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Animal experiments traditionally identify sensitizers in cosmetic materials. However, with growing concerns over animal ethics and bans on such experiments globally, alternative methods like machine learning are gaining prominence for their efficiency and cost‐effectiveness. In this study, to develop a robust sensitizer detector model, we first constructed benchmark data sets using data from previous studies and a public database, then 589 sensitizers and 831 nonsensitizers were collected. In addition, a graph‐based autoencoder and Mondrian conformal prediction (MCP) were combined to build a robust sensitizer detector, iSKIN. In the independent test set, the Matthews correlation coefficient (MCC) and the area under the receiver operating characteristic curve (ROCAUC) values of the iSKIN model without MCP were 0.472 and 0.804, respectively, which are higher than those of the three baseline models. When setting the significance level in MCP at 0.7, the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927, respectively. Regrouping experiments proved that the MCP method is robust in the improvement of model performance. Through key structure analysis, seven key substructures in sensitizers were identified to guide cosmetic material design. Notably, long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho‐positions were potential sensitizers. Finally, a user‐friendly web tool (http://www.iskin.work/) of the iSKIN model was deployed to be used by other researchers. In summary, the proposed iSKIN model has achieved state‐of‐the‐art performance so far, which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials. [ABSTRACT FROM AUTHOR]

Abstract Animal experiments traditionally identify sensitizers in cosmetic materials. However, with growing concerns over animal ethics and bans on such experiments globally, alternative methods like machine learning are gaining prominence for their efficiency and cost‐effectiveness. In this study, to develop a robust sensitizer detector model, we first constructed benchmark data sets using data from previous studies and a public database, then 589 sensitizers and 831 nonsensitizers were collected. In addition, a graph‐based autoencoder and Mondrian conformal prediction (MCP) were combined to build a robust sensitizer detector, iSKIN. In the independent test set, the Matthews correlation coefficient (MCC) and the area under the receiver operating characteristic curve (ROCAUC) values of the iSKIN model without MCP were 0.472 and 0.804, respectively, which are higher than those of the three baseline models. When setting the significance level in MCP at 0.7, the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927, respectively. Regrouping experiments proved that the MCP method is robust in the improvement of model performance. Through key structure analysis, seven key substructures in sensitizers were identified to guide cosmetic material design. Notably, long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho‐positions were potential sensitizers. Finally, a user‐friendly web tool (http://www.iskin.work/) of the iSKIN model was deployed to be used by other researchers. In summary, the proposed iSKIN model has achieved state‐of‐the‐art performance so far, which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials.

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Copyright of Detergent & Cosmetics is the property of Detergent & Cosmetics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An aging animal model was established and the anti-aging effects of Sanqi extract as a cosmetic ingredient were evaluated. D-galactose was injected subcutaneously for 45 days to obtain an aging mouse model. The samples were continuously applied to the hairless area for 50 days. Multifunctional skin analyzer was used to measure the skin color, pigment, and pores of the mouse skin. Collagen I (Col-I), Collagen III (Col-III) and vimentin (VIM) were evaluated by immunohistochemical staining. Total superoxide dismutase (T-SOD), catalase (CAT), glutathione peroxidase (GSH-Px) and malondialdehyde (MDA) in skin tissue homogenates were measured to evaluate the changes in antioxidant capacity after sample treatment. The results showed that the ointment containing 4% Sanqi extract could significantly increase the content of Col-I, Col-III and VIM by immunohistochemical staining. Sanqi ointment had a good dosage-dependent effect on the up-regulation of T-SOD and GSH-Px. Compared with the blank matrix group, the T-SOD activity was increased by 36.62%, the CAT activity was increased by 20.39%, and the GSH-Px activity was increased by 68.77% after treatment with the ointment containing 4% Sanqi extract. [ABSTRACT FROM AUTHOR]

The present situation of cosmetic raw materials has been introduced, mainly including the measures of risk analysis and quality control for cosmetic raw materials. Through the inspection of cosmetic raw materials, the formulation design and development, and the incoming management, the departments in the enterprise have worked together to achieve the risk analysis and quality control of cosmetic raw materials, to ensure the quality and safety of cosmetics. [ABSTRACT FROM AUTHOR]