An updated mode of action and human relevance framework evaluation for Formaldehyde-Related nasal tumors.
Chad M ThompsonRobinan GentrySeneca FitchKun LuHarvey J ClewellPublished in: Critical reviews in toxicology (2021)
Formaldehyde is a reactive aldehyde naturally present in all plant and animal tissues and a critical component of the one-carbon metabolism pathway. It is also a high production volume chemical used in the manufacture of numerous products. Formaldehyde is also one of the most well-studied chemicals with respect to environmental fate, biology, and toxicology-including carcinogenic potential, and mode of action (MOA). In 2006, a published MOA for formaldehyde-induced nasal tumors in rats concluded that nasal tumors were most likely driven by cytotoxicity and regenerative cell proliferation, with possible contributions from direct genotoxicity. In the past 15 years, new research has better informed the MOA with the publication of in vivo genotoxicity assays, toxicogenomic analyses, and development of ultra-sensitive methods to measure endogenous and exogenous formaldehyde-induced DNA adducts. Herein, we review and update the MOA for nasal tumors, with particular emphasis on the numerous studies published since 2006. These new studies further underscore the involvement of cytotoxicity and regenerative cell proliferation, and further inform the genotoxic potential of inhaled formaldehyde. The data lend additional support for the use of mechanistic data for the derivation of toxicity criteria and/or scientifically supported approaches for low-dose extrapolation for the risk assessment of formaldehyde.
Keyphrases
- room temperature
- cell proliferation
- low dose
- risk assessment
- stem cells
- human health
- chronic rhinosinusitis
- high glucose
- endothelial cells
- mesenchymal stem cells
- electronic health record
- diabetic rats
- big data
- cystic fibrosis
- randomized controlled trial
- drug induced
- high resolution
- circulating tumor
- cell therapy
- machine learning
- high throughput
- climate change
- deep learning
- induced pluripotent stem cells
- signaling pathway