The cannabinoid CB1 receptor is a G protein-coupled receptor and plays an important role in many biological processes and physiological functions. A variety of CB1 receptor agonists and antagonists, including endocannabinoids, phytocannabinoids and synthetic cannabinoids have been discovered or developed over the past 20 years. In 2005 it was discovered that the CB1 receptor contains allosteric site(s) which can be recognized by small molecules, or allosteric modulators. A number of CB1 receptor allosteric modulators, both positive and negative, have since been reported and importantly, they display pharmacological characteristics that are distinct from those of orthosteric agonists and antagonists. Given the psychoactive effects commonly associated with CB1 receptor agonists and antagonists/inverse agonists, allosteric modulation may offer an alternate approach to attain potential therapeutic benefits while avoiding inherent side effects of orthosteric ligands. This review details the complex pharmacological profiles of these allosteric modulators, their structure-activity relationships, and efforts in elucidating binding modes and mechanisms of actions of reported CB1 allosteric modulators. The ultimate development of CB1 receptor allosteric ligands could potentially lead to improved therapies for CB1-mediated neurological disorders.

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Allosteric modulators of the cannabinoid CB1 receptor have recently been reported as an alternative approach to modulate the CB1 receptor for therapeutic benefits. In this study, we report the design and synthesis of a series of diarylureas derived from PSNCBAM-1 (2). Similar to 2, these diarylureas dose-dependently inhibited CP55,940-induced intracellular calcium mobilization and [35S]GTP-γ-S binding while enhancing [3H]CP55,940 binding to the CB1 receptor. Structure-activity relationship studies revealed that the pyridinyl ring of 2 could be replaced by other aromatic rings and the pyrrolidinyl ring is not required for CB1 allosteric modulation. 34 (RTICBM-74) had similar potencies as 2 in all in vitro assays but showed significantly improved metabolic stability to rat liver microsomes. More importantly, 34 was more effective than 2 in attenuating the reinstatement of extinguished cocaine-seeking behavior in rats, demonstrating the potential of this diarylurea series as promising candidates for the development of relapse treatment of cocaine addiction.

BackgroundSingle-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree.ResultsThis approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed most existing the-state-of-the-art random forests. The top 25 SNPs in Parkinson data set were identified by the proposed model including four interesting genes associated with neurological disorders.ConclusionThe presented approach has shown to be effective in selecting informative sub-groups of SNPs potentially associated with diseases that traditional statistical approaches might fail. The new RF works well for the data where the number of case-control objects is much smaller than the number of SNPs, which is a typical problem in gene data and GWAS. Experiment results demonstrated the effectiveness of the proposed RF model that outperformed the state-of-the-art RFs, including Breiman's RF, GRRF and wsRF methods.

The cannabinoid receptor 1 (CBR1) is involved in a variety of physiological pathways and has long been considered a golden target for therapeutic manipulation. A large body of evidence in both animal and human studies suggests that CB1R antagonism is highly effective for the treatment of obesity, metabolic disorders and drug addiction. However, the first-in-class CB1R antagonist/inverse agonist, rimonabant, though demonstrating effectiveness for obesity treatment and smoking cessation, displays serious psychiatric side effects, including anxiety, depression and even suicidal ideation, resulting in its eventual withdrawal from the European market. Several strategies are currently being pursued to circumvent the mechanisms leading to these side effects by developing neutral antagonists, peripherally restricted ligands, and allosteric modulators. In this review, we describe the progress in the development of therapeutics targeting the cannabinoid receptor 1 in the last two decades.