DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential

作者：Ricardo Scheufen Tieghi, Marielle Rath, José Teófilo Moreira‐Filho, James Wellnitz, Holli‐Joi Martin, Kathleen M. Gates, Helena T. Högberg, Nicole Kleinstreuer, Alexander Tropsha, Eugene Muratov · 发表于：Environmental Health Perspectives · 年份：2025 · DOI：10.1021/ehp.6c01149 · 被引用次数：4 · 研究领域：Animal testing and alternatives、Pharmaceutical studies and practices、Effects and risks of endocrine disrupting chemicals

BACKGROUND: eplacing) of animal testing. OBJECTIVES: This study aimed to collect and curate a database of compounds classified according to their developmental toxicity potential, use this database to develop and validate QSAR models for predicting prenatal developmental toxicity, and implement models via a user-friendly online platform to support regulatory assessments of drug candidates. METHODS: We compiled and curated data from the FDA and Teratogen Information System (TERIS) databases and validated annotations with rigorous literature searches. The database was leveraged to create QSAR models using machine learning algorithms (RF, SVM, LightGBM) with Bayesian hyperparameter optimization. These models were implemented into a web tool. RESULTS: trimester). Models showed a sensitivity between 53% and 90%, specificity between 46% and 100%, and coverage of 76% assessed using a five-fold external validation protocol. We established a publicly accessible web portal (https://detox.mml.unc.edu/) for developmental toxicity prediction of both overall and trimester-specific toxicity predictions. CONCLUSIONS: icity), at https://detox.mml.unc.edu/. https://doi.org/10.1289/EHP15307.