Reverse-Design toward Optimized Labeled Chemical Probes – Examples from the Endocannabinoid System

Labeled chemical probes are of utmost importance to bring drugs from the laboratory through the clinic and ultimately to market. They support and impact all research and discovery phases: target verification and validation; assay development; lead optimization; and biomarker engagement in the context of preclinical studies and human trials. Probes should display high potency and selectivity as well as fulfill specific criteria in connection with absorption, distribution, metabolism, excretion and toxicology (ADMET) profile. Progress in fields such as imaging and proteomics increased the need for specialized probes to support drug discovery. Labeled probes carrying an additional reporter group are valuable tools to meet specific application requirements, but pose significant challenges in design and construction. In the reverse-design approach, small molecules previously optimized in medicinal chemistry programs form the basis for the generation of such high-quality probes. We discuss the reverse design concept for the generation of labeled probes targeting the endocannabinoid system (ECS), a complex lipid signaling network that plays a key role in many human health and disease conditions. The examples highlighted include diverse reporter units for a range of applications. In several cases the reported probes were the product of mutually rewarding and highly cross-fertilizing collaborations among academic and industry research programs, a strategy that can serve as a blueprint for future probe generation efforts

A potent and selective inhibitor for the modulation of MAGL activity in the neurovasculature.

Chronic inflammation and blood-brain barrier dysfunction are key pathological hallmarks of neurological disorders such as multiple sclerosis, Alzheimer's disease and Parkinson's disease. Major drivers of these pathologies include pro-inflammatory stimuli such as prostaglandins, which are produced in the central nervous system by the oxidation of arachidonic acid in a reaction catalyzed by the cyclooxygenases COX1 and COX2. Monoacylglycerol lipase hydrolyzes the endocannabinoid signaling lipid 2-arachidonyl glycerol, enhancing local pools of arachidonic acid in the brain and leading to cyclooxygenase-mediated prostaglandin production and neuroinflammation. Monoacylglycerol lipase inhibitors were recently shown to act as effective anti-inflammatory modulators, increasing 2-arachidonyl glycerol levels while reducing levels of arachidonic acid and prostaglandins, including PGE2 and PGD2. In this study, we characterized a novel, highly selective, potent and reversible monoacylglycerol lipase inhibitor (MAGLi 432) in a mouse model of lipopolysaccharide-induced blood-brain barrier permeability and in both human and mouse cells of the neurovascular unit: brain microvascular endothelial cells, pericytes and astrocytes. We confirmed the expression of monoacylglycerol lipase in specific neurovascular unit cells in vitro, with pericytes showing the highest expression level and activity. However, MAGLi 432 did not ameliorate lipopolysaccharide-induced blood-brain barrier permeability in vivo or reduce the production of pro-inflammatory cytokines in the brain. Our data confirm monoacylglycerol lipase expression in mouse and human cells of the neurovascular unit and provide the basis for further cell-specific analysis of MAGLi 432 in the context of blood-brain barrier dysfunction caused by inflammatory insults

Cannabinoid CB2 Receptors Modulate Microglia Function and Amyloid Dynamics in a Mouse Model of Alzheimer's Disease

The distribution and roles of the cannabinoid CB2 receptor in the CNS are still a matter of debate. Recent data suggest that, in addition to its presence in microglial cells, the CB2 receptor may be also expressed at low levels, yet biologically relevant, in other cell types such as neurons. It is accepted that the expression of CB2 receptors in the CNS is low under physiological conditions and is significantly elevated in chronic neuroinflammatory states associated with neurodegenerative diseases such as Alzheimer's disease. By using a novel mouse model (CB2EGFP/f/f), we studied the distribution of cannabinoid CB2 receptors in the 5xFAD mouse model of Alzheimer's disease (by generating 5xFAD/CB2EGFP/f/f mice) and explored the roles of CB2 receptors in microglial function. We used a novel selective and brain penetrant CB2 receptor agonist (RO6866945) as well as mice lacking the CB2 receptor (5xFAD/CB2-/-) for these studies. We found that CB2 receptors are expressed in dystrophic neurite-associated microglia and that their modulation modifies the number and activity of microglial cells as well as the metabolism of the insoluble form of the amyloid peptide. These results support microglial CB2 receptors as potential targets for the development of amyloid-modulating therapies.Funding The present work has been supported by a grant from Ministerio de Ciencia e Innovacion (ref PID2019-108992RB-I00 and ref PID2019-107548RB-I00) to JR and PG, respectively, by the Basque Government (ref IT1230-19) to PG, and the Research and Education Component of the Advancing a Healthier Wisconsin Endowment at the Medical College of Wisconsin to CJH

Biased probability judgment: Evidence of incidence and relationship to economic outcomes from a representative sample

International audienceMany economic decisions involve a substantial amount of uncertainty, and therefore crucially depend on how individuals process probabilistic information. In this paper, we investigate the capability for probability judgment in a representative sample of the German population. Our results show that almost a third of the respondents exhibits systematically biased perceptions of probability. The findings also indicate that the observed biases are related to individual economic outcomes, which suggests potential policy relevance of our findings

Detection of cannabinoid receptor type 2 in native cells and zebrafish with a highly potent, cell-permeable fluorescent probe.

Despite its essential role in the (patho)physiology of several diseases, CB2R tissue expression profiles and signaling mechanisms are not yet fully understood. We report the development of a highly potent, fluorescent CB2R agonist probe employing structure-based reverse design. It commences with a highly potent, preclinically validated ligand, which is conjugated to a silicon-rhodamine fluorophore, enabling cell permeability. The probe is the first to preserve interspecies affinity and selectivity for both mouse and human CB2R. Extensive cross-validation (FACS, TR-FRET and confocal microscopy) set the stage for CB2R detection in endogenously expressing living cells along with zebrafish larvae. Together, these findings will benefit clinical translatability of CB2R based drugs

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure–activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.ISSN:1064-3745ISSN:1940-602

Mutually Rewarding Academia-Industry Collaborations in the Field of Chemical Biology

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Simple User-Friendly Reaction Format

Leveraging the increasing volume of chemical reaction data can enhance synthesis planning and improve suc- cess rates. However, machine learning applications for retrosynthesis planning and forward reaction prediction tools depend on having readily available, high-quality data in a structured format. While some public and licensed reaction databases are available, they frequently lack essential information about reaction condi- tions. To address this issue and promote the principles of findable, accessible, interoperable, and reusable (FAIR) data reporting and sharing, we introduce the Simple User-Friendly Reaction Format (SURF). SURF standardizes the documentation of reaction data through a structured tabular format, requiring only a basic understanding of spreadsheets. This format enables chemists to record the synthesis of molecules in a format that is both human- and machine-readable, making it easier to share and integrate directly into machine- learning pipelines. SURF files are designed to be interoperable, easily imported into relational databases, and convertible into other formats. This complements existing initiatives like the Open Reaction Database (ORD) and Unified Data Model (UDM). At Roche, SURF plays a crucial role in democratizing FAIR reaction data sharing and expediting the chemical synthesis process