Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., <i>in vitro</i>/<i>in vivo</i> data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO₂, SiO₂, Ag, CeO₂, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

本研究构建了一款贝叶斯网络（Bayesian Network，BN），聚焦金属及金属氧化物纳米材料，用于预测其危害潜能与生物效应，以支撑人体健康风险评估工作。该贝叶斯网络以国际专家咨询结果与科学文献（如体外（in vitro）/体内（in vivo）实验数据）为基础，可全面系统地捕捉暴露途径、纳米材料理化性质与最终生物效应之间的（相互）关联。本研究所构建的贝叶斯网络采用已发表研究中的独立数据进行验证，其对纳米材料危害潜能的预测准确率达72%，对生物效应的预测准确率则为71%。研究通过针对二氧化钛（TiO₂）、二氧化硅（SiO₂）、银（Ag）、二氧化铈（CeO₂）、氧化锌（ZnO）纳米材料的情景案例，展示了该贝叶斯网络的应用方式。结果表明，该贝叶斯网络可供不同利益相关方在风险评估的多个阶段使用：既可预测信息匮乏的纳米材料的相关特性，也可对纳米材料进行优先级排序以开展后续筛选。