Studies to develop the prediction system for drug metabolism, pharmacokineties and toxicity.

我们利用大鼠药物反应芯片研究了肝毒性药物给药后的基因表达谱是否存在相关性。从典型的肝中毒大鼠中提取肝脏mRNA，进行包含1,097个药物反应基因的DMA微阵列。所有测试的化学物质都产生了一种特定的基因表达模式。APAP与其他化学物质的分类结果不同。从血清生化标志物图谱中，我们确定了10个上调的基因和10个下调的基因作为潜在的肝毒性标志物。通过QT聚类，我们确定了每种化学物质上调和下调的主要表达模式。有趣的是，这些表达模式的手段与生化标记图谱中的表达模式有很好的相关性。综上所述，我们确定了20个潜在的毒性标记物，并通过对给药的表达谱分析，可以独立于毒性水平，使用…来推测中毒时间点此外，这种基因表达谱分析也可以作为检测潜在肝毒物质的工具之一。我们以典型的肝毒物质硫代乙酰胺(TA)为模型，构建肝毒模型大鼠，同时进行基因表达谱检测，并寻找剂量和时间之间的相关性。血清AST、ALT升高，且呈剂量依赖关系，24小时达最大。所有剂量组的系统聚类分析显示有2个主要聚类群。此外，各剂量组的系统聚类分析显示出与所有剂量组相同的模式。此外，QT聚类分析得到的基因表达多数具有相同的TA毒性最大时间点。我们以前的研究选择的单个基因表达谱与它们的剂量无关。这些发现表明，在没有毒性发生的情况下，潜在的毒性效应与其剂量无关，遵循特定的基因表达谱和一般的基因表达谱可以作为毒性的敏感标志。较少

We investigated whether there is relationship in gene expression profiles of hepatotoxic chemicals administration by using Rat Drug Response Chip. Liver mRNA was isolated from the typical hepatotoxicant-administered rats and performed DMA microarray containing 1,097 drug response genes. All the tested chemicals generated a specific gene expression patterns. APAP was sorted different cluster from other chemicals. From serum biochemical markers profiles, we identified up-regulated 10 genes and down-regulated 10 genes as a potential marker of hepatotoxicity. By using QT clustering, we identified major up-regulated and down-regulated expression patterns from each chemical. Interestingly, these means of expression patterns had good correlation with expression patterns from biochemical markers profiles. In conclusion, we identified 20 potential toxicity markers and expression profile analysis of chemicals administration can guess toxic time point with independent of toxicity level and using … More this expression profile analysis was also available to be one of the tools of detecting potential hepatotoxicant.We used typical hepatotoxicant thioacetamide (TA), constructed liver toxicity model rats to perform simultaneous measurement of gene expression profiles and to find correlation between dosage and time. Serum AST and ALT were increased by TA administration with dose dependent manner and reached the maximal value at 24 hours. Hierarchical clustering analysis of all dosage groups showed 2 major clusters. Moreover, hierarchical clustering analysis of each dosage group showed the same pattern as shown in all dosage groups. Furthermore, gene expression majority obtained by QT clustering analysis had the same maximal time point of TA toxicity. Individual gene expression profiles selected our previous studies were independent on their dosage. These finding suggest that potential toxic effects in the absense of the occurrence of toxicity are independent on their dosage, and following specific gene expression profiles and general gene expression profiles can be a sensitive marker of toxicity. Less