Wang Tong, Jia Xuelian, Aleksunes Lauren M, Shen Hui, Deng Hong-Wen, Zhu Hao
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ, USA.
Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.
Trends Pharmacol Sci. 2025 Jun;46(6):486-502. doi: 10.1016/j.tips.2025.04.005. Epub 2025 May 15.
Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetuses. However, defining developmental toxicity endpoints and optimal analysis of associated public big data remain challenging. Recently, artificial intelligence (AI) approaches have had a critical role in analyzing complex, high-dimensional data, uncovering subtle relationships between chemical exposures and associated developmental risks. Here, we present an overview of major big data resources and data-driven models that focus on predicting various toxicity endpoints. We also highlight emerging, interpretable AI models that integrate multimodal data and domain knowledge to reveal toxic mechanisms underlying complex endpoints, and outline a potential framework that leverages multiple interpretable models to comprehensively evaluate chemical-induced developmental toxicity.
监管机构要求对产前药物暴露进行全面的毒性测试，包括处于研发阶段的新药，以减少对发育毒性的担忧，即药物对孕妇和胎儿的毒性及不良反应。然而，确定发育毒性终点以及对相关公共大数据进行最佳分析仍然具有挑战性。最近，人工智能（AI）方法在分析复杂的高维数据、揭示化学暴露与相关发育风险之间的微妙关系方面发挥了关键作用。在此，我们概述了主要的大数据资源和数据驱动模型，这些资源和模型专注于预测各种毒性终点。我们还强调了新兴的、可解释的人工智能模型，这些模型整合了多模态数据和领域知识，以揭示复杂终点背后的毒性机制，并概述了一个潜在的框架，该框架利用多个可解释模型来全面评估化学物质诱导的发育毒性。
