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首页> 外文期刊>Journal of Biotechnology >Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action
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Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action

机译:开发大型化学基因组数据库,以改善候选药物的选择并了解化学毒性和作用机理

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摘要

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.
机译:成功的药物发现需要准确的决策,以便使最佳候选人从最初的潜在顾客识别到最终批准。化学基因组学是在药理学和毒理学中使用基因组学工具,对靶标识别/验证,潜在顾客识别,功效评估和毒性评估的传统方法提供了有希望的增强。为了实现化学基因组学信息的价值,需要一个上下文数据库来将多种化合物诱导的生理结果与同一动物中测得的基因表达模式相关联。大规模并行基因表达表征与候选药物的传统评估相结合,可提供其他重要的机械信息,因此可提高关键决策的准确性。从体内治疗的大鼠开发的大规模化学基因组学数据库提供了背景信息和支持数据,以增强和加速对化学药品和药物的毒性和药理作用机理的准确解释。迄今为止,已经在多达7种不同的大鼠组织中对大约600种不同的化合物进行了分析,其中包括FDA批准的400多种药物,欧洲和日本批准的60种药物,撤回的药物25种和有毒的100种毒物(代表3200多种不同的药物,剂量-时间-组织组合)。要完成此任务,需要评估和改进许多体内和微阵列方案,包括80多个严格的质量控制步骤。使用三种抗肿瘤药物:卡莫司汀,甲氨蝶呤和硫代鸟嘌呤,说明了将临床病理评估与基因表达数据配对的效用,它们对血区的影响相似,但对肝毒性的影响却不同。我们将证明在肝脏中监测的基因表达事件可用于预测在该组织以及造血组织中发生的病理事件。

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