Understanding the Variable Drivers of Toxicity for the Broad Class of Carbon Nanotubes and Nanofibers from US Facilities

Details

• Personal Author:
  Bauer A ; Birch E ; Bishop L ; Boots T ; Bunker K ; Casuccio G ; Dahm, Matthew M. ; Erdely A ; Evans D ; Eye T ; Foster S ; Fraser K ; Friend S ; Hubbs A ; Hubczak J ; Kodali V ; Lersch T ; Lowry D ; Orandle M ; Sargent L ; Schubauer-Berigan, Mary K. ; Siegrist K ; Stephaniak A ; Yanamala N
• Description:
  Pulmonary exposure to carbon nanotubes or nanofibers (CNT/F) is known to induce inflammation, toxicity, or tumorigenesis, and is a concern in the occupational setting. Previously, we established toxicity profiles from male C57BL6/J mice aged 8-10 weeks exposed to either 4 or 40 microg of one of nine different CNT/F via oropharyngeal aspiration as well as human epithelial BEAS-2B cells (0-24 microg/ml), differentiated THP-1 cells (0-120 microg/ml), and human fibroblasts (0-2 microg/ml) for four primary outcomes of genotoxicity, inflammation, pathology, and translocation. An overarching goal of our expansive study was to use machine learning to determine the relationship between particle physicochemical characteristics with respective toxicity outcomes and the relationship between those four primary outcomes. The nine materials had a wide range of characteristics including diameter (6-397 nm), length (0.1-50 microm), surface area (18-238 m2/g), aspect ratio (2-1396), residual metal catalyst (0.3-6.2 %), density (0.007-0.220 g/cm3), etc., to consider. Unsupervised approaches were used to identify and define subsets of materials with similar outcomes. Subsequently, supervised learning approaches were used to identify physicochemical characteristics that define these outcomes. While some physicochemical characteristics were determined to be key drivers of specific toxicity outcomes, different characteristics were essential when considering other toxicity endpoints. More specifically, drivers of inflammation and/or pathology were not the factors driving translocation and/or genotoxicity. No single characteristic could be used as a toxicity predictor, therefore, multifactorial processes, or combination of characteristics, were necessary for an accurate and effective prediction model for responses. The study identified physicochemical drivers of CNT/F toxicity using an integrated approach, combining experimental evidence with computational modeling, with potential for broad application. [Description provided by NIOSH]
• Subjects:
  Chemistry Chromosomes Hazardous Substances Immune System Laboratories Occupational Health Particulate Matter Pathology Safety Software Toxicology

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• Keywords:
  Algorithms Carbon Nanotubes Chemical Properties Chromosome Damage Computer Models Genotoxicity Humans Immune Reaction Laboratory Animals Mathematical Models Nanofibers Nanotechnology Nanotoxicology Nanotubes Particle Diameter Particle Size Physical Properties Statistical Analysis Surface Properties Toxic Effects Toxic Materials

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• ISSN:
  1096-6080
• Document Type:
  Text
• Genre:
  Abstract or Summary
• Place as Subject:
  California ; Colorado ; Ohio ; OSHA Region 3 ; OSHA Region 5 ; OSHA Region 8 ; OSHA Region 9 ; Pennsylvania ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division ; DFSE - Division of Field Studies and Engineering
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  174
• Issue:
  1
• NIOSHTIC Number:
  nn:20058926
• Citation:
  Toxicologist 2020 Mar; 174(1):263
• CAS Registry Number:
  Carbon nanotubes (CAS RN 308068-56-6)
• Federal Fiscal Year:
  2020
• Peer Reviewed:
  False
• Source Full Name:
  The Toxicologist. Society of Toxicology 59th Annual Meeting and ToxExpo, March 15-19, 2020, Anaheim, California
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:2e7e56e69f71a0e3cbc82c4d10e4d53f5c3f9df669458f7cd5ee9d437c38734e605920f5c2e82c6af3c115f8d884902c840ffcfa4840be0c45360cba91591d6b
• Download URL:
  https://stacks.cdc.gov/view/cdc/223284/cdc_223284_DS1.pdf
• File Type:
  [PDF - 523.89 KB ]