Allergic contact dermatitis caused by a moisturizer containing iodopropynyl butylcarbamate

Kwon, Hyo-eun; Kim, J. M.; Oh, Won Sup; Park, K. Y.; Kim, M. N.; Seo, Sang Woo

doi:10.1111/ced.13242

Cited by 2 publications

(1 citation statement)

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“…The Cosmetics Ingredients Review (CIR) report on IBPC showed that this compound was not considered a sensitizer in guinea pigs nor in rabbits at concentrations up to 50% (w/w solutions) and that the ingredient was safe for human consumption in concentrations smaller than or equal to 0.1% (w/w solutions) . However, IBPC skin contact allergies have been broadly reported, − and it is considered a less frequent allergen. , Although the Bayesian model predicted IBPC as a “non-sensitizer”, LLNA, DPRA, h-CLAT, and KeratinoSenS models predictions for IBPC were “sensitizer”. In this case, there is a stronger weight of evidence in the direction of the “sensitizer” prediction; however, this compound might not achieve the positive response in 5% of the human population threshold of human patch tests, which explains why the Bayesian model still predicted this compound as “non-sensitizer”.…”

Section: Case Studymentioning

confidence: 99%

Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers

Borba

Braga²,

Alves

et al. 2020

Chem. Res. Toxicol.

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Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure–activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.

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