• • The hepatic spheroid model achieved an AUC of 0.92 (95% CI: 0.88-0.96) for DILI prediction, significantly outperforming 2D cultures (AUC=0.78) and animal models (AUC=0.71), enabling more accurate early-stage hepatotoxicity screening.
• • The model demonstrated a positive predictive value of 89% for severe DILI compounds, with enhanced sensitivity for cholestatic and steatotic hepatotoxicants, reducing the risk of overlooking clinically relevant injuries.
• • Transcriptomic analysis identified key predictive biomarkers, including genes involved in oxidative stress, mitochondrial dysfunction, and bile acid transport, providing mechanistic insights into DILI pathways.
• • Integration of high-content imaging and biochemical assays with machine learning classifiers (e.g., random forest) yielded a robust predictive framework, achieving a sensitivity of 85% and specificity of 88% on the validation set, facilitating high-throughput screening in drug development.