Detailed aggregate exposure analysis shows that exposure to fragrance ingredients in consumer products is low: Many orders of magnitude below thresholds of concern.

The Research Institute for Fragrance Materials (RIFM) and Creme Global Cremeglobal.com partnered to develop an aggregate exposure model for fragrance ingredients. The model provides a realistic estimate of the total exposure of fragrance ingredients to individuals across a population. The Threshold of Toxicological Concern (TTC) and Dermal Sensitization Threshold (DST) were used to demonstrate the magnitude of low exposure to fragrance materials. The total chronic systemic, inhalation, and dermal 95th percentile exposures on approximately 3000 fragrance ingredients in RIFM's inventory were compared to their respective TTC or DST. Additionally, representative fragrance ingredients were randomly selected and analyzed for exposure distribution by product type (i.e., cosmetic/personal care, household care, oral care, and air care) and route of exposure. It was found that 76 % of fragrance ingredients fall below their respective TTC limits when compared to 95th percentile systemic exposure, while 99 % are below inhalation TTC limits. The lowest 95th percentile aggregate exposure by product type was from household care products, then air care, and oral care products. The highest exposure was from personal care/cosmetic products. The volume of use for most fragrance ingredients (63 %) was <1 metric ton, estimating that environmental exposure to fragrance ingredients is likely low.

From worst-case to reality - Case studies illustrating tiered refinement of consumer exposure to cosmetic ingredients.

Consumer exposure to cosmetic ingredients is estimated in a tiered manner. Simple Tier1 deterministic aggregate exposure modelling generates a worst case estimate of exposure. Tier1 assumes that a consumer uses all cosmetic products concomitantly daily, at maximum frequency, and products always contain the ingredient at the maximum allowed % w/w concentration. Refining exposure assessment from worst case to more realistic estimates uses evidence from surveys of actual use levels of ingredients and Tier2 probabilistic models, where distributions of consumer use data can be applied. In Tier2+ modelling, occurrence data provides evidence of products on the market actually containing the ingredient. Three case studies are presented using this tiered approach to illustrate progressive refinement. The scale of refinements from Tier1 to Tier2+ modelling for the ingredients, propyl paraben, benzoic acid and DMDM hydantoin were: 0.492 to 0.026; 1.93 to 0.042 and 1.61 to 0.027 mg/kg/day exposure dose. For propyl paraben, moving from Tier1 to Tier2+ represents a refinement from 49-fold to 3-fold overestimate of exposure when compared to a maximum estimate of 0.01 mg/kg/day exposure seen in human studies. Such refinements from worst case to realistic levels of exposure estimation can be critical in the demonstration of consumer safety.

Assessing and reporting uncertainties in dietary exposure analysis: Mapping of uncertainties in a tiered approach.

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties.