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. 2020 Jun;128(6):67010.
doi: 10.1289/EHP6508. Epub 2020 Jun 12.

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles

Affiliations

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles

Yang Huang et al. Environ Health Perspect. 2020 Jun.

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 (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.

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Figures

Figure 1 is a workflow diagram. Metal oxide nanoparticle library (30 MeONPs) includes characteristics like size and zeta potential, cytokine production (In vitro), and oropharyngeal instillation (In vivo). The in vitro and in vivo lead to each other. Metal oxide nanoparticle library (30 MeONPs) leads to (Q) SARs, comprising a tree diagram, where X subscript 1 leads to No and X subscript 2 by including less than or equal to 3.10 and greater than 3.10, respectively. X subscript 2 leads to yes and no by including less than or equal to 1.20 and greater than 1.20, respectively, and a graph, where Y equals f (x subscript 1, x subscript 2, to x subscript n). (Q) SARs leads to experimental validation, comprising seven new MeONPs and mechanism interpretation. Quantum chemistry calculations leads to (Q) SARs using In Silico.
Figure 1.
Schematic workflow of metal oxide nanomaterials (MeONPs) library construction, predictive modeling, mechanism interpretation, and experimental validation.
Figure 2 is a heatmap of a cluster diagram, plotting concentration (microgram per milliliter), ranging from 0 to 6.2 in increments of 3.1, 6.2 to 13 in increments of 6.8, 13 to 25 in increments of 12, 25 to 50 in increments of 25, 50 to 100 in increments of 50, and 100 to 200 in increments of 100 (y-axis) across Sm subscript 2 O subscript 3, Eu subscript 2 O subscript 3, La subscript 2 O subscript 3, Yb subscript 2 O subscript 3, Gd subscript 2 O subscript 3, Dy subscript 2 O subscript 3, Y subscript 2 O subscript 3, Nd subscript 2 O subscript 3, Er subscript 2 O subscript 3, Mn subscript 2 O subscript 3, MnO subscript 2, Fe subscript 3 O subscript 4, In subscript 2 O subscript 3, Al subscript 2 O subscript 3, Fe subscript 2 O subscript 3, ZrO subscript 2, HfO subscript 2, TiO subscript 2, Sb subscript 2 O subscript 3, CuO, Ni subscript 2 O subscript 3, WO subscript 3, Cr subscript 2 O subscript 3, NiO, Bj subscript 2 O subscript 3, Co subscript 3 O subscript 4, SnO subscript 2, CeO subscript 2, CoO, and ZnO (x-axis) for fold changes of IL-1 beta production, namely, 0, 1, 2, 5, 10, 20, 30, and 50.
Figure 2.
Fold changes of IL-1β production (FCIL-1β) in metal oxide nanomaterials (MeONPs)-treated THP-1 cells. THP-1 cells were exposed to 0, 3.1, 6.2, 13, 25, 50, 100, and 200μg/mL MeONPs for 24 h. IL-1β levels in supernatants were quantified by ELISA. FCIL-1β was calculated by Equation 3. The FCIL-1β was expressed as the mean of three replicates and added in the heatmap.
Figure 3 (a) is a clustered bar graph, plotting pictogram per milliliter, ranging from 0 to 500 in increments of 100 (y-axis) across Ctrl, Quartz, Gd subscript 2 O subscript 3, La subscript 2 O subscript 3, Co subscript 3 O subscript 4, ZnO, TiO subscript 2, and WO subscript 3 (x-axis) for IL–1 beta production in BALF and MCP-1 production in BALF. Figure 3 (b) is a schematic of H and E staining of lung tissues after exposed to Ctrl, Quartz, Gd subscript 2 O subscript 3, La subscript 2 O subscript 3, Co subscript 3 O subscript 4, ZnO, TiO subscript 2, and WO subscript 3.
Figure 3.
Pulmonary inflammation of six selected metal oxide nanomaterials (MeONPs) in mice. (A) Cytokine production (IL-1β and MCP-1) in BALF, and (B) H&E staining of lung tissues after 40 h exposure to MeONPs. C57Bl/6 mice (n=6) were exposed to La2O3, Gd2O3, Co3O4, ZnO, TiO2, and WO3 at 2mg/kg by oropharyngeal instillation. Quartz was used as positive control to treat animals (5mg/kg). After 40 h, animals were sacrificed to measure IL-1β and MCP-1 production in BALF by ELISA. The lung tissues were fixed for H&E staining (three sections for each mouse). Normal distribution was confirmed by Kolmogorov-Smirnov test (significance>0.05). *p<0.05 compared with vehicle control by two-tailed Student’s t-test.
Figure 4 is tree diagram, representing the predicting of the inflammatory potential of metal oxide nanomaterials (MeONPs) in five stages. Stage 1. Electronegativity leads to Cation charge, comprising less than or equal to 1.55 and concentration (microgram per milliliter), comprising greater than 1.55. Stage 2. Cation charge leads to concentration (microgram per milliliter), comprising less than 3.15 and NO, comprising greater than 3.15. Concentration (microgram per milliliter) leads to NO, comprising less than or equal to 65.0 and electronegativity, comprising greater than 65.0. Stage 3. Concentration (microgram per milliliter) leads to concentration (microgram per milliliter), comprising less than or equal to 6.20 and YES, comprising greater than 6.20. Electronegativity leads to electronegativity, comprising less than or equal to 1.83 and NO, comprising greater than 1.83. Stage 4. Concentration (microgram per milliliter) leads to NO, comprising less than or equal to 3.10 and electronegativity, comprising greater than 3.10. Electronegativity leads to concentration (microgram per milliliter), comprising less than or equal to 1.78 and YES, comprising greater than 1.78. Stage 5. Electronegativity leads to YES, comprising less than or equal 1.20 and NO, comprising greater than 1.20. Concentration (microgram per milliliter) leads to NO, comprising less than or equal to 100.00 and YES, comprising greater than 100.00.
Figure 4.
C4.5 decision tree for predicting the inflammatory potential of metal oxide nanomaterials (MeONPs). The root and interior nodes are drawn in rectangles with splitting descriptors inside, and the splitting criteria are under the rectangles. The leaf nodes are presented in oval circles, which are marked as “YES” and “NO,” indicating the nanomaterials in the leaf nodes are predicted to be inflammatory potential and noninflammatory potential, respectively.
Figure 5A is a graph, plotting predicted FC subscript IL-1 beta, ranging from negative 10 to 40 in increments of 10 (y-axis) across experimental FC subscript IC-1 beta, ranging from negative 10 to 40 in increments of 10 (x-axis) for training set and test set. Figure 5B is a graph, plotting O superscript asterisk, ranging from negative 4 to 4 in unit increments (y-axis) across h, ranging from 0.0 to 1.2 in increments of 0.2 (x-axis) for training set and test set, where h superscript asterisk equals 0.6.
Figure 5.
Performance of the continuous model. (A) Plot of experimentally determined data (x-axis) vs. predicted fold change of IL-1β production (FCIL1β) values (y-axis). The straight solid line represents perfect agreement between experimental and calculated values. Squares represent values predicted for the metal oxides (MeONPs) from the training set; triangles represent MeONPs from the test set. The distance of each symbol from the solid line corresponds to its deviation from the related experimental value. The dotted lines showing the range encompassing 90% of the predictions. (B) Model applicability domain: Williams plot of standardized residuals (σ*) vs. leverage values (hi) for FCIL1β. MeONPs having hi>h* or |σ|>3 should be identified as outliers.
Figure 6 is a periodic table of predicted inflammatory potential of metal oxide nanomaterials (MeONPs), including Lanthanides and Actinides, plotting minimal doses eliciting inflammations (MDEI in micrograms per milliliter), namely, less than or equal to 12.5, 25, 50, 100, and greater than or equal to 200.
Figure 6.
Periodic table of predicted inflammatory potential of metal oxide nanomaterials (MeONPs). Sixty-six MeONPs were plotted according to the metal element position in the periodic table. The number in each color square represents MDEI. *, experimental data is unavailable. The predicted value is not shown when the experimental value is available.
Figure 7 is a bar graph, plotting adsorption energy (kilocalorie per mole), ranging from 0 to 300 in increments of 50 (y-axis) across X subscript me less than or equal to 1.55, namely, Nd subscript 2 O subscript 3, Y subscript 2 O subscript 3, and Gd subscript 2 O subscript 3 and X subscript me greater than 1.55, namely, CU O, Sn O subscript 2, CoO (x-axis) for Ip-MeONPs and Nip-MeONPs.
Figure 7.
Adsorption energy (|Ead|) of protons onto metal oxide nanomaterials (MeONPs) with different metal atom electronegativity (χme). Bar heights represent calculated values of |Ead|. MeONPs with higher χme (black) have much weaker proton adsorption than those with lower χme (red). Normal distribution was confirmed by Kolmogorov-Smirnov test (significance>0.05). *p<0.05 compared with ip-MeONPs by two-tailed Student’s t-test.
Figure 8 is a schematic diagram representing image of inflammatory mechanisms by metal oxide nanomaterials in three stages. Stage a titled Endocytosis includes MeONPs, leading to electrons and ross, leading to IL–1 beta. Stage b titled Proton sponge effect includes lysosomes, comprising v-ATPase, MeONPs, enzymes, positively charged H ions, negatively charged Cl ions, H subscript 2 O, and chloride channel, leading to each other. Stage c titled release of toxic ions includes biomolecules and Me superscript n positive ions, leading to electrons and IL-1 beta and inflammation in stage a. Stage a leads to stage b and stage b leads back to stage a. MeONPs are the input to stages a and c.
Figure 8.
Proposed schematic image of inflammatory mechanisms by metal oxide nanomaterials (MeONPs). (A) Endocytosis: MeONPs with a positive ζ-potential were most internalized by THP-1 cells and lysosomes. (B) Proton sponge effect. MeONPs with metal atom electronegativity 1.55 tend to trigger a proton sponge effect, followed by lysosome damages, leakage of lysosomal contents and excess IL-1β production. (C) Release of toxic ions.

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