Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms-Reference-Cited by-同舟云学术

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

Published:2024-05-28 Issue:7 Volume:2 Page:474-485
ISSN:2833-8278
Container-title:Environment & Health
language:en
Short-container-title:Environ. Health

Author:

Daood Nada J.¹,Russo Daniel P.¹,Chung Elena¹²^ORCID,Qin Xuebin³,Zhu Hao¹²^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States

2. Center for Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States

3. Tulane National Primate Research Center, Tulane University School of Medicine, Covington, Louisiana 70433, United States

Funder

National Institute of Environmental Health Sciences

National Institute of Child Health and Human Development

National Institute of General Medical Sciences

Division of Information and Intelligent Systems

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/envhealth.4c00026

Reference80 articles.

1. U.S. Congress; Office of Technology Assessment. Identifying and Controlling Immunotoxic Substances - Background Paper, OTA-BP-BA-75; U.S. Government Printing Office: Washington, DC, 1991.

2. Immunotoxicology: A brief history, current status and strategies for future immunotoxicity assessment

3. Toxicity Testing in the 21st Century: A Vision and a Strategy

4. In Vitro Testing for Direct Immunotoxicity: State of the Art

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