Jeong Jaeseong, Choi Jinhee
School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
Environ Int. 2020 Apr;137:105557. doi: 10.1016/j.envint.2020.105557. Epub 2020 Feb 18.
Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the major contributors to environmental and human toxicity of microplastics. In this context, to better understand the hazardous effect of microplastics, the toxicity of chemical additives was investigated. Fifty most common chemicals presented in plastics were selected as target additives. Their toxicity was systematically identified using apical and molecular toxicity databases, such as ChemIDplus and ToxCast™. Among the vast ToxCast assays, those having intended gene targets were selected for identification of the mechanism of toxicity of plastic additives. Deep learning artificial neural network models were further developed based on the ToxCast assays for the chemicals not tested in the ToxCast program. Using both the ToxCast database and deep learning models, active chemicals on each ToxCast assays were identified. Through correlation analysis between molecular targets from ToxCast and mammalian toxicity results from ChemIDplus, we identified the fifteen most relevant mechanisms of toxicity for the understanding mechanism of toxicity of plastic additives. They are neurotoxicity, inflammation, lipid metabolism, and cancer pathways. Based on these, along with, previously conducted systemic review on the mechanism of toxicity of microplastics, here we have proposed potential adverse outcome pathways (AOPs) relevant to microplastics pollution. This study also suggests in vivo and in vitro toxicity database and deep learning model combined approach is appropriate to provide insight into the toxicity mechanism of the broad range of environmental chemicals, such as plastic additives.
塑料制品中使用了各种添加剂来改善塑料的性能和耐久性。当塑料在环境中破碎成微米和纳米尺寸时，人们怀疑这些添加剂可能会从塑料中溶出，这是微塑料对环境和人类产生毒性的主要原因之一。在此背景下，为了更好地了解微塑料的有害影响，对化学添加剂的毒性进行了研究。选择了塑料中最常见的50种化学物质作为目标添加剂。使用诸如ChemIDplus和ToxCast™等顶端和分子毒性数据库系统地鉴定了它们的毒性。在大量的ToxCast分析中，选择具有预期基因靶点的分析来确定塑料添加剂的毒性机制。基于ToxCast分析，进一步开发了深度学习人工神经网络模型，用于ToxCast程序中未测试的化学物质。利用ToxCast数据库和深度学习模型，确定了每个ToxCast分析中的活性化学物质。通过ToxCast的分子靶点与ChemIDplus的哺乳动物毒性结果之间的相关性分析，我们确定了15种与理解塑料添加剂毒性机制最相关的毒性机制。它们是神经毒性、炎症、脂质代谢和癌症通路。基于这些，以及之前对微塑料毒性机制进行的系统综述，我们在此提出了与微塑料污染相关的潜在不良结局途径（AOPs）。本研究还表明，体内和体外毒性数据库与深度学习模型相结合的方法适用于深入了解广泛的环境化学物质（如塑料添加剂）的毒性机制。
