Academic Journal
Artificial intelligence-augmented mixture toxicology: reframing unresolved risk at Camp Lejeune.
| Τίτλος: | Artificial intelligence-augmented mixture toxicology: reframing unresolved risk at Camp Lejeune. |
|---|---|
| Συγγραφείς: | Pressman P; Department of Sociology, University of Maine, Orono, ME 04469, United States., Hayes AW; College of Public Health, University of South Florida, Tampa, FL 33620, United States. |
| Πηγή: | Toxicological sciences : an official journal of the Society of Toxicology [Toxicol Sci] 2026 Jun 05; Vol. 209 (6). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 9805461 Publication Model: Print Cited Medium: Internet ISSN: 1096-0929 (Electronic) Linking ISSN: 10960929 NLM ISO Abbreviation: Toxicol Sci Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1999- : Cary, NC : Oxford University Press Original Publication: Orlando, FL : Academic Press, c1998- |
| Ιατρικοί όροι (MeSH): | Toxicology*/methods , Volatile Organic Compounds*/toxicity , Water Pollutants, Chemical*/toxicity , Complex Mixtures*/toxicity , Artificial Intelligence* , Drinking Water*, Risk Assessment ; Humans ; Animals |
| Περίληψη: | Persistent uncertainty regarding health risks associated with contaminated drinking water at Camp Lejeune reflects a broader limitation in toxicology: the difficulty of evaluating complex chemical mixtures in genetically heterogeneous populations. Historical assessments have relied on single-chemical paradigms, assumptions of dose additivity, and population-average susceptibility. These approaches are not well aligned with exposures involving multiple volatile organic compounds (VOCs) that share metabolic pathways and generate reactive intermediates. This Forum article advances the position that artificial intelligence (AI)-enabled computational toxicology can strengthen mixture risk assessment by integrating toxicokinetic, toxicodynamic, and toxicogenomic data into mechanistically coherent, testable models. Using the Camp Lejeune VOC mixture-trichloroethylene, tetrachloroethylene, benzene, and vinyl chloride-as a case study, we illustrate how AI-augmented approaches can identify plausible interaction mechanisms, quantify genotype-dependent variability in internal dose, and generate probabilistic estimates of risk. We argue that the principal unresolved issue at Camp Lejeune is whether chronic low-level exposure to interacting mixtures disproportionately affected susceptible subpopulations. AI-driven approaches provide a tractable framework to address this question and to clarify the interface between mechanistic toxicology and public health decision-making. (© The Author(s) 2026. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
| Contributed Indexing: | Keywords: PBPK; artificial intelligence; camp Lejeune; genetic susceptibility; mixture toxicology; volatile organic compounds |
| Substance Nomenclature: | 0 (Volatile Organic Compounds) 0 (Water Pollutants, Chemical) 0 (Drinking Water) 0 (Complex Mixtures) |
| Entry Date(s): | Date Created: 20260601 Date Completed: 20260622 Latest Revision: 20260627 |
| Update Code: | 20260628 |
| DOI: | 10.1093/toxsci/kfag066 |
| PMID: | 42225985 |
| Βάση Δεδομένων: | MEDLINE |
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