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Discovering the pharmacodynamics of conolidine and cannabidiol using a cultured neuronal network based workflow

机译:使用基于神经网络的培养工作流程发现可可啶和大麻二酚的药效学

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摘要

Determining the mechanism of action (MOA) of novel or naturally occurring compounds mostly relies on assays tailored for individual target proteins. Here we explore an alternative approach based on pattern matching response profiles obtained using cultured neuronal networks. Conolidine and cannabidiol are plant-derivatives with known antinociceptive activity but unknown MOA. Application of conolidine/cannabidiol to cultured neuronal networks altered network firing in a highly reproducible manner and created similar impact on network properties suggesting engagement with a common biological target. We used principal component analysis (PCA) and multi-dimensional scaling (MDS) to compare network activity profiles of conolidine/cannabidiol to a series of well-studied compounds with known MOA. Network activity profiles evoked by conolidine and cannabidiol closely matched that of ω-conotoxin CVIE, a potent and selective Cav2.2 calcium channel blocker with proposed antinociceptive action suggesting that they too would block this channel. To verify this, Cav2.2 channels were heterologously expressed, recorded with whole-cell patch clamp and conolidine/cannabidiol was applied. Remarkably, conolidine and cannabidiol both inhibited Cav2.2, providing a glimpse into the MOA that could underlie their antinociceptive action. These data highlight the utility of cultured neuronal network-based workflows to efficiently identify MOA of drugs in a highly scalable assay.
机译:确定新化合物或天然化合物的作用机理(MOA)主要取决于为单个目标蛋白量身定制的分析方法。在这里,我们探索基于使用培养的神经元网络获得的模式匹配响应配置文件的替代方法。可可啶和大麻二酚是具有已知抗伤害感受活性但未知MOA的植物衍生物。可可啶/大麻二酚在培养的神经元网络中的应用以高度可重复的方式改变了网络放电,并对网络特性产生了类似的影响,表明与一个共同的生物靶标有关。我们使用主成分分析(PCA)和多维缩放(MDS)来比较可可力丁/大麻二酚与一系列经过充分研究的已知MOA化合物的网络活性曲线。可可啶和大麻二酚引起的网络活性谱与ω-conotoxinCVIE(一种有效的选择性Cav2.2钙通道阻滞剂,具有拟议的抗伤害作用)紧密匹配,表明它们也将阻断该通道。为了证实这一点,Cav2.2通道被异源表达,用全细胞膜片钳记录并应用了可可啶/大麻二酚。值得注意的是,可可啶和大麻二酚均抑制Cav2.2,使人对MOA有所了解,这可能是其抗伤害感受作用的基础。这些数据突显了基于培养的神经元网络的工作流程在高度可扩展的分析中有效识别药物MOA的实用性。

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