Abstract

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 ($\text{IL-}1\mathrm{\beta }$) 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 $\text{IL-}1\mathrm{\beta }$ 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 ($\text{ACC}=86\%$). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive $\mathrm{\zeta }\text{-potential}$ were more likely to cause lysosomal damage and inflammation.

Conclusions:

$\text{IL-}1\mathrm{\beta }$ released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on $\text{IL-}1\mathrm{\beta }$ 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：

---

预测金属氧化物纳米颗粒炎症潜能的定量构效关系模型

摘要

背景：

尽管人们对工程纳米材料（ENM）的炎性效应引起了广泛关注，但通过实验评估各种ENM的毒性却是挑战性且耗时的。另外，定量结构-活性关系（QSAR）模型已用于评估纳米安全性。但是，以前没有尝试预测ENM的炎症潜能。

目标：

通过采用金属氧化物纳米颗粒（MeONPs）作为模型ENM，我们旨在开发QSAR模型以通过其物理化学性质预测炎症潜能。

方法：

我们建立了30个MeONP的综合数据集，以筛选促炎性细胞因子白介素（IL）-1 beta（$\text{白介素}\mathrm{1个}\mathrm{\beta }$）在THP-1细胞系中释放。在体外危险排名是由六个随机选择MeONPs的口咽滴注在小鼠肺验证。我们建立了QSAR模型，用于通过机器学习预测MeONP诱导的炎症潜能。该模型针对七个新的MeONP进行了进一步验证。密度泛函理论（DFT）计算被用来破译驱动MeONPs炎症反应的关键机制。

结果：

30个MeONP中有17个引起过量 $\text{白介素}\mathrm{1个}\mathrm{\beta }$在THP-1细胞中产生。体内疾病结局与体外数据高度相关。开发了QSAR模型用于发炎潜力，其预测准确性（ACC）超过90％。该模型针对7个独立的MeONP进行了实验验证（$\text{行政协调会}=86％$）。DFT计算和实验结果进一步揭示了其潜在机理：金属电负性低于1.55且正电的MeONP$\mathrm{\zeta }\text{-潜在}$ 更可能引起溶酶体损害和炎症。

结论：

$\text{白介素}\mathrm{1个}\mathrm{\beta }$在THP-1细胞中释放的TNFα可以作为对MeONPs炎症潜力进行排名的指标。基于QSAR模型$\text{白介素}\mathrm{1个}\mathrm{\beta }$能够预测MeONP的炎症潜能。我们的方法克服了耗时且费力的生物学实验的挑战，并通过表征其理化特性进行了MeONP炎症潜能的计算评估。https://doi.org/10.1289/EHP6508