Quantitative adverse outcome pathway modeling for cigarette smoke-inducible airway mucus hypersecretion. Part 2: Bayesian network modeling for probabilistic risk estimation DOI Creative Commons
Shigeaki Ito,

Sakuya Ichikawa,

Risa Matsumoto

и другие.

Frontiers in Toxicology, Год журнала: 2025, Номер 7

Опубликована: Май 15, 2025

The development of in vitro tests that reproduce real-world situations is crucial for toxicity- and disease-risk assessment without animal testing. Because signs symptoms health concerns can be complex, it helpful to create a simplified representation such manifestations using conceptual framework as an adverse outcome pathway (AOP). Combining AOP with computational models could potential tool the extrapolation results scenarios. Here, we applied Bayesian network-based probabilistic quantitative disease-related risk estimation dataset on mucus hypersecretion—a known representative symptom chronic airway disease—obtained by repeated exposure human bronchial epithelial cells whole cigarette smoke. We also used aerosol dosimetry model account differences between concentrations revealed dose- repetition-dependent increases probability, suggesting reflects continuum smoking. Furthermore, under certain assumptions, modeling indicated our were similar actual smoking As exercise, calculated odds ratios bronchitis comparable range due Our combinatory risk-assessment approach valuable estimating inhalation effects inhalable products chemicals.

Язык: Английский

Quantitative adverse outcome pathway modeling for cigarette smoke-inducible airway mucus hypersecretion. Part 2: Bayesian network modeling for probabilistic risk estimation DOI Creative Commons
Shigeaki Ito,

Sakuya Ichikawa,

Risa Matsumoto

и другие.

Frontiers in Toxicology, Год журнала: 2025, Номер 7

Опубликована: Май 15, 2025

The development of in vitro tests that reproduce real-world situations is crucial for toxicity- and disease-risk assessment without animal testing. Because signs symptoms health concerns can be complex, it helpful to create a simplified representation such manifestations using conceptual framework as an adverse outcome pathway (AOP). Combining AOP with computational models could potential tool the extrapolation results scenarios. Here, we applied Bayesian network-based probabilistic quantitative disease-related risk estimation dataset on mucus hypersecretion—a known representative symptom chronic airway disease—obtained by repeated exposure human bronchial epithelial cells whole cigarette smoke. We also used aerosol dosimetry model account differences between concentrations revealed dose- repetition-dependent increases probability, suggesting reflects continuum smoking. Furthermore, under certain assumptions, modeling indicated our were similar actual smoking As exercise, calculated odds ratios bronchitis comparable range due Our combinatory risk-assessment approach valuable estimating inhalation effects inhalable products chemicals.

Язык: Английский

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