Current opinion on risk assessment of cosmetics
Abstract
Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and in vitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.
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Consumers support cosmetic products that are cruelty-free and not tested on animals.
“Additional to that, the consumers also support cosmetic products which are cruelty free and not tested on animals (130, 135).”
From The dark side of beauty: an in-depth analysis of the health hazards and toxicological impact of synthetic cosmetics and personal care products (2024) · cites this paper as [130]
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Alnuqaydan, A. M. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1439027 · Cited by 84 · see citing works
“Additional to that, the consumers also support cosmetic products which are cruelty free and not tested on animals (130, 135).”
BackgroundMethod
- Authors
- Kim, K.-B., Kwack, S. J., Lee, J. Y., Kacew, S., & Lee, B.-M.
- Affiliations
- Dankook University (KR)
- Changwon National University (KR)
- Catholic University of Korea (KR)
- University of Ottawa (CA)
- Sungkyunkwan University (KR)
- Corresponding author
- Kim, Kyu-Bong
- Venue
- Journal of Toxicology and Environmental Health Part B
- Publisher
- Informa UK Limited
- Published
- 2021-04-08
- Volume
- 24
- Issue
- 4
- Pages
- 137-161
- ISSN
- 1093-7404 (ISSN-L)
- 1521-6950
- OpenAlex
- W3146669585
- Cited by
- 63
- References
- 123
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 12 · 2023: 15 · 2022: 7 · 2021: 6
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Toxicology and Environmental Health Part B (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Food and Drug Safety (19172MFDS221)
- National Institute of Food and Drug Safety Evaluation (21172MFDS161)
- Topics
- Health Sciences › Medicine › Dermatology › Contact Dermatitis and Allergies
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
- Keywords
- cosmetic ingredients, hazard identification, dose-response assessment, exposure assessment, risk characterization, alternative toxicity testing, read-across, in silico analysis, in vitro methods, omics, artificial intelligence, aggregate exposure, animal testing ban, EU cosmetics regulation
- MeSH terms
- Animals, Animal Testing Alternatives, Artificial Intelligence, Computer Simulation, Consumer Product Safety, Cosmetics, Humans, Risk Assessment, In Vitro Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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