alert, University of Illinois-Urbana Champaign