A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats

• Published in: Archives of toxicology 2024-08, Vol.98 (8), p.2711-2730
• Main Authors: Gi, Min, Suzuki, Shugo, Kanki, Masayuki, Yokohira, Masanao, Tsukamoto, Tetsuya, Fujioka, Masaki, Vachiraarunwong, Arpamas, Qiu, Guiyu, Guo, Runjie, Wanibuchi, Hideki
• Format: Article
• Language: English
• Subjects: Administration, Oral; Animals; Biomarkers; Biomedical and Life Sciences; Biomedicine; Carcinogenicity; Carcinogenicity Tests - methods; Carcinogens; Carcinogens - toxicity; DNA microarrays; Effectiveness; Environmental Health; Gene expression; Gene Expression Profiling; Genes; Genotoxicity; Genotoxicity and Carcinogenicity; Health risks; Liver - drug effects; Liver - metabolism; Male; Mutagens - toxicity; Occupational Medicine/Industrial Medicine; Oligonucleotide Array Sequence Analysis; Oral administration; Pharmacology/Toxicology; Prediction models; Rats; Reliability; Reproducibility of Results; Risk assessment; Risk Assessment - methods; Sensitivity analysis; Support Vector Machine; Support vector machines
• ISSN: 0340-5761; 1432-0738; 1432-0738
• DOI: 10.1007/s00204-024-03755-w

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24 h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.