School of Environmental Engineering , University of Seoul , 163 Seoulsiripdae-ro , Dongdaemun-gu, Seoul 02504 , Republic of Korea.
United States Army Engineer Research and Development Center (ERDC) Environmental Laboratory , 3909 Halls Ferry Road , Vicksburg , Mississippi 39180 , United States.
Chem Res Toxicol. 2019 Jun 17;32(6):1212-1222. doi: 10.1021/acs.chemrestox.9b00040. Epub 2019 Jun 7.
Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor β (TGF-β) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an adverse outcome pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted a systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing a potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in the ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.
接触某些化学物质，如通过吸入消毒剂，被怀疑与肺纤维化的发展有关，肺纤维化是一种肺部组织受损和结疤的疾病。已知肺纤维化受转化生长因子β（TGF-β）和过氧化物酶体增殖物激活受体γ（PPARγ）的调节。在这里，我们开发了一个不良结局途径（AOP），以更好地定义 PPARγ 拮抗作用与肺纤维化不良结局的联系。然后，我们使用 ToxCast 数据库和深度学习人工神经网络模型，进行了系统分析，以确定与该 AOP 相关的潜在化学物质。我们从数据库中确定了具有潜在吸入危害和暴露危害的化学物质，这些化学物质可能与该 AOP 有关。对于不在 ToxCast 数据库中的化学物质，基于与 AOP 相关的 ToxCast 测定法，开发了多层感知器模型。然后使用这些深度学习模型预测 ToxCast 未测试化学品的反应性。这两种方法都确定了一组可用于验证 AOP 的化学物质。这项研究表明，使用 ToxCast 等现有数据库分类的化学物质可用于验证 AOP，并且深度学习方法可用于表征与感兴趣的 AOP 相关的一系列潜在活性化学物质。
