Adjustment of a no expected sensitization induction level derived from Bayesian network integrated testing strategy for skin sensitization risk assessment

Abstract

Skin sensitization is a key adverse effect to be addressed during hazard identification and risk assessment of chemicals, because it is the first step in the development of allergic contact dermatitis. Multiple non-animal testing strategies incorporating in vitro tests and in silico tools have achieved good predictivities when compared with murine local lymph node assay (LLNA). The binary test battery of KeratinoSens TM and h-CLAT could be used to classify non-sensitizers as the first part of bottomup approach. However, the quantitative risk assessment for sensitizing chemicals requires a No Expected Sensitization Induction Level (NESIL), the dose not expected to induce skin sensitization in humans. We used Bayesian network integrated testing strategy (BN ITS-3) for chemical potency classification. BN ITS-3 predictions were performed without a pre-processing step (selecting data from their physicchemical applicability domains) or post-processing step (Michael acceptor chemistry correction), neither of which necessarily improve prediction accuracy. For chemicals within newly defined applicability domain, all under-predictions fell within one potency class when compared with LLNA results, indicating no chemicals that were incorrectly classified by more than one class. Considering the potential under-prediction by one class, a worst case value to each class from BN ITS-3 was used to derive a NESIL. When in vivo and human data from suitable analogs cannot be used to estimate the uncertainty, adjusting the NESIL derived from BN ITS-3 may help perform skin sensitization risk assessment. The overall workflow for risk assessment was demonstrated by incorporating the binary test battery of KeratinoSens TM and h-CLAT.

The 3Ranker: An AI-based Algorithm for Finding Non-animal Alternative Methods

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