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Advances in the application of computational toxicology in cosmetic safety assessment.

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Τίτλος: Advances in the application of computational toxicology in cosmetic safety assessment.
Συγγραφείς: Zhou Z; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China., Guo D; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China. g1334524567@163.com.; Guangdong Provincial Biomedical Science and Technology Collaborative Innovation Center, Guangdong Institute for Drug Control, Guangzhou, 510006, China. g1334524567@163.com., Liang J; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China., Li Y; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China.; Guangdong Provincial Biomedical Science and Technology Collaborative Innovation Center, Guangdong Institute for Drug Control, Guangzhou, 510006, China., Wu Q; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China.; Guangdong Provincial Biomedical Science and Technology Collaborative Innovation Center, Guangdong Institute for Drug Control, Guangzhou, 510006, China., Fang J; NMPA Key Laboratory for Safety Risk Assessment of Cosmetics, Guangdong Institute for Drug Control, Guangzhou, 510006, China. jhfayy@163.com.; Guangdong Provincial Biomedical Science and Technology Collaborative Innovation Center, Guangdong Institute for Drug Control, Guangzhou, 510006, China. jhfayy@163.com.
Πηγή: Archives of toxicology [Arch Toxicol] 2026 Apr; Vol. 100 (4), pp. 1205-1223. Date of Electronic Publication: 2026 Jan 14.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0417615 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0738 (Electronic) Linking ISSN: 03405761 NLM ISO Abbreviation: Arch Toxicol Subsets: MEDLINE
Imprint Name(s): Original Publication: Berlin, New York, Springer-Verlag.
Ιατρικοί όροι (MeSH): Cosmetics*/toxicity , Cosmetics*/chemistry , Cosmetics*/pharmacokinetics , Toxicology*/methods , Toxicity Tests*/methods , Computational Biology*/methods , Consumer Product Safety*, Animal Testing Alternatives/methods ; Animals ; Humans ; Quantitative Structure-Activity Relationship ; Risk Assessment ; Computer Simulation ; Machine Learning
Περίληψη: With the global ban on animal testing for cosmetics, computational toxicology has emerged as a pivotal alternative for safety assessment. This review systematically analyzes technical frameworks, literature trends, multi-endpoint applications, and global regulatory developments. The study integrates core in silico methods, including quantitative structure–activity relationship (QSAR) models, expert systems and read-across, molecular simulations, physiologically based pharmacokinetic models, and machine learning, to evaluate their effectiveness in predicting key endpoints such as skin sensitization, genotoxicity, and endocrine disruption. Research shows that in silico methods can now predict metabolites and mixtures beyond single ingredients, with representative integrated models achieving ~86% accuracy in skin sensitization assessment, outperforming traditional assays (78%), and QSAR tools reaching 71.4–100% accuracy for OECD chemicals via in vitro data fusion. Global regulatory frameworks are gradually accepting defined approaches that integrate computational chemistry with in vitro data, with the EU, US, and China having formally adopted defined approaches for skin sensitization. However, challenges persist in model interpretability, stereoisomer coverage, and fragmented regulatory standards.  This review recommends prioritizing AI-driven multi-source data fusion to enhance transparency, comprehensive absorption-distribution-metabolism-excretion frameworks to resolve data gaps, and stereochemistry-aware evaluation to improve precision. Establishing standardized high-quality datasets, fostering interdisciplinary expertise, and constructing open data-sharing platforms will promote widespread application of computational toxicology, ultimately contributing to animal testing replacement.
Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflict of interest.
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Grant Information: 2023ZDZ11 the Scientific and technological innovation project of Guangdong Provincial Drug Administration; 2024ZDZ04 the Scientific and technological innovation project of Guangdong Provincial Drug Administration; 2025YDZ13 the Scientific and technological innovation project of Guangdong Provincial Drug Administration; B2024237 Guangdong Provincial Medical Science and Technology Research Fund; 2023A1111120025 the Science and Technology Plan Project of Guangdong Provincial
Contributed Indexing: Keywords: Computational toxicology; Cosmetic safety assessment; New approach methodologies; Non-animal testing
Substance Nomenclature: 0 (Cosmetics)
Entry Date(s): Date Created: 20260114 Date Completed: 20260627 Latest Revision: 20260627
Update Code: 20260627
DOI: 10.1007/s00204-025-04282-y
PMID: 41535589
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1432-0738
DOI:10.1007/s00204-025-04282-y