College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
Int J Mol Sci. 2023 Aug 31;24(17):13548. doi: 10.3390/ijms241713548.
Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q, QH, E, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.
邻苯二甲酸酯（PAEs）作为塑料中的增塑剂广泛存在于环境中，已被发现对环境和健康造成重大危害，尤其是对人类内分泌的干扰。为了研究 PAEs 对内分泌干扰作用的作用机制，本研究采用三种机器学习技术构建了针对人体的不良结局途径（AOP）。根据三种机器学习技术的结果，随机森林和 XGBoost 模型在预测方面表现良好。随后，进行了敏感性分析，以确定影响 PAEs 对人体内分泌干扰作用的初始事件、关键事件和关键特征。关键特征，如 Mol.Wt、Q、QH、E、minHCsats、MEDC-33 和 EG，与分子结构密切相关。因此，构建了 PAEs 的三维定量构效关系（3D-QSAR）模型，基于三维势能面信息，发现 PAEs 的疏水性、立体性和静电场对其对人体的内分泌干扰作用有显著影响。最后，对氨基酸残基和结合能（BE）的贡献进行了分析，确定并证实氢键、疏水相互作用和范德华力是影响 PAEs 分子内分泌干扰作用 AOP 的重要因素。本研究定义并构建了 PAEs 对人体内分泌干扰作用的综合 AOP，并基于理论模拟开发了一种特征化 AOP 的方法，为研究其他污染物毒性机制提供了理论指导。
