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™ and h-CLAT could be used to classify non-sensitizers as the first part of bottom-up 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 physic-chemical 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-pre-diction 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 work-flow for risk assessment was demonstrated by incorporating the binary test battery of KeratinoSens™ and h-CLAT.