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

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.

Next generation risk assessment for skin allergy: Decision making using new approach methodologies.

Our aim is to develop and apply next generation approaches to skin allergy risk assessment (SARA) that do not require new animal test data and better quantify uncertainties. Significant progress has been made in the development of New Approach Methodologies (NAMs), non-animal test methods, for assessment of skin sensitisation and there is now focus on their application to derive potency information for use in Next Generation Risk Assessment (NGRA). The SARA model utilises a Bayesian statistical approach to infer a human-relevant metric of sensitiser potency and a measure of risk associated with a given consumer exposure based upon any combination of human repeat insult patch test, local lymph node, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we have applied the SARA model within our weight of evidence NGRA framework for skin allergy to three case study materials in four consumer products. Highlighting how to structure the risk assessment, apply NAMs to derive a point of departure and conclude on consumer safety risk. NGRA based upon NAMs were, for these exposures, at least as protective as the historical risk assessment approaches. Through such case studies we are building our confidence in using NAMs for skin allergy risk assessment.

A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products.

Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products - face cream containing 0.1% coumarin and deodorant containing 1% coumarin - demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.

Antioxidant properties and associated mechanisms of salicylates.

The pharmacological action of salicylates has been historically related to their ability to inhibit cyclooxygenases, thereby blocking the synthesis of prostaglandins and thromboxane A2. On the other hand, several studies have suggested that salicylates have a multitude of cyclooxygenase-independent actions specially related with their antioxidant properties, which might contribute to the overall salutary effects of these compounds. Although salicylates are well-known antioxidants through their ability to scavenge hydroxyl radical, their antioxidant mechanisms of action have not been fully compiled and characterized. In this context, several mechanisms of action have been suggested, namely i) scavenging of hydroxyl radical and chelation of transition metals; ii) upregulation of nitric oxide; iii) increased synthesis of lipoxins; iv) inhibition of neutrophil oxidative burst; v) inhibition of NF-κB and AP-1 protein kinases; and vii) inhibiton of lectin-like oxidized LDL receptor-1. The newly discovered acetyl salicylic acid-triggered lipoxins probably play a key role in the maintenance of the oxidative stress balance. Furthermore, salicylates have shown to protect low-density lipoprotein from oxidation and elicit an inhibitory effect on the expression of lectin-like receptors on endothelial cells. This review aims to provide an overview of the various proposed antioxidant mechanisms of salicylates.