Characterising the effects of per- and polyfluoroalkyl substances (PFASs) on health and disease: An opportunity for exposomics?

Marchiandi, Jaye; Green, Mark P.; Dagnino, Sonia; Anumol, Tarun; Clarke, Bradley O.

doi:10.1016/j.coesh.2020.05.006

Cited by 4 publications

(2 citation statements)

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“…Per- and poly-fluoroalkyl substances (PFASs) are a group of synthetic chemicals used in consumer products such as clothing and furniture for their water- and stain-repellent properties. , Studies have identified PFASs in various environmental settings including groundwater, dust, and edible fish . Their persistence in the environment leads to exposure and accumulation in the human body over time. ,− Researchers have detected perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid, and perfluorononanoic acid in human blood samples , and breast milk .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors

Azhagiya Singam,

Durkin,

La Merrill

et al. 2024

Environ. Sci. Technol.

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Per-and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.

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