Evaluation of Risk and Uncertainty for Model-Predicted NOAELs of Engineered Nanomaterials Based on Dose-Response-Recovery Clusters

Vignesh Ramchandran, Jeremy M. Gernand

Research output: Contribution to journalArticlepeer-review

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Experimental toxicology studies for the purposes of setting occupational exposure limits for aerosols have drawbacks including excessive time and cost which could be overcome or limited by the development of computational approaches. A quantitative, analytical relationship between the characteristics of emerging nanomaterials and related in vivo toxicity can be utilized to better assist in the subsequent mitigation of exposure toxicity by design. Predictive toxicity models can be used to categorize and define exposure limitations for emerging nanomaterials. Model-based no-observed-adverse-effect-level (NOAEL) predictions were derived for toxicologically distinct nanomaterial clusters, referred to as model-predicted no observed adverse effect levels (MP-NOAELs). The lowest range of MP-NOAELs for the polymorphonuclear neutrophil (PMN) response observed by carbon nanotubes (CNTs) was found to be 21-35 lg/kg (cluster "A"), indicating that the CNT belonging to cluster A showed the earliest signs of adverse effects. Only 25% of the MP-NOAEL values for the CNTs can be quantitatively defined at present. The lowest observed MP-NOAEL range for the metal oxide nanoparticles was Cobalt oxide nanoparticles (cluster III) for the macrophage (MAC) response at 54-189 lg/kg. Nearly 50% of the derived MP-NOAEL values for the metal oxide nanoparticles can be quantitatively defined based on current data. A sensitivity analysis of the MP-NOAEL derivation highlighted the dependency of the process on the shape and type of the fitted dose-response model, its parameters, dose selection and spacing, and the sample size analyzed.

Original languageEnglish (US)
Article number011205
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Issue number1
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Mechanical Engineering


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