This article presents the examination of the pulmonary toxicity of engineered metal oxide nanoparticles using a dose-response-recovery clustering model for the purpose of revealing toxicologically distinct clusters. Current recommended exposure limits published by the National Institute for Occupational Safety and Health (NIOSH) consider “ultrafine” metal oxide particles to pose significantly increased health risks as compared to larger particles. The unique combination of physical and chemical characteristics afforded by the metal oxide nanoparticles has enabled them to find use in various large-scale industrial setting leading to a risk of exposure for current and future workers. This paper presents an algorithmic examination of the metal oxide nanoparticles by their categorization into toxicologically distinct clusters. Based on a dataset composed of peer-reviewed in vivo experimental studies in rodents, the metal oxide nanoparticle variants are divided into sub-classes based on their dose-response-recovery similarity and the Akaike Information Criterion (AIC) of the family of models. Results indicate the presence of 4 toxicologically unique classes based on 5 toxicity endpoints selected. The cluster with greatest potency was found to be 400,000 times more potent than the cluster with the lowest potency indicative of substantial variation across all the responses. The absence of coherent characterization data for the metal oxide nanoparticle variants analyzed in this study prevents the designation of significant physical characteristic-based labels which could have assisted in identifying the key factors affecting the toxic potential of the metal oxide nanoparticles. The standardized potency of the 4 metal oxide nanoparticle clusters was compared: 3 clusters (I, II and IV) showed signs of elevated immune system activity and cell membrane damage. These clusters were primarily composed of iron oxide nanoparticles (cluster I) silica (cluster II), and titanium dioxide (cluster IV). One cluster (III) showed comparatively reduced toxicity across all 5 responses; this cluster primarily featured zinc oxide and cerium oxide nanoparticles.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Health, Toxicology and Mutagenesis