Amano Yuto, Yamane Masayuki, Honda Hiroshi
R&D Safety Science Research, Kao Corporation, Tochigi, Japan.
Front Pharmacol. 2022 Jul 22;13:879907. doi: 10.3389/fphar.2022.879907. eCollection 2022.
Chemical structure-based read-across represents a promising method for chemical toxicity evaluation without the need for animal testing; however, a chemical structure is not necessarily related to toxicity. Therefore, studies were often used for read-across reliability refinement; however, their external validity has been hindered by the gap between and conditions. Thus, we developed a virtual DNA microarray, regression analysis-based inductive DNA microarray (RAID), which quantitatively predicts gene expression profiles based on the chemical structure and/or transcriptome data. For each gene, elastic-net models were constructed using chemical descriptors and transcriptome data to predict data from data ( to extrapolation; IVIVE). In feature selection, useful genes for assessing the quantitative structure-activity relationship (QSAR) and IVIVE were identified. Predicted transcriptome data derived from the RAID system reflected the gene expression profiles of characteristic hepatotoxic substances. Moreover, gene ontology and pathway analysis indicated that nuclear receptor-mediated xenobiotic response and metabolic activation are related to these gene expressions. The identified IVIVE-related genes were associated with fatty acid, xenobiotic, and drug metabolisms, indicating that studies were effective in evaluating these key events. Furthermore, validation studies revealed that chemical substances associated with these key events could be detected as hepatotoxic biosimilar substances. These results indicated that the RAID system could represent an alternative screening test for a repeated-dose toxicity test and toxicogenomics analyses. Our technology provides a critical solution for IVIVE-based read-across by considering the mode of action and chemical structures.
基于化学结构的类推法是一种很有前景的化学毒性评估方法，无需进行动物试验；然而，化学结构不一定与毒性相关。因此，常常使用研究来完善类推法的可靠性；然而，它们的外部有效性受到[具体条件1]和[具体条件2]之间差距的阻碍。因此，我们开发了一种虚拟DNA微阵列，即基于回归分析的归纳DNA微阵列（RAID），它基于化学结构和/或转录组数据定量预测基因表达谱。对于每个基因，使用化学描述符和转录组数据构建弹性网络模型，以从[具体数据1]预测[具体数据2]数据（从[具体数据1]到[具体数据2]的外推；体外到体内外推法）。在特征选择中，确定了用于评估定量构效关系（QSAR）和体外到体内外推法的有用基因。源自RAID系统的预测转录组数据反映了特征性肝毒性物质的基因表达谱。此外，基因本体论和通路分析表明，核受体介导的异生物素反应和代谢激活与这些基因表达有关。确定的与体外到体内外推法相关的基因与脂肪酸、异生物素和药物代谢有关，表明[具体研究]在评估这些关键事件方面是有效的。此外，验证研究表明，与这些关键事件相关的化学物质可以被检测为肝毒性生物类似物。这些结果表明，RAID系统可以代表重复剂量毒性试验和毒理基因组学分析的替代筛选试验。我们的技术通过考虑作用模式和化学结构，为基于体外到体内外推法的类推法提供了关键解决方案。
