Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms  

作　　者：Nada J.Daood Daniel P.Russo Elena Chung Xuebin Qin Hao Zhu 

机构地区：[1]Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms [2]Department of Chemistry and Biochemistry,Rowan University,Glassboro,New Jersey 08028,United States [3]Center for Biomedical Informatics and Genomics,Tulane University School of Medicine,New Orleans,Louisiana 70112,United States [4]Tulane National Primate Research Center,Tulane University School of Medicine,Covington,Louisiana 70433,United States

出　　处：《Environment & Health》2024年第7期474-485,共12页环境与健康（英文）

基　　金：National Institute of General Medical Sciences(Grant R01GM148743);National Institute of Child Health and Human Development(Grant UHD113039);National Science Foundation(Grant 2402311);National Institute of Environmental Health Sciences(Grants R01ES031080 and R35ES031709).

摘　　要：Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immunotoxicity were reported,with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity.In this study,we employed a data-driven quantitative structure–activity relationship(QSAR)modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity.To this end,a probe data set of 6,341 chemicals was obtained from a high-throughput screening(HTS)assay testing for the activation of the aryl hydrocarbon receptor(AhR)signaling pathway,a key event leading to immunotoxicity.Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds.100 assays were selected to develop QSAR models based on their correlations to AhR agonism.Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints.5-fold cross-validation of the resulting models showed good predictivity(average CCR=0.73).A total of 20 assays were further selected based on QSAR model performance,and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals.This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints,which have limited training data and complicated toxicity mechanisms.

关 键 词：IMMUNOTOXICITY QSAR Machine learning Aryl hydrocarbon receptor Data mining 

分 类 号：X592[环境科学与工程—环境工程]