HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization

作者：Fanrong Zhao, Li Li, Penghui Lin, Yue Chen, Shipei Xing, Huili Du, Zheng Wang, Junjie Yang, Tao Huan, Cheng Long, Limao Zhang, Bin Wang, Mingliang Fang · 发表于：Environmental Health Perspectives · 年份：2023 · DOI：10.1289/ehp11305 · 被引用次数：47 · 研究领域：Health, Environment, Cognitive Aging、Effects and risks of endocrine disrupting chemicals、Computational Drug Discovery Methods

Background: Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. Objectives: Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern. Methods: We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after...