Is Mixtures' Additivity Supported by Empirical Data? A Case Study of Developmental Toxicity of PFOS and 6:2 FTS in Wildtype Zebrafish Embryos.
Megan E FeyPhilip E GoodrumN Roxanna RazaviChristopher Michael WhippsSujan FernandoJanet K AndersonPublished in: Toxics (2022)
Per- and polyfluoroalkyl substances (PFASs) are a major priority for many federal and state regulatory agencies charged with monitoring levels of emerging contaminants in environmental media and setting health-protective benchmarks to guide risk assessments. While screening levels and toxicity reference values have been developed for numerous individual PFAS compounds, there remain important data gaps regarding the mode of action for toxicity of PFAS mixtures. The present study aims to contribute whole-mixture toxicity data and advance the methods for evaluating mixtures of two key components of aqueous film-forming foams: perfluorooctanesulfonic acid (PFOS), and 6:2 fluorotelomer sulfonic acid (6:2 FTS). Wildtype (AB) zebrafish embryos were exposed to PFOS and 6:2 FTS, both as individual components and as binary mixtures, from 2 to 122 h post-fertilization. Five treatment levels were selected to encompass environmentally relevant exposure levels. Experimental endpoints consisted of mortality, hatching, and developmental endpoints, including swim bladder inflation, yolk sac area, and larval body length. Results from dose-response analysis indicate that the assumption of additivity using conventional points of departure (e.g., NOAEL, LOAEL) is not supported for critical effect endpoints with these PFAS mixtures, and that the interactions vary as a function of the dose range. Alternative methods for quantifying relative potency are proposed, and recommendations for additional investigations are provided to further advance assessments of the toxicity of PFAS mixtures to aquatic organisms.
Keyphrases
- ionic liquid
- oxidative stress
- electronic health record
- room temperature
- healthcare
- public health
- spinal cord injury
- risk assessment
- type diabetes
- oxide nanoparticles
- cardiovascular events
- transcription factor
- multidrug resistant
- gold nanoparticles
- gram negative
- health information
- reduced graphene oxide
- artificial intelligence
- life cycle