Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs)...

Descripción completa

Detalles Bibliográficos
Autores principales: Nicolas, Chantel I., Linakis, Matthew W., Minto, Melyssa S., Mansouri, Kamel, Clewell, Rebecca A., Yoon, Miyoung, Wambaugh, John F., Patlewicz, Grace, McMullen, Patrick D., Andersen, Melvin E., Clewell III, Harvey J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Pharmacology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586287/
https://www.ncbi.nlm.nih.gov/pubmed/36278238
http://dx.doi.org/10.3389/fphar.2022.980747

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586287/
https://www.ncbi.nlm.nih.gov/pubmed/36278238
http://dx.doi.org/10.3389/fphar.2022.980747

Ejemplares similares