Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro
Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro
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DOI：
10.1073/pnas.1632587100
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发表时间：
2003-08-05
影响因子：
11.1
通讯作者：
Heyes, MP
中科院分区：
文献类型：
--
作者：
Gunther, EC;Stone, DJ;Heyes, MP
药物作用的测定通常评价生物化学活性。然而，将治疗效果与生物化学活性准确匹配是一个挑战。高含量的细胞测定试图通过捕获关于药物作用的细胞生理学的广泛信息来弥合这一差距。在这里，我们提出了一种方法，预测的一般治疗类，各种精神活性药物属于，基于高内容的统计分类的基因表达谱诱导这些药物。当我们使用分类树和随机森林监督分类算法来分析微阵列数据时，我们得出了与抗抑郁药、抗精神病药和阿片类药物对体外原代人类神经元作用相关的生物标志物基因表达的一般“功效概况”。这些特征被用作预测模型来对初始体外药物治疗进行分类，准确率为83.3%（随机森林）和88.9%（分类树）。因此，基因组表达数据中包含的详细信息足以将新药在细胞水平上的生理作用与其临床相关性相匹配。这种基于体外基因表达特征鉴定治疗功效的能力在药物发现和药物靶点验证中具有潜在的实用性。
Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general "efficacy profiles" of biomarker gene expression that correlate with antidepressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naive in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.