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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| Items | – Name: Title Label: Title Group: Ti Data: Artificial intelligence-augmented mixture toxicology: reframing unresolved risk at Camp Lejeune. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Pressman+P%22">Pressman P</searchLink>; Department of Sociology, University of Maine, Orono, ME 04469, United States.<br /><searchLink fieldCode="AU" term="%22Hayes+AW%22">Hayes AW</searchLink>; College of Public Health, University of South Florida, Tampa, FL 33620, United States. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229805461%22">Toxicological sciences : an official journal of the Society of Toxicology</searchLink> [Toxicol Sci] 2026 Jun 05; Vol. 209 (6). – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Oxford+University+Press%22">Oxford University Press </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9805461 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1096-0929 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2210960929%22">10960929 </searchLink><i>NLM ISO Abbreviation: </i>Toxicol Sci <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Publication</i>: 1999- : Cary, NC : Oxford University Press<br /><i>Original Publication</i>: Orlando, FL : Academic Press, c1998- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Toxicology%22">Toxicology*</searchLink>/<searchLink fieldCode="MM" term="%22Toxicology+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Volatile+Organic+Compounds%22">Volatile Organic Compounds*</searchLink>/<searchLink fieldCode="MM" term="%22Volatile+Organic+Compounds+toxicity%22">toxicity</searchLink> <br /><searchLink fieldCode="MM" term="%22Water+Pollutants%2C+Chemical%22">Water Pollutants, Chemical*</searchLink>/<searchLink fieldCode="MM" term="%22Water+Pollutants%2C+Chemical+toxicity%22">toxicity</searchLink> <br /><searchLink fieldCode="MM" term="%22Complex+Mixtures%22">Complex Mixtures*</searchLink>/<searchLink fieldCode="MM" term="%22Complex+Mixtures+toxicity%22">toxicity</searchLink> <br /><searchLink fieldCode="MM" term="%22Artificial+Intelligence%22">Artificial Intelligence*</searchLink> <br /><searchLink fieldCode="MM" term="%22Drinking+Water%22">Drinking Water*</searchLink><br /><searchLink fieldCode="MH" term="%22Risk+Assessment%22">Risk Assessment</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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.<br /> (© 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.) – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>PBPK; artificial intelligence; camp Lejeune; genetic susceptibility; mixture toxicology; volatile organic compounds – Name: NumberCAS Label: Substance Nomenclature Group: ID Data: 0 (Volatile Organic Compounds)<br />0 (Water Pollutants, Chemical)<br />0 (Drinking Water)<br />0 (Complex Mixtures) – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260601 <i>Date Completed: </i>20260622 <i>Latest Revision: </i>20260627 – Name: DateUpdate Label: Update Code Group: Date Data: 20260628 – Name: DOI Label: DOI Group: ID Data: 10.1093/toxsci/kfag066 – Name: AN Label: PMID Group: ID Data: 42225985 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/toxsci/kfag066 Languages: – Code: eng Text: English Subjects: – SubjectFull: Risk Assessment Type: general – SubjectFull: Humans Type: general – SubjectFull: Animals Type: general – SubjectFull: Toxicology methods Type: general – SubjectFull: Volatile Organic Compounds toxicity Type: general – SubjectFull: Water Pollutants, Chemical toxicity Type: general – SubjectFull: Complex Mixtures toxicity Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Drinking Water Type: general Titles: – TitleFull: Artificial intelligence-augmented mixture toxicology: reframing unresolved risk at Camp Lejeune. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pressman P – PersonEntity: Name: NameFull: Hayes AW IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 06 Text: 2026 Jun 05 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1096-0929 Numbering: – Type: volume Value: 209 – Type: issue Value: 6 Titles: – TitleFull: Toxicological sciences : an official journal of the Society of Toxicology Type: main |
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