Risk assessment-based verification of the CertiPURTM limit values for toluene diamine and methylene dianiline in flexible polyurethane foam.

Flexible polyurethane foams (PUF) are used in many consumer products. PUF may contain trace levels of aromatic diamine impurities that could represent a potential health risk. The risk associated with sleeping on a PUF mattress was evaluated. Toxicity benchmarks for sensitization and non-cancer endpoints were derived from the respective points-of-departure using standard assessment factors. For the cancer endpoints, toxicity benchmarks were derived from the 25th-percentile values of animal studies. Recently published emission and migration data allowed to link exposure with the CertiPURTM voluntary quality limits of ≤5 mg.kg-1 for 2,4-toluene diamine and 4,4'-methylene dianiline in PUF. Using conservative exposure scenarios, lifetime-average daily internal doses from the combined inhalation and dermal exposures were calculated. Margins of safety for non-cancer and sensitization endpoints were >104. The theoretical excess cancer risk was ≤1.5 × 10-7. It is concluded that sleeping on a mattress that satisfies the CertiPUR limit value does not pose undue risk to consumers.

Quantitative Structure Use Relationships: Highlights from a technical summit meeting.

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

Threshold of Toxicological Concern: Extending the chemical space by inclusion of a highly curated dataset for organosilicon compounds.

The Threshold of Toxicological Concern (TTC) for non-genotoxic substances, a risk assessment tool to establish safe exposure levels for chemicals with insufficient toxicological data, is based on the 5th percentile of cumulated distributions of Point of Departures in a high amount of repeat-dose, developmental and reproductive toxicity studies, grouped by Cramer Classes. The lack of organosilicon compounds in this dataset has resulted in regulatory concerns over the applicability of the TTC concept for this chemistry. We collected publicly available, scientifically robust oral repeat-dose and DART studies for 71 organosilicon substances for inclusion in the existing TTC dataset, using criteria for evaluation of studies and derivation of points of departure analogous to the Munro and COSMOS TTC publications. The resulting 5th percentile of this dataset was 13-fold higher than the 5th percentile for Cramer Class III compounds reported by Munro (which is the default for silicon-containing substances). Both the existing TTC for Cramer Class III compounds from Munro (1.5 µg/kg bw/day) and the COSMOS TTC (2.3 µg/kg bw/day), recommended by the SCCS for cosmetics-related substances, provide a conservative and sufficiently protective approach for this class of chemistry.

Perspectives on advancing consumer product exposure models.

Predictive models are used to estimate exposures from consumer products to support risk management decision-making. These model predictions may be used alone in the absence of measured data or integrated with available exposure data. When different models are used, the resulting estimates of exposure and conclusions of risk may be disparate and the origin of these differences may not be obvious. This Perspectives Paper provides recommendations that could promote more systematic evaluation and a wider range of applicability of consumer product exposure models and their predictions, improve confidence in model predictions, and result in more accurate communication of consumer exposure model estimates. Key insights for the exposure science community to consider include: consistency in product descriptions, exposure routes, and scenarios; consistent and explicit definitions of exposure metrics; situation-dependent benefits from using one or multiple models; distinguishing between model algorithms and exposure factors; and corroboration of model predictions with measured data.

Refinement of the oral exposure description in the cyclic siloxane PBPK model for rats and humans: Implications for exposure assessment.

The multi-compound, and multi-dose (MC-MD) route physiologically based pharmacokinetic (PBPK) model for cyclic siloxanes reported by McMullin et al. (2016) brought together the series of models for octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) in rat and human into a unified code structure that would allow simulation of both compounds following the inhalation and dermal routes of exposure. The refined MC-MD PBPK model presented here expands upon this effort to include representation of rat kinetic data in plasma, tissues and exhaled breath for the parent compounds after oral bolus administration. Additional refinements were made with regards to hepatic induction of metabolism in the liver and allometric scaling of rate constants for the deep tissue compartments which will allow the MC-MD model to be used in uncertainty analysis. Overall, the refined MC-MD model was able to reproduce both parent D4 and D5 kinetic data in rat and human after inhalation exposure (rat and human) or dermal exposure (human). The inclusion of sequestered (i.e., lipid associated) oral absorption into plasma after oral bolus dosing successfully described the lack of exhalation as well as the initial distribution of siloxane to the liver which was higher than simple partitioning from plasma would allow. The refined MC-MD PBPK model presented here can be incorporated into uncertainty and variability analysis and cross-species dosimetry for both D4 and D5.

Illustrative case using the RISK21 roadmap and matrix: prioritization for evaluation of chemicals found in drinking water.

The HESI-led RISK21 effort has developed a framework supporting the use of twenty-first century technology in obtaining and using information for chemical risk assessment. This framework represents a problem formulation-based, exposure-driven, tiered data acquisition approach that leads to an informed decision on human health safety to be made when sufficient evidence is available. It provides a transparent and consistent approach to evaluate information in order to maximize the ability of assessments to inform decisions and to optimize the use of resources. To demonstrate the application of the framework's roadmap and matrix, this case study evaluates a large number of chemicals that could be present in drinking water. The focus is to prioritize which of these should be considered for human health risk as individual contaminants. The example evaluates 20 potential drinking water contaminants, using the tiered RISK21 approach in combination with graphical representation of information at each step, using the RISK21 matrix. Utilizing the framework, 11 of the 20 chemicals were assigned low priority based on available exposure data alone, which demonstrated that exposure was extremely low. The remaining nine chemicals were further evaluated, using refined estimates of toxicity based on readily available data, with three deemed high priority for further evaluation. In the present case study, it was determined that the greatest value of additional information would be from improved exposure models and not from additional hazard characterization.