46 Solubility and the Toxicity of Metal Oxide Nanoparticles: Looking Through the Lens of Toxicogenomics and DNA Damage
Bibliographic record
Abstract
Abstract Metal oxide nanoparticles (MONPs) are amongst the most widely utilized classes of nanomaterials worldwide. The mechanisms underlying their pulmonary toxicity are dependant on the physical-chemical properties of the particles. Solubility plays a critical role, as proportions of nanoparticles and dissolved metals change over time; however their relative contribution to the resulting toxicity has yet to be thoroughly investigated. The objective of this study was to mechanistically evaluate the impact of solubility on the toxic potential of MONPs. In this study, mouse lung epithelial cells were exposed for 2 – 48 Hrs to copper, zinc, nickel, aluminum and titanium oxides (CuO, ZnO, NiO, Al2O3, TiO2) as MONPs and microparticles, and corresponding metal chloride salts. Viability was assessed at each timepoint using Trypan Blue staining. DNA damage was measured after 2 and 4 Hrs exposure via the CometChip assay, while transcriptomic changes were measured using microarrays after 2, 24, and 48 Hrs of exposure. Benchmark dose modelling of viability and transcriptomics data indicates that ZnO and CuO MONPs induced more pronounced cytotoxicity and gene expression, earlier, than NiO, Al2O3, and TiO2 MONPs. With respect to DNA damage potential, NiO, CuO and ZnO MONPs showed the highest potency, with ZnCl2 being the only chloride to induce DNA damage at either timepoint. The metal chlorides induced similar pathway perturbations as their respective MONPs, while corresponding microparticle oxides (except ZnO microparticles) exhibited muted responses. This presentation will further explore these findings, with an emphasis on the effect of solubility on toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".