Computational Prediction of Drug-Induced Hematotoxicity: Mechanisms and Model Development

Abstract Hematotoxicity, encompassing adverse effects such as anemia, leukopenia, thrombocytopenia, and coagulation disorders, is a critical yet underexplored area of toxicological research. These toxic effects can lead to severe clinical outcomes, including heightened risks of infection, bleeding, and mortality. Despite its significance, hematotoxicity research lags behind general toxicities like hepatotoxicity and nephrotoxicity, and traditional evaluation methods such as animal models and in vitro assays often fail to accurately predict human responses. To address this gap, we curated a dataset of thousands of compounds with and without hematotoxic effects and performed in-depth analyses of their molecular properties and mechanisms using clustering and target prediction. These analyses revealed key pathways and targets underlying hematotoxicity, demonstrating its complex and multifactorial nature. We developed predictive models using fingerprints, combined with machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The best-performing model achieved an AUC of 0.78, highlighting its potential for accurately identifying hematotoxic compounds. This study provides a computational framework for understanding hematotoxicity mechanisms and offers a practical tool for early screening of blood-toxic compounds during drug development. These advancements pave the way for safer therapeutic strategies, improved patient safety, and reduced risks of drug-induced hematological disorders.