RAID: Regression Analysis-Based Inductive DNA Microarray for Precise Read-Across.

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.