School of Environment, MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, South China Normal University, Guangzhou 510006, China.
School of Environment, MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, South China Normal University, Guangzhou 510006, China.
Environ Int. 2024 Sep;191:108995. doi: 10.1016/j.envint.2024.108995. Epub 2024 Sep 2.
Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-mediated adverse outcomes on female zebrafish fertility. This model started with chemical fingerprints as the input layer and was implemented through a five-layer virtual AR-induced adverse outcome pathway (AOP). Results indicated that the AR-DNN effectively and accurately screens new reproductive toxicants (AUC = 0.94, accuracy = 0.85), providing potential toxicity pathways. Furthermore, 1477 and 2448 chemicals that could lead to infertility were identified in the plastic additives list (PLASTICMAP, n = 7112) and the Inventory of Existing Chemical Substances in China (IECSC, n = 17741), respectively. Colourants containing steroid-like structures are the major active plastic additives that might lower female zebrafish fertility through AR binding, DNA binding, and transcriptional activation. While active IECSC chemicals primarily have the same fragments, such as benzonitrile, nitrobenzene, and quinolone. The predicted toxicity pathways were consistent with existing fish evidence, demonstrating the model's applicability. This knowledge-based approach offers a promising computational toxicology strategy for predicting and characterising the endocrine-disrupting effects and toxic mechanisms of organic chemicals, potentially leading to more efficient and cost-effective screening of EDCs.
传统的鉴定激活雄激素受体（AR）的内分泌干扰化学物质（EDCs）的方法既昂贵又耗时，且通量低。本研究开发了一种基于知识的深度神经网络模型（AR-DNN），用于预测 AR 介导的对雌性斑马鱼生育力的不良后果。该模型以化学指纹作为输入层，并通过五层虚拟 AR 诱导的不良结局途径（AOP）实现。结果表明，AR-DNN 能够有效且准确地筛选新的生殖毒物（AUC = 0.94，准确性 = 0.85），提供潜在的毒性途径。此外，在塑料添加剂清单（PLASTICMAP，n = 7112）和中国现有化学物质清单（IECSC，n = 17741）中分别鉴定出 1477 种和 2448 种可能导致不孕的化学物质。含有类甾体结构的着色剂是主要的活性塑料添加剂，可能通过 AR 结合、DNA 结合和转录激活降低雌性斑马鱼的生育能力。而活性 IECSC 化学物质主要具有相同的片段，如苯甲腈、硝基苯和喹诺酮。预测的毒性途径与现有的鱼类证据一致，证明了该模型的适用性。这种基于知识的方法为预测和描述有机化学物质的内分泌干扰效应和毒性机制提供了一种有前途的计算毒理学策略，可能会导致更高效和更具成本效益的 EDC 筛选。
