Decision making in next generation risk assessment for skin allergy: Using historical clinical experience to benchmark risk.

ABSTRACT

Our aim is to develop and apply next generation approaches to skin allergy risk assessment that do not require new animal test data and better quantify uncertainties. Quantitative risk assessment for skin sensitisation uses safety assessment factors to extrapolate from the point of departure to an acceptable human exposure level. It is currently unclear whether these safety assessment factors are appropriate when using non-animal test data to derive a point-of departure. Our skin allergy risk assessment model Defined Approach uses Bayesian statistics to infer a human-relevant metric of sensitiser potency with explicit quantification of uncertainty, using any combination of human repeat insult patch test, local lymph node assay, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we describe the incorporation of benchmark exposures pertaining to use of consumer products with clinical data supporting a high/low risk categorisation for skin sensitisation. Margins-of-exposure (potency estimate to consumer exposure level ratio) are regressed against the benchmark risk classifications, enabling derivation of a risk metric defined as the probability that an exposure is low risk. This approach circumvents the use of safety assessment factors and provides a simple and transparent mechanism whereby clinical experience can directly feed-back into risk assessment decisions.