Chem-Bio Informatics Journal
Online ISSN : 1347-0442
Print ISSN : 1347-6297
ISSN-L : 1347-0442
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Machine-learning-driven oral-to-inhalation extrapolation for predicting inhalation toxicity values
Kazushi Matsumura
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2025 Volume 25 Pages 1-18

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Abstract

Relevant exposure routes need to be taken into account when performing chemical risk assessments for humans. Chemical risks are often assessed by performing route-to-route extrapolations based on oral repeated dose toxicity studies if route-specific toxicity data are unavailable. When performing a route-to-route extrapolation, an extrapolation factor derived from differences in absorption after exposure through different routes needs to be estimated. In this study, we used a machine learning (ML) and regression-based approach to estimate extrapolation-factor-like coefficients for oral-to-inhalation extrapolations using chemical structures and physicochemical properties. We used well-reviewed chemicals with human chronic toxicity values for specific administration routes (oral reference dose and inhalation reference concentration) available. ML regression models for predicting inhalation reference concentrations were developed using oral reference doses and molecular features as descriptors. The ML-based regression models gave better predictions than models using only molecular features or even single constant extrapolation factors, suggesting that the ML-based approach offers advantages over other oral-to-inhalation extrapolation methods.

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この記事はクリエイティブ・コモンズ [表示 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by/4.0/deed.ja
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