Machine learning classification of steatogenic compounds using toxicogenomics profiles.

The transition toward new approach methodologies for toxicity testing has accelerated the development of computational models that utilize transcriptomic data to predict chemical-induced adverse effects. Here, we applied supervised machine learning to gene expression data derived from primary human hepatocytes and rat liver models (in vitro and in vivo) to predict drug-induced hepatic steatosis. We evaluated five machine learning classifiers using microarray data from the Open TG-GATEs database. Among these, support vector machine (SVM) consistently achieved the highest performance, with area under the receiver operating characteristic curve (ROC-AUC) of 0.820 in primary human hepatocytes, 0.975 in the rat in vitro model, and 0.966 in the rat in vivo model. To gain mechanistic insights, we functionally profiled the top-ranked predictive genes. Enrichment analyses revealed strong associations with lipid metabolism, mitochondrial function, insulin signalling, oxidative stress, all biological processes central to steatosis pathogenesis. Key predictive genes such as CYP1A1, PLIN2, and GCK mapped to lipid metabolism networks and liver disease annotations, while others highlighted novel transcriptomics signals. Integration with differentially expressed genes and known steatosis markers highlighted both overlapping and distinct molecular features, suggesting that machine learning models capture biologically relevant signals. These findings demonstrate the potential of machine learning models guided by transcriptomic data to identify early molecular signatures of drug-induced hepatic steatosis. The support vector machine model's strong predictive accuracy across species highlights its promise as a scalable and interpretable tool for chemical risk assessment. As data limitations in human toxicology persist, expanding high-quality transcriptomic resources will be critical to further advance non-animal approaches in regulatory toxicology.