BACKGROUND 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. OBJECTIVES 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. METHODS We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta (IL-1β) release in THP-1 cell line. The in vitro 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. RESULTS Seventeen out of 30 MeONPs induced excess IL-1β production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (ACC=86%). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive ζ-potential were more likely to cause lysosomal damage and inflammation. CONCLUSIONS IL-1β released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on IL-1β 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.

中文翻译：

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用于预测金属氧化物纳米颗粒炎症潜力的定量结构-活性关系模型。

背景尽管人们对工程纳米材料（ENM）的炎症效应产生了极大的关注，但通过实验评估各种ENM的毒性具有挑战性且耗时。另外，定量构效关系（QSAR）模型已被用来评估纳米安全性。然而，之前尚未尝试预测 ENM 的炎症潜力。目的 通过使用金属氧化物纳米颗粒 (MeONP) 作为 ENM 模型，我们旨在开发 QSAR 模型，通过其物理化学特性预测炎症潜力。方法 我们建立了包含 30 个 MeONP 的综合数据集，以筛选 THP-1 细胞系中促炎细胞因子白细胞介素 (IL)-1 β (IL-1β) 的释放。通过口咽滴注六种随机选择的 MeONP，在小鼠肺部验证了体外危险等级。我们建立了 QSAR 模型，通过机器学习预测 MeONP 诱导的炎症潜力。该模型针对七个新的 MeONP 进行了进一步验证。利用密度泛函理论 (DFT) 计算来破译驱动 MeONP 炎症反应的关键机制。结果 30 个 MeONP 中有 17 个诱导 THP-1 细胞产生过量的 IL-1β。体内疾病结果与体外数据高度相关。QSAR 模型是针对炎症潜力而开发的，预测准确度 (ACC) 超过 90%。该模型进一步针对七个独立的 MeONP (ACC=86%) 进行了实验验证。DFT计算和实验结果进一步揭示了潜在的机制：金属电负性低于1.55和正电位的MeONP更容易引起溶酶体损伤和炎症。结论 THP-1细胞中释放的IL-1β可以作为MeONPs炎症潜力排序的指标。基于 IL-1β 的 QSAR 模型能够预测 MeONP 的炎症潜力。我们的方法克服了耗时耗力的生物实验的挑战，并允许通过表征 MeONP 的理化性质来计算评估 MeONP 炎症潜力。https://doi.org/10.1289/EHP6508。