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

Abstract

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, in vitro studies were often used for read-across reliability refinement; however, their external validity has been hindered by the gap between in vitro and in vivo conditions. Thus, we developed a virtual DNA microarray, regression analysis-based inductive DNA microarray (RAID), which quantitatively predicts in vivo gene expression profiles based on the chemical structure and/or in vitro transcriptome data. For each gene, elastic-net models were constructed using chemical descriptors and in vitro transcriptome data to predict in vivo data from in vitro data (in vitro to in vivo 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 in vivo 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 in vitro 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.

Keywords: alternative method; gene expression analysis; hepatotoxicity; new approach methodology; oligonucleotide array.

FIGURE 1

Development and implementation of a virtual microarray (RAID) for read-across. GE: gene expression. f(x): predictive models (formula). (A) RAID system development. The predictive model for in vivo transcriptome data for each gene was individually constructed by elastic net regression employing chemical descriptors and in vitro data. The models constructed were defined as a RAID system (a virtual microarray). (B) Workflow of safety evaluation using the RAID system. Chemical descriptors and in vitro gene expression data were inputted to the RAID system and in vivo gene expression data were outputted. The predicted results were analyzed by PCA and enrichment analysis for read-across. This procedure would replace toxicogenomics analysis in in vivo repeated dose study.

FIGURE 2

PCA score plots for chemical substances and the gene loading in the transcriptome data of (A) in vivo, (B) virtual microarray (RAID), and (C) in vitro data. PCA score plot with (D) chemical descriptor data. Uppercase letters in PCA score plots: abbreviations of chemical substances are described in Table 1. Blue: nontoxic substances. Red: hepatotoxic substances. Gene symbols are presented on the arrowhead (loading).

FIGURE 3

List of genes that have high loading values in the (A) fourth quadrant and (B) first quadrant in the PCA plot of in vivo data, where the first group (TAA, MP, and HCB) and the second group (WY, FFB, BBr, and GFZ) plotted, and their pathway map. The loading value was defined as the loading length in the first or fourth quadrant calculated using the Pythagorean theorem. The pathway map was drawn by upstream regulator analysis using IPA.

FIGURE 4

Commonalities of principal component–related genes and their biological functions analyzed by gene ontology and pathway analyses. Venn diagram of genes related to the first and second principal components of in vivo, a virtual microarray (RAID), and in vitro data.

FIGURE 5

Enrichment analysis of in vitro–in vivo extrapolation (IVIVE)–related genes identified in a virtual microarray (RAID) system. Top 20 most important (contribution) genes from the predictive models were analyzed.

FIGURE 6

Distribution of RMSEs of a virtual microarray (RAID) and in vitro data of (A) all genes and (B) in vitro genes having importance (contribution) in predictive models. **p < 0.01 (Welch’s t-test).

FIGURE 7

Read-across using PCA plot of external data predicted by a virtual microarray (RAID). (A) Cyp1a and (B) Cyp4a inducing chemical substances were analyzed for validation.