Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian, China.
State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou, Jiangsu, China.
Environ Health Perspect. 2020 Jun;128(6):67010. doi: 10.1289/EHP6508. Epub 2020 Jun 12.
Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.
By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.
We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta () release in THP-1 cell line. The hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs.
Seventeen out of 30 MeONPs induced excess production in THP-1 cells. disease outcomes were highly relevant to the data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive were more likely to cause lysosomal damage and inflammation.
released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508.
尽管人们对工程纳米材料（ENM）的炎症效应提出了诸多担忧，但评估各种 ENM 的毒性具有挑战性且耗时。相反，定量构效关系（QSAR）模型已被用于评估纳米安全性。然而，目前尚无尝试预测 ENM 的炎症潜力。
我们采用金属氧化物纳米颗粒（MeONP）作为模型 ENM，旨在通过其理化性质开发用于预测炎症潜力的 QSAR 模型。
我们构建了一个包含 30 个 MeONP 的综合数据集，以筛选在 THP-1 细胞系中促炎细胞因子白细胞介素（IL）-1β（）的释放。危害排名通过对 6 种随机选择的 MeONP 进行口咽滴注在小鼠肺部进行验证。我们通过机器学习建立了用于预测 MeONP 诱导的炎症潜力的 QSAR 模型。该模型进一步针对 7 种新的 MeONP 进行了验证。我们利用密度泛函理论（DFT）计算来揭示驱动 MeONP 炎症反应的关键机制。
在 30 个 MeONP 中有 17 个可诱导 THP-1 细胞产生过量的 。疾病结局与 数据高度相关。建立了炎症潜力的 QSAR 模型，预测准确性（ACC）超过 90%。该模型进一步通过 7 种独立的 MeONP （）进行了实验验证。DFT 计算和实验结果进一步揭示了潜在机制：具有低于 1.55 的金属电负性和正值的 MeONP 更有可能导致溶酶体损伤和炎症。
在 THP-1 细胞中释放的 可以作为对 MeONP 炎症潜力进行排序的指标。基于 的 QSAR 模型能够预测 MeONP 的炎症潜力。我们的方法克服了耗时且费力的生物学实验的挑战，并允许通过对其理化性质进行表征来对 MeONP 的炎症潜力进行计算评估。https://doi.org/10.1289/EHP6508.
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