Profiling 58 compounds including cosmetic-relevant chemicals using ToxRefDB and ToxCast.

Safety assessment for cosmetic-relevant chemicals (CRCs) in the European Union has been reshaped by restrictions on animal testing, and new approach methodologies (NAMs) for predicting toxicity are critical to ensure new cosmetic product safety. To demonstrate NAMs for safety assessment, we surveyed in vitro bioactivity and in vivo systemic toxicity data in the US Environmental Protection Agency's (EPA's) Toxicity Forecaster (ToxCast) and Toxicity Reference databases (ToxRefDB), respectively, for 58 chemicals identified as CRCs, including cosmetic ingredients as well as trace contaminants. CRCs were diverse in use types as suggested by broad chemical use categories. In terms of both target organ effects and study type, the median of the lowest effect level (LEL) doses in ToxRefDB for CRCs tended to be slightly higher than the median for the remaining 928 chemicals with study data in ToxRefDB, though the ranges of LELs were similar. For 17 of the 58 CRCs, high-throughput toxicokinetic data were used to calculate administered equivalent doses (AEDs) in mg/kg/day units for the in vitro bioactivity observed, and these AEDs served as conservative estimators of the systemic LELs observed in vivo. This work suggests that NAMs for bioactivity may inform a conservative point-of-departure estimate for diverse CRCs.

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log10 mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.

In vitro assessment of eye irritancy using the Reconstructed Human Corneal Epithelial SkinEthic HCE model: application to 435 substances from consumer products industry.

The 7th amendment of the EU Cosmetics Directive led to the ban of eye irritation testing for cosmetic ingredients in animals, effective from March 11th 2009. Over the last 20years, many efforts have been made to find reliable and relevant alternative methods. The SkinEthic HCE model was used to evaluate the in vitro eye irritancy potential of substances from a cosmetic industry portfolio. An optimized protocol based on a specific 1-h treatment and a 16-h post-treatment incubation period was first assessed on a set of 102 substances. The prediction model (PM) based on a 50% viability cut-off, allowed to draw up two classes (Irritants and Non-Irritants), with good associated sensitivity (86.2%) and specificity (83.5%). To check the robustness of the method, the evaluated set was expanded up to 435 substances. Final performances maintained a high level and were characterized by an overall accuracy value > 82% when using EU or GHS classification rules. Results showed that the SkinEthic HCE test method is a promising in vitro tool for the prediction of eye irritancy. Optimization datasets were shared with the COLIPA Eye Irritation Project Team and ECVAM experts, and reviewed as part of an ongoing progression to enter an ECVAM prospective validation study for eye irritation.