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首页> 外文期刊>Applied in vitro toxicology. >Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient Receptor Potential Vanilloid Subfamily Type 1
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Mechanistic Profiling of Inhalation Sensory Irritants with a Computational Model for Transient Receptor Potential Vanilloid Subfamily Type 1

机译:机械分析吸入的感觉刺激物的计算模型瞬时受体电位草酸亚科1型

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Introduction: We are using big data mining to develop computational models that predict potential interaction with important biological pathways. Transient receptor potential vanilloid subfamily type 1 (TRPV1) is one of several nociceptors that contribute to sensory irritation. Because sensory irritation is frequently used as a critical effect in setting occupational exposure limits (OELs), we developed a model that predicts interaction with TRPV1 and used it to mechanistically profile two inhalation databases (DBs).Methods: We built a random forest machine learning model to predict whether a novel compound will or will not interact with TRPV1 by fingerprinting a large DB curated primarily from public in vitro data. Our model has high sensitivity (90.2%), specificity (99.2%), and balanced accuracy (94.8%). We mechanistically profiled (1) a rodent RD50 DB (concentrations causing a 50% decrease in respiratory rate; N = 190) and (2) a subset of the American Conference of Governmental Industrial Hygienists DB with OELs that were primarily based on sensory irritation (N = 109).Results: For both DBs, a high percentage of compounds were identified for potential interaction with TRPV1. Further screening of DBs with a profiler for facile chemical reactivity gave similar results, with many compounds flagged for both mechanisms. The more potent compounds in either DB were often chemically reactive, suggesting potential involvement of a related nociceptor known to serve as a sentinel for electrophiles—transient receptor potential ankyrin subfamily type 1.Conclusion: Our findings emphasize the need for an integrated testing approach using tiered in silico and in vitro screening for these nociceptors to derive OELs without using animals.
机译:简介:我们使用大数据挖掘开发预测的计算模型潜在的相互作用与重要的生物通路。亚类型1 (TRPV1)是其中之一痛觉受器的感觉过敏。常用的设置至关重要的影响职业暴露极限(伍),我们开发了一个模型,预测与TRPV1和交互从力学上看用它来配置两个吸入数据库(DBs)。机器学习模型来预测是否一本小说复合会或不会与TRPV1的交互指纹识别主要从大型数据库策划公共体外数据。敏感性(90.2%)、特异性(99.2%)平衡精度(94.8%)。异形(1)一个啮齿动物RD50 DB(浓度导致呼吸速率下降50%;190)和美国会议(2)的一个子集政府工业卫生DB伍,主要是基于感官刺激(N = 109)。很高比例的化合物被确定与TRPV1潜在交互。肤浅的筛检DBs的分析器化学反应了类似的结果,许多化合物标记两种机制。化合物在DB往往更有效化学反应,表明潜力参与的一个相关的伤害感受器作为一个为electrophiles-transient前哨受体潜在锚蛋白亚类型1.一个集成测试方法使用分层硅和体外筛选痛觉受器获得伍不使用动物。

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