Academic Journal
Artificial intelligence for predictive mixture toxicology.
| Τίτλος: | Artificial intelligence for predictive mixture toxicology. |
|---|---|
| Συγγραφείς: | Domingo JL; Universitat Rovira i Virgili, School of Medicine, Laboratory of Toxicology and Environmental Health, Sant Llorens 21, Reus, Catalonia, 43201, Spain. Electronic address: joseluis.domingo@urv.cat., Souza MCO; School of Pharmaceutical Sciences of Ribeirão Preto, Department of Biomolecular Sciences, University of São Paulo, Av. do Café s/n, Ribeirão Preto, SP, 14040-903, Brazil., Barbosa F Jr; School of Pharmaceutical Sciences of Ribeirão Preto, Department of Clinical, Toxicological and Bromatological Analysis, University of São Paulo, Ribeirão Preto, SP, Brazil. |
| Πηγή: | Toxicology [Toxicology] 2026 Dec; Vol. 527, pp. 154589. Date of Electronic Publication: 2026 Sep 19. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Ireland NLM ID: 0361055 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-3185 (Electronic) Linking ISSN: 0300483X NLM ISO Abbreviation: Toxicology Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Original Publication: Amsterdam. Elsevier/North-Holland. |
| Ιατρικοί όροι (MeSH): | Toxicology*/methods , Environmental Pollutants*/toxicity , Complex Mixtures*/toxicity , Artificial Intelligence*, Humans ; Animals ; Machine Learning ; Risk Assessment |
| Περίληψη: | Human populations and ecosystems are continuously exposed to complex mixtures of environmental contaminants rather than to individual chemicals in isolation. These mixtures include pesticides, metals and metalloids, persistent organic pollutants, endocrine-disrupting chemicals, per- and polyfluoroalkyl substances, pharmaceuticals, plastic-associated compounds, air pollutants, nanomaterials, and numerous poorly characterized substances. Their combined effects may be additive, synergistic, or antagonistic, and are strongly influenced by dose, component ratio, timing, exposure sequence, and biological susceptibility. Experimental evaluation of all environmentally relevant mixtures is infeasible because the number of possible combinations increases combinatorially. Artificial intelligence (AI) offers a possible way to address this limitation. Machine learning, deep learning, graph neural networks, Bayesian approaches, and natural-language processing can integrate heterogeneous data, including chemical structures, molecular descriptors, toxicokinetics, high-throughput screening results, omics profiles, adverse outcome pathways, biomonitoring data, and epidemiological findings. However, the evidence base is uneven. Relatively few studies have applied AI directly to experimentally characterized mixtures, and much of the current optimism is extrapolated from single-chemical toxicology. This review distinguishes explicitly between applications demonstrated in mixtures, proof-of-concept mixture applications, and approaches whose mixture use remains prospective. It further examines the methodological requirements for mixture prediction, including dose and ratio representation, additivity reference models, applicability domains, external validation, and mechanistic interpretability, and proposes a framework for regulatory-grade implementation. Current evidence does not support autonomous AI-driven regulation of mixtures. AI should complement, not replace, experimental and expert evaluation, supporting a transition toward more predictive, mechanism-informed assessment of real-world chemical exposures. (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Chemical mixtures; Computational toxicology; Exposomics; Machine learning; Predictive toxicology |
| Substance Nomenclature: | 0 (Environmental Pollutants) 0 (Complex Mixtures) |
| Entry Date(s): | Date Created: 20260919 Date Completed: 20260929 Latest Revision: 20260929 |
| Update Code: | 20260930 |
| DOI: | 10.1016/j.tox.2026.154589 |
| PMID: | 42762870 |
| Βάση Δεδομένων: | MEDLINE |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial intelligence for predictive mixture toxicology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Domingo+JL%22">Domingo JL</searchLink>; Universitat Rovira i Virgili, School of Medicine, Laboratory of Toxicology and Environmental Health, Sant Llorens 21, Reus, Catalonia, 43201, Spain. Electronic address: joseluis.domingo@urv.cat.<br /><searchLink fieldCode="AU" term="%22Souza+MCO%22">Souza MCO</searchLink>; School of Pharmaceutical Sciences of Ribeirão Preto, Department of Biomolecular Sciences, University of São Paulo, Av. do Café s/n, Ribeirão Preto, SP, 14040-903, Brazil.<br /><searchLink fieldCode="AU" term="%22Barbosa+F+Jr%22">Barbosa F Jr</searchLink>; School of Pharmaceutical Sciences of Ribeirão Preto, Department of Clinical, Toxicological and Bromatological Analysis, University of São Paulo, Ribeirão Preto, SP, Brazil. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%220361055%22">Toxicology</searchLink> [Toxicology] 2026 Dec; Vol. 527, pp. 154589. <i>Date of Electronic Publication: </i>2026 Sep 19. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Review – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>Ireland <i>NLM ID: </i>0361055 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-3185 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220300483X%22">0300483X </searchLink><i>NLM ISO Abbreviation: </i>Toxicology <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Publication</i>: Limerick : Elsevier<br /><i>Original Publication</i>: Amsterdam. Elsevier/North-Holland. – 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="%22Environmental+Pollutants%22">Environmental Pollutants*</searchLink>/<searchLink fieldCode="MM" term="%22Environmental+Pollutants+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="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> ; <searchLink fieldCode="MH" term="%22Machine+Learning%22">Machine Learning</searchLink> ; <searchLink fieldCode="MH" term="%22Risk+Assessment%22">Risk Assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Human populations and ecosystems are continuously exposed to complex mixtures of environmental contaminants rather than to individual chemicals in isolation. These mixtures include pesticides, metals and metalloids, persistent organic pollutants, endocrine-disrupting chemicals, per- and polyfluoroalkyl substances, pharmaceuticals, plastic-associated compounds, air pollutants, nanomaterials, and numerous poorly characterized substances. Their combined effects may be additive, synergistic, or antagonistic, and are strongly influenced by dose, component ratio, timing, exposure sequence, and biological susceptibility. Experimental evaluation of all environmentally relevant mixtures is infeasible because the number of possible combinations increases combinatorially. Artificial intelligence (AI) offers a possible way to address this limitation. Machine learning, deep learning, graph neural networks, Bayesian approaches, and natural-language processing can integrate heterogeneous data, including chemical structures, molecular descriptors, toxicokinetics, high-throughput screening results, omics profiles, adverse outcome pathways, biomonitoring data, and epidemiological findings. However, the evidence base is uneven. Relatively few studies have applied AI directly to experimentally characterized mixtures, and much of the current optimism is extrapolated from single-chemical toxicology. This review distinguishes explicitly between applications demonstrated in mixtures, proof-of-concept mixture applications, and approaches whose mixture use remains prospective. It further examines the methodological requirements for mixture prediction, including dose and ratio representation, additivity reference models, applicability domains, external validation, and mechanistic interpretability, and proposes a framework for regulatory-grade implementation. Current evidence does not support autonomous AI-driven regulation of mixtures. AI should complement, not replace, experimental and expert evaluation, supporting a transition toward more predictive, mechanism-informed assessment of real-world chemical exposures.<br /> (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.) – Name: Abstract Label: Competing Interests Group: Ab Data: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>Artificial intelligence; Chemical mixtures; Computational toxicology; Exposomics; Machine learning; Predictive toxicology – Name: NumberCAS Label: Substance Nomenclature Group: ID Data: 0 (Environmental Pollutants)<br />0 (Complex Mixtures) – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260919 <i>Date Completed: </i>20260929 <i>Latest Revision: </i>20260929 – Name: DateUpdate Label: Update Code Group: Date Data: 20260930 – Name: DOI Label: DOI Group: ID Data: 10.1016/j.tox.2026.154589 – Name: AN Label: PMID Group: ID Data: 42762870 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.tox.2026.154589 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 154589 Subjects: – SubjectFull: Humans Type: general – SubjectFull: Animals Type: general – SubjectFull: Machine Learning Type: general – SubjectFull: Risk Assessment Type: general – SubjectFull: Toxicology methods Type: general – SubjectFull: Environmental Pollutants toxicity Type: general – SubjectFull: Complex Mixtures toxicity Type: general – SubjectFull: Artificial Intelligence Type: general Titles: – TitleFull: Artificial intelligence for predictive mixture toxicology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Domingo JL – PersonEntity: Name: NameFull: Souza MCO – PersonEntity: Name: NameFull: Barbosa F Jr IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2026 Dec Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1879-3185 Numbering: – Type: volume Value: 527 Titles: – TitleFull: Toxicology Type: main |
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