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Validation of the Epi2SensA Method Using the EpiDerm™ Model for Skin Sensitization Testing Under OECD TG442D.

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

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  • 1L'Oréal, Research & Innovation, 1Eugène Schueller, 93600 Aulnay-sous-Bois, France.

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Summary

The cosmetic industry leads in animal-free risk assessment using defined approaches to predict skin sensitization potency. This study introduces a Bayesian network model to derive continuous points of departure, enhancing reliability in next-generation risk assessments.

Keywords:
Bayesian networkdefined approachpoint of departurerisk assessmentskin sensitization

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Area of Science:

  • Cosmetic Science
  • Toxicology
  • Computational Chemistry

Background:

  • Cosmetic ingredient regulations are advanced in adopting new approach methodologies (NAMs).
  • The cosmetic industry pioneers animal-free next-generation risk assessment (NGRA) using defined approaches (DAs).

Purpose of the Study:

  • To develop and validate an animal-free DA for predicting skin sensitization potency.
  • To establish a continuous point of departure (PoD) for risk assessment using NGRA.

Main Methods:

  • A Bayesian network DA (SkinSens-BN) was developed using Local Lymph Node Assay data for 297 substances.
  • A weighted sum of SkinSens-BN probabilities was calculated to derive continuous PoDs.
  • Confidence levels were assigned to predictions to inform uncertainty evaluation.

Main Results:

  • The SkinSens-BN achieved predictive performance comparable to other DAs.
  • Derived PoDs for non-sensitizers were distinct, and 77% were more conservative than LLNA EC3 values.
  • The approach demonstrated promise for informing NGRA.

Conclusions:

  • The developed PoD derivation approach contributes to reliable skin sensitization NGRAs.
  • This method supports the cosmetic industry's role in advancing animal-free testing.
  • Defined approaches and NGRA are crucial for modern risk assessment.