• Toxicological Research
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• v.32 no.3
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• pp.259-267
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• 2016

Animal testing was used traditionally in the cosmetics industry to confirm product safety, but has begun to be banned; alternative methods to replace animal experiments are either in development, or are being validated, worldwide. Research data related to test substances are critical for developing novel alternative tests. Moreover, safety information on cosmetic materials has neither been collected in a database nor shared among researchers. Therefore, it is imperative to build and share a database of safety information on toxicological mechanisms and pathways collected through in vivo, in vitro, and in silico methods. We developed the CAMSEC database (named after the research team; the Consortium of Alternative Methods for Safety Evaluation of Cosmetics) to fulfill this purpose. On the same website, our aim is to provide updates on current alternative research methods in Korea. The database will not be used directly to conduct safety evaluations, but researchers or regulatory individuals can use it to facilitate their work in formulating safety evaluations for cosmetic materials. We hope this database will help establish new alternative research methods to conduct efficient safety evaluations of cosmetic materials.

• Toxicological Research
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• v.33 no.3
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• pp.191-203
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• 2017

Human eyes and skin are frequently exposed to chemicals accidentally or on purpose due to their external location. Therefore, chemicals are required to undergo the evaluation of the ocular and dermal irritancy for their safe handling and use before release into the market. Draize rabbit eye and skin irritation test developed in 1944, has been a gold standard test which was enlisted as OECD TG 404 and OECD TG 405 but it has been criticized with respect to animal welfare due to invasive and cruel procedure. To replace it, diverse alternatives have been developed: (i) For Draize eye irritation test, organotypic assay, in vitro cytotoxicity-based method, in chemico tests, in silico prediction model, and 3D reconstructed human cornealike epithelium (RhCE); (ii) For Draize skin irritation test, in vitro cytotoxicity-based cell model, and 3D reconstructed human epidermis models (RhE). Of these, RhCE and RhE models are getting spotlight as a promising alternative with a wide applicability domain covering cosmetics and personal care products. In this review, we overviewed the current alternatives to Draize test with a focus on 3D human epithelium models to provide an insight into advancing and widening their utility.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.