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<p>Per- and polyfluoroalkyl substances (PFASs) are pervasive in airborne particles and aerosols, making inhalation a critical exposure pathway; however, the lack of inhalation toxicity data hinders accurate risk assessment and public health protection. In this study, we developed quantitative structure–activity relationship (QSAR) and quantitative read-across structure–activity relationship (q-RASAR) models to predict the acute inhalation toxicity of PFASs. The models were constructed using mechanistically interpretable two-dimensional molecular descriptors, and the integration of similarity-based descriptors enhanced predictive performance while maintaining model simplicity and interpretability. All validated models were applied to untested PFASs for toxicity prediction and priority ranking. In addition, interspecies toxicity (iST) models were established to explore toxicity relationships between rats and mice, enabling cross-species extrapolation. Collectively, these QSAR, q-RASAR, and iST models address the critical data gap in PFAS inhalation toxicology, providing a rapid and reliable tool for regulators and researchers to support science-driven risk assessment and public health protection against airborne PFAS exposure.</p>

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Unveiling the structural determinants of PFASs acute inhalation toxicity: an integrated approach using QSAR, q-RASAR, and interspecies extrapolation

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Manyi Qiu,
  • Jieyi Yang,
  • Ying’ai Pang,
  • Yunman Wen,
  • Le Yang,
  • Guoliang Li,
  • Qiaoyuan Yang,
  • Lili Liu

摘要

Per- and polyfluoroalkyl substances (PFASs) are pervasive in airborne particles and aerosols, making inhalation a critical exposure pathway; however, the lack of inhalation toxicity data hinders accurate risk assessment and public health protection. In this study, we developed quantitative structure–activity relationship (QSAR) and quantitative read-across structure–activity relationship (q-RASAR) models to predict the acute inhalation toxicity of PFASs. The models were constructed using mechanistically interpretable two-dimensional molecular descriptors, and the integration of similarity-based descriptors enhanced predictive performance while maintaining model simplicity and interpretability. All validated models were applied to untested PFASs for toxicity prediction and priority ranking. In addition, interspecies toxicity (iST) models were established to explore toxicity relationships between rats and mice, enabling cross-species extrapolation. Collectively, these QSAR, q-RASAR, and iST models address the critical data gap in PFAS inhalation toxicology, providing a rapid and reliable tool for regulators and researchers to support science-driven risk assessment and public health protection against airborne PFAS exposure.