The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mechanistic Applicability Domain Classification of a Local Lymph Node Assay Dataset for Skin Sensitization
Abstract
The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.
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Cites
Sköld, M., Börje, A., Harambasic, E., & Karlberg, A.-T. (2004) · Chemical Research in Toxicology · doi:10.1021/tx049831z · Cited by 202
Cited by
Assessing skin sensitization hazard in mice and men using non-animal test methods
Urbisch, D., Mehling, A., Guth, K., Ramirez, T., Honarvar, N., Kolle, S., Landsiedel, R., Jaworska, J., Kern, P. S., Gerberick, F., et al. (2015) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2014.12.008 · Cited by 398
Self-citationTowards reproducible computational drug discovery
Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0408-x · Cited by 208
Non-animal methods to predict skin sensitization (I): the Cosmetics Europe database
Hoffmann, S., Kleinstreuer, N., Alépée, N., Allen, D., Api, A. M., Ashikaga, T., Clouet, E., Cluzel, M., Desprez, B., Gellatly, N., et al. (2018) · Critical Reviews in Toxicology · doi:10.1080/10408444.2018.1429385 · Cited by 188
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Metadata
- Authors
- Roberts, D. W., Patlewicz, G., Kern, P. S., Gerberick, F., Kimber, I., Dearman, R. J., Ryan, C. A., Basketter, D. A., & Aptula, A. O.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-06-08
- Volume
- 20
- Issue
- 7
- Pages
- 1019-1030
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W1993795069
- Cited by
- 1,862
- References
- 1
- FWCI
- 18.27
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.