Prediction of skin sensitization using machine learning

局部淋巴结试验 三元运算 工具箱 支持向量机 敏化 皮肤致敏 计算机科学 二进制数 机器学习 欧盟委员会 人工智能 欧洲联盟 医学 数学 免疫学 业务 经济政策 算术 程序设计语言
作者
Jueng Eun Im,Jung Dae Lee,Hyang Yeon Kim,Hak Rim Kim,Dong Wan Seo,Kyu‐Bong Kim
出处
期刊:Toxicology in Vitro [Elsevier BV]
卷期号:93: 105690-105690
标识
DOI:10.1016/j.tiv.2023.105690
摘要

As global awareness of animal welfare spreads, the development of alternative animal test models is increasingly necessary. The purpose of this study was to develop a practical machine-learning model for skin sensitization using three physicochemical properties of the chemicals: surface tension, melting point, and molecular weight. In this study, a total of 482 chemicals with local lymph node assay results were collected, and 297 datasets with 6 physico-chemical properties were used to develop Random Forest (RF) model for skin sensitization. The developed model was validated with 45 fragrance allergens announced by European Commission. The validation results showed that RF achieved better or similar classification performance with f1-scores of 54% for penal, 82% for ternary, and 96% for binary compared with Support Vector Machine (SVM) (penal, 41%; ternary, 81%; binary, 93%), QSARs (ChemTunes, 72% for ternary; OECD Toolbox, 89% for binary), and a linear model (Kim et al., 2020) (41% for penal), and we recommend the ternary classification based on Global Harmonized System providing more detailed and precise information. In the further study, the proposed model results were experimentally validated with the Direct Peptide Reactivity Assay (DPRA, OECD TG 442C approved model), and the results showed a similar tendency. We anticipate that this study will help to easily and quickly screen chemical sensitization hazards.
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