北京若生智能科技有限公司
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nabilone (Canemes)

✓ Approved

AOP Health · CNR1

什么是 nabilone？

nabilone 是一种治疗药物，由AOP Health研发。该药已获批，用于治疗相关适应症，给药途径：Oral (PO)。

药物档案

商品名Canemes
公司AOP Health
分子靶点CNR1
给药途径Oral (PO)
状态Approved
来源： ClinicalTrials.gov, AOP Health

作用机制

分子靶点

nabilone 作用于 1 个分子靶点：

CNR1cannabinoid receptor 1 (CNR, CB1A)
需要更深入的分析？Noah AI 可解释复杂机制并与同类药物比较。

相关研究文献

PubMedFungal biology and biotechnology2026-09-11

Removal of selected pharmaceuticals from wastewater using spawn-based pellets of Pleurotus ostreatus: efficiency and environmental implications.

Hultberg Malin M, Surra Elena E, Golovko Oksana O

Pharmaceutical residues are increasingly detected in aquatic environments, and conventional wastewater treatment plants often provide insufficient removal. In this study, we evaluated the capacity of spawn-based pellets of the white-rot fungus Pleurotus ostreatus to remove 11 pharmaceuticals from synthetic wastewater and the secondary effluent from a municipal wastewater treatment plant and conducted a preliminary life cycle assessment (LCA) of the treatment process. In synthetic wastewater, living fungal pellets reduced the pharmaceutical concentration by > 30% within 10 min compared to the control. Significant removal of nine compounds, particularly macrolide and sulfonamide antibiotics, was observed. Biosorption contributed to removal but accounted for only a minor fraction. In the secondary effluent, total pharmaceutical concentrations decreased by 51-66% after 2-24 h, with time-dependent removal observed for several compounds. LCA results indicated that fungal treatment combined with incineration of spent biomass had lower environmental impacts than landfilling, largely due to energy recovery. The major contributors to environmental burden were energy demands, particularly due to sterilisation during grain spawn preparation and aeration. Use of spawn-based pellets of P. ostreatus is a biologically based treatment technology for pharmaceutical removal. Future work should address transformation products, toxicity, and process optimization to improve sustainability at scale.

PMID 42723082
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PubMedGlobal advances in integrative medicine and health2026-09-11

Reported Distribution Patterns of Five Kampo Formulations in the United States: Observations from a Single Manufacturer and Comparison with Japanese Prescribing Trends.

Williams Nathan L NL, Yoshino Tetsuhiro T, Shumer Gregory G, Watanabe Kenji K et al.

Kampo medicine is a traditional Japanese herbal medical system that is integrated into contemporary healthcare in Japan but remains less widely characterized in the United States. Publicly available information regarding Kampo formulation utilization in the United States is limited. Manufacturer-reported distribution information was obtained through direct correspondence with Honso Pharmaceuticals, a company distributing Kampo formulations in the United States. The company provided a ranked list of its most commonly distributed Kampo formulations. The five highest-ranked formulations were reviewed descriptively and compared with published reports of Kampo prescribing patterns in Japan. The five highest-ranked formulations reported by Honso Pharmaceuticals were Kamishoyosan, Shosaikoto, Hachimijiogan, Keishibukuryogan, and Juzentaihoto. These formulations differ from commonly prescribed Kampo formulations reported in Japanese health insurance and outpatient prescription datasets. Potential explanations may include differences in regulatory status, practitioner education, product availability, and healthcare-system structure between the United States and Japan. Manufacturer-reported distribution patterns suggest that Kampo formulation use in the United States may differ from prescribing trends reported in Japan. Because these observations are derived from a single manufacturer and lack patient-level utilization data, they should be interpreted cautiously. Additional research is needed to better characterize Kampo medicine utilization in the United States.

PMID 42724176
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PubMedExperimental & molecular medicine2026-09-11

Pharmacological modulation of GPR84 revealed by dual states structures and immune functional assays.

Choi Myung Kyung MK, Park Dong Jin DJ, Kim Pankyung P, Choi Hee Seong HS et al.

G-protein-coupled receptor 84 (GPR84) is an orphan class A GPCR selectively activated by medium chain fatty acids and highly expressed in immune cells, where it modulates pro-inflammatory signaling. The structural basis of GPR84 inactivation and antagonism has remained unclear, limiting the rational design of pathway-selective modulators despite its clinical relevance in metabolic inflammation and fibrotic diseases. Here, we report cryo-electron microscopy structures of human GPR84 in inactive and active states. The 3.5 Å inactive structure bound to the antagonist GLPG1205 reveals a lid-like conformation of extracellular loop 2 and an inward reorientation of Arg172, with the antagonist head group blocking the allosteric sodium-binding site. Molecular dynamics simulations further support these findings, identifying an aberrant TM5, TM6 lateral entry gate. By contrast, the 3.17 Å agonist ZQ-16, Gαi complex, shows a rearranged toggle switch and comparative analyses highlight extracellular loop 2 conformational plasticity. Immune functional assays in THP-1 cells demonstrated that ZQ-16 elicited GPR84-dependent activation and cytokine production, which were effectively abrogated by GLPG1205. Mutagenesis combined with functional assays validates key ligand interactions, providing a framework for the rational design of pathway selective GPR84 modulators.

PMID 42722702
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PubMedScientific reports2026-09-11

Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.

Magnusson Rasmus R, Johansson Markus M, Hagvall Sepideh S, Synnergren Jane J

Drug repurposing has emerged as an attractive strategy in contemporary pharmaceutical research, presenting an opportunity to expedite drug discovery, minimize developmental costs, and mitigate risks associated with developing new pharmaceuticals. In this study, we investigated a novel approach based on deep learning of human transcriptomic mechanisms for systematic identification of additional therapeutic potential in preexisting drugs. We trained a composite feedforward neural network model using gene expression data sourced from the ARCHS4 compilation of the GEO, encompassing extensive human datasets. Subsequently, disease-associated gene expression data were generated from our stem cell-derived in vitro model of cardiac hypertrophy induced by Endothelin-1 stimulation. These data were employed to identify latent variables associated with genes showing differential expression due to Endothelin-1 stimulation. By examining the differential expression profiles within the model's latent space, we successfully correlated the disease signal with known drug targets found in pharmaceutical compounds cataloged in DrugBank. The model accurately encoded additional disease-related genes beyond the curated gene set, demonstrating its ability to generalize disease associations. Leveraging the model, we identified potential drug candidates, such as lapatinib and amiodarone showing promise in mitigating proBNP concentration associated with cardiac hypertrophy. This study demonstrates the power of deep learning of human transcriptomic mechanisms in swiftly identifying new therapeutic potentials for existing drugs, highlighting the pivotal role of artificial intelligence technologies in accelerating drug development for other complex medical conditions.

PMID 42722777
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PubMedEuropean journal of pediatrics2026-09-11

Changes in the incidence and profile of pediatric tuberculosis in France from 2014 to 2023: impact of COVID-19 non-pharmaceutical interventions implementation and lifting.

Castello Gautier G, Ouldali Naïm N, Fafi Ines I, Valtuille Zaba Z et al.

While the COVID-19 pandemic led to important variations in numerous community-acquired bacterial infections, very few data concerning pediatric tuberculosis evolution during this period are available. We aimed to assess the impact of COVID-19 related non-pharmaceuticals interventions (NPI) and their lifting on the epidemiology of active tuberculosis in children in France. We conducted a national, population-based surveillance study in France. All patients under 18 years of age hospitalized for active TB between January 2014 and December 2023 were included. The primary outcome was the evolution of the incidence of active TB in France, analyzed using a quasi-Poisson model. Over the study period, 8503 children were hospitalized for active TB in France. The implementation of NPIs in March 2020 was associated with a significant decrease in TB incidence (- 33.7%, 95% confidence interval [CI]: - 42.7 to - 24.7). This decline was followed by a resurgence beginning in November 2022 (+ 22.9%, 95% CI: 4.0 to 41.8). This resurgence was particularly pronounced for respiratory and miliary TB (+ 31.6%, 95% CI: 11.4 to 51.9 and + 64.6%, 95% CI: 11.3 to 117.8 respectively). Our study highlights the impact of NPI implementation and subsequent lifting on the incidence of active TB in children, with changes in the clinical spectrum of the disease. • The impact of COVID-19-related non-pharmaceutical interventions (NPIs) on tuberculosis epidemiology has been documented in adults, but data in children remain scarce. • This nationwide study shows changes in both the incidence and clinical presentation of childhood tuberculosis following the implementation and subsequent lifting of COVID-19-related NPIs.

PMID 42722892
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PubMedToxicological sciences : an official journal of the Society of Toxicology2026-09-10

A high-throughput method to computationally develop candidate adverse outcome pathways in humans: a proof of concept with insecticides and Parkinson Disease.

Rollin Dorian D, Shen Chenyu C, Groh Ksenia K, Kosnik Marissa B MB

Adverse outcome pathways (AOPs) describe stressor non-specific sequences of events between a first molecular trigger (molecular initiating event, MIE), causally linked key events (KEs), and an adverse outcome (AO). AOPs are intended to aid in chemical toxicity testing as a new approach methodology. However, commonly used AOP development methods depend on manual curation, which is labor intensive. As a result, there are still relatively few AOPs and a huge number of toxicity mechanisms and possible adverse outcomes remain undescribed. Therefore, systematic and high-throughput approaches to predict new AOPs are needed. Here, we developed and implemented a data integration-based framework to generate new candidate AOPs using insecticides and Parkinson Disease as a proof of concept. We integrated and statistically linked disconnected databases (e.g., Comparative Toxicogenomics Database, Human Protein Atlas, and Gene Ontology) to form MIE - KE (cell level) - KE (tissue level) - AO candidate AOPs. Through this systematic process, we generated 562,117 candidate AOPs, which we then scored using a weight of evidence (WoE) approach and prioritized 12,756 AOPs with a WoE >0.5. The prioritized AOPs describe varied mechanisms of toxicity related to e.g., MAPK, PTEN, and FGFR signaling pathways, with "increases phosphorylation of MAPK1" as the most frequent MIE. Through analysis of 100 random prioritized AOPs, we found 70% had external literature supporting their biological plausibility, and only 15% represented identifiably implausible associations. Our AOP generating approach yields consistently structured AOPs and can complement existing and emerging development methods to expand AOP coverage across different stressors and outcomes.

PMID 42720601
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