QSAR’s abilities advance by ATSDR’s con- tinuance of method development, evalua- tion, and re-application. • Assessing Exposure Levels: By combining data on chemical properties, environmen- tal concentrations, and human exposure data, computational models can estimate the levels of exposure to hazardous sub- stances in dierent populations. Exposure- dose reconstruction uses computational models and other simulation techniques to estimate cumulative concentrations of hazardous substances in individuals at risk of exposure to substances associated with hazardous waste sites. Environmental fate and transport modeling and water distri- bution system modeling enhance ATSDR’s capacity to assess exposures and doses, especially past exposures, to support bet- ter health assessments and consultations, health studies, and exposure registries. ATSDR scientists have published research papers documenting their reconstruction of historical drinking water contamination at U.S. Marine Corps Base Camp Lejeune. Between 1950 and 1985, the drinking water at the base was contaminated with volatile organic compounds (VOCs) such as tetrachloroethylene. ATSDR research- ers evaluated the association between the exposures and adverse birth outcomes (Ruckart et al., 2013). • Identifying Vulnerable Populations: Mod- els can simulate how dierent population groups might be more susceptible to the adverse eects of certain chemicals due to factors such as age, genetics, or pre-exist- ing health conditions. Computational sys- tems biology enables the identification of potential linkages between environmental toxicants, biological pathways, and health outcomes using bioinformatics modeling, mathematics, high-throughput data, gene networks, protein interactions, and cellular processes. This approach justifies mecha- nistic hypotheses that can be investigated via in vivo and in vitro experimentation. ATSDR scientists incorporated computa- tional systems biology, PBPK modeling, and gene expression data to uncover the potential mechanisms by which toluene, ethylbenzene, and xylene mixtures interact and understand their long- and short-term exposure eects on health outcomes as a mixture (Ruiz et al., 2020).
The Main Computational Modeling Approaches of the Simulation Science Section Within the Agency for Toxic Substances and Disease Registry (ATSDR) FIGURE 1
Computational Modeling and Applications Within ATSDR ATSDR utilizes simulation science tools to enhance its understanding of the potential health eects of exposure to hazardous sub- stances. Key applications of computational modeling within ATSDR include the following: • Predicting Toxicity: Computational mod- els estimate the toxicity of chemicals based on their structure and properties. QSAR modeling is employed to predict the tox- icity activity of chemicals in human risk assessment. These models assist in priori-
tizing chemicals for further testing, pro- viding insight into their potential risks to human health. ATSDR has been working toward this goal by developing and validat- ing QSAR models for major toxicological endpoints, such as endocrine disruption (Ruiz et al., 2017) and developmental toxicity (Ruiz et al., 2011). This work has spread awareness via collaborative eorts within predictive toxicology (Mansouri et al., 2020; Myatt et al., 2018). QSAR model- ing has permitted significant achievements and findings in toxicology at ATSDR.
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November 2024 • Journal of Environmental Health
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