The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R’s three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research.

Huntington’s disease (HD) is a neurodegenerative disorder caused by the expansion of the CAG trinucleotide in the exon 1 of the huntingtin gmodellerene. This abnormal expansion produces a mutant huntingtin (mHTT) protein with extended polyglutamine (polyQ) tracts. Although the molecular mechanisms underlying HD onset and progression remain poorly understood, aberrant folding, aggregation, and membrane interactions of mHTT are considered central to disease pathogenesis. In this study, we used molecular dynamics (MD) simulations to investigate the structural properties, dimerization propensity, and membrane lipid interaction of mHTT carrying 70 polyQ repeats (mHTT-Q70). Our analyses revealed that mHTT-Q70 retains partially structured α-helical conformations with increased flexibility within the polyQ domain, thus being predisposed to misfolding. Coarse-grained MD simulations further revealed a strong tendency of mHTT-Q70 to dimerize, indicating that early oligomerization may represent a critical step in protein aggregation. Interestingly, we show that membrane cholesterol content dose-dependently promotes dimeric mHTT-Q70—but not monomeric mHTT-Q70—association with neuronal membrane models, which was observed for 70% of simulation time at 40% cholesterol content. Such a cholesterol-dependent membrane binding of dimeric mHTT-Q70 suggests that membrane lipid composition may represent a critical checkpoint in the early stages of mHTT-Q70 aggregation, and of cytotoxicity thereof. Moreover, distinct neuronal membrane lipids like phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine differently contributed to mHTT-Q70 binding, highlighting the complexity of such a lipid-dependent modulation. Taken together, these findings underscore the dynamic interplay between polyQ-driven misfolding, dimerization, and membrane lipids in HD pathogenesis, suggesting that modulation of membrane composition, and in particular of cholesterol levels, may be a novel action point to design therapeutic drugs for HD.