Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Ruparel, Nakul; Islas-Robles, Argel; Hilberer, Allison; Cantrell, Kayla; Madrid, Megan; Ryan, Cindy; Gerberick, G. Frank; Persaud, Ravi

doi:10.1016/j.fct.2024.114725

Food and Chemical Toxicology

2024

DOI: 10.1016/j.fct.2024.114725

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Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Nakul Ruparel,

Argel Islas-Robles,

Allison Hilberer

et al.

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Cited by 2 publications

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Predictive capabilities and limitations of non-animal methods for skin sensitization: Comments on the paper by Ruparel et al. Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Roberts

2024

Food and Chemical Toxicology

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Predictive capabilities and limitations of non-animal methods for skin sensitization: Comments on the paper by Ruparel et al. Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Roberts

2024

Food and Chemical Toxicology

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Deriving a Continuous Point of Departure for Skin Sensitization Risk Assessment Using a Bayesian Network Model

Tourneix,

Carron,

Jouffe

et al. 2024

Toxics

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Regulations of cosmetic ingredients and products have been the most advanced in embracing new approach methodologies (NAMs). Consequently, the cosmetic industry has assumed a forerunner role in the development and implementation of animal-free next-generation risk assessment (NGRA) that incorporates defined approaches (DAs) to assess the skin sensitization potency of ingredients. A Bayesian network DA predicting four potency categories (SkinSens-BN) was constructed against reference Local Lymph Node Assay data for a total of 297 substances, achieving a predictive performance similar to that of other DAs. With the aim of optimally informing risk assessment with a continuous point of departure (PoD), a weighted sum of the SkinSens-BN probabilities for four potency classes (non-, weak, moderate, and strong/extreme sensitizer) was calculated, using fixed weights based on associated LLNA EC3-values. The approach was promising, e.g., the derived PoDs for substances classified as non-sensitizers did not overlap with any others and 77% of PoDs were similar or more conservative than LLNA EC3. In addition, the predictions were assigned a level of confidence based on the probabilities to inform the evaluation of uncertainty in an NGRA context. In conclusion, the PoD derivation approach can substantially contribute to reliable skin sensitization NGRAs.

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Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Cited by 2 publications

References 50 publications

Predictive capabilities and limitations of non-animal methods for skin sensitization: Comments on the paper by Ruparel et al. Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Predictive capabilities and limitations of non-animal methods for skin sensitization: Comments on the paper by Ruparel et al. Deriving a point of departure for assessing the skin sensitization risk of wearable device constituents with in vitro methods

Deriving a Continuous Point of Departure for Skin Sensitization Risk Assessment Using a Bayesian Network Model

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