Cannabinoid Receptor Type 2 Ligands: An Analysis of Granted Patents Since 2010

Brennecke, Benjamin; Gazzi, Thais; Atz, Kenneth; Fingerle, Jürgen; Kuner, Pascal; Schindler, Thorsten; Weck, G. De; Nazaré, Marc; Grether, Uwe

doi:10.4155/ppa-2021-0002

Cited by 20 publications

(10 citation statements)

References 191 publications

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“…GPCRs are very important as drug targets, and 34% of FDA approved drugs act on GPCRs 43,44 . CB2, in particular, is the target of multiple ongoing drug discovery efforts 45 . Thus, we start by demonstrating high-throughput screening of synthetic compound libraries using inexpensive and accessible equipment.…”

Section: Discussionmentioning

confidence: 99%

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

Miettinen

Leelahakorn

Almeida

et al. 2022

Preprint

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The decriminalization of cannabis and the growing interest in cannabinoids as therapeutics require efficient methods to discover novel compounds and monitor cannabinoid levels in human samples and products. However, current methods are limited by the structural diversity of the active compounds. Here, we construct a G-protein coupled receptor-based yeast whole-cell biosensor, optimize it to achieve high sensitivity and dynamic range, and prove its effectiveness in three real-life applications. First, we screen a library of compounds to discover two novel agonists and four antagonists and demonstrate that our biosensor can democratize GPCR drug discovery by enabling low-cost high-throughput analysis using open-source automation. Subsequently, we bioprospect 54 plants to discover a novel phytocannabinoid, dugesialactone. Finally, we develop a robust portable device, analyze body-fluid samples, and confidently detect illicit synthetic drugs like “Spice”/“K2”. Taking advantage of the extensive sensing repertoire of GPCRs, this technology can be extended to detect numerous other compounds.

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Section: Discussionmentioning

confidence: 99%

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

Miettinen

Leelahakorn

Almeida

et al. 2022

Preprint

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“…Accordingly, the generation of subtype-selective ligands is extremely difficult. To the best of our knowledge, the ∼100 most popular synthetic cannabinoids that act as CB 1 R and CB 2 R agonists fall into different chemical categories: classical, nonclassical, amino-alkylindole, eicosanoids, and others. − The classical family of CB 1 R and CB 2 R agonists is constituted by the dibenzopyran derivatives. Two especially notable examples are (Δ)-9-tetrahydrocannabinol (Δ9-THC), the primary psychoactive component of cannabis, and (Δ)-11-hydroxy-8-THC-dimethylheptyl (HU-210), a synthetic analog of (Δ)-8-THC.…”

Section: Introductionmentioning

confidence: 99%

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

Gambacorta,

Ciriaco,

Amoroso

et al. 2023

J. Chem. Inf. Model.

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The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB 1 R and CB 2 R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB 1 R and CB 2 R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB 1 R and CB 2 R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

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“…GPCRs are very important as drug targets, and 34% of FDA-approved drugs act on GPCRs 56 , 57 . CB 2 , in particular, is the target of multiple ongoing drug discovery efforts 58 . Our CB 2 biosensor displays sensitivity and robustness on par with that of mammalian systems 24 , which are clearly more costly, complicated to use, and require specialized equipment, infrastructure, and trained personnel.…”

Section: Discussionmentioning

confidence: 99%

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

et al. 2022

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Eukaryotic cells use G-protein coupled receptors to sense diverse signals, ranging from chemical compounds to light. Here, we exploit the remarkable sensing capacity of G-protein coupled receptors to construct yeast-based biosensors for real-life applications. To establish proof-of-concept, we focus on cannabinoids because of their neuromodulatory and immunomodulatory activities. We construct a CB2 receptor-based biosensor, optimize it to achieve high sensitivity and dynamic range, and prove its effectiveness in three applications of increasing difficulty. First, we screen a compound library to discover agonists and antagonists. Second, we analyze 54 plants to discover a new phytocannabinoid, dugesialactone. Finally, we develop a robust portable device, analyze body-fluid samples, and confidently detect designer drugs like JWH-018. These examples demonstrate the potential of yeast-based biosensors to enable diverse applications that can be implemented by non-specialists. Taking advantage of the extensive sensing repertoire of G-protein coupled receptors, this technology can be extended to detect numerous compounds.

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Cannabinoid Receptor Type 2 Ligands: An Analysis of Granted Patents Since 2010

Cited by 20 publications

References 191 publications

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

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