Mining mouse behavior for patterns predicting psychiatric drug classification.
Mining mouse behavior for patterns predicting psychiatric drug classification.
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DOI：
10.1007/s00213-013-3230-6
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发表时间：
2014-01
影响因子：
3.4
通讯作者：
Elmer GI
中科院分区：
文献类型：
--
作者：
Kafkafi N;Mayo CL;Elmer GI
在精神药物发现中，关键的一步是在开发过程的早期预测新（或重新利用）化合物的精神药理学作用和治疗潜力。这一过程受到需要利用多种疾病特异性和劳动密集型行为测定的阻碍。本研究的目的是探讨一个单一的高通量行为分析的可行性，精神药物分为多个精神药理学类。使用模式阵列，一种用于小鼠探索行为的数据挖掘程序，我们挖掘了约100，000种复杂的运动模式，以最好地预测精神药理学类别和剂量。最佳模式被整合到一个分类模型中，该模型将精神药理学化合物分配到六个临床相关类别之一-抗精神病药，抗抑郁药，阿片类药物，拟精神病药，精神兴奋剂和α-肾上腺素能。令人惊讶的是，成功的类预测只需要少量精心选择的行为。其中之一，被称为“通用药物检测器”的行为是剂量依赖性地减少从所有类别的药物，从而提供了一个敏感的精神药理学活动的指标。在以盲法进行的独立验证中，模拟体内临床前药物筛选过程，分类模型正确分类了11种"未知"化合物中的9种。有趣的是，即使是“错误分类”匹配已知的替代治疗适应症，说明药物“再利用”的潜力。与标准动物模型不同，发现的分类模型可以系统地更新以提高其预测能力，并随着数据库的每次额外多样化而添加治疗类别和子类。我们的研究证明了数据挖掘方法在行为分析中的作用，同时使用多种方法进行药物筛选和行为表型分析。
In psychiatric drug discovery, a critical step is predicting the psychopharmacological effect and therapeutic potential of novel (or repurposed) compounds early in the development process. This process is hampered by the need to utilize multiple, disorder-specific and labor intensive behavioral assays. This study aims to investigate the feasibility of a single high-throughput behavioral assay to classify psychiatric drugs into multiple psychopharmacological classes. Using Pattern Array, a procedure for data-mining exploratory behavior in mice, we mined ~100,000 complex movement patterns for those that best predict psychopharmacological class and dose. The best patterns were integrated into a classification model that assigns psychopharmacological compounds to one of six clinically-relevant classes – antipsychotic, antidepressant, opioids, psychotomimetic, psychomotor stimulant and α-adrenergic. Surprisingly, only a small number of well-chosen behaviors were required for successful class prediction. One of them, a behavior termed “universal drug detector” was dose-dependently decreased by drugs from all classes, thus providing a sensitive index of psychopharmacological activity. In independent validation in a blind fashion, simulating the process of in vivo pre-clinical drug screening, the classification model correctly classified 9 out of 11 “unknown” compounds. Interestingly, even “misclassifications” match known alternate therapeutic indications, illustrating drug “repurposing” potential. Unlike standard animal models, the discovered classification model can be systematically updated to improve its predictive power, and add therapeutic classes and subclasses with each additional diversification of the database. Our study demonstrates the power of data-mining approaches for behavior analysis, using multiple measures in parallel for drug screening and behavioral phenotyping.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
3.6
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
2.5
作者：
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通讯作者：
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影响因子：
3.6
作者：
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通讯作者：
Elmer, GI